report about kpi analysis and methods of comparison · 2018-03-06 · executive summary based on...

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under

grant agreement No 646.531

Report about KPI analysis and methods of comparison

D8.1 – Release 2

2015 The UPGRID Consortium

Real proven solutions to enable active demand and distributed

generation flexible integration, through a fully controllable

LOW Voltage and medium voltage distribution grid

WP8 - Monitoring & Impact Assessment of

Project Demonstrations

Monitoring & Impact Assessment of Project Demonstrator

D.8.1 REPORT ABOUT KPI ANALYSIS AND METHODS OF COMPARISON

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PROGRAMME H2020 – Energy Theme

GRANT AGREEMENT NUMBER 646.531

PROJECT ACRONYM UPGRID

DOCUMENT D8.1 – Release 2

TYPE (DISTRIBUTION LEVEL) ☒ Public

☐ Confidential

☐ Restricted

DUE DELIVERY DATE 31/12/2017

DATE OF DELIVERY 15/12/2017

STATUS AND VERSION Release 2 / v09

NUMBER OF PAGES 124

WP / TASK RELATED WP8 / T8.1

WP / TASK RESPONSIBLE COMILLAS / ITE

AUTHOR (S) ITE (Ignacio Delgado, Irene Aguado, Amparo

Mocholí)

PARTNER(S) CONTRIBUTING IBERDROLA (Ana González, Raúl Bachiller, Marta

Elorduy, Roberto González), EDP Distribuição (Pedro

Nunes, João Filipe Nunes, Pedro Felício, Rui Miguel

Gonçalves, Pedro Godinho Matos), VATTENFALL

(Anders Kim Johansson, Erica Lidström), ENERGA

(Slawomir Noske, Dominik Falkowski, Maciej

Glombioeski), TECNALIA (Eduardo García, Karmele

Herranz, Ángel Díaz), COMILLAS (Álvaro Sánchez),

INESC (Luis Seca), ZIV (Laura Marrón, Txetxu

Arzuaga), WITHUS (Tiago Duarte, Luis Oliveira), NOS

(Marijn Van Overveld), POWEL (Kjetil Kvamne), SE

(David Pampliega, Tomas Sánchez), GE (Manuel

Weindorf, David Kirkland), IEN (Aleksander Babs)

OFFICIAL REVIEWER/S IBERDROLA (Raúl Bachiller) / TECNALIA (Eduardo

García)

FILE NAME UPBRID_WP8_D8 1_KPIs_Release 2_v09



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DOCUMENT HISTORY

VERS. ISSUE DATE CONTENT AND CHANGES

v00 12/09/2016 TOC first draft.

v01 24/10/2016 Integration of demos data (first release).

v02 18/06/2017 First release ready for official review with data sent by demos

until 16/06/2017

v03 27/06/2017 First release after official and demos review.

v04 30/06/2017 Minor modifications.

v05 21/11/2017 Integration of demos’ data (second release).

v06 28/11/2017 Integration of feedback from demo leaders’ review.

v07 01/12/2017 Integration of feedback from demo leaders’ second review.

v08 13/12/2017 Integration of feedback from official review.

v09 13/12/2017 Second release after official and demos review.



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EXECUTIVE SUMMARY

Based on the European Electricity Grid Initiative (EEGI) methodology for Key Performance Indicators

(KPI) calculation, two levels of KPIs were defined for the UPGRID project in D1.4 – Report on common

KPIs: High level KPIs and Detailed KPIs. High level KPIs are directly linked with the EEGI Function

Objectives (in fact, they share the names). UPGRID project has defined twenty-five detailed KPIs which

are combined to calculate the high level KPIs linked with the EEGI Function Objectives. Table 1 shows

the list of detailed KPIs included in each high level KPI calculation. It is worth mentioning that one

detailed KPI can be associated to more than one high level KPI.

TABLE 1: DETAILED KPIS DISTRIBUTION AMONG HIGH LEVEL KPIS SELECTED IN UPGRID.

High Level KPIs

(EEGI Function Objectives)

Number of associated

Detailed KPIs

D1 Active Demand for increased network flexibility 7

D2 Enabling maximum energy efficiency in new or refurbished

urban using smart distribution grids 0

D3 Integration of DER at low voltage 5

D4 Integration of DER at medium voltage / high voltage 0

D5 Integration of storage in network management 0

D6 Integration of infrastructure to host Electrical Vehicles 4

D7 Monitoring and control of LV networks 13

D8 Automation and control of MV networks 4

D9 Network management methodologies for network operation 7

D10 Smart metering data utilization 8

D11 Novel planning approaches for distribution networks 1

D12 Novel approaches to asset management 8

D13 New approaches for market design 9

The proposed UPGRID methodology for the KPI calculation consists on combining the detailed KPIs

through a weight sum to obtain the high level KPIs. These weigh matrixes were adapted in [1] to each

demo depending on the components, developments to be deployed in the scope of the UPGRID project

and the own vision / objectives of each demo. This means, the relative weight might differ from one

demo to another.

A first release of D8.1 was prepared in M30 (June 2017) including the detailed and high level KPIs

calculation with the data provided by the demos by that time. This data included the business as usual



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scenario (BAU) information (before UPGRID deployment in the demo sites) and most of the research

and innovation scenario (R&I) information (after UPGRID deployment in the demo sites). This means

that most of the detailed KPIs were calculated and also a first approach of the high level KPIs to assess

the UPGRID contribution to the EEGI objectives.

This second release of D8.1 includes the review and calculation of the whole set of detailed KPIs and the

final version of high level KPIs based on enriched information that has been provided from the demo

leaders as a result of the UPGRID components and solutions deployed in the demo sites. In addition, it

has been added a section summarizing main results and conclusions based on these KPIs.



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TABLE OF CONTENTS

EXECUTIVE SUMMARY _________________________________________________________________ 4

TABLE OF CONTENTS __________________________________________________________________ 6

LIST OF FIGURES ______________________________________________________________________ 9

LIST OF TABLES ______________________________________________________________________ 10

ABBREVIATIONS AND ACRONYMS ______________________________________________________ 13

1. INTRODUCTION ___________________________________________________________________ 18

2. UPGRID KPI CALCULATION METHODOLOGY _____________________________________________ 19

3. DETAILED KPIS CALCULATION ________________________________________________________ 23

3.1 SPANISH DEMO ________________________________________________________________________ 23

3.1.1 KPI 4: FULFILMENT OF VOLTAGE LIMITS ___________________________________________________________ 25

3.1.2 KPI 5: AVERAGE TIME FOR LV FAULTS _____________________________________________________________ 26

3.1.3 KPI 7: QUALITY OF SUPPLY IMPROVEMENT IN LV ____________________________________________________ 28

3.1.4 KPI 10: MONITORING INFORMATION CATEGORIES ___________________________________________________ 29

3.1.5 KPI 11: AVAILABLE INFORMATION CATEGORIES _____________________________________________________ 32

3.1.6 KPI 12: CHARACTERIZED INFORMATION CATEGORIES ________________________________________________ 35

3.1.7 KPI 13: AVAILABILITY OF INTELLIGENT NETWORK COMPONENTS _______________________________________ 36

3.1.8 KPI 14: SUCCESS INDEX IN METER READING ________________________________________________________ 38

3.1.9 KPI 15: SUCCESS INDEX IN EVENT READING _________________________________________________________ 39

3.1.10 KPI 16: SUCCESS INDEX IN ADVANCED FUNCTIONALITIES _____________________________________________ 41

3.1.11 KPI 17: SUCCESS INDEX IN METERS CONNECTIVITY __________________________________________________ 42

3.1.12 KPI 18: CONSUMERS BEING METERED AUTOMATICALLY _____________________________________________ 42

3.1.13 KPI 19: IMPROVED LIFE-TIME OF TRANSFORMERS __________________________________________________ 43

3.1.14 KPI 20: PARTICIPANT RECRUITMENT _____________________________________________________________ 45

3.1.15 KPI 21: ACTIVE PARTICIPATION __________________________________________________________________ 45

3.1.16 KPI 23: USE OF EQUIPMENT STANDARDS __________________________________________________________ 46

3.1.17 KPI 24: USE OF PROTOCOL STANDARDS ___________________________________________________________ 47

3.2 PORTUGUESE DEMO ____________________________________________________________________ 50

3.2.1 KPI 1: DEMAND FLEXIBILITY _____________________________________________________________________ 52

3.2.2 KPI 3: HOSTING CAPACITY OF ELECTRIC VEHICLES ____________________________________________________ 52



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3.2.9 KPI 14: SUCCESS INDEX IN METER READING ________________________________________________________ 62

3.2.10 KPI 18: CONSUMERS BEING METERED AUTOMATICALLY _____________________________________________ 62



3.2.13 KPI 23: USE OF EQUIPMENT STANDARDS __________________________________________________________ 65

3.2.14 KPI 24: USE OF PROTOCOL STANDARDS ___________________________________________________________ 66

3.3 SWEDISH DEMO _______________________________________________________________________ 68



3.3.3 KPI 6: AVERAGE TIME NEEDED FOR FAULT LOCATION IN MV ___________________________________________ 71

3.3.4 KPI 8: QUALITY OF SUPPLY IMPROVEMENT IN MV ___________________________________________________ 72



3.3.7 KPI 23: USE OF EQUIPMENT STANDARDS___________________________________________________________ 77

3.3.8 KPI 24: USE OF PROTOCOL STANDARDS ____________________________________________________________ 79

3.4 POLISH DEMO _________________________________________________________________________ 81

3.4.1 KPI 2: GENERATION FLEXIBILITY __________________________________________________________________ 82


3.4.3 KPI 6: AVERAGE TIME NEEDED FOR FAULT LOCATION IN MV ___________________________________________ 84

3.4.4 KPI 7: QUALITY OF SUPPLY IMPROVEMENT IN LV ____________________________________________________ 85

3.4.5 KPI 8: QUALITY OF SUPPLY IMPROVEMENT IN MV ___________________________________________________ 85

3.4.6 KPI 9: ENERGY LOSSES __________________________________________________________________________ 87




3.4.10 KPI 13: AVAILABILITY OF INTELLIGENT NETWORK COMPONENTS ______________________________________ 96

3.4.11 KPI 15: SUCCESS INDEX IN EVENT READING ________________________________________________________ 98




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3.4.14 KPI 23: USE OF EQUIPMENT STANDARDS _________________________________________________________ 100

3.4.15 KPI 24: USE OF PROTOCOL STANDARDS __________________________________________________________ 101

4. HIGH LEVEL KPIS CALCULATION ______________________________________________________ 103

4.1 SPANISH DEMO _______________________________________________________________________ 103

4.1.1 NETWORK OPERATIONS _______________________________________________________________________ 104

4.1.2 NETWORK PLANNING AND ASSET MANAGEMENT __________________________________________________ 105

4.1.3 MARKET DESIGN _____________________________________________________________________________ 106

4.1.4 CONTRIBUTION TO EEGI OBJECTIVES _____________________________________________________________ 106

4.2 PORTUGUESE DEMO ___________________________________________________________________ 108

4.2.1 INTEGRATION OF SMART CUSTOMERS ___________________________________________________________ 108

4.2.2 INTEGRATION OF DER AND NEW USES ___________________________________________________________ 109



4.2.5 MARKET DESIGN _____________________________________________________________________________ 111


4.3 SWEDISH DEMO ______________________________________________________________________ 113



4.4 POLISH DEMO ________________________________________________________________________ 116

4.4.1 INTEGRATION OF DER AND NEW USES ___________________________________________________________ 116



4.4.4 MARKET DESIGN _____________________________________________________________________________ 119


5. CONCLUSIONS ___________________________________________________________________ 121

REFERENCES _______________________________________________________________________ 123



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LIST OF FIGURES

FIGURE 1: HIGH LEVEL CALCULATION PROCEDURE FROM DETAILED KPIS. ................................................ 20

FIGURE 2: HIGH LEVEL KPIS DEMO SPAIN .................................................................................................106

FIGURE 3: HIGH LEVEL KPIS PORTUGUESE DEMO. ....................................................................................111

FIGURE 4: HIGH LEVEL KPIS SWEDISH DEMO. ...........................................................................................115

FIGURE 5: HIGH LEVEL KPIS POLISH DEMO. ..............................................................................................119

FIGURE 6: UPGRID CONTRIBUTION TO EEGI CLUSTERS AND FUNCTION OBJECTIVES. .............................121



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LIST OF TABLES

TABLE 1: DETAILED KPIS DISTRIBUTION AMONG HIGH LEVEL KPIS SELECTED IN UPGRID. _____________ 4

TABLE 2: UPGRID HIGH LEVEL KPIS. ______________________________________________________ 19

TABLE 3: PROPOSED UPGRID DETAILED KPIS AND THEIR RELATION TO OTHER EEGI PROJECTS. _______ 21

TABLE 4: SPANISH DEMO DETAILED KPIS SUMMARY. ________________________________________ 23

TABLE 5: MAIN EQUIPMENT AND INFORMATION CATEGORIES FOR THE SPANISH DEMO ____________ 29

TABLE 6: HOW THE SPANISH DEMONSTRATOR MAKES INFORMATION CATEGORIES AVAILABLE FOR

MAIN SYSTEMS AND PROCEDURES ______________________________________________________ 34

TABLE 7: RELEVANCE OF THE SPANISH DEMO REGARDING EQUIPMENT AND SYSTEMS _____________ 36

TABLE 8: SUMMARY OF RESULTS OBTAINED REGARDING THE NUMBER OF EVENTS THAT SHOULD BE

RECEIVED AFTER THE INCIDENT HAPPENED ON 12/02/2016 THAT AFFECTED 14 SSS. _______________ 39

TABLE 9: SIAF WITH MAXIMUM DELAY 1 SECOND ___________________________________________ 41

TABLE 10: SPANISH DEMO EQUIPMENT STANDARDS ________________________________________ 46

TABLE 11: SPANISH DEMO PROTOCOL STANDARDS _________________________________________ 48

TABLE 12: PORTUGUESE DEMO KPIS SUMMARY ____________________________________________ 50

TABLE 13: BAU DATA FOR KPI 4 (PORTUGUESE DEMO) _______________________________________ 54

TABLE 14: R&I DATA FOR KPI 4 (PORTUGUESE DEMO) _______________________________________ 55

TABLE 15: MAIN EQUIPMENT AND INFORMATION CATEGORIES FOR THE PORTUGUESE DEMO _______ 57

TABLE 16: PORTUGUESE DEMO INFORMATION CATEGORIES AVAILABLE FOR MAIN SYSTEMS AND

PROCEDURES _______________________________________________________________________ 58

TABLE 17: RELEVANCE OF THE PORTUGUESE DEMO REGARDING EQUIPMENT AND SYSTEMS ________ 60

TABLE 18: PORTUGUESE DEMO EQUIPMENT STANDARDS ____________________________________ 65

TABLE 19: PORTUGUESE DEMO PROTOCOL STANDARDS _____________________________________ 67

TABLE 20: SWEDISH DEMO KPIS SUMMARY _______________________________________________ 68

TABLE 21: SWEDISH DEMO DATA FOR KPI 8 _______________________________________________ 73

TABLE 22: MAIN EQUIPMENT AND INFORMATION CATEGORIES FOR THE SWEDISH DEMO __________ 74

TABLE 23: RELEVANCE OF THE SWEDISH DEMO REGARDING EQUIPMENT AND SYSTEMS ____________ 76

TABLE 24: SWEDISH DEMO EQUIPMENT STANDARDS ________________________________________ 78

TABLE 25: SWEDISH DEMO PROTOCOL STANDARDS _________________________________________ 80



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TABLE 26: POLISH DEMO KPIS SUMMARY _________________________________________________ 81

TABLE 27: MAIN EQUIPMENT AND INFORMATION CATEGORIES FOR THE POLISH DEMO ____________ 88

TABLE 28: POLISH DEMO INFORMATION CATEGORIES AVAILABLE FOR MAIN SYSTEMS AND PROCEDURES

__________________________________________________________________________________ 90

TABLE 29: PARAMETRIZATION CHANGE FOR 1-PHASE CUSTOMER METER _______________________ 92



TABLE 32: RELEVANCE OF THE POLISH DEMO REGARDING EQUIPMENT AND SYSTEMS _____________ 97

TABLE 33: POLISH DEMO EQUIPMENT STANDARDS ________________________________________101

TABLE 34: POLISH DEMO PROTOCOL STANDARDS _________________________________________102

TABLE 35: HIGH LEVEL KPIS CALCULATED FOR SPANISH DEMO. _______________________________103

TABLE 36: WEIGH MATRIX FOR HIGH LEVEL KPI D7 (MONITORING AND CONTROL OF LV NETWORK) FOR

SPANISH DEMO. ____________________________________________________________________104

TABLE 37: WEIGH MATRIX FOR HIGH LEVEL KPI D9 (NETWORK MANAGEMENT METHODOLOGIES FOR

NETWORK OPERATION) FOR SPANISH DEMO. _____________________________________________104

TABLE 38: WEIGH MATRIX FOR HIGH LEVEL KPI D10 (SMART METERING DATA UTILIZATION) FOR

SPANISH DEMO. ____________________________________________________________________105

TABLE 39: WEIGH MATRIX FOR HIGH LEVEL KPI D11 (NEW PLANNNG APPROACHES FOR DISTRIBUTION

NETWORKS) FOR SPANISH DEMO. ______________________________________________________105

TABLE 40: WEIGH MATRIX FOR HIGH LEVEL KPI D12 (NOVEL APPROACHES TO ASSET MANAGEMENT)

FOR SPANISH DEMO. ________________________________________________________________105

TABLE 41: WEIGH MATRIX FOR HIGH LEVEL KPI D13 (NOVEL APPROACHES FOR MARKET DESIGN) FOR

SPANISH DEMO. ____________________________________________________________________106

TABLE 42: HIGH LEVEL KPIS CALCULATED FOR PORTUGAL DEMO. _____________________________108

TABLE 43: WEIGH MATRIX FOR HIGH LEVEL KPI D1 (ACTIVE DEMAND FOR INCREASED NETWORK

FLEXIBILITY) FOR PORTUGUESE DEMO. __________________________________________________108

TABLE 44: WEIGH MATRIX FOR HIGH LEVEL KPI D6 (INTEGRATION OF INFRASTRUCTURE TO HOST

ELECTRICAL VEHICLES) FOR PORTUGUESE DEMO. __________________________________________109


PORTUGUESE DEMO. ________________________________________________________________109


PORTUGUESE DEMO. ________________________________________________________________110


FOR PORTUGUESE DEMO. ____________________________________________________________110



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PORTUGUESE DEMO. ________________________________________________________________111

TABLE 49: HIGH LEVEL KPIS CALCULATED FOR SWEDEN DEMO. _______________________________113


SWEDISH DEMO. ____________________________________________________________________113

TABLE 51: WEIGH MATRIX FOR HIGH LEVEL KPI D8 (AUTOMATION AND CONTROL OF MV NETWORK)

FOR SWEDISH DEMO. ________________________________________________________________114


NETWORK OPERATION) FOR SWEDISH DEMO. ____________________________________________114


SWEDISH DEMO. ____________________________________________________________________114

TABLE 54: HIGH LEVEL KPIS CALCULATED FOR POLISH DEMO. ________________________________116

TABLE 55: WEIGH MATRIX FOR HIGH LEVEL KPI D3 (INTEGRATION OF DER AT LOW VOLTAGE) FOR

POLISH DEMO. _____________________________________________________________________116


POLISH DEMO. _____________________________________________________________________117

TABLE 57: WEIGH MATRIX FOR HIGH LEVEL KPI D8 (AUTOMATION AND CONTROL OF MV NETWORK)

FOR DEMO POLAND. _________________________________________________________________117


NETWORK OPERATION) FOR POLISH DEMO. ______________________________________________117

TABLE 59: WEIGH MATRIX FOR HIGH LEVEL KPI D10 (SMART METERING DATA UTILIZATION) FOR POLISH

DEMO. ____________________________________________________________________________118


FOR POLISH DEMO. __________________________________________________________________118


POLISH DEMO. _____________________________________________________________________119



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ABBREVIATIONS AND ACRONYMS

∆E Energy losses (Detailed KPI)

∆TLV Average time for LV faults (Detailed KPI)

∆TMV Average time needed for fault location in MV (Detailed KPI)

A Active participation (Detailed KPI)

ACER Agency for the Cooperation of Energy Regulators

ADV Available information categories (Detailed KPI)

AMI Advanced Metering Infrastructure

AMR Automatic Meter Reading

ASIDI Average System Interruption Duration Index

AV Availability of intelligent network components (Detailed KPI)

BAU Business as Usual

BMS Battery Management System

CAPEX Capital Expenditures

CDV Characterized information categories (Detailed KPI)

CEER Council of European Energy Regulators

CIM Common Information Model

CML Customer Minutes Lost

CT Current Transformers

DC Data Concentrator

DER Distributed Energy Resource

DLMS Distribution Line Message Specification

DMS Distribution Management System

DRES Distributed Renewable Energy Sources

DSM Demand Side Management



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DSO Distribution System Operator

DTC Distribution Transformer Controller

EDP EDP Distribuição – Energia, S. A.

EDSO4SG European Distribution System Operators for Smart Grids

EEGI European Electricity Grid Initiative

ENERGA Energa Operator S.A.

ENTSO-E European Network of Transmission System Operators for

Electricity

EU European Union

EV Electric Vehicle

FB Fuse Base

FCS Field Crew Support

FPI Fault Passage Indicator

GE General Electric

GHG GreenHouse Gas

GHG-ER Reduction in greenhouse gas emissions (Detailed KPI)

GIS Geographic Information System

GPRS General Packet Radio Service

GTP Gateway of Telecommunications PRIME

HAN Home Area Network

HCEV Hosting Capacity of Electric Vehicles (Detailed KPI)

HV High Voltage

ID Identification

IEA International Energy Agency

IED Intelligent Electronic Device

ILT Improved Life-time of transformers (Detailed KPI)

INESC Instituto de engenharia de sistemas e computadores do Porto

IP Internet Protocol



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IT Information Technology

ITC Information Technology and Communication

ITE Instituto Tecnológico de la Energía

KPI Key Performance Indicator

LCC Life Cycle Cost

LPID Light Protocol Implementation Document

LV Low Voltage

LV NMS Low Voltage Network Management System

MDMS Meter Data Management System

MDV Monitoring information categories (Detailed KPI)

MIB Management Information Base

MV Medium Voltage

NOC Network Operation Centre

NOS Optimus – NOS Comunicações, S. A.

O&M Operation and Management

OMS Outage Management System

OPEX Operating Expense or Operational Expenditure

PDER Generation flexibility (Detailed KPI)

PDSM Demand flexibility (Detailed KPI)

PHEV Plug-in Hybrid Electric Vehicle

PLC Power Line Communication

POWEL Powel AS

PRIME PoweRline Intelligent Metering Evolution

PS Primary Substation

PV Photovoltaic

QSLV Quality of Supply Improvement in LV (Detailed KPI)

QSMV Quality of Supply Improvement in MV (Detailed KPI)



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Quota Consumers being metered automatically (Detailed KPI)

R Participant recruitment (Detailed KPI)

R&I Research and Innovation

RC Replacement Costs

RD&D Research, Development and Demonstration

RES Renewable Energy Source

RTU Remote Terminal Unit

SAIDI System Average Interruption Duration Index

SAIFI System Average Interruption Frequency Index

SAP Simple Acquisition Protocol

SCADA Supervisory Control And Data Acquisition

SE Schneider Electric Industries

SET-PLAN Strategic Energy Technology Plan

SG Smart Grid

SIAF Success index in advanced functionalities (Detailed KPI)

SIER Success index in event reading (Detailed KPI)

SIMC Success index in meters connectivity (Detailed KPI)

SIMR Success index in meter reading (Detailed KPI)

SM Smart Meter

SNMP Simple Network Management Protocol

SS Secondary Substation

SSS Smart Secondary Substation

SW Software

T Task

TOC Table of Contents

TSO Transmission System Operator

UDP User Data Panel



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UES Use of equipment standards (Detailed KPI)

UPS Use of protocol standards (Detailed KPI)

VF Load curve valley filling (Detailed KPI)

VL Fulfilment of voltage limits (Detailed KPI)

VS Versus

VTF Vattenfall Eldistribution AB

WP Work Package



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1. INTRODUCTION

The main objective of WP8 – Monitoring & Impact Assessment of Project Demonstration is the analysis

of the demonstrators (also referred to as demos) implemented in order to compare results, advantages

and disadvantages of implementation and operation, expected life cycles and cost-benefit analysis,

taking into account the indirect impacts like environmental issues and social benefits. The main purpose

of this work package first task, Task 8.1 – Key performance indicators (KPIs) analysis, is to review and

validate the KPIs previously defined in WP1 for each demo in order to be able to evaluate the results of

demonstrators developed in WP3 (Spanish demonstrator), WP4 (Portuguese demonstrator), WP5

(Swedish demonstrator) and WP6 (Polish demonstrator). Therefore this task has been focused on the

calculation and evaluation of the KPIs defined for each demo in WP1 [2].

UPGRID intended from the beginning to apply, as a general framework, the KPIs that have been used in

other smart grids projects in Europe and see how these KPIs can be adapted to the features and

objectives of the UPGRID demos. Thus, enable a robust and feasible demo performance monitoring. For

this reason, the European Electricity Grid Initiative (EEGI, [3]) methodology for KPI calculation has been

followed for the UPGRID KPIs definition [4].

The main output of Task 8.1 is D8.1–Report about KPIs analysis and methods of comparison. It is a

document that calculates and evaluates the selected KPIs for monitoring the UPGRID demos. It contains

KPI results and rationale about the contribution of each demo to the EEGI objectives based on the

UPGRID KPI definition included in D1.4 – Report on common KPIs.

Task 8.1 is being performed in parallel with the four demonstrators WPs. D8.1 has been split up into two

releases. The first release contained the data provided for the business as usual scenario (before the

deployment of the UPGRID components and solutions) and the data provided up to date for the

research and development scenario (after the deployment of the UPGRID components and solutions).

This second release of D8.1 includes the calculation of the whole set of detailed and high level KPIs for

the four demos and even the recalculation of some of them to enrich them with new data coming from

the demo sites.

This document has been divided into four main chapters. After a brief introduction (chapter 1), chapter

2 provides a brief description of the UPGRID KPI calculation methodology as a background based on the

information included in D1.4. Then, chapter 3 describes the calculation of each detailed KPI per demo.

This chapter has been divided into one chapter per demo and includes a summary of the detailed KPIs

results at the beginning. Chapter 4 includes the calculation of the UPGRID high level KPIs per demo. As

explained for chapter 3, chapter 4 has been divided into one chapter per demo and includes an

evaluation of the contribution of each demo to the EEGI objectives as a conclusion. Finally, chapter 5

includes the main conclusions of this deliverable.



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2. UPGRID KPI CALCULATION METHODOLOGY

This chapter pretends to be a summary of the KPI methodology adopted in the scope of the UPGRID

project based on the detailed description included in D1.4 – Report on common KPIs at the beginning of

the project.

UPGRID project has chosen the EEGI roadmap structure as the framework to classify the UPGRID

contributions to the research and innovation activities on Electricity Grids at European level by the SET-

PLAN. The EEGI1 is one of the European Industrial Initiatives under the Strategic Energy Technologies

Plan (SET-PLAN) and proposes a 9-year European research, development and demonstration (RD&D)

programme to accelerate innovation and the development of the electricity networks of the future in

Europe [3].

EEGI’s objectives are the base of the EEGI Roadmap 2013-22 and Implementation Plan 2013-2022, which

has been prepared by ENTSO-E2 and EDSO3 in close collaboration with the European Commission, CEER4

and other relevant stakeholders. UPGRID project has followed the EEGI roadmap structure of the EEGI

roadmap 2013-2022 [4]. This document is an upgraded version of the June 2010 EEGI R&I roadmap

which was initiated early 2012 in response to EU energy policy evolutions.

In order to evaluate the contribution of each UPGRID demo in achieving the EU goals, the UPGRID high

level KPIs were matched with the EEGI functional objectives. An output of the KPI calculation will be the

fulfilment of a table by each demo which format is presented in Table 2. It represents the contribution

of each UPGRID demo to each EEGI functional objective from the BAU scenarios to the R&I scenario

after applying the UPGRID innovations.

TABLE 2: UPGRID HIGH LEVEL KPIS.

EEGI Cluster

High Level KPI (EEGI Function Objectives)

UPGRID Demos

selection

Integration of smart customers

D1. Active Demand for increased network flexibility

X

D2. Enabling maximum energy efficiency in new or refurbished urban using smart distribution grids

1 In July 2016 the European Technology and Innovation Platform for Smart Networks for the Energy Transition (ETIP SNET) was created. The ETIP SNET merges and replaces the EEGI and the European Technology Platform (ETP) SMARTGRIDS. In the scope of this document, all the references are to EEGI and not to ETIP SNET as the EEGI methodology was selected as the framework of the project at the beginning (January 2015).

2 ENTSO-E: European Network of Transmission System Operators for Electricity (www.entsoe.eu).

3 EDSO4SG: European Distribution System Operators’ Association for Smart Grids (www.edsoforsmartgrids.eu).

4 CEER: Council of European Energy Regulators (www.ceer.eu).



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Integration of DER and new uses

D3. Integration of DER at low voltage

X

D4. Integration of DER at medium voltage / high voltage

D5. Integration of storage in network management

D6. Integration of infrastructure to host Electrical Vehicles

X

Network operations

D7. Monitoring and control of LV networks

X

D8. Automation and control of MV networks

X

D9. Network management methodologies for network operation

X

D10. Smart metering data utilization

X

Network planning

and asset management

D11. New planning approaches for distribution networks

X

D12. Novel approaches to asset management

X

Market design

D13. Novel approaches for market design

X

High level KPIs have been built as the weighted sum of detailed KPIs. These detailed KPIs have been

calculated once per demo (combining as defined in each case the BAU and R&I scenarios). They measure

the impact of the new components or developments deployed in each demo. The results of one or more

demo developments may impact on one or more detailed KPIs. Using a weight matrix, the detailed KPIs

have been combined to calculate each high level KPI. This means that each detailed KPI have been used

in one or more high level KPI calculation. Figure 1 illustrates the high level calculation procedure from

detailed KPIs.

FIGURE 1: HIGH LEVEL CALCULATION PROCEDURE FROM DETAILED KPIS.



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The UPGRID detailed KPIs correspond to the “project KPIs” defined in the EEGI methodology. These KPIs

are the indicators that show the achievements of the individual R&I projects. The definitions of these

KPIs are specific to each individual project and therefore it is not possible for the EEGI to provide a

universal set of KPI definitions and calculation methodologies that could be practically applied to all

projects. However, the EEGI expects that, in some cases, projects would have some of the same or

similar KPIs. For this reason, most of these KPIs have been adapted on the basis of other EEGI labelled

projects [5] and some other KPIs, to a lesser extent, some of the proposed KPIs have been borrowed

directly from other projects. Nevertheless, seven of them were defined specifically for UPGRID project

to cover all the sub-functionalities. The EEGI labelled project that were analysed during Task 1.4 –

Definition of common KPIs to monitor the performance of the demos were GRID+ [6], IGREENGRID [7],

GRID4EU [8], DISCERN [9] and IDE4L [10].

Table 3 includes the proposed UPGRID detailed KPIs and their relation to other EEGI labelled project as

they were defined in D1.4 – Report on common KPIs at the beginning of the project.

TABLE 3: PROPOSED UPGRID DETAILED KPIS AND THEIR RELATION TO OTHER EEGI PROJECTS.

# UPGRID KPI EEGI

LABELLED PROJECTS

DEFINED FOR

UPGRID PROJECT

1 PDSM Demand flexibility x

2 PDER Generation flexibility x

3 HCEV Hosting Capacity of Electric Vehicles x

4 VL Fulfilment of voltage limits x

5 ∆TLV Average time for LV faults x

6 ∆TMV Average time needed for fault location in MV x

7 QSLV Quality of Supply Improvement in LV x

8 QSMV Quality of Supply Improvement in MV x

9 ∆E Energy losses x

10 MDV Monitoring information categories x

11 ADV Available information categories x

12 CDV Characterized information categories x

13 AV Availability of intelligent network components

x

14 SIMR Success index in meter reading x

15 SIER Success index in event reading x

16 SIAF Success index in advanced functionalities x

17 SIMC Success index in meters connectivity x

18 Quota Consumers being metered automatically x

19 ILT Improved Life-time of transformers x

20 R Participant recruitment x



22 | 124

# UPGRID KPI EEGI

LABELLED PROJECTS

DEFINED FOR

UPGRID PROJECT

21 A Active participation x

22 VF Load curve valley filling x

23 UES Use of equipment standards x

24 UPS Use of protocol standards x

25 GHG Reduction in greenhouse gas emissions x



23 | 124

3. DETAILED KPIS CALCULATION

This chapter includes the calculation procedure and rationale of each detailed KPI for each UPGRID

demo. It has been divided into four chapters (one per demo). All the chapters start with a summary of

the KPIs results with a brief explanation to clarify the output of each calculation.

3.1 SPANISH DEMO

This sub-chapter includes the calculation and rationale of the detailed KPIs calculated in the scope of the

Spanish demonstrator. Table 4 provides a summary and understanding of the KPIs and the subsequent

sections include a further description of each one of them.

Further information about the Spanish demo can be found in the deliverables of WP3 - Demonstration in

real user environment: Iberdrola –Spain, specifically in D3.4 – Demonstration results: evaluation and

opportunities [11].

TABLE 4: SPANISH DEMO DETAILED KPIS SUMMARY.

SPANISH DEMO

UPGRID KPI VALUE DESCRIPTION

KPI 4 Fulfilment of voltage limits 3.46% The fulfilment of voltage limits has been improved 3.46% in the R&I scenario compared with the BAU scenario.

KPI 5 Average time for LV faults 14.52% The average time for LV faults has been reduced 14.52% in the R&I scenario compared with the BAU scenario.

KPI 7 Quality of Supply Improvement in LV 31.9% There is a 31.9% of improvement in the accuracy in the calculation of the quality of supply in LV in the R&I scenario compared with the BAU scenario.

KPI 10 Monitored information categories 6.31% There is a 6.31% increase of monitoring information categories in the R&I scenario compared with the BAU scenario.

KPI 11 Available information categories 431.97% There is a 431.97% increase of available information categories in the R&I scenario compared with the BAU scenario.

KPI 13 Availability of intelligent network components

111.13% There is a 111.13% increase of intelligent components in the R&I scenario compared with the BAU scenario.

KPI 14 Success index in meter reading 1.00%

The success index in meter reading has been enhanced in 1.00% in the R&I scenario compared with the BAU scenario (albeit the BAU was already good: 98%)



24 | 124

SPANISH DEMO


KPI 15 Success index in events reading 72.00% The success index in meter event reading stands at 72.00% .

KPI 16 Success index in PRIME advanced functionalities

17.50% The success index in PRIME advanced functionalities stands at 17.50%.

KPI 17 Success index in meter connectivity 48.08% The success index in meter connectivity has been enhanced in 48.08% in the R&I scenario compared with the BAU scenario.

KPI 18 Consumers being metered automatically 1.00%

The consumers being metered automatically has been enhanced in 1.00% in the R&I scenario compared with the R&I scenario (albeit the BAU was already good: 95%).

KPI 19 Improved Life-time of Transformers 0.14% The improved life-time of transformers has been enhanced in 0.14% in the R&I scenario compared with the BAU scenario.

KPI 20 Participant recruitment 20.93% The participant recruitment stands at 20.93% in relation with the contacted participants.

KPI 21 Active participation 87.42% The active participation stands at 87.42% in relation with the recruited participants.

KPI 23 Use of equipment standards 125.00%

All the equipment standards declared to use at the beginning of the project have been used. An additional 25% of these standards have been extended in the scope of the project.

KPI 24 Use of protocol standards 121.42%

All the protocol standards declared to use at the beginning of the project have been used. An additional 21.42% of these standards have been extended in the scope of the project.

In the scope of the Spanish demo there has been some KPIs declared to be calculated in [1] but not

finally calculated in this deliverable due to:

• KPI 9 Energy losses. This KPI will not be finally calculated for the Spanish demo because there

were not foreseen actions to reduce the energy losses. This KPI was only included for this demo

in [1] to evaluate potential collateral effects of other actions that finally have not been produced

after the analysis of the R&I scenario.

• KPI 12 Characterized information categories. This KPI has not been calculated for the Spanish

demo with quantitative data, nevertheless chapter 3.1.6 provides a qualitative analysis of the

results obtained regarding characterised information categories.

• KPI 25 Reduction in greenhouse gas emissions. This KPI will not be finally calculated for the

Spanish demo because there were not foreseen actions to actively reduce the greenhouse gas

emissions (linked in D1.4 with KPI 9). This KPI was only included for this demo in [1] to evaluate



25 | 124

potential collateral effects of the energy losses that finally have not been produced after the

analysis of the R&I scenario.

3.1.1 KPI 4: FULFILMENT OF VOLTAGE LIMITS

This KPI is used to evaluate the fulfilment of regulatory voltage limits in distribution networks and it is

calculated using the following formula:

𝑉(%) =𝑉𝐵𝐴𝑈 − 𝑉𝑅&𝐼

𝑉𝐵𝐴𝑈 ( 1 )

where:

𝑉𝐵𝐴𝑈 Percentage of time that the voltage is out of limits (undervoltage and overvoltage) in BAU scenario (mean value per customer).

𝑉𝑅&𝐼 Percentage of time that the voltage is out of limits (undervoltage and overvoltage) in R&I scenario (mean value per customer).

The Spanish demo has identified, through Smart Meter (SM) event analyses, the worst secondary

substations (SSs) and fuse boxes (FBs) regarding voltage limit issues in the demo area (although results

from the SM event analysis have been already delivered to the maintenance responsible for this

network area). A report containing this information has been given to the maintenance responsible to

perform corrective field works (e.g. change distribution transformers tap changer position). Until the

date of delivering this release of D8.1, these actions have been not performed in the demo area.

However, the same procedure was followed in other LV network areas managed by the DSO such as

Burgos province, where the maintenance crews were able to perform these corrective works.

For this reason, this KPI has been calculated using the data of a distribution transformer installed in

Burgos comparing the voltage results in December 2015 (BAU scenario) and December 2016 (R&I

scenario), after modifying the tap changer. Same month has been chosen for the monitoring period in

BAU and R&I scenarios to minimize the impact that the level of charge of the transformer has on the

voltage.

Fulfilment of voltage limits has been calculated using the following formula:


𝑉𝐵𝐴𝑈 ( 2 )



26 | 124

where:

𝑉𝐵𝐴𝑈 The 95% percentage voltage value during monitoring period (one month) in selected critical point in LV network for BAU scenario.

𝑉𝑅&𝐼 The 95% percentage voltage value during monitoring period (one month) in selected critical point in LV network for R&I scenario.

According to the data provided, the KPI “Fulfilment of voltage limits” for the Spanish demo has the

following value:


𝑉𝐵𝐴𝑈=

231 − 223

231= 3.46% ( 3 )

3.1.2 KPI 5: AVERAGE TIME FOR LV FAULTS

This KPI represents the percentage of reduction in time required for LV fault awareness, location and

isolation (i.e. the last affected customer recovers the electricity supply). The Spanish demo has applied

the new LV NMS, including the Mobile solution for Field Crews, for LV incident management procedure

through a validation process based on the analysis of 583 LV incidents registered in the LV NMS and 52

LV incidents managed by Field Crews with the LV NMS Mobile solution. It is worth mentioning that 22

out of the 52 incidents (42%) managed with the Mobile solution were not responsibility of the DSO. This

means that thanks to the capability of requesting SM measurements on demand from the Mobile

solution the Field Crew was not mobilised unnecessarily.

The main outcomes of the Spanish demo regarding the time for LV management are the following:

• Improvement in the LV fault awareness time. In the BAU scenario, the DSO was aware of the

fault with a consumer call. Meanwhile, in the R&I scenario, thanks to the new functionalities of

the LV NMS, the DSO can be aware of the LV incident earlier thanks to receive SM events

(additionally to the consumers calls). Based on the analyses of the registered incidents that have

events and calls grouped (30 incidents), this differential time would allow to start working on the

LV incident earlier. The obtained value, in average, has been 36 minutes.

• Improvement in the LV fault restoration time. The use of the new LV NMS Mobile solution by

field crews has shown a reduction in the LV restoration time of 16 minutes comparing 30

incidents5 managed with the LV NMS Mobile solution and similar ones in both, season and

description, before using the LV NMS. It is worth mentioned that as time goes a more incidents

are managed with the Mobile solution, more accurate results can be obtained.

5 These 30 incidents might not be the same 30 incidents included in the previous bullet point.



27 | 124

Only in the scope of this document and with the objective of calculating a value for the global time for

LV faults (for high level KPI calculation purpose) considering the aforementioned results, the LV fault

awareness and restoration time will be summed although these results are not based on the same

incidents. Based on this idea, the time needed for fault awareness, location and isolation in LV has been

calculated using the following formula:

∆𝑇LV(%) =(∆𝑇𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠 + ∆𝑇𝑟𝑒𝑠𝑡𝑜𝑟𝑎𝑡𝑖𝑜𝑛)𝐵𝐴𝑈 − (∆𝑇𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠 + ∆𝑇𝑟𝑒𝑠𝑡𝑜𝑟𝑎𝑡𝑖𝑜𝑛)𝑅&𝐼

(∆𝑇𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠 + ∆𝑇𝑟𝑒𝑠𝑡𝑜𝑟𝑎𝑡𝑖𝑜𝑛)𝐵𝐴𝑈 ( 4 )

where:

(∆𝑇𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠)𝐵𝐴𝑈

The time it takes for a DSO to be aware of an incidence

(without using the new LV NMS, that is, based on

customer calls)

(∆𝑇𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠)𝑅&𝐼

The time it takes for a DSO to be aware of an incidence

(using the event coming from the SMs to the LV NMS

before consumer calls).

(∆𝑇𝑟𝑒𝑠𝑡𝑜𝑟𝑎𝑡𝑖𝑜𝑛)𝐵𝐴𝑈

Time needed to restore the electrical service since the

DSO is aware of an incidence in R&I scenario (before

using LV NMS Mobile solution.

(∆𝑇𝑟𝑒𝑠𝑡𝑜𝑟𝑎𝑡𝑖𝑜𝑛)𝑅&𝐼 Time needed to restore the electrical service using LV

NMS Mobile solution.

According to the data provided, the KPI “Average time for LV faults” for the Spanish demo has the

following value:

∆𝑇LV(%) =(∆𝑇𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠 + ∆𝑇𝑟𝑒𝑠𝑡𝑜𝑟𝑎𝑡𝑖𝑜𝑛)𝐵𝐴𝑈 − (∆𝑇𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠 + ∆𝑇𝑟𝑒𝑠𝑡𝑜𝑟𝑎𝑡𝑖𝑜𝑛)𝑅&𝐼

(∆𝑇𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠 + ∆𝑇𝑟𝑒𝑠𝑡𝑜𝑟𝑎𝑡𝑖𝑜𝑛)𝐵𝐴𝑈

=(36.73 𝑚𝑖𝑛 + 327.35 𝑚𝑖𝑛) − (0 𝑚𝑖𝑛 + 311.23 𝑚𝑖𝑛)

(36.73 𝑚𝑖𝑛 + 327.35 𝑚𝑖𝑛)= 14.52%

( 5 )

This means that the deployment of the new LV NMS has enabled to reduce the mean time needed to

restore the service after a LV fault in a 14.52% compared with the BAU scenario.



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3.1.3 KPI 7: QUALITY OF SUPPLY IMPROVEMENT IN LV

During the Spanish demonstrator, the new LV NMS has been running in parallel with the existing

incident reporting system. This means that all incidents happen during the demo period have been

registered by both systems. This has allowed to compare the improvement regarding the incident scope

accuracy. Therefore, for the calculation of this KPI the R&I scenario is the LV incident management with

the new LV NMS and the BAU scenario the management of the same LV incidents but with the existing

incident reporting system in operation.

It is important to mention here that the LV NMS provides more reporting details information regarding

the incident management thanks to the capability of introducing connectivity information. In those SSs,

with LV advanced supervision installed, the real connectivity information has been used while in the rest

of SSs consumers have been assigned equitably to each feeder phase until the real connectivity is

known. In this way, the DSO is able to keep a register of the number of consumers in the LV NMS that

recover the electricity supply and the time they have been without service after each field actuation

within the management of an incident (what it is called interruption). This capability was not available in

the demo area before the UPGRID LV NMS deployment.

Then this KPI does not show the improvement in the quality of supply but the improvement in the

calculation of the quality of supply in LV based in a monitoring period from 02/05/2017 to 08/09/2017

regarding 212 LV incidents. It has been calculated using the following formula:

𝑄𝑆𝐿𝑉(%) = 1 −CMLR&I

CMLBAU ( 6 )

where:

CMLBAU Calculation of the Customer Minutes Lost in BAU scenario

(before new LV NMS deployment).

CMLR&𝐼 Calculation of the Customer Minutes Lost in R&I scenario (after

the LV NMS deployment).

According to the data provided, the KPI “Quality of supply improvement in LV” for the Spanish demo has

the following value:

𝑄𝑆𝐿𝑉(%) = 1 −CMLR&𝐼

CMLBAU= 1 −

167.56

246.08= 31.90% ( 7 )



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3.1.4 KPI 10: MONITORING INFORMATION CATEGORIES

This KPI lists main equipment and information categories available before and after (during)6 the

Spanish demonstrator (Table 5). It provides an insight of how the work performed in the demonstrator

impacts on the existence of new information categories accessible from the DSO control centre. This

does not mean necessarily that the demonstrator makes use of these data.

TABLE 5: MAIN EQUIPMENT AND INFORMATION CATEGORIES FOR THE SPANISH DEMO

Equipment

Number of deployed devices

Main Information Category

Monitored data

Before UPGRID After UPGRID Before

UPGRID

After

UPGRID

SM 179,694 190,838

Energy registers Yes Yes

Standard events Yes Yes

Cut-off element control

events Yes Yes

Quality events Yes Yes

Fraud events Yes Yes

Demand response events Yes Yes

High occurrence –

common events Yes Yes

Security events Yes Yes

Data

Concentrators 936 939 Data Concentrator Events Yes Yes

Advanced LV

supervision 100 495

Energy register Yes Yes

Voltage and current

register Yes Yes

Standard events Yes Yes

Quality events Yes Yes

6 From now on “after” include also “during” the execution of the demonstrator.



30 | 124

Equipment



Monitored data


UPGRID

After

UPGRID

GTP7 – Master

PRIME base

node role

0 2 SNMP MIB8 PRIME No Yes

GTP - Slave

PRIME service

node role

0 2 SNMP MIB PRIME No Yes

It is important to contextualise the information included in the previous table to understand it correctly.

For this reason the following remarks are provided:

▪ SMs

- 179,694 means supply points of type T59 with SMs installed. The difference between the latter

figure and the total supply points (before the demonstrator) in the area is mainly due to the

existence of some supply points of type T410 (not included in the mandatory Advanced Metering

Infrastructure (AMI) deployment by the regulation yet). Only the Bilbao area has been

considered11.

- The SM installation is out of the demonstrator scope and it has been already done in the demo

area because of the mandatory SMs roll out in Spain. Any difference, before and after UPGRID, is

exclusively due to roll out reasons (e.g. existence of some meter that was pending to be

changed).

- SMs are able to register events and measurements. It happened before and it will happen after

UPGRID. Then, no differences with this regard. For this reason it is said YES in both columns of

Table 5. The increased number of devices does not impact on the existence of new information

categories. However, one of the objectives of the Spanish demonstrator is to explore the

information provided through certain SM events to enhance and improve operation and

maintenance (O&M) of the LV network. This is shown in KPI 11.

▪ Data concentrators

7 GTP stands for Gateway of Telecommunications PRIME

8 MIB stands for Management Information Base

9 Supply points type 5 are those with power contracted <= 15kW (Spanish Royal Decree 1110/2007).

10 Supply points type 4 are those with power contracted > 15kW and <= 50kW (Spanish Royal Decree 1110/2007).

11 The demo area extension conducted to increase the network managed by the LV NMS has not been considered in this calculation. This was a decision taken in the middle of the demo execution. Since both KPI terms are referred to the same basis there is not an impact on it. Moreover, the KPI result should be the same if the extended network area would have been considered.



31 | 124

- The installation of data concentrators is out of the scope of the Spanish demonstrator as well.

Same explanation provided for SMs applies here.

- The total number of data concentrators does not coincide with the number of SSs because in

those SSs with less than five supply points data concentrators are not installed (another solution

is installed instead).

▪ Advanced LV supervision

- The installation of advanced LV supervision equipment is out of the scope of the Spanish

demonstrator as well. Important to note that this smart solution is not mandatory by law as the

metering is. The difference in figures before and after UPGRID is due to the recent decision (also

out of the demonstrator scope) of deploying more devices (in addition to those installed in the

demo base).

- Events and energy, voltage and current measurements were registered before UPGRID and it will

be still so after UPGRID. However, the demonstrator uses part of this data in the LV NMS (see KPI

11). The increased number of devices does not impact on the existence of new information

categories.

▪ GTP Master PRIME base node role and GTP - Slave PRIME service node role

- Physically (hardware) both devices are the same. The differences (software) are the role in the

PoweRline Intelligent Metering Evolution (PRIME) hierarchy (only one master node is allowed in

each PRIME network) and the information managed (the base node has PRIME architecture

information). They have been developed in UPGRID (reason of the difference between before

and after the demonstrator shown in Table 5).

- Data managed by these devices are not sent to the AMI Head End as it happens with metering. It

has been defined that the PRIME base node registers information. This information is not sent if

the web base PRIME management tool (also developed in the demonstrator) does not request it.

There are two types of values: instantaneous (punctual value that is not stored and available

only if the tool asks for it) and periodic (defined to be stored and covered approximately during

the last minute).

- The PRIME management tool accesses remotely to PRIME base nodes through the Simple

Network Management Protocol (SNMP) communication protocol. All PRIME monitoring registers

through SNMP have been defined and implemented in the UPGRID Spanish demonstrator. The

interface of the management tool is new as well. It shows not only PRIME topological

information but also detailed data about PRIME network performance and use.

As conclusion, it can be said that, apart from the GTP PRIME, all types of field devices involved in the

demonstrator existed before UPGRID and so as the information categories sent to the AMI Head End.

Then, one of the main contributions of the Spanish demonstrator is the new uses given to part of this

information for LV O&M purposes (see KPI 11).

The count criterion chosen by the Spanish demo is the second one proposed in [1]: counting each

information category available in each LV network measuring point (i.e. SMs provide measuring



32 | 124

information, power quality information, events, etc.). Taking into account this count criterion and based

on Table 5, the KPI “Monitoring information categories” for the Spanish demo has the following value:

𝑀𝐷𝑉(%) =𝑀𝐷𝑅&𝐼 − 𝑀𝐷𝐵𝐴𝑈

𝑀𝐷𝐵𝐴𝑈=

1,529,627 − 1,438,888

1,438,888= 6.31% ( 8 )

3.1.5 KPI 11: AVAILABLE INFORMATION CATEGORIES

This KPI identifies how the work performed in the Spanish demonstrator has changed the use of certain

information categories identified in KPI 10. This means for example data already collected and available

in the AMI Head End before the demonstrator but not exploited for O&M of LV network until UPGRID.

Table 6 crosses most relevant demonstrator systems and procedures with the latter information

categories (i.e. LV NMS, Mobile solution12 for grid crews, web based tool to monitor PRIME networks,

event analysis procedure and web portal for consumer empowering). This table remarks mainly those

cases when the demonstrator entailed changes in the use of data before and after it. If data is still not

explicitly exploited by UPGRID (even being available, see KPI 10), cells (before and/or after UPGRID) are

kept empty (-); if data and/or system/procedure only exists because UPGRID then the first column says

n/a and finally cells are in grey if the information categories are not related to that particular system or

procedure.

It is important to contextualise the information included in Table 6 to understand it correctly. For this

reason some remarks are provided below.

▪ SMs

- SMs are able to register events (almost 200 types of events in the PRIME companion). It

happened before and it will happen after UPGRID. However, one of the objectives of the Spanish

demonstrator is to explore the information provided through SM events to enrich and improve

the maintenance of LV network. The demonstrator is being focused only in certain set of events

(those more useful for O&M of LV network based on experience): undervoltage, overvoltage,

high impedance, loss of neutral and loss of phase (belonging to the standard and quality groups).

In more detail:

o Undervoltage, overvoltage, high impedance and loss of neutral for offline analyses.

o Loss of phase events are be communicated spontaneously (as soon as they are register)

by the three phase meters to the LV NMS. They are processed as consumer calls when

12 Since basically the Mobile solution is a portable version of the LV NMS (i.e. a tablet accessing the LV NMS servers), same information is shown in Table 6 for both systems with the difference that the mobile solution did not existed before UPGRID (for this reason the first column for it is empty).



33 | 124

reporting an incident. This was not done before. Then, Table 6 says NO before UPGRID

and YES after it.

- SM event analyses had not been performed before UPGRID. For this reason it is said n/a in Table

6. The LV NMS version installed in the LV pilot of the demo base Bidelek Sareak13 project did not

use SM events event thought the information was available. For this reason it is said NO in Table

6. Since the Mobile solution was not available before UPGRID, it is said n/a in Table 6.

▪ Data concentrators

- Events of this equipment are not being used in the Spanish demonstrator. For this reason it is

said NO before and after UPGRID.

▪ Advanced LV supervision

- As mentioned in KPI 10, energy, voltage and current measurements were registered before

UPGRID and it will be still done so after UPGRID. The difference is that this information is being

used now by the UPGRID LV NMS as follows:

o possibility to access to historical measurements

o measurements can be used to validate power flow results

- Moreover, some of the deployed equipment (approximately 50 of the total number of units) is

able to determine the phase connectivity of the SMs (i.e. knowing the LV feeder phase in which

each SM is connected to). This information is also used in the LV NMS for having a sound

mapping of SMs in the LV network diagram what is of interest to power flow and network

operations. Before having this information, the mapping was done randomly (e.g. third of the

supply points to each LV feeder phase).

- As mentioned in KPI 10, this equipment registers the same events before and after UPGRID.

However, one type of event (blown fuse) is used in the LV NMS to display an alarm in case some

LV feeder phase is opened at LV switchboard level (in a SS). For this reason, it is said NO before

and YES after UPGRID in Table 6.

▪ GTP Master PRIME base node role and GTP - Slave PRIME service node role

- Due to the reasons pointed out in KPI 10 it is said NO before and YES after UPGRID in Table 6.

13 http://bidelek.com/

http://bidelek.com/



34 | 124

TABLE 6: HOW THE SPANISH DEMONSTRATOR MAKES INFORMATION CATEGORIES AVAILABLE FOR MAIN SYSTEMS AND

PROCEDURES

Equipment

Number of

deployed

devices Information

Category

Available data for…

LV Network

management

system

Mobile devices

(grid crews)

Web based tool

to monitor

PRIME sub-

network

SM event

analysis

Web portal

(consumer

empowering)

Bef

ore

UP

GR

ID

Aft

er

UP

GR

ID

Bef

ore

UP

GR

ID

Aft

er

UP

GR

ID

Bef

ore

UP

GR

ID

Aft

er

UP

GR

ID

Bef

ore

UP

GR

ID

Aft

er

UP

GR

ID

Bef

ore

UP

GR

ID

Aft

er

UP

GR

ID

Bef

ore

UP

GR

ID

Aft

er

UP

GR

ID

SM 179,694 190,838

Energy registers Yes Yes n/a Yes

Yes Yes

Standard events No Yes n/a Yes

n/a Yes

Cut-off element

control events - - n/a -

- -

Quality events - - n/a Yes

n/a Yes

Fraud events - - n/a -

- -

Demand

response events - - n/a -

- -

High occurrence

– common

events

- - n/a -

- -

Security events - - n/a - - -

Data

Concentrators 936 939

Data

Concentrator

Events

- - n/a -

Advanced LV

Supervision 100 495

Energy register No Yes n/a Yes

Voltage and

current register No Yes n/a Yes

Standard events No Yes n/a Yes

Quality events No Yes n/a -

GTP - Master

PRIME base

node role

0 2 SNMP MIB

PRIME n/a Yes

GTP - Slave

PRIME service

node role

0 2 SNMP MIB

PRIME n/a Yes

The count criterion chosen by the Spanish demo is the second one proposed in D1.4: counting each

information category available in each new LV network measuring point that may be visualized through

one of the platforms. If a measuring point information category is available through three different

platforms, it will be counted three times. Taking into account this criterion and according to the data



35 | 124

provided in Table 6, the KPI “Available information categories” for the Spanish demo has the following

value:

𝐴𝐷𝑉(%) =𝐴𝐷𝑅&𝐼 − 𝐴𝐷𝐵𝐴𝑈

𝐴𝐷𝐵𝐴𝑈=

1,911,849 − 359,388

359,388= 431.97% ( 9 )

3.1.6 KPI 12: CHARACTERIZED INFORMATION CATEGORIES

Although this KPI has not been calculated for the Spanish demo with quantitative data, the following

paragraphs provide a qualitative analysis of the results obtained regarding characterised information

categories.

This KPI aim is to specify how particular processes, calculations, task, etc. performed by the main

systems and procedures identified in KPI 11 are impacted by the work done in the Spanish

demonstrator. The development of new equipment, the availability of new information categories and

the leverage of existing ones for first time (KPI 10, KPI 11) have introduced differences in how certain

operations are either done or enhanced. In this regards it is worth mentioning the following particular

cases:

▪ Data for defining a sound LV network representation

- Having a sound LV network representation is one objective of the Spanish demonstrator around

which the O&M relies on. The type of information that is being uploaded during the

demonstrator is basically the same managed before (during the LV pilot of the demo base

Bideleak Sareak project). However there are differences:

o New network asset attributes and descriptive text have been added. This avoids

Operators opening other data bases and systems to check this information.

o The capability to manage SMs measurements from the LV NMS has been added. This is a

feature developed in the demonstrator. Thanks to that, the Operator is able to access

SMs measurements on demand through the LV NMS.

▪ Algorithm to determine connectivity of SMs to the grid

- The advanced LV supervisor devices that are installed in the demonstrator area are able to

determine the connectivity of SMs. This facilitates knowing in which LV feeder and even in some

cases in which phase of the feeder each SM is connected to. This is relevant since it allows

validating the information that is registered in the Geographic Information System (GIS) that it

might not be completely accurate in this regard. So far this information has not been uploaded

into the LV NMS.

▪ Support Power flow analyses in the LV NMS

- The LV NMS availability of measurements taken by the advanced LV supervisors deployed in the

demonstrator area allows checking the accuracy of power flow results achieved by LV NMS

calculations. That means comparing voltage and currents measured with the distribution power

flow results for those LV feeders equipped with these devices.



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- Different approach to run distribution power flows regarding the data used is also being testing

in the demonstrator. The time stamp (day and hour) for peak demand in each distribution

transformer supervisor (that might be different in each SS) is collected. For this day and hour all

SMs are interrogated to calculate the weight of each type of consumer that is assigned to each

FB. These values, combined with typical load profiles for each type of supply points, are used for

running power flows. This is a new approach since contracted powers were applied before for

these calculations. More accurate results are expected.

▪ Identification of LV incidents

- The demonstrator has made possible to treat some SM events (i.e. phase loss) as a trigger for

registering LV incidents that have happened in the LV network. This means that not only

incidents are opened after consumer calls but when SMs register this event automatically. This

can start the process to solve them even before some consumers are aware of it (e.g. if they are

not in their houses).

- Moreover, alarms due to blown fuse registered by advanced LV supervisors are also used by the

LV NMS to open automatically an incident report.

▪ LV NMS Mobile solution

- Having developed a LV NMS Mobile solution for Field Crews has not introduced changes in

processes, calculations, task, etc. regarding the use of data apart from those mentioned for the

LV NMS itself since it is basically a portable version of the LV NMS available in a control centre.

What has happened is that the LV NMS has been modified (software architecture) to allow the

existence of the Mobile solution.

3.1.7 KPI 13: AVAILABILITY OF INTELLIGENT NETWORK COMPONENTS

This KPI aim is to weight the relative importance of the intelligent components and systems used in the

demonstrator. Based on KPI 10, it is known that most of the equipment existed before starting the

demonstrator and that slight changes have been implemented on them due to UPGRID. This is not the

case of the LV NMS, GTP PRIME devices, and PRIME monitoring tool.

TABLE 7: RELEVANCE OF THE SPANISH DEMO REGARDING EQUIPMENT AND SYSTEMS

Intelligent Component and

Systems

Weight Factor

(Component

Relative

Importance)

Before UPGRID After UPGRID

IC

(0.5= component modified or with

new functionalities)

(1=component installed during the

UPGRID project)

SMs 9% 179,694 190,838 0.5

Data Concentrators 7% 936 939 0.5

Advanced LV supervision 9% 100 495 0.5

GTP - Master PRIME base node

role & GTP - Slave PRIME service 20% 0 4 (2+2) 1



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Systems

Weight Factor

(Component

Relative

Importance)


IC




UPGRID project)

node role

LV Network Management

System (LV NMS) 40% 0 1 0.5

SW system for collecting MIB’s 15% 0 1 1

Total 100%

In Table 7 it can be observed that the component with the highest relative importance is the LV NMS

what is aligned with the demonstrator objectives. Since a first version of LV NMS was installed in the LV

pilot of the demo base Bidelek Sareak project, the last column of Table 7 says 0.5.

The availability of intelligent network components has been calculated using the following formula:

𝐴𝑉(%) =𝐼𝐶𝑅&𝐼 − 𝐼𝐶𝐵𝐴𝑈

𝐼𝐶𝐵𝐴𝑈 ( 10 )

where:

𝐼𝐶𝑅&𝐼

Amount of the intelligent components deployed in R&I

scenario and/or intelligent components with enhanced

functionalities.

𝐼𝐶𝐵𝐴𝑈 Amount of the intelligent components deployed in BAU

scenario.

In the former formula, the intelligent components (devices and systems) have been weighted since the

specific work that is being developed in the demonstrators has not the same impact over them. In

addition, this KPI also considers new components installed in the scope of the UPGRID project and those

in which new functionalities have been enabled. The count criterion for the intelligent components is

the following one:

• IC = 0.5: if the intelligent component has been modified to include new functionalities during the

UPGRID project.

• IC = 1: if the intelligent component has been installed during the UPGRID project.



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According to the former data, the KPI “Availability of intelligent network components” for the Spanish

demo has the following value:


𝐼𝐶𝐵𝐴𝑈= 111.13% ( 11 )

This result means that there is a 111.13% increase of intelligent components in the R&I scenario

compared with the BAU scenario.

3.1.8 KPI 14: SUCCESS INDEX IN METER READING

This KPI determines the success index in SM reading in BAU and R&I scenarios. It is an indicator to

evaluate the performance of the metering infrastructure.

The criterion used by the Spanish demo to provide this KPI is the rate of meter reading during the last 30

days. This index is based on report S05 [12]. It contains one measurement per day. Then it is checked for

how many SMs this report is filled in for the last 30 days. The number of SMs considered in the formula

is the group of equipment already installed, connected to the AMI infrastructure and registered in the

AMI Head End.

Success index in meter reading has been calculated using the following formulas:

𝑆𝐼𝑀𝑅(%) = 𝑆𝐼𝑅&𝐼 − 𝑆𝐼𝐵𝐴𝑈 ( 12 )

𝑆𝐼(%) =𝐶𝑆𝑢𝑐𝑐𝑒𝑠𝑠

𝐶𝑇𝑜𝑡𝑎𝑙 ( 13 )

where:

𝐶𝑆𝑢𝑐𝑐𝑒𝑠𝑠

Total number of measurements and actions that are correct

and successfully performed by the first data retrieval and can

be used for the respective process.

𝐶𝑇𝑜𝑡𝑎𝑙 Total number of triggered measurements and actions with

the relevant period of time

According to the data provided, the KPI “Success index in meter reading” for the Spanish demo has the

following value:

𝑆𝐼𝑀𝑅(%) = 𝑆𝐼𝑅&𝐼 − 𝑆𝐼𝐵𝐴𝑈 = 0.99 − 0.98 = 1 % ( 14 )



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This results means that the success index in SM reading has been enhanced in 1.00% in the R&I scenario

compared with the BAU scenario. It is important to mention here that the success index in meter

reading was already very high in the BAU scenario and Spanish demo was not focused in improving the

SMs performance. Nevertheless this is an important result because it also means that the works

performed in the demo like the retrieval of events from the meters do not affect the SM performance.

3.1.9 KPI 15: SUCCESS INDEX IN EVENT READING

The objective of this KPI is to quantify how many SMs, that should send an expected event after a known incident really, do that. The methodology is based on analysing events after an incident affecting all SMs fed by one or more SSs. Then, the calculation of this KPI is subjected to the occurrence of this kind of incidents. Only four specific non-spontaneous events associated to supply failure are searched. An index is calculated on basis of these events recording.

Although there were SMs installed before starting the demonstrator and the collection of events commenced by then, the latter registers have not been analysed in detail until UPGRID project. Therefore there are not previous performance indicators (BAU scenario) to be compared with.

On 12/02/2016 there was an outage affecting a total of 14 SSs in the demonstrator area (Bilbao). An index (i.e. total number of searched events received / theoretical number of events to be received) is calculated (see Table 8) on basis of the events recording. The closer the index is to 0, the more events are missed.

TABLE 8: SUMMARY OF RESULTS OBTAINED REGARDING THE NUMBER OF EVENTS THAT SHOULD BE RECEIVED AFTER THE

INCIDENT HAPPENED ON 12/02/2016 THAT AFFECTED 14 SSS.

SS id Number of SMs Total number of searched

events received Index

(% of events received)

1 269 635 0.79

2 390 1069 0.91

3 208 634 1.02

4 339 859 0.72

5 285 761 0.89

6 107 321 1

7 327 74 0.08

8 257 528 0.68

9 97 324 1.1114

10 230 263 0.38

11 358 783 0.73

12 325 683 0.7

13 264 496 0.63

14 9 12 0.44

14 Being higher than 1 means that there are SMs registering the situation (the supply shortage) more than once during the incident. Since the index is higher than 1 it implies that there are not missing events what was the purpose of the test.



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SS id Number of SMs Total number of searched

events received Index

(% of events received)

Average index value 0.72

It is important to mention here that the initial formula proposed for the KPI calculation (included in [1])

was based on the comparison between the success index in events reading in the R&I scenario and in

the BAU scenario:

𝑆𝐼𝐸𝑅(%) = 𝑆𝐼𝑅&𝐼 − 𝑆𝐼𝐵𝐴𝑈 ( 15 )



where:

𝐶𝑆𝑢𝑐𝑐𝑒𝑠𝑠 Number of meters sending correctly their events after a grid

issue.

𝐶𝑇𝑜𝑡𝑎𝑙

Number of meters that the DSO knows that should be

sending their events after a grid issue (i.e. after a loss of

energy supply).

As it has been explained before, although there were SMs installed before starting the demonstrator,

the events were not processed at that time and it has been not possible to provide data for the BAU

scenario. Therefore the KPI formula has been slightly modified as follows to consider only R&I scenario

information:

𝑆𝐼𝐸𝑅(%) = (𝐶𝑆𝑢𝑐𝑐𝑒𝑠𝑠

𝐶𝑇𝑜𝑡𝑎𝑙)

𝑅&𝐼

= 72 % ( 17 )

The conclusion obtained after analysing the data collected was that 28% of events were missed in the studied incident, Table 8. This figure is consistent with the expected value. Then, it could be extrapolated to the global recording of events at Meter Data Management System (MDMS).

From the demonstrator it is determined that the main cause behind this behaviour is that non-spontaneous events are recorded weekly into the MDMS, during a certain time-slot. When this time-slot ends, the SM events which have not been sent are not recorded there. Therefore, a new configuration of SMs would be needed in order to discard some events, such as communications events, useful during the roll out, but dispensable after finishing it.



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3.1.10 KPI 16: SUCCESS INDEX IN ADVANCED FUNCTIONALITIES

In the scope of UPGRID Spanish demonstrator, it has been tested the latency of IP over PRIME traffic in

conditions of isolated Internet Protocol (IP) traffic and mixed IP and Advance Metering Infrastructure

(AMI) traffic. The tests were aimed to identify possible traffic loss when IP and AMI traffic share PRIME

layer.

This KPI measures the latency in communications calculating the percentage of successful

communications of “ping” traffic in less than the delay objective.

Maximum delay has been fixed to 1 second for ping traffic, as the regular timeout used in common

transmission media for AMI data, such as General Packet Radio Service (GPRS).

Success index in advanced functionalities has been calculated using the following formula (from [2]):

𝑆𝐼𝐴𝐹(%) =𝐶𝑆𝑢𝑐𝑐𝑒𝑠𝑠


where:

𝐶𝑆𝑢𝑐𝑐𝑒𝑠𝑠 Successful communication attempts (lower latency than the

target).

𝐶𝑇𝑜𝑡𝑎𝑙 Total number of communication attempts.

Two success indexes have been calculated, summarized in the following Table 9:

TABLE 9: SIAF WITH MAXIMUM DELAY 1 SECOND

Test scope SIAF value

IP over PRIME traffic 4%

IP over PRIME traffic + AMI traffic 17.5%

As conclusion, it can be said that combining IP and AMI traffic introduces delays in packet transmission,

but it is worth mentioning that during the tests no packet was missing. Also, average delay for packets

with delays less than the maximum defined, is very similar in both cases described.

In order to calculate high level KPIs included in chapter 4, it will be considered the maximum value of

the aforementioned calculated KPIs: 17.5%.



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3.1.11 KPI 17: SUCCESS INDEX IN METERS CONNECTIVITY

This KPI evaluates the improvements performed in the Spanish demo to know the phase and feeder to

which each SM is connected to. This is mainly thanks to the sound LV network representation, the

deployment of the LV NMS and the more accurate connectivity information.

Initially, this KPI combined two new connectivity functionalities: the ability to know the location of the

SM (consumer) regarding the associated FB and aforementioned functionality of knowing the phase and

feeder to which each SM is connected. Due to lack of information regarding the BAU scenario, the KPI is

only calculated considering the phase and feeder connectivity.

In the R&I scenario there are 493 SSs (112,042 consumers) with the advanced LV supervision solution

connected to the LV NMS that provide information about the connectivity at feeder phase level. In the

BAU scenario there were only 50 SSs equipped with this functionality.

Therefore success index in meters connectivity has been calculated using the following formula:

𝑆𝐼𝑀𝐶(%) = (𝐶𝑆𝑢𝑐𝑐𝑒𝑠𝑠


𝑅&𝐼

− (𝐶𝑆𝑢𝑐𝑐𝑒𝑠𝑠


𝐵𝐴𝑈

( 19 )

where:

𝐶𝑆𝑢𝑐𝑐𝑒𝑠𝑠 Number of SM which phase and feeder is known.

𝐶𝑇𝑜𝑡𝑎𝑙 Total number of SMs in the scope of the demo in BAU or R&I scenarios.

According to the data provided, the KPI “Success index in meters connectivity” for the Spanish demo has


𝑆𝐼𝑀𝐶(%) = (112,042

190,838)

𝑅&𝐼

− (19,106

179,694)

𝐵𝐴𝑈

= 58.71% − 10.63% = 48.08% ( 20 )

3.1.12 KPI 18: CONSUMERS BEING METERED AUTOMATICALLY

Consumers being metered automatically state the quota of consumers which have their meter

information remotely gathered by the DSO, i.e. with SMs connected via communication network to the

data collection system in BAU and R&I scenarios. The number of consumers being metered



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automatically is an indicator to evaluate the evolution of the metering infrastructure together with the

success in index meter reading (KPI 14).

The Spanish demo has provided directly the BAU and R&I values, due to the fact the data is collected

directly by the AMI Head End. The criterion decided to calculate this KPI has been the following: rate of

meter reading during the last 30 days. This index is based on report S02 (STG-DD definition, [12]). It

contains one measurement per hour. Then it is checked for how many SMs this report is filled for the

last 30 days. The number of SMs considered in the formula is the group of equipment already installed,

connected to the AMI infrastructure and registered in the AMI Head End.

“Consumers being metered automatically” has been calculated using the following formula:

𝑄𝑢𝑜𝑡𝑎(%) = (𝑆𝑀𝐴𝑅

𝑆𝑀)

𝑅&𝐼− (

𝑆𝑀𝐴𝑅

𝑆𝑀)

𝐵𝐴𝑈 ( 21 )

where:

𝑆𝑀𝐴𝑅

Total number of SMs installed on field (meters connected to

the communication network and able to be remotely

accessed and read).

𝑆𝑀

Total number of SMs (meters connected to the

communication network and able to be remotely accessed

and read).

According to the data provided this KPI has the following value:


𝑆𝑀)

𝑅&𝐼− (

𝑆𝑀𝐴𝑅

𝑆𝑀)

𝐵𝐴𝑈= 96% − 95% = 1% ( 22 )

This results means that the consumers being metered automatically have been enhanced in 1.00% in

the R&I scenario compared with the BAU scenario. It is important to mention here that the success

index in meter reading was already very high in the BAU scenario as Spanish demo was not focused in

deploying the SMs infrastructure.

3.1.13 KPI 19: IMPROVED LIFE-TIME OF TRANSFORMERS

Distribution transformer load is an indicator of the electrical stress that this equipment might be

subjected to. The life span of transformers is reduced dramatically in case of exceeding the nominal

values during a long time period or after having very accentuated peaks. This is much related to the

excess of current circulating in the internal transformer winding (i.e. deterioration of the insulation due

to the over heat generated by the current). It is worth noting that the overload of a transformer might



44 | 124

be due to a continue increase of demand or for some network management operation (i.e. switching

loads from one transformer to another or laying out new lines).

Before the deployment of supervision meters in SSs, overloaded transformers were only detected when

the equipment fail. This means not only the impact of changing the unit but also the impact that might

have on the surrounding infrastructure, on the consumers (i.e. power cut) and the corresponding

penalties. However, thanks to the leveraging data collected by transformer supervision meters at SSs

and the processing of it, it is possible that network maintenance responsible has at its disposal reports

for transformer load. Then, it is feasible to identify a potential risk of equipment failure before it

happened triggering work orders to change them by other equipment of higher capacity. This can be

scheduled limiting the impact to the customers.

This KPI is calculated based on the work orders resulted for the analysis of data retrieved from the

supervisor meters in SS. It is considered that each time an overloaded transformer is detected a work

order for replacing this transformer will be launched. This is the R&I scenario.

Improved life-time of transformers has been calculated using the following formula:

∆Tr𝑙𝑖𝑓𝑒(%) =𝑁𝑐ℎ𝑎𝑛𝑔𝑒𝑠𝑅&𝐼 − 𝑁𝑐ℎ𝑎𝑛𝑔𝑒𝑠𝐵𝐴𝑈

𝑁𝑡𝑟𝑎𝑛𝑠𝑓 ( 23 )

where:

𝑁𝑐ℎ𝑎𝑛𝑔𝑒𝑠𝐵𝐴𝑈 Number of distribution transformers changed in the BAU

time period

𝑁𝑐ℎ𝑎𝑛𝑔𝑒𝑠𝑅&𝐼 Number of distribution transformers changed in the R&I time

period

Ntransf Number of total transformer in the selected area of the

demo

During the UPGRID project of releasing the first version of D8.1, a total of 25 distribution transformers

have been changed during the last two years in Vizcaya; while only 2 of them belong to the demo area

(Bilbao). The total number of distribution transformers is 7,609 and 1,456 respectively.

According to the data provided, the KPI “Improved life-time of transformers” for the Spanish demo has


∆Tr𝑙𝑖𝑓𝑒(%) =𝑁𝑐ℎ𝑎𝑛𝑔𝑒𝑠𝑅&𝐼 − 𝑁𝑐ℎ𝑎𝑛𝑔𝑒𝑠𝐵𝐴𝑈

𝑁𝑡𝑟𝑎𝑛𝑠𝑓=

2 − 0

1,456= 0.14% ( 24 )



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3.1.14 KPI 20: PARTICIPANT RECRUITMENT

Recruitment is an indication of the fraction of consumers accepting participation in the different demos.

In the scope of the UPGRID project, the Spanish demo has addressed two different groups of

consumers. The first group has been reached through the president of the Federation of Neighbourhood

Associations of Bilbao (FAAVVB) that integrates 24 associations [13]. The second group includes co-

workers of Tecnalia, Iberdrola, EVE, and ZIV (demo participants), as well as relatives and friends of them,

all of them living in the Spanish demo area, mainly in the city of Bilbao. This KPI has been calculated as

the sum of the amount of consumers participating in the UPGRID demos (weighted in function of

diversification of stakeholders) in relation to the total consumers contacted to be part of them.

It has been considered a weight of 30% co-workers from the companies previously mentioned and a

70% for neighbourhood associations, as the former might be involved more in the electric sector and

has a background in this area; while the neighbourhood associations represent better the point of view

of the final customer without any previous knowledge.

To sum up, this KPI regarding recruitment has been calculated using the following formula:

𝑅(%) = 0.3 (𝑛𝑎𝑐𝑐𝑒𝑝𝑡

𝑛𝑡𝑜𝑡𝑎𝑙)

𝑐𝑜−𝑤𝑜𝑟𝑘𝑒𝑟𝑠

+ 0.7 (𝑛𝑎𝑐𝑐𝑒𝑝𝑡


𝐹𝐴𝐴𝑉𝑉

( 25 )

where:

𝑛𝑎𝑐𝑐𝑒𝑝𝑡 Number of users that finally accepted to be part of the

demo.

𝑛𝑡𝑜𝑡𝑎𝑙 Number of users contacted to be part of the demo.

According to the data provided, the KPI “Participant recruitment” for the Spanish demo has the

following value:

𝑅(%) = 0.3 (𝑛𝑎𝑐𝑐𝑒𝑝𝑡



+ 0.7 (𝑛𝑎𝑐𝑐𝑒𝑝𝑡


𝐹𝐴𝐴𝑉𝑉

= 0.3 ·90

444+ 0.7 ·

56

264= 20.93%

( 26 )

3.1.15 KPI 21: ACTIVE PARTICIPATION

Active participation is an indication of the fraction of consumers actively taking part in the different

demos. As it has been explained in the previous KPI, the Spanish demo has addressed two different



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groups: co-workers of Tecnalia, Iberdrola, EVE and ZIV (demo participants) and member of the

Federation of Neighbourhood associations of Bilbao (FAAVVB). The participation groups have been

weighted as in the previous KPI (mates 30% and FAAVVB 70%) for the same reasons explained before.

This KPI has been calculated as the sum of the amount of users actively participating and willing to

continue participating in the Spanish UPGRID demo in relation with the total that accepted participating.

According to the data provided, the KPI “Active participation” for the Spanish demo has the following

value:

𝐴(%) =𝑁𝐴

𝑁𝑃= 0.3 (

𝑁𝐴

𝑁𝑃)


+ 0.7 (𝑁𝐴

𝑁𝑃)

𝐹𝐴𝐴𝑉

= 0.3 ·55

56+ 0.7 ·

56

90= 87.42%

( 27 )

3.1.16 KPI 23: USE OF EQUIPMENT STANDARDS

Use of equipment standards is an indication of the effective use of equipment-related standards with

respect to the declared use. Task 1.3 of UPGRID project gathered how the four demos were considering

the implementation of standardized solutions regarding equipment and thus how to improve some of

the proposed demos by using of interoperable and standardized protocols.

Specifically, the equipment standards were divided into the standards already being used in the demo

and the standards to be developed or extended under UPGRID project. Table 7 of UPGRID deliverable

D1.3 [12] contain all this information for the Spanish demo.

Based on the aforementioned table included in D1.3, Table 10 shows the improvement in the

equipment standard in the Spanish demo due to UPGRID project. It shows that the only new

standardised devices implemented during the demonstrator are the GTP - Master PRIME base node role

and GTP - Slave PRIME service node role. The rest already existed before UPGRID and they have not

been changed due to the demonstrator.

TABLE 10: SPANISH DEMO EQUIPMENT STANDARDS

Standardised Equipment

ESEU

(ESEU=0; no change between the status of the equipment standard before and

after UPGRID project)

(ESEU=0.5; equipment standard application extended during UPGRID project)

(ESEU=1; new equipment standard implemented during UPGRID project)

SMs 0

Data Concentrators 0

Advanced LV supervision 0



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ESEU





GTP - Master PRIME base node

role and GTP - Slave PRIME

service node role

1

The use of protocol standards has been calculated using the following formula:

𝑈𝐸𝑆(%) = 1 +𝐸𝑆𝐸𝑈

𝐸𝑆𝐷𝑈 ( 28 )

where:

𝐸𝑆𝐸𝑈 Equipment standards effectively used according to the count

criterion.

𝐸𝑆𝐷𝑈 Equipment standards declared to be used in D1.3 of UPGRID

project.

According to the data provided, the KPI “Use of equipment standards” for the Spanish demo has the

following value:

𝑈𝐸𝑆(%) =𝐸𝑆𝐸𝑈

𝐸𝑆𝐷𝑈= 1 +

1

4= 125 % ( 29 )

3.1.17 KPI 24: USE OF PROTOCOL STANDARDS

Use of protocols standards is an indication of the effective use of protocol-related standards with

respect to the declared use. Task 1.3 of UPGRID project tries gathered how the four demos were

considering the implementation of standardized solutions regarding protocols and thus how to improve

some of the proposed demo projects by maximizing the use of interoperable and standardized

equipment.

Specifically, the protocol standards were divided into the standards already being used in the demo and

the standards to be developed or extended under UPGRID project. Table 6 of UPGRID deliverable D1.3

[12] contains all this information for the Spanish demo.



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Based on the aforementioned table included in D1.3, Table 11 shows the improvement in the protocol

standards in the Spanish demo due to UPGRID project.

TABLE 11: SPANISH DEMO PROTOCOL STANDARDS

Protocol standards

PSEU

(PSEU=0; no change between the status of the protocol standard before

and after UPGRID project)

(PSEU=0.5; protocol standard application extended during UPGRID

project)

(PSEU=1; protocol standard implemented during UPGRID project)

DLMS COSEM 0

PRIME 1.3.6 0

ICCP / TASE2 (IEC 60870-6-503) 0

ICE 60870-5-104 0

CIM (IEC61968, IEC61970, IEC62325) 0.5

STG-DC 32 for SMs management 0

SNMPv3 for MIB collection 1

The standard protocol SNMPv3 for MIB collection (associated to the GTP - Master PRIME base node

role) is the only standard protocol implemented during the UPGRID demonstrator that was not used

before (in the demo base).

The extension of Common Information Model (CIM) has been categorised in Table 11 with 0.5. Iberdrola

alpha-numeric data has been importing into CIM to represent the LV network in the LV NMS. To comply

with all the Iberdrola requirements stablished for this process and the importing tool characteristics, it

has been necessary to perform customise CIM extensions.

The rest of standard protocols have not been changed as a result of the demonstrator.

“Use of protocol standards” has been calculated using the following formula:

UPS(%) = 1 +𝑃𝑆𝐸𝑈

𝑃𝑆𝐷𝑈 ( 30 )

where:

𝑃𝑆𝐸𝑈 Protocols standards effectively used according to the count

criterion.



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𝑃𝑆𝐷𝑈 Protocols standards declared to be used in D1.3 of UPGRID

project.

According to the data provided, the KPI “Use of protocol standards” for the Spanish demo has the

following value:

UPS(%) =𝑃𝑆𝐸𝑈

𝑃𝑆𝐷𝑈= 1 +

1.5

7= 121.42 % ( 31 )



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3.2 PORTUGUESE DEMO


Portuguese demonstration. Table 12 provides a summary and understanding of the KPIs and the

subsequent sections include a further description of each one of them.

Further information about the Portuguese demo can be found in the deliverables of WP4 -

Demonstration in real user environment: EDPD –Portugal, specifically in D4.3 – Evaluation of

Demonstration Results and Data Collection [14].

TABLE 12: PORTUGUESE DEMO KPIS SUMMARY

PORTUGUESE DEMO


KPI 1 Demand flexibility 32.70% The demand flexibility stands at 32.70% in relation with the maximum electricity demand in the demo area.

KPI 3 Hosting Capacity of Electric Vehicles 99.95% There is a 99.95% capacity to host additional electric vehicles in the demo area.

KPI 4 Fulfilment of voltage limits 41.72% The fulfilment of voltage limits has been improved 41.72% in the R&I scenario compared with the BAU scenario.

KPI 5 Average time for LV faults 8.10% The average time for LV faults has been reduced in 8.10% in the R&I scenario compared with the BAU scenario.



KPI 12 Characterized information categories 98.90% The share of new characterized data (R&I scenario) is 98.90% in relation with the total characterized data (BAU + R&I scenarios).


100.00% There is a 100% increase of intelligent components in the R&I scenario compared with the BAU scenario.

KPI 14 Success index in meter reading 16.00% The success index in meter reading has been enhanced in 16.00% in the R&I scenario compared with the BAU scenario.

KPI 18 Consumers being metered automatically 15.78% The consumers being metered automatically has been enhanced in 15.78% in the R&I scenario compared with the BAU scenario.



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PORTUGUESE DEMO


KPI 20 Participant recruitment 62.82% The participant recruitment stands at 62.82% in relation with the contacted participant.




KPI 24 Use of protocol standards 100.00% All the protocol standards declared to use at the beginning of the project have been used.

In the scope of the Portuguese demo there has been some KPIs declared to be calculated in [1] but not


• KPI 8 Quality of Supply Improvement in MV. This KPI will not be finally calculated for the

Portuguese demo because there were not foreseen actions to improve the MV quality of supply.

This KPI was only included for this demo in [1] to evaluate potential collateral benefits that other

actions performed of the scope of the UPGRID project might have in the MV quality of supply.

• KPI 9 Energy losses. As the Portuguese demo area network is quite recent there were not

identified topological manoeuvres with impact in energy losses reduction. Although the impact

of other processes in the reduction of energy losses has been analysed by the Portuguese demo,

the results are not relevant enough to calculate this KPI.

• KPI 22 Load curve valley filling. The load curve valley filling is an indication of the change in kWh

used at valley or through time due to technical signal to increase consumption (DSO order).

Throughout the demo several signals were sent and accepted by the consumers. Nevertheless, in

the Portuguese demo, due to i) the lack of specific DSM incentives to change consumption to the

valley period and ii) the majority of the consumers have simple tariff (i.e. the final consumer

energy price is constant to all hours of the day), it was not possible to achieve a relevant change

in consumption patterns and consequently this KPI cannot be calculated.

• KPI 25 Reduction in greenhouse gas emissions. The actions implemented by Portuguese demo

to reduce the GHG emissions are mainly related to the implementation of demand side

management policies and topological maneuvers with impact in energy losses reduction. Due to

the reasons mentioned in KPI 9 and KPI 25, it will not be possible to calculate this KPI ( i) The

Portuguese demo grid is already optimized; ii) No relevant change in consumption pattern

behavior).



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3.2.1 KPI 1: DEMAND FLEXIBILITY

Flexibility is an indication of the ability of the electricity system to respond to –and balance- supply and

demand in real time. Demand flexibility is mainly measured through demand response capabilities.

Nevertheless other grid initiatives may also enhance the demand flexibility of the grid, such as the

integration of storage resources and specifically the integration of their operation in the distribution

network operation.

For the Portuguese demo, this KPI has been calculated as the sum of the amount of load capacity

participating in demand side management using the following formula:

𝑃𝐷𝑆𝑀(%) =(𝑃𝐷𝑆𝑀)𝑅&𝐼 − (𝑃𝐷𝑆𝑀)𝐵𝐴𝑈

𝑃𝑝𝑒𝑎𝑘 ( 32 )

where:

𝑃𝐷𝑆𝑀 Represents the sum of the amount of load capacity that can

be shifted thanks to DSM in the BAU and R&I scenarios.

𝑃𝑝𝑒𝑎𝑘 Represents the maximum electricity demand in the area

under evaluation.

In the BAU scenario there were no implemented tools or functionalities that would allow for a DSM

request, therefore, (𝑃𝐷𝑆𝑀)𝐵𝐴𝑈 = 0 𝑘𝑉𝐴. Thanks to the deployment of DSM tools in the scope of the

UPGRID project, the Portuguese demo in now able to manage the demand, (𝑃𝐷𝑆𝑀)𝑅&𝐼 = 79.35 𝑘𝑉𝐴.

According to the data provided, the KPI “Demand flexibility” for the Portuguese demo has the following

value:

𝑃𝐷𝑆𝑀(%) =(𝑃𝐷𝑆𝑀)𝑅&𝐼 − (𝑃𝐷𝑆𝑀)𝐵𝐴𝑈

𝑃𝑝𝑒𝑎𝑘=

79.35 − 0

242.65= 32.70% ( 33 )

3.2.2 KPI 3: HOSTING CAPACITY OF ELECTRIC VEHICLES

This KPI intends to measure the contribution that UPGRID project has in increasing the capacity of the

distribution network to host EVs. A direct contribution to this KPI may be enhancing the grid capacity

(lines and transformers) or even the allocation of new charging points in the demo area. An indirect

contribution may be the management or the analysis of the usage information of the existing charging

points to characterise the user’s behaviour and host more charging points with the same grid capability.



53 | 124

Regarding this topic, UPGRID project is mainly addressing indirect actions to enhance the hosting

capacity of electric vehicles.

For the Portuguese demo, this KPI has been calculated as the sum of the available power of the

characterized EV charging points (maximum power capability for each charging station). Hosting

capacity of electric vehicles has been calculated using the following formula:

𝐻𝐶𝐸𝑉(%) = 1 −(𝐻𝐶𝐸𝑉)𝑅&𝐼 − (𝐻𝐶𝐸𝑉)𝐵𝐴𝑈

𝑃𝐸𝑉 ( 34 )

where:

𝐻𝐶𝐸𝑉

Represents the sum of the power consumed by the

characterized EV charging points in the BAU and R&I

scenarios.

𝑃𝐸𝑉 Represents the sum of the installed charging points power.

Each electric vehicle charging station in the UPGRID demo area, connected to distribution network, has

20.7 KVA of nominal power. There are 16 charging stations in the scope of the Portuguese demo. It has

been analysed the usage of the charging station infrastructure in BAU and R&I scenarios in order to

calculate the potential of these charging stations to host additional vehicles. The data provided for the

BAU scenario corresponds to the sum of the mean power in year 2015 (0.343 kW) and for the R&I

scenario in year 2016 (0.517 kW).

According to the data provided, the KPI “Hosting capacity of electric vehicles” for the Portuguese demo

has the following value:

𝐻𝐶𝐸𝑉(%) = 1 −(𝐻𝐶𝐸𝑉)𝑅&𝐼 − (𝐻𝐶𝐸𝑉)𝐵𝐴𝑈

𝑃𝐸𝑉= 1 −

0.517 − 0.343

331.2

= 100% − 0.0525% = 99.9475% ( 35 )

The result of this KPI shows that the charging stations already installed in the Portuguese demo area still

have most of their capability to host additional electric vehicles in the area.


The fulfilment of voltage limits is a common KPI used to evaluate the power quality and quality of supply

of distribution networks. UPGRID project is addressing some actions to be implemented in the demo

areas which will impact positively in the fulfilment of voltage limits. Some of these actions regard to the



54 | 124

remote management of DER. Other actions are related to the implementation of new algorithms to

identify the optimum topological configuration of the distribution grid or even its remote

reconfiguration after a fault. In addition, new voltage regulation capabilities will be implemented by

deploying smart devices and tools.



𝑉𝐵𝐴𝑈 ( 36 )

where:

𝑉𝐵𝐴𝑈 The 95% percentage voltage value during monitoring period (two months), the value for which 95% of all voltage line measurements fall below in BAU scenario.

𝑉𝑅&𝐼 The 95% percentage voltage value during monitoring period (two months), the value for which 95% of all voltage line measurements fall below in R&I scenario.

The Portuguese demo has identified, in BAU scenario, the feeders with highest overvoltage and

undervoltage parameters, not only in SSs but also in customers’ side.

The BAU scenario data was provided by the Portuguese demo as follows:

TABLE 13: BAU DATA FOR KPI 4 (PORTUGUESE DEMO)

Metering Point VBAU

(In the worst feeders) Comments

Secondary substation

252V (+9.57% of Vn)

220V (-4.35% of Vn)

Note: Vn = 230 V

100% secondary substations in

demo area

Data collection period: from

01/02/2017 to 21/02/2017

Customers side 250.5V (+8.91% of Vn)

219V (-4.78% of Vn)

2,000 SMs


01/02/2017 to 21/02/2017

The R&I scenario data was provided by the Portuguese demo as follows:



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TABLE 14: R&I DATA FOR KPI 4 (PORTUGUESE DEMO)

Metering Point VBAU

(In the worst feeders) Comments

Secondary substation

237.7 V (+3.35% of Vn)

230.3 V (+0.13% of Vn)

Note: Vn = 230 V

100% secondary substations in

demo area


01/06/2017 to 02/09/2017

Customers side 249.5V (+8.48% of Vn)

219V (-4.78% of Vn)

2,000 SMs


01/06/2017 to 02/09/2017

According to the data provided, the KPI “Fulfilment of voltage limits” for the Portuguese demo has the

following value:

𝑉(%) =1

4· ∑

|𝑉𝐵𝐴𝑈𝑖

− 𝑉𝑛

𝑉𝑛| − |

𝑉𝑅&𝐼𝑖− 𝑉𝑛

𝑉𝑛|

|𝑉𝐵𝐴𝑈𝑖

− 𝑉𝑛

𝑉𝑛|

4

𝑖=1

=1

4· [

9.57 − 3.35

9.57+

4.35 − 0.13

4.35+

8.91 − 8.48

8.91+

4.78 − 4.78

4.78]

= 41.72%

( 37 )


The average time needed for fault location in LV is a common KPI used to evaluate the power quality

and quality of supply of distribution networks. This KPI represents the percentage of reduction in time

required for fault awareness, location and isolation (the last affected customer recovers the supply).

In the scope of the Portuguese demo, the time of non-supplied energy will be reduced through a faster

outage detection allowed by the implementation of new tools during the UPGRID project (UPGRID

Control) as well as its integration in the outage management process

One of the improvements (among others), of the implementation of this tools, is the new ability of DSO

to detect a fault before the end-customer calls to the call-centre, meaning that the assignment of the

crew process starts before than in the BAU scenario.

Time needed for fault awareness, location and isolation in LV has been calculated using the following

formula:



56 | 124

∆𝑇LV(%) =(∆𝑇𝑓𝑎𝑢𝑙𝑡)

𝐵𝐴𝑈− (∆𝑇𝑓𝑎𝑢𝑙𝑡)

𝑅&𝐼

(∆𝑇𝑓𝑎𝑢𝑙𝑡)𝐵𝐴𝑈

( 38 )

where:



first user calls to notify an incidence in BAU scenario.

Mean value of LV faults that occur during the BAU

scenario monitoring period.

(∆𝑇𝑓𝑎𝑢𝑙𝑡)𝑅&𝐼


first user calls to notify an incidence in R&I scenario.

Mean value of LV faults that occur during the R&I

scenario monitoring period.

The Portuguese demo provided the BAU data for all the incidents of 2015 dividing the mean time

needed to restore the electrical service since the fault occurs (103.37 min) in the following periods:

• Mean time of call centre: 8.37 min

• Assignment of crew: 2.3 min

• Response time of crew: 66 min

• Service restore: 26.7 min

The average time for LV faults in the Portuguese demo in the R&I scenario was reduced to 95 min.

According to the data provided, the KPI “Average time for LV faults” for the Portuguese demo has the

following value:



𝑅&𝐼


=103.37 − 95

103.37= 8.10% ( 39 )


Monitoring data volume is an indication of the increase of data amount for new monitored currents,

powers or voltages in SS or customer level. One of the main objectives of UPGRID project is the

integration of measurement data for LV network control tools, for supporting state estimation, power

flow algorithms or outage management procedures, among others.

This KPI will measure the amount of monitored information, through the installation of “intelligent

network components” to support LV network control tools, like LV Network Management Systems as

well as, in the case of Portuguese demo, tools to support DSM functionalities



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For the Portuguese demo, the monitored information through the intelligent network components has

been summarized in Table 15.

TABLE 15: MAIN EQUIPMENT AND INFORMATION CATEGORIES FOR THE PORTUGUESE DEMO

Equipment



Monitored data


UPGRID

After

UPGRID

SM 12,252 13,495


Meter events No Yes

Load Diagram (V, P) No Yes

Data

Concentrators 270 302


Data Concentrator Events No Yes

Load Diagram (V, P) No Yes

Smart Plugs 0 150 Load Diagram (I, V) No Yes

Monitoring data volume has been calculated using the following formula:


𝑀𝐷𝐵𝐴𝑈 ( 40 )

where:

𝑀𝐷𝐵𝐴𝑈 Total monitored data according to the count criterion in BAU

scenario.

𝑀𝐷𝑅&𝐼 Total monitored data according to the count criterion in R&I

scenario.

The count criterion chosen by the Portuguese demo is the second one proposed in [1]: counting each


information, power quality information, events, etc.). According to the data provided, the KPI

“Monitoring information categories” for the Portuguese demo has the following value:



41,541 − 12,522

12,522= 231.74% ( 41 )



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Available data volume is an indication of the increase of data amount for new visualized currents,

powers or voltages in primary substations, SSs or at customer level. One of the main objectives of

UPGRID project is to enhance the availability of the information gathered by the smart grid

infrastructure for the distribution system operator and also for the final customer. This information will

be integrated in the LV management tools visualization for the distributed system operator. Also the

consumption information will be available in a web portal for increasing the customer awareness.

Finally, the information will be also depicted in smart mobile devices to support maintenance grid

crews.

The Portuguese demo is addressing sub-functionalities to enhance the data visualization and grid

operation through the LV Network Management System (UPGRID Control) for the DSO, and the web

portal for increasing customers’ awareness in order to leverage their participation in electricity market.

This information has been summarized in Table 16.

TABLE 16: PORTUGUESE DEMO INFORMATION CATEGORIES AVAILABLE FOR MAIN SYSTEMS AND PROCEDURES

Equipment

Number of deployed

devices

Information Category

Available data for…

LV Network

management system Web portal

Before

UPGRID

After

UPGRID

Before

UPGRID

After

UPGRID

Before

UPGRID

After

UPGRID

SM 12,252 13,495

Energy register Yes Yes No Yes

Meter events No Yes No No

Load Diagram (V, P) No Yes No Yes

Data Concentrators 270 302

Energy register Yes Yes No No

Data Concentrator Events No Yes No No

Load Diagram (V, P) No Yes No No

Smart Plugs 0 150 Load Diagram (I, V) No No No Yes

Available data volume has been calculated using the following formula:


𝐴𝐷𝐵𝐴𝑈 ( 42 )



59 | 124

where:

𝐴𝐷𝐵𝐴𝑈 Total available data according to count criterion in BAU

scenario.

𝐴𝐷𝑅&𝐼 Total available data according to count criterion in R&I

scenario.

The count criterion chosen by the Portuguese demo is the second one proposed in [1]: counting each


each platform. If a measuring point information category is available in two different platforms, it will be

counted two times.

According to the data provided, the KPI “Available information categories” for the Portuguese demo has




68,531 − 12,522

12,522= 447.28 % ( 43 )


Characterized data volume is an indication of the increase of data amount for new characterization

analysis of currents, powers or voltages in primary substations, SS or customer level. UPGRID addresses

the data analytic based on the information gathered by the Smart metering infrastructure to

characterize LV consumption, to characterize the EV charging point’s behaviour and the grid state to

assist network planning and maintenance.

This KPI will measure the portion of information from the monitored information that will be

characterized in the scope of UPGRID project to support DSO operation and planning tools and to

provide information about the grid state and grid user behaviour. The data characterization is a measure

of how the information gathered in the smart grid is being used for real applications giving value and

justifying the smart grids deployment.

Characterized data volume has been calculated using the following formula:

𝐶𝐷𝑉(%) =𝐶𝐷𝑅&𝐼

𝐶𝐷𝐵𝐴𝑈 + 𝐶𝐷𝑅&𝐼 ( 44 )

where:



60 | 124

𝐶𝐷𝐵𝐴𝑈 Total characterized data according to count criterion in BAU

scenario.

𝐶𝐷𝑅&𝐼 Total characterized data according to count criterion in R&I

scenario.

In the scope of the Portuguese demo, in the BAU scenario the characterized information was:

• LV customer consumption characterization: o Active Power (1 daily parameter) in 10k SMs

• Consumption characterization of EV charging points: o Active Power (15’ minutes, 96 measures/day) in 16 Charging Stations

In the R&I scenario, the characterized information has been increased as follows:

• LV customer consumption characterization:

o Active Power (1 daily parameter) in 2,705 SMs

o Active Power (15’ minutes, 96 measures/day) in 10,790 SMs

• Consumption characterization of EV charging points:

o Active Power (15’ minutes, 96 measures/day) in 16 Charging Stations

According to the data provided, the KPI “Characterized information categories” for the Portuguese


𝐴𝐶𝐷𝑉(%) =𝐶𝐷𝑅&𝐼

𝐶𝐷𝐵𝐴𝑈 + 𝐶𝐷𝑅&𝐼=

1,040,081

11,536 + 1,040,081= 98.90 % ( 45 )


The availability of intelligent network components evaluates the increase of the total amount of

intelligent network components (SMs, smart transformers, new intelligent protection, smart plugs, etc.)

deployed in the scope of each demo. UPGRID project is addressing several actions to be implemented in

the demo areas which will increase the availability of intelligent network components. Some of these

actions regard to the deployment of new devices such as SMs, concentrators, smart transformers or

new fault detectors. Other actions are related to making smarter some of the already deployed devices,

i.e. concept test of PLC-PRIME advanced queries in the deployed Smart metering infrastructure.

In the scope of the Portuguese demo, Table 17 summarizes the availability of intelligent network

components:

TABLE 17: RELEVANCE OF THE PORTUGUESE DEMO REGARDING EQUIPMENT AND SYSTEMS



61 | 124


Systems

Weight Factor

(Component

Relative

Importance)


IC




UPGRID project)

SMs 10% 12,252 13,495 0

Data Concentrators 10% 270 302 0

Smart Plugs 40% 0 150 1

LV Network Management

System 40% 0 1 1

Total 100%




where:

𝐼𝐶𝑅&𝐼



functionalities.


scenario.

In the former formula, the intelligent components have been weighted as it is not the same installing a

SM than installing an advanced LV supervisor in terms of investment but also in terms of amount and

quality of the gathered information. As not all the UPGRID demos are deploying the same components,

each demo will have its own matrix.

According to the former data, the KPI “Availability of intelligent network components” for the

Portuguese demo has the following value:


𝐼𝐶𝐵𝐴𝑈= 100% ( 47 )



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3.2.9 KPI 14: SUCCESS INDEX IN METER READING

Success index in meter reading determines the success index in meter reading in BAU and R&I scenarios.

As it has been already mentioned before, one of the main objectives of UPGRID project is the integral

deployment of the Smart metering infrastructure. The success in meter reading is a simple indicator to

evaluate the performance of the metering infrastructure and it may be the result of simple (i.e. load

profile) and more complex queries to request meter data.

Success index in meter reading has been calculated using the following formula:

𝑆𝐼𝑀𝑅(%) = 𝑆𝐼𝑅&𝐼 − 𝑆𝐼𝐵𝐴𝑈 ( 48 )



where:

𝐶𝑆𝑢𝑐𝑐𝑒𝑠𝑠

Total number of measurements and actions that are correct

and successfully performed by the first data retrieval and can

be used for the respective process.

𝐶𝑇𝑜𝑡𝑎𝑙 Total number of triggered measurements and actions with

the relevant period of time

According to the data provided, the KPI “Success index in meter reading” for the Portuguese demo has


𝑆𝐼𝑀𝑅(%) = 𝑆𝐼𝑅&𝐼 − 𝑆𝐼𝐵𝐴𝑈 = 91% − 75% = 16% ( 50 )

3.2.10 KPI 18: CONSUMERS BEING METERED AUTOMATICALLY

Consumers being metered automatically states the quota of consumers which have their meter

information remotely gathered by the distribution system operator, i.e. with SMs connected via

communication network to the data collection system in BAU and R&I scenarios. As it has been already

mentioned before, one of the main objectives of UPGRID project is the integral deployment of the Smart

metering infrastructure. The number of consumers being metered automatically is a simple indicator to

evaluate the evolution of the metering infrastructure together with the Success index in meter reading

KPI.



63 | 124

“Consumers being metered automatically” KPI has been calculated using the following formula:


𝑆𝑀)

𝑅&𝐼− (

𝑆𝑀𝐴𝑅

𝑆𝑀)

𝐵𝐴𝑈 ( 51 )

where:

𝑆𝑀𝐴𝑅

Total number of SMs installed on field (meters connected to

the communication network and able to be remotely

accessed and read).

𝑆𝑀

Total number of SMs (meters connected to the

communication network and able to be remotely accessed

and read).

According to the data provided, the KPI “Consumers being metered automatically” for the Portuguese



𝑆𝑀)

𝑅&𝐼− (

𝑆𝑀𝐴𝑅

𝑆𝑀)

𝐵𝐴𝑈=

12,250

13,495−

10,120

13,495

= 90.77% − 74.99% = 15.78% ( 52 )



UPGRID project is addressing several actions to be implemented in the demo areas which require the

participation of consumers and producers. In the Portuguese demo, these actions are related to the

implementation of a web portal for customers’ awareness and the implementation of demand side

management actions. .

This KPI will be calculated as the sum of the amount of consumers participating in relation to the total of

customers contacted to be part of them. It will only measure if the user decides to join; another KPI will

measure if the user’s participation is active or not.

In the scope of the Portuguese demo, customers will participate in the demo through the installation of

HEMS (smart plugs for demand side management), and through the web portal.

Recruitment has been calculated using the following formula:

𝑅(%) =𝑛𝑎𝑐𝑐𝑒𝑝𝑡

𝑛𝑡𝑜𝑡𝑎𝑙 ( 53 )



64 | 124

where:


demo.


According to the data provided, the KPI “Participant recruitment” for the Portuguese demo has the

following value:

𝑅(%) =49

78= 62.82% ( 54 )

The data used to calculate this KPI only includes the customers participating in the demo by allowing the

DSO to install HEMS (smart plugs) in their homes. At the present moment, 49 of a total of 78 customers

that were invited to have a HEMS’, have accepted.



demos. UPGRID project is addressing several actions to be implemented in the demo areas which

require the participation of users.

In the Portuguese demo, these actions are related to the implementation of a web portal for customer

awareness and the implementation of demand side management actions.

Active participation has been calculated using the following formula:

𝐴(%) =𝑁𝐴

𝑁𝑃 ( 55 )

where:

𝑁𝐴 Number of consumers that have an active participation in

the UPGRID demo.

𝑁𝑃 Number of consumers that accepted participating in the

demo.



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In the scope of the Portuguese demo, the actions regarding customer involvement are related to the

percentage of response for a load shift (DSM) when requested by DSO, as well as the usage of the Web

Portal by customers (e.g. number of times they check their consumption data).

According to the data provided, the KPI “Active participation” for the Portuguese demo has the

following value:

𝐴(%) =𝑁𝐴

𝑁𝑃=

22

49= 44.90% ( 56 )




the implementation of standardized solutions regarding equipment and thus how to improve some of

the proposed demo projects by maximizing the use of interoperable and standardized protocols.



D1.3 [12] contains all this information for the Portuguese demo.

“Use of equipment standards” KPI has been calculated using the following formula:


𝐸𝑆𝐷𝑈 ( 57 )

where:


criterion.


project.

TABLE 18: PORTUGUESE DEMO EQUIPMENT STANDARDS


ESEU





PRIME SM - EDP Box 0



66 | 124


ESEU





DTC- Distribution

Transformer Controller 0

Router 0

HEMS (Gateway and

Homeplug) 1

According to the data provided, the KPI “Use of equipment standards” for the Portuguese demo has the

following value:

𝑈𝐸𝑆(%) =𝐸𝑆𝐸𝑈


1

4= 125 % ( 58 )




considering the implementation of standardized solutions regarding protocols and thus, how to improve

some of the proposed demo projects by maximizing the use of interoperable and standardized

equipment.



[12] contains all this information for the Portuguese demo.

Use of protocol standards has been calculated using the following formula:


𝑃𝑆𝐷𝑈 ( 59 )

where:


criterion.


project.



67 | 124

TABLE 19: PORTUGUESE DEMO PROTOCOL STANDARDS

Protocol standards

PSEU




project)


ICE 60870-5-104 0

PRIME 0

DLMS COSEM 0

Web services SOAP (STG-DC 3.1c) 0

FTP (RFC959) 0

MODBUS over serial line 0

HAN interface 0

According to the data provided, the KPI “Use of protocol standards” for the Portuguese demo has the

following value:

UPS(%) =𝑃𝑆𝐸𝑈


0

7= 100 % ( 60 )

This result (100%) does not mean that the Portuguese demo has not implemented any standard, but

that the demo uses the same standards that were being applied before the UPGRID project and

consequently no protocol standards have been implemented or extended during UPGRID.



68 | 124

3.3 SWEDISH DEMO

This sub-chapter includes the calculations and rationale of the detailed KPIs calculated in the scope of

the Swedish demonstration. Table 20 provides a summary and understanding of the KPIs and the

subsequent sections include a further description of each one of them.

Further information about the Swedish demo can be found in the deliverables of WP5 - Demonstration

in real user environment: VTF - Sweden, specifically in D5.3 – Results of the demonstration project [15].

TABLE 20: SWEDISH DEMO KPIS SUMMARY

SWEDISH DEMO


KPI 4 Fulfilment of voltage limits 4.40% The fulfilment of voltage limits has been enhanced in 4.40% in the R&I scenario compared with the BAU scenario.


KPI 6 Average time needed for fault location in MV

37.97% The average time needed for fault location in MV has been reduced in 37.97% in the R&I scenario compared with the BAU scenario.

KPI 8 Quality of Supply Improvement in MV 61.29% The quality of supply improvement in MV has been enhanced in 61.29% in the R&I scenario compared with the BAU scenario.







All the protocol standards declared to use at the beginning of the project have been used. An additional 35% of these standards have been extended in the scope of the project.

In the scope of the Swedish demo there has been some KPIs declared to be calculated in [1] but not


• KPI 14 Success index in meter reading. This KPI will not be finally calculated for the Swedish

demo because there were not foreseen actions to improve the success in meter reading. This KPI

was only included for this demo in [1] to evaluate potential collateral effects of other actions

that finally have not been produced after the analysis of the R&I scenario.

• KPI 17 Success index in meter connectivity. In the scope of UPGRID project, the Swedish demo

has enhanced the connection between the LV NMS and the AMI adding an improving the



69 | 124

information about the meters connectivity available for the DSO. In example, this objective was

summarized in the sub-functionality “Algorithm to determine connectivity of SM to the grid

(identification of phase and line to which each SM is connected to)”.Before UPGRID project in

the Swedish Demo the meters had already been connected to all the customers on the LV

network. All the meters are connected by three phases. Typically there is a fuse box with a group

of fuses for all outgoing LV feeders from the sub-station. The information of meter's location

regarding the associated LV feeder is registered in meter documentation. There was no other

system and method than actually doing field visits and measuring coordinates in order to check

which LV feeder the meter was connected to. During UPGRID the functionality, LV topology

discovery algorithm, is available from Schneider Electric. This sub-function uses the

measurements from the substation RTU on outgoing LV circuits and the data from the underlying

meters to evaluate the hourly meter time-series and assess if the meter lies on the correct LV

feeder. In LV monitoring application of the Swedish Demo this algorithm was used to examine

the association of 42 three-phase meters to 4 feeders of a secondary substation on the

demonstration site. The result of the examination showed that no meters were found to be

documented on the wrong feeder which implied that the documentation was already correct

and therefore no more activities were made to improve further. For the aforementioned

reasons, this KPI has not been finally calculated for the Swedish demo.

• KPI 18 Consumers being metered automatically. This KPI is related to KPI 14 “Success index in

meter reading” (chapter 3.3.7). For the same reasons that have been exposed in that chapter,

this KPI will not be finally calculated for the Swedish demo because there were not foreseen

actions to improve the success in meter reading, as the deployment of the advance metering

infrastructure was completed before the UPGRID project. This KPI was only included for this

demo in [1] to evaluate potential collateral effects of other actions that finally have not been

produced after the analysis of the R&I scenario.


The fulfilment of voltage limits is a common KPI used to evaluate the power quality and quality of supply

of distribution networks. UPGRID project is addressing some actions to be implemented in the demo

areas which will impact positively in the fulfilment of voltage limits.



𝑉𝐵𝐴𝑈=

437.7 − 418.46

437.7= 4.40% ( 61 )

where:



70 | 124

𝑉𝐵𝐴𝑈 Measured voltage level prior auto-voltage regulation by a “smart transformer” in secondary substation.

𝑉𝑅&𝐼 Measured voltage level after auto-voltage regulation, using a “smart transformer” in secondary substation.

In the scope of the Swedish demo, the main action that will impact positively in the fulfilment of voltage

limits is the deployment of a smart transformer with the functionality of automatically regulating the

voltage. In the BAU scenario, the measured voltage level prior to the smart transformer installation was

provided (𝑉𝐵𝐴𝑈 = 437.7 𝑉). It corresponds to the maximum line voltage measured from 22/04/2016 to

29/04/2016 at the site with power supply from the SS where the smart transformer will be installed. In

the R&I scenario, the voltage was measured after the smart transformer was installed at the four sites

with power supply from this transformer from 10/04/2017 to 14/06/2017 (𝑉𝐵𝐴𝑈 = 437.7 𝑉).





One of the main objectives of UPGRID project is to enhance the tools to reduce the average time

needed for fault location in LV like the integration of processing meter event or/and other sources in the

outage management process.


formula:



𝑅&𝐼


( 62 )

where:


Estimated time from fault to restoration in LV network.


Estimated time needed to restore a fault in LV network

taking into account outage improvement in LV

Management system for fault awareness.



71 | 124

According to the data provided, the KPI “Average time for LV faults” for the Swedish demo has the

following value:



𝑅&𝐼


=9.7 − 4.83

9.7= 50.21% ( 63 )

3.3.3 KPI 6: AVERAGE TIME NEEDED FOR FAULT LOCATION IN MV

The average time needed for fault location in MV is a common KPI used to evaluate the power quality


required for fault awareness, location and isolation. UPGRID project is addressing several actions to be

implemented in the demo areas which will reduce the average time needed for fault location. Some of

these actions regard to the revision and implementation of the DSO business processes in relation to the

outage management integrating and processing fault detectors events or/and other sources. In addition,

new smart devices will be deployed and tested to detect the fault and to support the maintenance grid

crews.

Time needed for fault location in MV has been calculated using the following formula:

∆𝑇𝑀𝑉(%) =(∆𝑇𝑓𝑎𝑢𝑙𝑡)


𝑅&𝐼


( 64 )

where:


Estimated time from fault to restoration in MV network.


Estimated time needed to restore a fault in MV network

taking into account outage improvement in MV

SCADA/DMS for fault awareness, e.g. using Fault Passage

Indicators (FPI)

According to the data provided, the KPI “Average needed for fault location in MV” for the Swedish demo




𝑅&𝐼


=12.3 − 7.63

12.3= 37.97% ( 65 )



72 | 124

3.3.4 KPI 8: QUALITY OF SUPPLY IMPROVEMENT IN MV

UPGRID project is addressing several actions to be implemented in the demo areas which will impact

positively in the reduction of duration and frequency of interruptions. Some of these actions regard to

the revision and implementation of the DSO business processes in relation to the outage management

integrating and processing meter events or/and other sources. Other actions are related to the

implementation of new algorithms to the remote reconfiguration of the distribution grid after a fault. In

addition, new smart devices will be deployed and tested to detect the fault and to support the

maintenance grid crews.

Quality of supply in MV has been calculated as the weighted sum of two classical indicators: SAIFI

(System Average Interruption Frequency Index) and SAIDI (System Average Interruption Duration Index):

𝑄𝑆𝑀𝑉(%) = CSAIFI · ∆SAIFI + CSAIDI · ∆SAIDI ( 66 )

∆SAIFI=SAIFIBAU − SAIFIR&𝐼

SAIFIBAU ( 67 )

𝑆𝐴𝐼𝐹𝐼 =#𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠

#𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 ( 68 )

∆SAIDI=SAIDIBAU − SAIDIR&𝐼

SAIDIBAU ( 69 )

𝑆𝐴𝐼𝐷𝐼 =#𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛_𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠


where:

CSAIFI Weight factor for SAIFI.

CSAIDI Weight factor for SAIDI.

interruptions Total number of customer’s interruptions within the observed

period.

customers Total number of customers served (average within the period).

duration_interruptions Sum of all end customer interruptions duration within the

observed period

The information provided by the Swedish demo regarding the BAU scenario is:



73 | 124

• Average number of interruptions per year within the observed period (from 01/01/2011 to 31/12/2015) in BAU scenario: 17 interruptions/ year.

• Average end customer interruptions duration per year within the observed period in BAU scenario: 105,256 customer minutes /year.

• Total number of customers served in BAU scenario: 534 customers.

The information provided by the Swedish demo regarding the R&I scenario is:

• Total number of interruptions within the observed period (from 26/04/2017 to 30/09/2017) in R&I scenario: 5 interruptions in five months.

• Sum of all end customer interruptions duration within the observed period in R&I scenario: 3,071 customer minutes in five months.

• Total number of customers served (average within the period observed) in R&I scenario: 534 customers.

As the monitoring period in the BAU scenario has been one year and in the R&I scenario it has been five

months, for the calculation of this KPI all the data provided have been transformed to a monthly basis as

shown in Table 21.

TABLE 21: SWEDISH DEMO DATA FOR KPI 8

Parameter BAU scenario R&I scenario

Average number of interruptions per month

(interruptions / month) 1.42 1.00

Average end customer interruptions duration per month

(customer minutes / month) 8,771.33 614.20

Total of customers served

(customers) 534 534

According to the data provided, the KPI “Quality of Supply Improvement in MV” for the Swedish demo


𝑄𝑆𝑀𝑉(%) = CSAIFI · ∆SAIFI + CSAIDI · ∆SAIDI

= CSAIFI ·(

#𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠#𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠

)BAU

− (#𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠

#𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠)

R&𝐼

(#𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠


BAU

+ CSAIDI

·(

#𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛_𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠#𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠

)BAU

− (#𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛_𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠


R&𝐼

(#𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛_𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠


BAU

= 0.5 ·

1.42534

−1

5341.42534

+ 0.5 ·

8,771.33534

−614.20

5348,771.33

534

= 61.29%



74 | 124



powers or voltages in primary substations, SS or customer level. One of the main objectives of UPGRID

project is the integration of measurement data for LV network control tools, for supporting state

estimation and power flow algorithms or for the outage management procedures, among others.

For the Swedish demo, the monitored information through the intelligent network components has

been summarized in Table 22.

TABLE 22: MAIN EQUIPMENT AND INFORMATION CATEGORIES FOR THE SWEDISH DEMO

Equipment



Monitored data


UPGRID After

UPGRID

MV protection relay

5 5 Current and fault alarm

type Yes Yes

Remote switch 1 1 Switch position Yes Yes

SM

534 534


Meter events No Yes

Meter events - including voltage level for

disturbance No Yes

Meter parameters Yes Yes

0 340 Meter parameters-

reactive power No Yes

Fault passage indicators (FPI)

0 6

MV line monitoring and fault passage and location

information No Yes

Line current No Yes

PQ data from line sensor FPI

No Yes

Conductor temperature for dynamic line rating

applications No Yes

Fault location central software co mapping

sensor measurements No Yes

Weather station with information at line sensor

location No Yes

MV voltage phase measurement at location

of line sensors No Yes

Advanced LV supervisors (RTU in SS)

0 16

Measuring information No Yes

Power quality information No Yes

Integrated FPI No Yes

LV group monitoring of voltage to determine

broken conductor and fuse status

No Yes



75 | 124

Equipment



Monitored data


UPGRID After

UPGRID

Advanced functions to deduce MV status from LV

measurements No Yes

Smart transformers

0 1

LV voltage management No Yes

Monitoring of LV grid to SCADA at MV/LV

transformer No Yes




where:


scenario.


scenario.

The count criterion chosen by the Swedish demo is the second one proposed in [1]: counting each


information, power quality information, events, etc.).

According to the data provided, the KPI “Monitoring information categories” for the Swedish demo has




2,606 − 1,074

1,074= 142.64% ( 72 )



intelligent network components (SMs, smart transformers, new intelligent protection, etc.) deployed in

the scope of each demo. UPGRID project is addressing several actions to be implemented in the demo

areas which will increase the availability of intelligent network components. Some of these actions

regard to the deployment of new devices such as SMs, concentrator, smart transformer or new fault



76 | 124

detectors. Other actions are related to making smarter some of the already deployed devices, i.e.

concept test of PLC-PRIME advanced queries in the deployed Smart metering infrastructure.




where:

𝐼𝐶𝑅&𝐼



functionalities.


scenario.

In the former formula, the intelligent components will be weighted as it is not the same installing a SM

than installing an advanced LV supervisor in terms of investment but also in terms of amount and quality

of the gathered information. As not all the UPGRID demos are deploying the same components, each

demo will have its own matrix.

Table 23 shows the availability of intelligent components in the Swedish demo area before the UPGRID

project (BAU scenario) and after it (R&I scenario):

TABLE 23: RELEVANCE OF THE SWEDISH DEMO REGARDING EQUIPMENT AND SYSTEMS


Systems

Weight Factor

(Component

Relative

Importance)


IC




UPGRID project)

SMs 10% 534 534 0

Data Concentrators 10% 51 51 0

Current transformers 10% 9 48 0.5

DER controllers 10% 5 5 0

Fault passage indicators (FPI) or

RTU with integrated FPI

(RTU/FPI)

10% 0 6 1

LV network management system 10% 0 2 1



77 | 124


Systems

Weight Factor

(Component

Relative

Importance)


IC




UPGRID project)

Modem/router for GPRS/2G/3G

or other secured communication 10% 3 21 0.5

New intelligent protection 5% 2 2 0

RTU, or RTU/IED 10% 0 16 1

Smart transformers 15% 0 1 1

Total 100%

In the former formula, the intelligent components (devices and systems) have been weighted since the

specific work that is being developed in the demonstrators has not the same impact over them. In

addition, this KPI also considers new components installed in the scope of the UPGRID project and those

in which new functionalities have been enabled. The count criterion for the intelligent components is

the following one:

• IC = 0.5: if the intelligent component has been modified to included new functionalities during

the UPGRID project.

• IC = 1: if the intelligent component has been installed during the UPGRID project.

• IC=0: if there are no relevant changes in the intelligent component

According to the former data, the KPI “Availability of intelligent network components” for the Swedish



𝐼𝐶𝐵𝐴𝑈= 158.83% ( 74 )

This result means that there is a 158.83% increase of intelligent components in the R&I scenario

compared with the BAU scenario.




the implementation of standardized solutions regarding to equipment and thus how to improve some of

the proposed demo projects by maximizing the use of interoperable and standardized protocols.



78 | 124



D1.3 [12] contains all this information for the Swedish demo.

Use of equipment standards has been calculated using the following formula:


𝐸𝑆𝐷𝑈 ( 75 )

where:


criterion.


project.

TABLE 24: SWEDISH DEMO EQUIPMENT STANDARDS


ESEU

(ESEU=0; no change between the status of the

equipment standard before and after UPGRID project)

(ESEU=0.5; equipment standard application extended

during UPGRID project)

(ESEU=1; new equipment standard implemented during

UPGRID project)

Echelon SMs 0.5

Echelon Data Concentrators (DC), (with built in GPRS communication

modem or with Ethernet connection for an external modem/router) 0

Schneider Electric Smart Transformer 1

RTU devices (or equivalent IED) for MV and LV measurement in

secondary substations (10-20/0.4 kV) 1

FPI - Fault passage Indicators for fault detection and localisation on

MV network 0.5

Modem/router for GPRS/2G/3G or other secured communication 0

Re-closer/breaker for remote network operation/automation 0

CT - Current Transformers in secondary substation.

(Others, which may be tested are Rogowski Current Transformers,

micro snap-on CT)

0

CT - Current Transformers for MV network FPIs 0



79 | 124

According to the data provided, the KPI “Use of equipment standards” for the Swedish demo has the

following value:



3

10= 130 % ( 76 )




considering the implementation of standardized solutions regarding to protocols and thus how to

improve some of the proposed demo projects by maximizing the use of interoperable and standardized

equipment.



[1] contains all this information for Swedish demo.

This KPI has been calculated for all UPGRID demos as all of them are considering the implementation of

standards.



𝑃𝑆𝐷𝑈 ( 77 )

where:


criterion.


project.



80 | 124

TABLE 25: SWEDISH DEMO PROTOCOL STANDARDS


ESEU

(ESEU=0; no change between the status of the

equipment standard before and after UPGRID project)

(ESEU=0.5; equipment standard application extended

during UPGRID project)

(ESEU=1; new equipment standard implemented during

UPGRID project)

OSGP ETSI GS OSG 001 - Open Smart Grid Protocol for both

measurements and events between SM<->DC<->AMI Head End

system

0.5

GS2- Message based protocol for measurement values (meter

stands and hourly values) between AMI Head End and

Vattenfall (MDMS)

0

XML - Message based protocol for events from SM from AMI

Head End system and Vattenfall PER-system

(PerformanceEventReport system)

0

PLC - Power Line Communication, using both A and C band, and

different frequencies. Communication carrier between the SM

and DC.

0

GPRS/3G - Communication between the field installed IED, e.g.

DC, and telecommunication service provider hardware

environment

0

IEC-60870-5-104 - Communication between FPI and SCADA-

DMS and/or fault analysis tool in MV substation 0.5

IEC-60870-5-104 - Communication between secondary

substation (10-20/0.4 kV) and SCADA-DMS 0.5

ZigBee (IEEE 802.15.4) - Communication between wireless

current sensor and RTU 1

CIM - Common Information Model for data exchange between

Network Information System and LV SCADA 1

FTP (RFC959) over GPRS 0

According to the data provided, the KPI “Use of protocol standards” for the Swedish demo has the

following value:



3.5

10= 135 % ( 78 )



81 | 124

3.4 POLISH DEMO


Polish demonstration. Table 26 provides a summary and understanding of the KPIs and the subsequent

sections include a further description of each one of them.

Further information about the Polish demo can be found in the deliverables of WP6 - Demonstration in

real user environment: Poland, specifically in D6.5 – System testing and optimization [16].

TABLE 26: POLISH DEMO KPIS SUMMARY

POLISH DEMO


KPI 2 Generation flexibility 3.55% The generation flexibility has been enhanced 3.55% in the R&I scenario compared with the BAU scenario.


KPI 6 Average time needed for fault location in MV

7.75% The average time needed for fault location in MV has been reduced in 7.75% in the R&I scenario compared with the BAU scenario.

KPI 7 Quality of Supply Improvement in LV 52.20% The quality of supply in LV has been improved 52.20% in the R&I scenario compared with the BAU scenario.

KPI 8 Quality of Supply Improvement in MV 74.95% The quality of supply improvement in MV has been enhanced in 74.95% in the R&I scenario compared with the BAU scenario.

KPI 9 Energy losses 69.08% There has been a reduction of 69.08% in the energy losses of secondary substations in the R&I scenario compared to the BAU scenario.



KPI 12 Characterized information categories 76.03% There is a 76.03% increase of characterized information categories in the R&I scenario compared with the BAU scenario.



KPI 15 Success index in events reading 97.92% The success index in meter reading stands at 97.92% .

KPI 20 Participant recruitment 12.17% The participant recruitment stands at 12.17% in relation with the contacted participants.




82 | 124

POLISH DEMO



All the equipment standards declared to use at the beginning of the project have been used. An additional 41.67% of these standards have been extended in the scope of the project.


All the protocol standards declared to use at the beginning of the project have been used. An additional 11.11% of these standards have been extended in the scope of the project.

In the scope of the Polish demo there has been one KPI declared to be calculated in [1] but not finally

calculated in this deliverable due to:

• KPI 4 Fulfilment of voltage limits. This KPI will not be finally calculated for the Polish demo

because there were not foreseen actions to improve the fulfilment of voltage limits. This KPI was

only included for this demo in [1] to evaluate potential collateral effects of other actions that

finally have not been produced after the analysis of the R&I scenario.

3.4.1 KPI 2: GENERATION FLEXIBILITY

Flexibility is an indication of the ability of the electricity system to respond to –and balance- supply and

demand in real time. Generation flexibility is mainly measured through generation response capabilities.

Nevertheless other grid initiatives may also enhance the generation flexibility of the grid, such as the

integration of storage resources and specifically the integration of their operation in the distribution

network operation.

For the Polish demo, this KPI has been calculated as the sum of the amount of generation capacity

managed by the distribution network operator in LV and MV. Generation flexibility has been calculated

using the following formula:

𝑃𝐷𝐸𝑅(%) =(𝑃𝐷𝐸𝑅)𝑅&𝐼

∑(𝑃𝑅)𝑅&𝐼−

(𝑃𝐷𝐸𝑅)𝐵𝐴𝑈

∑(𝑃𝑅)𝐵𝐴𝑈 ( 79 )

where:

𝑃𝐷𝐸𝑅

Represents the sum of the amount of flexible generation

capabilities that the distribution network operator can shift

in the BAU and R&I scenarios.

𝑃𝑅 Represents the sum of the generation installed capacity on

the system in the BAU and R&I scenarios.



83 | 124

According to the data provided, the KPI “Demand flexibility” for the Polish demo has the following value:

𝑃𝐷𝐸𝑅(%) =(𝑃𝐷𝐸𝑅)𝑅&𝐼

∑(𝑃𝑅)𝑅&𝐼−

(𝑃𝐷𝐸𝑅)𝐵𝐴𝑈

∑(𝑃𝑅)𝐵𝐴𝑈=

1

28.2−

0

28.2= 3.55 % ( 80 )





One of the main objectives of UPGRID project is to enhance the tools to reduce the average time

needed for fault location in LV like the integration of processing meter event or/and other sources in the

outage management process.


formula:



𝑅&𝐼


( 81 )

where:



user calls to notify an incidence in BAU scenario. Mean

value of all the faults that occur during the BAU scenario

monitoring period.



user calls to notify an incidence in R&I scenario. Mean

value of all the faults that occur during the R&I scenario

monitoring period.

After the analysis of 30 incidents from one year ago and using in the demo the existing system before

the UPGRID deployment, the mean time needed to restore the electrical service since the user calls to

notify an incidence in BAU scenario is (∆𝑇𝑓𝑎𝑢𝑙𝑡)𝐵𝐴𝑈

= 166.6 𝑚𝑖𝑛. After the deployment of UPGRID

solutions, this time was reduced to (∆𝑇𝑓𝑎𝑢𝑙𝑡)𝑅&𝐼

= 123 𝑚𝑖𝑛.

According to the data provided, the KPI “Average time for LV faults” for the Polish demo has the

following value:



84 | 124



𝑅&𝐼


=166.6 − 123

166.6= 26.17% ( 82 )

3.4.3 KPI 6: AVERAGE TIME NEEDED FOR FAULT LOCATION IN MV

The average time needed for fault location in MV is a common KPI used to evaluate the power quality


required for fault awareness, location and isolation. UPGRID project is addressing several actions to be

implemented in the demo areas which will reduce the average time needed for fault location. Some of

these actions regard to the revision and implementation of the DSO business processes in relation to the

outage management integrating and processing fault detectors events or/and other sources. In addition,

new smart devices will be deployed and tested to detect the fault and to support the maintenance grid

crews.

Time needed for fault location in MV has been calculated using the following formula:



𝑅&𝐼


( 83 )

where:



user calls to notify an incidence in BAU scenario. Mean

value of all the faults that occur during the BAU scenario

monitoring period.



user calls to notify an incidence in R&I scenario. Mean

value of all the faults that occur during the R&I scenario

monitoring period.

Up to date, the Polish demo has provided the BAU scenario information. After the analysis, the

incidents in MV recorded during one year ago and using in the demo the existing system before UPGRID

deployment, the mean time needed to restore the electrical service since the user calls to notify an

incidence in BAU scenario is (∆𝑇𝑓𝑎𝑢𝑙𝑡)𝐵𝐴𝑈

= 63.2 𝑚𝑖𝑛. After the deployment of UPGRID solutions, this

time was reduced to (∆𝑇𝑓𝑎𝑢𝑙𝑡)𝑅&𝐼

= 58.3 𝑚𝑖𝑛.

According to the data provided, the KPI “Average needed for fault location in MV” for the Polish demo




85 | 124



𝑅&𝐼


=63.2 − 58.3

63.2= 7.75% ( 84 )

3.4.4 KPI 7: QUALITY OF SUPPLY IMPROVEMENT IN LV

Although other KPIs have been defined in the scope of UPGRID project to evaluate the quality of supply

of distribution networks, also a general quality of supply improvement KPI has been considered to

evaluate the improvement in the frequency and duration of interruptions in LV.

Quality of supply in LV has been calculated using the Customer Minutes Lost (CML) indicator in BAU and

R&I scenario using the following formula:

𝑄𝑆𝐿𝑉(%) =CMLBAU − CMLR&𝐼

CMLBAU ( 85 )

where:

CMLBAU Customer Minutes Lost in BAU scenario.

CMLR&𝐼 Customer Minutes Lost in R&I scenario

Up to date, the Polish demo has provided the BAU scenario information. The average customer minutes

lost in BAU scenario is CMLBAU = 3.87 𝑚𝑖𝑛 and it has been calculated as the sum of all faults in the

demo area, multiplied by the duration of each fault and divided by the total number of customers in the

demo area. During one year, 30 incidents have been identified and used to provide this information

using the existing system before UPGRID deployment. After the deployment of UPGRID solutions, this

time was reduced to CMLR&I = 1.85 𝑚𝑖𝑛 .

According to the data provided, the KPI “Quality of supply improvement in LV” for the Polish demo has


𝑄𝑆𝐿𝑉(%) =CMLBAU − CMLR&𝐼

CMLBAU=

3.87 − 1.85

3.87= 52.20% ( 86 )

3.4.5 KPI 8: QUALITY OF SUPPLY IMPROVEMENT IN MV

UPGRID project is addressing several actions to be implemented in the demo areas which will impact

positively in the reduction of duration and frequency of interruptions. Some of these actions regard to



86 | 124

the revision and implementation of the DSO business processes in relation to the outage management

integrating and processing meter events or/and other sources. Other actions are related to the

implementation of new algorithms to the remote reconfiguration of the distribution grid after a fault. In

addition, new smart devices will be deployed and tested to detect the fault and to support the

maintenance grid crews.

Quality of supply in MV will be calculated as the weighted sum of two classical indicators: SAIFI (System

Average Interruption Frequency Index) and SAIDI (System Average Interruption Duration Index):

𝑄𝑆𝑀𝑉(%) = CSAIFI · ∆SAIFI + CSAIDI · ∆SAIDI ( 87 )

∆SAIFI=SAIFIBAU − SAIFIR&𝐼

SAIFIBAU ( 88 )

𝑆𝐴𝐼𝐹𝐼 =#𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠


∆SAIDI=SAIDIBAU − SAIDIR&𝐼

SAIDIBAU ( 90 )

𝑆𝐴𝐼𝐷𝐼 =#𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛_𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠


where:

CSAIFI Weight factor for SAIFI.

CSAIDI Weight factor for SAIDI.

interruptions Total number of customer’s interruptions within the observed

period.

customers Total number of customers served (average within the period).

duration_interruptions Sum of all end customer interruptions duration within the

observed period

The information provided by the Polish demo regarding the BAU scenario is:

• Total number of interruptions within the observed period (one year) in BAU scenario: 7,190 interruptions.

• Sum of all end customer interruptions duration within the observed period in BAU scenario: 332,833.33 minutes.



87 | 124

• Total number of customers served (average within the period observed) in BAU scenario: 14,658 customers.

The information provided by the Polish demo regarding the R&I scenario is:

• Total number of interruptions within the observed period in R&I scenario: 1,524 interruptions.

• Sum of all end customer interruptions duration within the observed period in R&I scenario: 96,216 minutes.

• Total number of customers served (average within the period observed) in R&I scenario: 14,658 customers.

According to the data provided, the KPI “Quality of Supply Improvement in MV” for the Polish demo has


𝑄𝑆𝑀𝑉(%) = CSAIFI · ∆SAIFI + CSAIDI · ∆SAIDI

= CSAIFI ·(

#𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠#𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠

)BAU

− (#𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠


R&𝐼

(#𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠


BAU

+ CSAIDI

·(

#𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛_𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠#𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠

)BAU

− (#𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛_𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠


R&𝐼

(#𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛_𝑖𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠


BAU

= 0.5 ·

7,19014,658

−1,524

14,6587,190

14,658

+ 0.5 ·

332,833.3314,658

−96,21614,658

332,833.3314,658

= 74.95%

3.4.6 KPI 9: ENERGY LOSSES

The energy losses improvement evaluates the reduction of energy technical losses in the distribution

network. UPGRID project is addressing some actions to be implemented in the demo areas which will

impact positively in the reduction of energy technical losses. Some of these actions regard to the remote

management of DER. Other actions are related to the implementation of new algorithms to identify the

optimum topological configuration of the distribution grid.

The reduction in energy technical losses has been calculated using the following formula:

∆𝐸(%) =𝐸𝐵𝐴𝑈 − 𝐸𝑅&𝐼

𝐸𝐵𝐴𝑈 ( 92 )

where:

𝐸𝐵𝐴𝑈 BAU scenario energy technical losses.

𝐸𝑅&𝐼 Energy technical losses after the R&I deployment.

In the scope of the Polish demo, the technical losses of the demo area have been calculated using

external software that needs the implementation and normal operation of the new Distribution



88 | 124

Management System (DMS) for LV. Thanks to this tool, an optimization has been made in 42 SSs to

better matching the needs and power consumption of the customers connected to each SS.

According to the data provided, the KPI “Energy losses” for the Polish demo has the following value:

∆𝐸(%) =𝐸𝐵𝐴𝑈 − 𝐸𝑅&𝐼

𝐸𝐵𝐴𝑈=

421,758.11 𝑘𝑊ℎ − 164,142.67 𝑘𝑊ℎ

421,758.11 𝑘𝑊ℎ= 61.08% ( 93 )



powers or voltages in primary substations, SSs or customer level. One of the main objectives of UPGRID

project is the integration of measurement data for LV network control tools, for supporting state

estimation and power flow algorithms or for the outage management procedures, among others.

For the Polish demo, the monitored information through the intelligent network components has been

summarized in Table 27.

TABLE 27: MAIN EQUIPMENT AND INFORMATION CATEGORIES FOR THE POLISH DEMO

Equipment

Number of deployed devices Main Information

Category Parameter / Report

Monitored data


UPGRID

After

UPGRID

SM 12,000 12,000

Energy register Billing values 1 1

Energy values 1 1

Meter events

Instant voltages 0 1

Quality and power failures 0 1

Fraud detection 0 1

DCU with

substation

meter

48 48

Meter list Basic topology 1 1

Enhanced PLC topology 0 1

Energy register

Energy values 1 1

Instantaneous values 1 1

Quality measurements 0 1

RTU in SS 6 48 MV control and

monitoring

fault detection 1 1

MV Current monitoring 1 1

Security monitoring(access

to SS) 1 1

Active power 1 1



89 | 124

Equipment

Number of deployed devices Main Information

Category Parameter / Report

Monitored data


UPGRID

After

UPGRID

Reactive power 1 1

MV Voltage monitoring 0 1

LV monitoring

Blown fuse monitoring 0 1

Current monitoring on LV 0 1

Voltage monitoring on LV 0 1

Advanced LV

supervisors (LV

smart cabinet)

0 9 LV monitoring

Current monitoring on LV 0 1

Voltage monitoring on LV 0 1

fault detection 0 1

DER controllers 0 1 Energy register Energy values 0 1




where:


scenario.


scenario.

The count criterion chosen by the Polish demo is the third one proposed in [1]: counting each


information, power quality information, events, etc.).

According to the data provided, the KPI “Monitoring information categories” for the Polish demo has the

following value:



60,700 − 24,174

24,174= 151.10 % ( 95 )



90 | 124


Available data volume is an indication of the increase of data amount for new visualized currents,

powers or voltages in primary substations, SS or customer level. One of the main objectives of UPGRID

project is to enhance the availability of the information gathered by the Smart metering infrastructure

for the distribution system operator and also for the final customer. This information will be integrated

in the LV management tools visualization for the distributed system operator. Also the consumption

information will be available in a web portal for increasing the customer awareness. Finally, the

information will be also depicted in smart mobile devices to support maintenance grid crews. This

information has been summarized in Table 28.

TABLE 28: POLISH DEMO INFORMATION CATEGORIES AVAILABLE FOR MAIN SYSTEMS AND PROCEDURES

Equipment

Number of

deployed

devices Information Category

Available data

Existing MV / LV

SCADA / DMS Web portal AMI

Before

UPGRID

After

UPGRID

Before

UPGRID

After

UPGRID

Before

UPGRID

After

UPGRID

Before

UPGRID

After

UPGRID

SM 12,000 12,000

Billing values No No Yes Yes Yes Yes

Energy values No Yes No No Yes Yes

Instant voltages No Yes No No No Yes

Quality and power failures No Yes No No No Yes

Fraud detection No Yes No No No Yes

Meter events No Yes No No No Yes

DCU with

substation meter 48 48

Basic topology No No No No Yes Yes

Enhanced PLC topology No No No No No Yes

Meter events No Yes No No No Yes

Energy values No Yes No No Yes Yes

Instant voltages No Yes No No No Yes

Instant current No Yes No No No Yes

Quality measurements No Yes No No No Yes

RTU in SS 6 48 fault detection Yes Yes No No No No

MV Current monitoring Yes Yes No No No No



91 | 124

Equipment

Number of

deployed

devices Information Category

Available data

Existing MV / LV

SCADA / DMS Web portal AMI

Before

UPGRID

After

UPGRID

Before

UPGRID

After

UPGRID

Before

UPGRID

After

UPGRID

Before

UPGRID

After

UPGRID

Security

monitoring(access to SS) Yes Yes No No No No

Active power Yes Yes No No No No

Reactive power Yes Yes No No No No

MV Voltage monitoring No Yes No No No No

Blown fuse monitoring No Yes No No No No

Current monitoring on LV No Yes No No No No

Voltage monitoring on LV No Yes No No No No

DER controllers 0 1 Energy values No Yes No No Yes Yes

Available data volume has been calculated using the following formula:


𝐴𝐷𝐵𝐴𝑈 ( 96 )

where:

𝐴𝐷𝐵𝐴𝑈 Total available data according to count criterion in BAU

scenario.

𝐴𝐷𝑅&𝐼 Total available data according to count criterion in R&I

scenario.

The count criterion chosen by the Polish demo is the second one proposed in [1]: counting each


one platform. If a measuring point information category is available in three different platforms, it will

be counted three times.



92 | 124

According to the data provided, the KPI “Available information categories” for the Polish demo has the

following value:



145,010 − 36,126

36,126= 301.40 % ( 97 )


Characterized data volume is an indication of the increase of data amount for new characterization

analysis of currents, powers or voltages in primary substations, SS or customer level. UPGRID addresses

the data analytic based on the information gathered by the Smart metering infrastructure to

characterize LV consumption, to characterize the EV charging point’s behaviour and the grid state data

to assist network planning and maintenance.

This KPI will measure the portion of information from the monitored information that will be

characterized in the scope of UPGRID project to support DSO operation and planning tools and to

provide information about the grid status and grid user behaviour. The data characterization is a

measure of how the information gathered in the smart grid is being used for real applications giving

value and justifying the smart grids deployment.

Characterized data volume has been calculated using the following formula:

𝐶𝐷𝑉(%) =𝐶𝐷𝑅&𝐼 − 𝐶𝐷𝐵𝐴𝑈

𝐶𝐷𝐵𝐴𝑈 ( 98 )

where:

𝐶𝐷𝐵𝐴𝑈 Total characterized data according to count criterion in BAU

scenario (number of entries).

𝐶𝐷𝑅&𝐼 Total characterized data according to count criterion in R&I

scenario (number of entries).

In the Polish demonstration area, the existing AMI infrastructure use has been enlarged. In order to

increase the level of monitoring and ensure additional data to the implemented AMI/DMS system, new

meter profiles have been prepared and implemented (for both meters installed in customers’ locations

and meters installed in SSs). In the pilot area, the following are installed: municipal meters and sets of

SSs (concentrator + meter for measuring the parameters).

The range of changes in parameters recorded by 1-phase municipal meters is presented in Table 29.

TABLE 29: PARAMETRIZATION CHANGE FOR 1-PHASE CUSTOMER METER



93 | 124

Captured objects


Acquired Acquisition

period Acquired

Acquisition period

load profile status X 60 min X 15 min

+A X 60 min X 15 min

-A X 60 min X 15 min

QI (+Ri) X 60 min X 15 min

QII (+Rc) -

X 15 min

QIII (-Ri) -

X 15 min

QIV (-Rc) X 60 min X 15 min

Pmax X 1 day X 15 min

U L1 - instantaneous -

X 15 min

biling profile status X 1 day X 1 day

+A T1 X 1 day X 1 day


-A T1 -

X 1 day

-A T2 -

X 1 day

QI (+Ri) T1 X 1 day X 1 day


QIV (-Rc) T1 X 1 day X 1 day


The range of changes in parameters recorded by 3-phase municipal meters is presented in Table 30.


Captured objects



period Acquired

Acquisition period

load profile status X 60 min X 15 min

+A X 60 min X 15 min

-A X 60 min X 15 min

QI (+Ri) X 60 min X 15 min

QII (+Rc) -

X 15 min



94 | 124

Captured objects



period Acquired

Acquisition period

QIII (-Ri) -

X 15 min

QIV (-Rc) X 60 min X 15 min

Pmax X 1 day X 15 min


X 15 min


X 15 min


X 15 min

biling profile status X 1 day X 1 day



-A T1 -

X 1 day

-A T2 -

X 1 day





The range of changes in parameters recorded by meters installed in SSs is presented in Table 31.


Captured objects



period Acquired

Acquisition period

LP3 profile status X 10 min X 10 min

Maximum voltage Vmax L1 X 10 min X 10 min

Instantaneous current for Vmax L1 X 10 min X 10 min





Minimum voltage Vmin L1 X 10 min X 10 min

Instantaneous current for Vmin L1 X 10 min X 10 min





Time-average of rms voltage value L1 X 10 min X 10 min

Time-average of rms current value L1 X 10 min X 10 min




95 | 124

Captured objects



period Acquired

Acquisition period




Instantaneous TTHD of voltage L1 -

X 10 min


X 10 min


X 10 min

Load profile 2 status X 1 day X 1 day

+A X 1 day X 1 day

-A X 1 day X 1 day

QI (+Ri) X 1 day X 1 day

QII (+Rc) X 1 day X 1 day

QIII (-Ri) X 1 day X 1 day

QIV (-Rc) X 1 day X 1 day

+S X 1 day X 1 day

-S X 1 day X 1 day

Pmax X 1 day X 1 day

I2h X 1 day X 1 day

U2h X 1 day X 1 day

Load profile 1 status X 15 minutes X 15 minutes

+A X 15 minutes X 15 minutes

-A X 15 minutes X 15 minutes

QI (+Ri) X 15 minutes X 15 minutes

QII (+Rc) X 15 minutes X 15 minutes

QIII (-Ri) X 15 minutes X 15 minutes

QIV (-Rc) X 15 minutes X 15 minutes

+S X 15 minutes X 15 minutes

-S X 15 minutes X 15 minutes

Pmax X 15 minutes X 15 minutes

U L1 X 15 minutes X 15 minutes



I L1 X 15 minutes X 15 minutes



I2h X 15 minutes X 15 minutes

U2h X 15 minutes X 15 minutes

P+ L1 -

X 15 minutes

P+ L2 -

X 15 minutes



96 | 124

Captured objects



period Acquired

Acquisition period

P+ L3 -

X 15 minutes

P- L1 -

X 15 minutes

P- L2 -

X 15 minutes

P- L3 -

X 15 minutes

Q+ L1 -

X 15 minutes

Q+ L2 -

X 15 minutes

Q+ L3 -

X 15 minutes

Q- L1 -

X 15 minutes

Q- L2 -

X 15 minutes

Q- L3 -

X 15 minutes

Based on the previous tables, the total characterized data in BAU scenario for the Polish demo is

𝐶𝐷𝐵𝐴𝑈 = 230,439,788 entries defined as the data and information transfer sent from the field devices

to the IT system. After the deployment of UPGRID solutions, the amount of characterised information

was enhanced to 𝐶𝐷𝑅&𝐼 = 405,631,659 entries.

According to the data provided, the KPI “Characterized information categories” for the Polish demo has


𝐶𝐷𝑉(%) =𝐶𝐷𝑅&𝐼 − 𝐶𝐷𝐵𝐴𝑈

𝐶𝐷𝐵𝐴𝑈=

405,631,659 − 230,439,788

230,439,788= 76.03% ( 99 )



intelligent network components (SMs, smart transformers, new intelligent protection, etc.) deployed in

the scope of each demo. UPGRID project is addressing several actions to be implemented in the demo

areas which will increase the availability of intelligent network components. Some of these actions

regard to the deployment of new devices such as SMs, concentrators, smart transformers or new fault

detectors. Other actions are related to making smarter some of the already deployed devices, i.e.

concept test of PLC-PRIME advanced queries in the deployed Smart metering infrastructure.



𝐼𝐶𝐵𝐴𝑈 ( 100 )



97 | 124

where:

𝐼𝐶𝑅&𝐼



functionalities.


scenario.

In the former formula, the intelligent components have been weighted as is not the same installing a SM

than installing an advanced LV supervisor in terms of investment but also in terms of amount and quality

of the gathered information. As not all the UPGRID demos are deploying the same components, each

demo will have its own matrix.

The following table summarizes for the Polish demo the intelligent components to be deployed, the

weight component to set its relative importance, the number of deployed components and the IC index.

In the scope of the Polish demo, Table 32 summarizes the availability of intelligent network

components:

TABLE 32: RELEVANCE OF THE POLISH DEMO REGARDING EQUIPMENT AND SYSTEMS


Systems

Weight Factor

(Component

Relative

Importance)


IC




UPGRID project)

RTU 20% 6 48 1

Fault passage indicators (FPI) 20% 12 64 1

Advanced LV supervisors (SS) 30% 0 9 1

DER controllers 30% 0 1 1

Total 100%

According to the former data, the KPI “Availability of intelligent network components” for the Polish



𝐼𝐶𝐵𝐴𝑈= 286.67% ( 101 )



98 | 124

3.4.11 KPI 15: SUCCESS INDEX IN EVENT READING

In the scope of UPGRID project, some demos will gather the grid events information registered by the

SMs in order to use it in the network operation processes. This objective was summarized in the sub-

functionality “Queries to request advanced meter data to support operation”. For this reason a specific

KPI has been defined to analyse if all the meters are sending correctly their registered grid events in BAU

and R&I scenario.

It is important to mention here that a meter only registers an event (and therefore sends it) when an

event happens. For this reason, to calculate this KPI only a group of meters that the DSO knows that

should be sending an event (i.e. after a loss of energy supply) will be taken into account. This means that

maybe not all the meters in the scope of the demo will be used to calculate this KPI.

It is important to mention here that the initial formula proposed for the KPI calculation (included in [1])

was based on the comparison between the success index in events reading in the R&I scenario and in

the BAU scenario:

𝑆𝐼𝐸𝑅(%) = 𝑆𝐼𝑅&𝐼 − 𝑆𝐼𝐵𝐴𝑈 ( 102 )



where:

𝐶𝑆𝑢𝑐𝑐𝑒𝑠𝑠 Number of meters sending correctly their events after a grid

issue.

𝐶𝑇𝑜𝑡𝑎𝑙

Number of meters that the DSO knows that should be

sending their events after a grid issue (i.e. after a loss of

energy supply).

As in the BAU scenario, there were not tools able to receive the alarms or events from the SMs. the KPI

formula has been slightly modified as follows to consider only R&I scenario information:

𝑆𝐼𝐸𝑅(%) = (𝐶𝑆𝑢𝑐𝑐𝑒𝑠𝑠


𝑅&𝐼

=47

48= 97.92 % ( 104 )



UPGRID project is addressing several actions to be implemented in the demo areas which require the

participation of consumers and producers. In the Polish demo, these actions are related to the

implementation of a web portal for customers’ awareness.



99 | 124

This KPI will be calculated as the sum of the amount of consumers participating in the UPGRID demos

(weighted in function of diversification of stakeholders) in relation with the total contacted to be part of

them. It will only measure if the user decides to join. Another KPI will measure if the user’s participation

is active or not. This KPI will be only calculated for an UPGRID demo if the demo is addressing sub-

functionalities which require consumers or producers participation.

Recruitment has been calculated using the following formula:


𝑛𝑡𝑜𝑡𝑎𝑙 ( 105 )

where:


demo.


According to the data provided, the KPI “Participant recruitment” for the Polish demo has the following

value:


𝑛𝑡𝑜𝑡𝑎𝑙=

14

115= 12.17% ( 106 )



demos. UPGRID project is addressing several actions to be implemented in the demo areas which

require the participation of users. In the Polish demo, these actions are related to the implementation

of a web portal for customers’ awareness and the deployment of demand side management policies.

This KPI will be calculated for each UPGRID demo as the sum of the amount of users actively

participating in the UPGRID demos in relation with the total that accepted participating. This KPI will be

only calculated for an UPGRID demo if the demo is addressing sub-functionalities which require

consumers or producers participation.

Active participation has been calculated using the following formula:

𝐴(%) =𝑁𝐴

𝑁𝑃 ( 107 )



100 | 124

where:

𝑁𝐴 Number of consumers that have an active participation in

the UPGRID demo.

𝑁𝑃 Number of consumers that accepted participating in the

demo.

According to the data provided, the KPI “Active participation” for the Polish demo has the following

value:

𝐴(%) =𝑁𝐴

𝑁𝑃=

6

14= 42.86% ( 108 )


Use of equipment standards is an indication of the effective use of standards with respect to the

declared use. Task 1.3 of UPGRID project gathered how the four demos were considering the

implementation of standardized solutions regarding equipment and thus how to improve some of the

proposed demo projects by maximizing the use of interoperable and standardized protocols.



D1.3 [12] contains all this information for the Polish demo.

Use of equipment standards has been calculated using the following formula:


𝐸𝑆𝐷𝑈 ( 109 )

where:


criterion.


project.



101 | 124

TABLE 33: POLISH DEMO EQUIPMENT STANDARDS


ESEU





PRIME SMs 0

PRIME Data Concentrators 0.5

Wireless GSM

GPRS/EDGE/UMTS routers 0.5

IEEE 60870-5-104

Monitoring and control

units for MV/LV substations

0

IEEE 1815 Monitoring and

control units for MV/LV

substations

0.5

DLMS/COSEM/PRIME M&C

units for LV DER equipment 1

According to the data provided, the KPI “Use of equipment standards” for the Polish demo has the

following value:



2.5

6= 141.67 % ( 110 )


Use of protocols standards is an indication of the effective use of standards with respect to the declared

use. Task 1.3 of UPGRID project tries gathered how the four demos were considering the

implementation of standardized solutions regarding protocols and thus how to improve some of the

proposed demo projects by maximizing the use of interoperable and standardized equipment.



[12] contains all this information for the Polish demo.



𝑃𝑆𝐷𝑈 ( 111 )



102 | 124

where:


criterion.


project.

TABLE 34: POLISH DEMO PROTOCOL STANDARDS

Protocol standards

PSEU




project)


DLMS COSEM 0.5

PRIME 0

STG-DC 3.1 0

DC-SAP 0

IEC 60870-5-104 0

IEEE 1815 0

IEC 61970 0

IEC 61968-100 0.5

IEC 62325-301 0

According to the data provided, the KPI “Use of protocol standards” for the Polish demo has the

following value:



1

9= 111.11 % ( 112 )



103 | 124

4. HIGH LEVEL KPIS CALCULATION

This chapter includes the calculation procedure and rationale of each high level KPI for each UPGRID

demo. It has been divided into four sub-chapters (one per demo). All the sub-chapters start with a

summary of the high level KPIs addressed by each demo in relation to the EEGI objectives. At the end of

each sub-chapter there is a summary of the high level KPIs results and a rationale of the contribution of

the demo to the EEGI objectives.

According to [1] in the tables used to calculate the high level KPIs are identical for all the demos. The

difference for each demo is the selected detailed KPIs and also the weight assigned for each of them.

This means that empty columns for “KPI value” and “weight” in these tables represent that the demo

has not selected that KPI for the calculation.

4.1 SPANISH DEMO

According to D1.4 – Report on common KPIs, Table 35 includes the high level KPIs that have been

calculated for the Spanish demo and their relation with the Clusters defined in the EEGI. According to

this table, in the scope of the UPGRID project the Spanish demo is mainly contributing to improve

network operations, and network planning and asset management.

TABLE 35: HIGH LEVEL KPIS CALCULATED FOR SPANISH DEMO.

Cluster Function objective

C3 Network operations

D7 Monitoring and control of LV networks

D9 Network management methodologies for network operation

D10 Smart metering data utilization

C4 Network planning


D11 Novel planning approaches for distribution networks

D12 Novel approaches to asset management

C5 Market design D13 New approaches for market design

The following sections show the UPGRID high level KPIs results based on the information collected in the

scope of the Spanish demo.



104 | 124

4.1.1 NETWORK OPERATIONS

TABLE 36: WEIGH MATRIX FOR HIGH LEVEL KPI D7 (MONITORING AND CONTROL OF LV NETWORK) FOR SPANISH DEMO.

CLUSTER OBJECTIVE Network operations

FUNCTIONAL OBJECTIVE D7 Monitoring and control of LV network

UPGRID KPI KPI value Weight

KPI 4 Fulfilment of voltage limits 3.46% 4%

KPI 5 Average time for LV faults 14.52% 20%

KPI 9 Energy losses

KPI 10 Monitored information categories 6.31% 10%

KPI 11 Available information categories 431.97% 14%

KPI 12 Characterized information categories

KPI 13 Availability of intelligent network components 111.13% 5%

KPI 15 Success index in events reading 72.00% 15%

KPI 16 Success index in PRIME advanced functionalities 17.50% 15%

KPI 18 Consumers being metered automatically 1.00% 5%

KPI 23 Use of equipment standards 125.00% 6%

KPI 24 Use of protocol standards 121.42% 6%

97.97% 100%

TABLE 37: WEIGH MATRIX FOR HIGH LEVEL KPI D9 (NETWORK MANAGEMENT METHODOLOGIES FOR NETWORK

OPERATION) FOR SPANISH DEMO.


FUNCTIONAL OBJECTIVE D9 Network management methodologies for

network operation



KPI 7 Quality of Supply Improvement in LV 31.90% 30%






81.56% 100%



105 | 124

TABLE 38: WEIGH MATRIX FOR HIGH LEVEL KPI D10 (SMART METERING DATA UTILIZATION) FOR SPANISH DEMO.


FUNCTIONAL OBJECTIVE D10 Smart metering data utilization



KPI 8 Quality of Supply Improvement in MV





KPI 14 Success index in meter reading 1.00% 10%


KPI 17 Success index in meter connectivity 48.08% 10%

89.41% 100%

4.1.2 NETWORK PLANNING AND ASSET MANAGEMENT

TABLE 39: WEIGH MATRIX FOR HIGH LEVEL KPI D11 (NEW PLANNNG APPROACHES FOR DISTRIBUTION NETWORKS) FOR

SPANISH DEMO.

CLUSTER OBJECTIVE Network planning and asset management

FUNCTIONAL OBJECTIVE D11 New planning approaches for distribution

networks



KPI 19 Improved Life-time of Transformers 0.14% 100%

0.14% 100%

TABLE 40: WEIGH MATRIX FOR HIGH LEVEL KPI D12 (NOVEL APPROACHES TO ASSET MANAGEMENT) FOR SPANISH DEMO.


FUNCTIONAL OBJECTIVE D12 Novel approaches to asset management



KPI 9 Energy losses




KPI 19 Improved Life-time of Transformers 0.14% 55%

KPI 25 Reduction in greenhouse gas emissions

70.54% 100%



106 | 124

4.1.3 MARKET DESIGN

TABLE 41: WEIGH MATRIX FOR HIGH LEVEL KPI D13 (NOVEL APPROACHES FOR MARKET DESIGN) FOR SPANISH DEMO.

CLUSTER OBJECTIVE Market design

FUNCTIONAL OBJECTIVE D13 Novel approaches for market design


KPI 1 Demand flexibility



KPI 20 Participant recruitment 20.93% 55%

KPI 21 Active participation 87.42% 40%

KPI 23 Use of equipment standards

KPI 24 Use of protocol standards


68.08% 100%

4.1.4 CONTRIBUTION TO EEGI OBJECTIVES

Figure 2 includes a summary of the high level KPIs results related to the Spanish demo.

FIGURE 2: HIGH LEVEL KPIS DEMO SPAIN

The Spanish demonstrator has been mainly focused on monitoring and control of LV network and

network management methodologies for network operations, although it must be underlined the

selection of two sub-functionalities related to novel approaches to asset management. The main

outcomes that the demonstrator envisages achieving were:

97,97%

81,56%

89,41%

0,14%

70,54%

68,08%

0% 20% 40% 60% 80% 100% 120%

D7 Monitoring and control of LV network

D9 Network management methodologies for networkoperation


D11 New planning approaches for distribution networks


D13 Novel approaches for market design



107 | 124

• Have a sound LV network representation, deploying tools to operate the network.

• Develop a dispatch tool to support LV network operations: LV NMS.

• Be able to do remote control operation over the LV grid using PRIME.

• Empower customers with information, knowledge skills and tools.

The quantitative results of the high level KPIs seem to match the expected results and benefits of the

Spanish demonstrator in the scope of UPGRID project. The most relevant high level KPI is “D7

Monitoring and control of LV network”, which reflects the contribution of the Spanish demo to this

EEGI Function Objective thanks to the tools and improved procedures in the scope of the LV Network

Management System, such as:

• Gathering detailed, enriched and accurate representation of the LV network, covering

components, topology, status, operation, connectivity, performance, loads, etc., in a real time

basis.

• Deployment of the LV NMS through two different solutions the LV NMS Desktop solution for

control centres and the LV NMS Mobile solution for LV Field Crews.

• Integration of the LV NMS with other systems in operation as the GIS, AMI and Supervisory

Control And Data Acquisition (SCADA). More detail can be found in [11].

In addition, “D10 Smart metering data utilization” and “D9 Network management methodologies for

network operation” are also key high level KPIs for the Spanish demo due to the following outcomes:

• The use of the existing PRIME infrastructure, not only to retrieve metering data from SMs, but

also to support Internet Protocol (IP) traffic which can serve to multiple purposes, such as

remote control operation in LV.

• The analysis of the SM events to develop a methodology and tools to perform a more rational,

automated structuring and offline processing of these SM events to support maintenance field

work.



108 | 124

4.2 PORTUGUESE DEMO


calculated for the Portuguese demo and their relation with the cluster objectives defined in the EEGI.

According to this table, in the scope of the UPGRID project the Portuguese demo is contributing to

improve all the cluster objectives defined by the EEGI: integration of smart customers, integration of

DER and new uses, network operations, network planning and asset management and market design.

TABLE 42: HIGH LEVEL KPIS CALCULATED FOR PORTUGAL DEMO.


C1 Integration of smart

customers D1 Active Demand for increased network flexibility

C2 Integration of DER

and new uses D6 Integration of infrastructure to host Electrical Vehicles

C3 Network operations D7 Monitoring and control of LV networks


C4 Network planning





scope of the Portuguese demo.

4.2.1 INTEGRATION OF SMART CUSTOMERS

TABLE 43: WEIGH MATRIX FOR HIGH LEVEL KPI D1 (ACTIVE DEMAND FOR INCREASED NETWORK FLEXIBILITY) FOR

PORTUGUESE DEMO.

CLUSTER OBJECTIVE Integration of smart customers

FUNCTIONAL OBJECTIVE D1 Active Demand for increased network

flexibility


KPI 1 Demand flexibility 32.70% 30%

KPI 12 Characterized information categories 98.90% 20%






109 | 124

CLUSTER OBJECTIVE Integration of smart customers

FUNCTIONAL OBJECTIVE D1 Active Demand for increased network

flexibility


KPI 22 Load curve valley filling


70.36% 100%

4.2.2 INTEGRATION OF DER AND NEW USES

TABLE 44: WEIGH MATRIX FOR HIGH LEVEL KPI D6 (INTEGRATION OF INFRASTRUCTURE TO HOST ELECTRICAL VEHICLES)

FOR PORTUGUESE DEMO.

CLUSTER OBJECTIVE Integration of DER and new uses

FUNCTIONAL OBJECTIVE D6 Integration of infrastructure to host

Electrical Vehicles


KPI 3 Hosting Capacity of Electric Vehicles 99.95% 30%




132.64% 100%


TABLE 45: WEIGH MATRIX FOR HIGH LEVEL KPI D7 (MONITORING AND CONTROL OF LV NETWORK) FOR PORTUGUESE

DEMO.






KPI 9 Energy losses





KPI 15 Success index in events reading




110 | 124




KPI 18 Consumers being metered automatically 15.78% 10%



142.04% 100%

TABLE 46: WEIGH MATRIX FOR HIGH LEVEL KPI D10 (SMART METERING DATA UTILIZATION) FOR PORTUGUESE DEMO.










KPI 14 Success index in meter reading 16.00% 25%


KPI 17 Success index in meter connectivity

136.85% 100%


TABLE 47: WEIGH MATRIX FOR HIGH LEVEL KPI D12 (NOVEL APPROACHES TO ASSET MANAGEMENT) FOR PORTUGUESE

DEMO.





KPI 9 Energy losses




KPI 19 Improved Life-time of Transformers


158.98% 100%



111 | 124

4.2.5 MARKET DESIGN

TABLE 48: WEIGH MATRIX FOR HIGH LEVEL KPI D13 (NOVEL APPROACHES FOR MARKET DESIGN) FOR PORTUGUESE DEMO.




KPI 1 Demand flexibility 32.70% 25%








125.62% 100%


Figure 3 includes a summary of the high level KPIs results related to the Portuguese demo.

FIGURE 3: HIGH LEVEL KPIS PORTUGUESE DEMO.

The Portuguese demonstrator has already installed Distribution Transformer Controllers (DTC) in all SSs

and EDP Box (SMs) in all LV clients. Apart from some sub-functionalities related to LV network

management, it is worth underlining the Portuguese approach to active demand topics, market design

and sub-functionalities based on DER integration and electrical vehicle. This is the demo most focused

70,36%

132,64%

142,04%

136,85%

158,98%

125,62%

0% 20% 40% 60% 80% 100% 120% 140% 160% 180%

D1 Active Demand for increased network flexibility

D6 Integration of infrastructure to host ElectricalVehicles







112 | 124

on demand management in UPGRID. The main outcomes that the demonstrator envisages achieving

were:

• Demonstrate in real environment end users engagement through the combined use of AMI and

Home Energy Management Systems.

• Specify, develop and testing Energy Management Systems.

• Improve the LV Network visibility, management and automation to support higher levels of DER

without compromising quality of supply.

• Engagement of stakeholders through a Neutral access platform that will highlight the distribution

network needs, supporting market operation.

• Allow an advanced assistance and support to the grid maintenance crews and grid operators.


Portuguese demonstrator in the scope of UPGRID project. This second release to D8.1 assesses the

relation of the quantitative results of the high level KPIs, the expected results and benefits of the

Portuguese demo and the EEGI objectives.



113 | 124

4.3 SWEDISH DEMO


calculated for the Swedish demo and their relation with the cluster objectives defined in the EEGI.

According to this table, in the scope of the UPGRID project the Swedish demo is contributing to improve

network operations.

TABLE 49: HIGH LEVEL KPIS CALCULATED FOR SWEDEN DEMO.




D8 Automation and control of MV networks




scope of the Swedish demo.


TABLE 50: WEIGH MATRIX FOR HIGH LEVEL KPI D7 (MONITORING AND CONTROL OF LV NETWORK) FOR SWEDISH DEMO.





KPI 5 Average time for LV faults

KPI 9 Energy losses


KPI 11 Available information categories





KPI 18 Consumers being metered automatically



131.04% 100%



114 | 124

TABLE 51: WEIGH MATRIX FOR HIGH LEVEL KPI D8 (AUTOMATION AND CONTROL OF MV NETWORK) FOR SWEDISH DEMO.


FUNCTIONAL OBJECTIVE D8 Automation and control of MV network


KPI 6 Average time needed for fault location in MV 37.97% 70%




70.94% 100%


OPERATION) FOR SWEDISH DEMO.



network operation



KPI 7 Quality of Supply Improvement in LV

KPI 10 Monitored information categories





132.50% 100%

TABLE 53: WEIGH MATRIX FOR HIGH LEVEL KPI D10 (SMART METERING DATA UTILIZATION) FOR SWEDISH DEMO.










KPI 14 Success index in meter reading



87.18% 100%



115 | 124


Figure 4 includes a summary of the high level KPIs results related to the Swedish demo.

FIGURE 4: HIGH LEVEL KPIS SWEDISH DEMO.

The Swedish demonstrator has been mainly focused on monitoring and control of LV network and Smart

metering data utilization; stressing one functionality related to automation and control of MV network

and other one focused on testing interoperability of equipment from different manufactures with the LV

management System. The main expected results and benefits of the Swedish demo were the following:

• Development of system functionalities for 10/0.4 kV network achieved by a full-scale

demonstration in real operational environment with system integration, network

synchronisation and cost-benefit analyses to control and secure operating networks.

• Installation, test and validation of supplier independent equipment in the 10/0.4 kV network to

improve Outage Management and Power Quality.

• Preparing the development of the next generation ICT infrastructure for Smart metering and

Smart Grids, indicating costs for investment and operation and future business possibilities.


Swedish demonstrator in the scope of UPGRID project. The main focus for the UPGRID Demo has been

to show how the LV network can be monitored from the Vattenfall Distribution Control Room, in a

technical solution which is installed in the Vattenfall medium and LV network and IT environment. For

this reason the most relevant EEGI Function objectives or high level KPIs for the Swedish demo have

been “D7 Monitoring and control of LV network” and “D9 Network management methodologies for

network operation”.

131%

71%

133%

87%

0% 20% 40% 60% 80% 100% 120% 140%


D8 Automation and control of MV network





116 | 124

4.4 POLISH DEMO


calculated for the Polish demo and their relation with the cluster objectives defined in the EEGI.

According to this table, in the scope of the UPGRID project the Polish demo is contributing to improve

most of the cluster objectives defined by the EEGI: integration of DER and new uses, network

operations, network planning and asset management and market design.

TABLE 54: HIGH LEVEL KPIS CALCULATED FOR POLISH DEMO.


C2 Integration of DER

and new uses D3 Integration of DER at low voltage



D8 Automation and control of MV networks



C4 Network planning





scope of the Polish demo.

4.4.1 INTEGRATION OF DER AND NEW USES

TABLE 55: WEIGH MATRIX FOR HIGH LEVEL KPI D3 (INTEGRATION OF DER AT LOW VOLTAGE) FOR POLISH DEMO.

CLUSTER OBJECTIVE Integration of DER and new uses

FUNCTIONAL OBJECTIVE D3 Integration of DER at low voltage


KPI 2 Generation flexibility 3.55% 100%

KPI 4 Fulfilment of voltage limits

KPI 9 Energy losses


3.55% 100%



117 | 124


TABLE 56: WEIGH MATRIX FOR HIGH LEVEL KPI D7 (MONITORING AND CONTROL OF LV NETWORK) FOR POLISH DEMO.




KPI 4 Fulfilment of voltage limits


KPI 9 Energy losses 61.08% 20%







KPI 18 Consumers being metered automatically



157.47% 100%

TABLE 57: WEIGH MATRIX FOR HIGH LEVEL KPI D8 (AUTOMATION AND CONTROL OF MV NETWORK) FOR DEMO POLAND.


FUNCTIONAL OBJECTIVE D8 Automation and control of MV network


KPI 6 Average time needed for fault location in MV 7.75% 35%

KPI 8 Quality of Supply Improvement in MV 74.95% 25%



115.78% 100%


OPERATION) FOR POLISH DEMO.



network operation



KPI 7 Quality of Supply Improvement in LV



118 | 124



network operation


KPI 10 Monitored information categories





112.60% 100%

TABLE 59: WEIGH MATRIX FOR HIGH LEVEL KPI D10 (SMART METERING DATA UTILIZATION) FOR POLISH DEMO.





KPI 8 Quality of Supply Improvement in MV 74.95% 25%





KPI 14 Success index in meter reading



130.90% 100%


TABLE 60: WEIGH MATRIX FOR HIGH LEVEL KPI D12 (NOVEL APPROACHES TO ASSET MANAGEMENT) FOR POLISH DEMO.





KPI 9 Energy losses 61.08% 100%




KPI 19 Improved Life-time of Transformers



119 | 124





61.08% 100%

4.4.4 MARKET DESIGN

TABLE 61: WEIGH MATRIX FOR HIGH LEVEL KPI D13 (NOVEL APPROACHES FOR MARKET DESIGN) FOR POLISH DEMO.




KPI 1 Demand flexibility






KPI 24 Use of protocol standards


64.61% 100%


Figure 5 includes a summary of the high level KPIs results related to the Polish demo.

FIGURE 5: HIGH LEVEL KPIS POLISH DEMO.

3,55%

157,47%

115,78%

112,60%

130,90%

61,08%

64,61%

0% 40% 80% 120% 160% 200%

D3 Integration of DER at low voltage


D8 Automation and control of MV network







120 | 124

The Polish demonstrator has been focused on monitoring and control of LV network but it is worth

mentioning the existence of one sub-functionality related to remote management of DER. The main

expected results and benefits of the Polish demo were the following:

• Utilisation of the LV network for communication devoted for transferring the data other than

metering.

• Implementation of SCADA/DMS LV with micro generation control.

• The use of CIM standard and integration data of SCADA/DMS – Geographic Information System

(GIS) – AMI.

• The use of AMI system for monitoring the performance of LV power grid.


Polish demonstrator in the scope of UPGRID project. The most relevant high level KPI is “D7 Monitoring

and control of LV network”, which reflects the contribution of the Polish demo to this EEGI Function

Objective thanks to the tools and improved procedures in the scope of the LV Network Management

System, such as:

• DMS for LV network – a system supporting management of an LV network.

• SCADA LV.

• Field Crew Support (FCS).

• Extension of User Data Panel (UDP).

In addition, “D8 Automation and control of LV network” and “D9 Network management

methodologies for network operation” are also key high level KPIs for the Polish demo due to the

following outcomes:

• Prototype solutions for monitoring and supervising SSs – integrated AMI/Smart Grid (SG)

cabinets. Due to the application of these solutions, two types of solutions for Smart Secondary

Substations (SSS) have been developed.

• Prototype devices monitoring electrical parameters in LV cable cabinets.

• A prototype device allowing for steering and monitoring the operation of micro-generation

installed in a customer’s location.

• In the existing AMI infrastructure, new software has been used to enable obtaining additional

data, both from meters installed in customers’ locations and meters installed in Secondary

Substations.

• The use of the existing PRIME infrastructure, not only to retrieve metering data from SMs, but

also to support Internet Protocol (IP) traffic which can serve multiple purposes, such as remote

control operation in LV.

• The analysis of the SM events to develop a methodology and tools to perform a more rational,

automated structuring and offline processing of SM events to support maintenance field work.



121 | 124

5. CONCLUSIONS

From the beginning of the project, UPGRID selected the European Electricity Grid Initiative (EEGI, [3])

methodology for KPI calculation [4]. The KPI definition was included in D1.4 – Report on common KPIs

[1] following this methodology. The first release of that document was finished in M6 (June 2015) and

the second release was delivered in M10 (October 2015) with a very detailed description of the KPIs

ready to be calculated when the data produced by the demos was available.

The KPI calculation has been included in this deliverable, D8.1 – Report about KPIs analysis and methods

of comparison. Also for this deliverable two releases have been prepared. The first one was delivered in

M30 (June 2017) with the data provided by the demos by that time and this second release (M36,

December 2017) with the review and calculation of the whole set of detailed KPIs and the final version

of high level KPIs based on enriched information that has been provided from the demo leaders.

The contribution of each demo to the EEGI objectives and the calculation of the high level KPIs have

been already included in the previous chapter 4, specifically: section 4.1.4 for the Spanish demo, section

4.2.6 for the Portuguese demo, section 4.3.2 for the Swedish demo and section 4.4.5 for the Polish

demo. In addition to that individual analysis, Figure 6 shows the contribution of the project UPGRID as a

whole to the clusters and function objectives proposed by the EEGI methodology.

C1: Integration of smart customers D1. Active Demand for increased network flexibility D2. Enabling maximum energy efficiency in new or refurbished urban using smart distribution grids C2: Integration of DER and new uses D3. Integration of DER at low voltage D4. Integration of DER at medium voltage / high voltage D5. Integration of storage in network management D6. Integration of infrastructure to host Electrical Vehicles C3: Network operations D7. Monitoring and control of LV networks D8. Automation and control of MV networks D9. Network management methodologies for network operation D10. Smart metering data utilization C4: Network planning and asset management D11. New planning approaches for distribution networks D12. Novel approaches to asset management C5: Market design

D13. Novel approaches for market design

FIGURE 6: UPGRID CONTRIBUTION TO EEGI CLUSTERS AND FUNCTION OBJECTIVES.



122 | 124

It is also important to mention in this chapter of conclusions the lessons learnt and main outcomes of

the KPI calculation methodology applied. UPGRID project had the opportunity of participating in the

workshop “The role of evaluating Smart Grids Projects” [17] within the framework of sharing

experiences and findings among EC projects. During this session UPGRID presented its experience on

defining KPIs. Once the KPI process has been finished with the release of the present deliverable, those

conclusions has been reviewed for validation and lesson learnt consolidation. As a result, the main

UPGRID outcomes with this regards are:

• KPI definitions were mainly based on EEGI framework as an initial starting point.

• A template approach was applied in most cases to define KPIs, the calculation methodology and

applicability.

• Important to implement a clear methodology (KPI framework) right at the beginning of the

project, despite high uncertainty of goals (of projects, tools, demos, etc.):

o Use Cases naturally evolve after closer interaction with the trial sites – thus, KPIs are

difficult to be planned early in a one-time effort.

o Implement an iterative approach to align KPI definition and goals (along the project.

o Do not impose KPIs; KPIs should be the result of a collaborative effort of all the involved

parties.

o Joint KPIs are rather difficult to reach; majority applied individual KPIs that are based on

individual projects / demo conditions and expected data availability.

• Data availability remains a challenge in itself and impacts the KPI definition and selection:

o Availability of Business as Usual (BaU) data has been expressed as real challenge; no clear

approach to define BaU values in the absence of BaU data.

o KPIs that require cost information are less incorporated in KPI frameworks (and even

sometimes avoided), as it remains a though challenge to collect cost data.

• Quantitative data needs to be enhanced with qualitative assessments.

• Ensure engagement by all parties to contribute to KPIs:

o Facilitated if KPI tasked is “lived” along the project (“get them working”)

o Appoint a central partner in charge for KPIs in the project to engage parties and

participation.

The main conclusions and lessons learnt for the application of the KPI calculation methodology

proposed by the EEGI are the following:

• KPIs need to be complemented with qualitative / contextual information.

• Monetizing KPIs could be useful, but not needed in all cases.

• The specific characteristics of the demos need to be considered for comparison of different

demo sites using KPIs.

• A catalogue of KPIs available for voluntary use in projects should be developed in the scope of

the EEGI methodological framework.



123 | 124

REFERENCES

[1] UPGRID Consortium, “D1.4: Report on common KPIs”.

[2] Upgrid Consortium, “D1.4: Report on common KPIs”.

[3] “European Electricity Grid Initiative (EEGI) web page,” [Online]. Available:

http://www.gridplus.eu/eegi.

[4] (EEGI), European Electricity Grid Initiative, “Research and Innovation Roadmap 2013-2022,” 2012.

[Online]. Available: http://www.gridplus.eu/Documents/20130228_EEGI%20Roadmap%202013-

2022_to%20print.pdf.

[5] “EEGI Labelled projects list,” [Online]. Available: http://www.gridplus.eu/node/172.

[6] “GRID+ Project web page,” [Online]. Available: http://www.gridplus.eu/.

[7] “IGREENGrid project web page,” [Online]. Available: http://www.igreengrid-fp7.eu/.

[8] “GRID4EU Project web page,” [Online]. Available: http://www.grid4eu.eu/.

[9] “DISCERN project web page,” [Online]. Available: http://www.discern.eu/.

[10] “IDE4L project web page,” [Online]. Available: http://ide4l.eu/.

[11] UPGRID Consortium, “D3.4: Demostration results: evaluation and opportunities (WP3:

Demonstrations in real user environment: Iberdrola - Spain)”.

[12] UPGRID Consortium, “D1.3: Report on standards and potencial synergies”.

[13] Federation of Neighbourhood Associations of Bilbao (FAAVVB), “http://www.bakarra.net/cms/,”

[Online].

[14] UPGRID Consortium, “D4.3: Evaluation of Demonstration Results and Data Collection (WP4:

Demonstrations in real user environment: EDPD - Portugal)”.

[15] UPGRID Consortium, “D5.3: Results of the demonstration project (WP5: Demonstrations in real user



124 | 124

environment: VTF - Sweden)”.

[16] UPGRID Consortium, “D6.5: System testing and optimization (WP6: Demonstrations in real user

environment: Poland)”.

[17] DISCERN KPI Workshop web page, “http://discern.eu/events/workshopIV.html,” [Online].

[18] DNV GL, «4th External Workshop "The role of KPIs in evaluating Smart Grid projects" (Workshop

Minutes)».

[19] Upgrid Consortium, “D1.3: Report on standards and potencial synergies”.

[20] Upgrid Consortium, “D3.4: Demostration results: evaluation and opportunities (WP3:

Demonstrations in real user environment: Iberdrola - Spain)”.

[21] Upgrid Consortium, “D4.3: Evaluation of Demonstration Results and Data Collection (WP4:

Demonstrations in real user environment: EDPD - Portugal)”.

[22] Upgrid Consortium, “D5.3: Results of the demonstration project (WP5: Demonstrations in real user

environment: VTF - Sweden)”.

[23] Upgrid Consortium, “D6.5: System testing and optimization (WP6: Demonstrations in real user

environment: Poland)”.