d4.5 prototype tool manual

Distribution grid planning and operational principles for EV mass roll-out while enabling DER integration

Deliverable (D) No: 4.5

Prototype tool manual Author: Frederik Geth / Tractebel Stefan Böcker (ICT Annex) / TU Dortmund Date: 28.01.2016 Version: 1.0

www.PlanGridEV.eu

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement

No. 608957.

Confidential (Y / N): Y

D4.5 Prototype tool manual

PGEV D4 5 Manual Public of 5

Title of the Deliverable

Prototype tool manual

WP number WP title WP leader WP4 Task title

Main Author Frederik Geth, Tractebel Stefan Böcker (Annex) / TU Dortmund

Project partners involved

Stephane Rapoport, Tractebel

Editors Eduardo Zabala / Tecnalia; Eoghan O'Callaghan / ESB Stefan Greve / RWE Armin Gaul / RWE

Type (Distribution level)

PU, Public PP, Restricted to other program participants (including the Commission Services) RE, Restricted to other a group specified by the consortium (including the Commission Services) CO, Confidential, only for members of the consortium (including the Commission Services)

Status In Process In Revision Approved

Further information www.PlanGridEV.eu

D4.5 Prototype Tool Manual

PGEV D4 5 Manual Public of 5

Executive Summary

The overall objective of the PlanGridEV project is to develop new network planning tools and methods for European DSOs to support an optimized large-scale roll-out of electromobility whilst at the same time maximizing the potential of DER integration. The project aims to update tools and methods to address local load and congestion issues, based on the management of electric vehicle (EV) charging processes. A prototype tool is developed as part of WP4. The developed prototype builds upon the analysis, models and methods developed and published in D4.1 and D4.2. Next, this tool is used in WP6 to develop case studies and to explore solutions to grid problems. An AC optimal power flow (OPF) methodology is developed as part of the prototype tool to optimize power system related operational choices. Next to unit characteristics, OPF problems take into account the physical behaviour of the grid. Technical information is required to be able solve such OPF problems. For instance, information on how the grid is structured, which technologies are used, what the operational limits are, etc. This manual to D4.5 (the prototype tool itself) therefore discusses which models are included. Next, it discusses which parameters are required to define a valid case study. Finally, the parameters, which are needed capitalize on the tool in a context to support grid planning processes, are discussed. The aim is to be able to support and validate grid investment decisions in controllability. The OPF simulation core tackles both low voltage and medium voltage distribution grid case studies. This means that mathematical methods used are valid, robust, and have sufficient numerical performance for simulation of radial networks of varying voltage levels, with varying combinations of underground cables and overhead lines, and for varying reactance-to-resistance ratios. The calculation core performs balanced power flow analysis. Furthermore, the OPF methodology includes support of multiperiod simulation (simulation of multiple time steps at once). In the unit models (e.g. EVs) a number of dynamics are taken into account, e.g. storage processes over time. A library of unit models is provided with the tool, including curtailable generators, sheddable loads, PV system models and EVs (including vehicle-to-grid). For DER and EV, the tool includes methods to generate representative behaviour, to simplify the setup of case studies by the user. Due to the large-scale and multiperiod nature of the simulation, the tool returns a significant amount of numerical results. It is rather time-consuming to explore such results using conventional spreadsheet software. Therefore, to streamline the interpretation and analysis of the results, next to the numerical results, the tool returns a number of figures, all adhering to a common visualization approach.

Distribution grid planning and operational principles for EV mass roll-out while

enabling DER integration

Deliverable (D) No: 4.5 Annex

Functional specification of ICT model and methods Author: Böcker, Stefan Date: 17.11.2015 Version: 1.5

www.PlanGridEV.eu

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement

No. 608957.

Confidential (Y / N): N

D4.2 Report on new methods to maximize integration of EV and DER in distribution grids

PlanGridEV_ICT_Model_Specification_D4_5_Annex_v15_final.docx

Title of the Deliverable

Functional specification of ICT model and methods

WP number WP title WP leader

4 Development of Grid Methods and Tools TRACTEBEL

Task title

Main Author Stefan Böcker / TUDo

Project partners involved

F. Geth / Tractebel

Editors Eoghan O’Callaghan / ESB Eduardo Zabala / Tecnalia Raúl Rodríguez / Tecnalia Armin Gaul / RWE Stefan Greve / RWE

Type (Distribution level)

PU, Public

PP, Restricted to other program participants (including the Commission Services)

RE, Restricted to other a group specified by the consortium (including the Commission Services)

CO, Confidential, only for members of the consortium (including the Commission Services)

Status

In Process

In Revision

Approved

Further information www.PlanGridEV.eu

Functional Specification of the ICT model and methods

Page 5 of 37

Table of contents

Abbreviations and Acronyms ..................................................................................................... 6 1. Introduction ......................................................................................................................... 7

1.1. ICT Model and Methods overview ............................................................................... 7 2. Functional Specification of ICT Model ................................................................................ 9

2.1. ICT Model Input ......................................................................................................... 10 2.1.1. Use Case Groups ............................................................................................... 10 2.1.2. ICT Requirements .............................................................................................. 12 2.1.3. Energy Network .................................................................................................. 14

2.2. Smart Grid Traffic Modeling ...................................................................................... 16 2.3. Network Dimensioning ............................................................................................... 17 2.4. Cost Modelling ........................................................................................................... 19

2.4.1. CAPEX Estimation ............................................................................................. 20 2.4.2. OPEX Estimation ................................................................................................ 20

2.5. ICT Model Output ...................................................................................................... 21 2.6. ICT Model Summary .................................................................................................. 22

3. Test Case Study ............................................................................................................... 24 3.1. Sensitivity Analysis .................................................................................................... 26

4. References ........................................................................................................................ 29 4.1. External documents ................................................................................................... 29 4.2. Project documents ..................................................................................................... 30

5. Revisions ........................................................................................................................... 31 5.1. Track changes ........................................................................................................... 31

6. Annex ................................................................................................................................ 32 6.1. ICT Model Input Overview ......................................................................................... 32 6.2. Detailed Use Case Element Overview ...................................................................... 34


Page 6 of 37

Abbreviations and Acronyms

Table 1 – Acronyms

AMR Automated Meter Reading

CAPEX Capital Expenditures

CO Central Office

CP Connection Point

CS Central System

DA Distribution Automation

DG/DS Distributed Generation/Distributed

Storage

DSM Demand Side Management

EV Electric Vehicle

EVSE Electric Vehicle Supply Equipment

FTTB Fibre-To-The-Building

GPON Gigabit Optical Network

HL High-Level

ICT Information and Communication

Technology

LAN Local Area Network

LC Local Controller

LTE Long Term Evolution

M2M Machine-to-Machine

NPV Net Present Value

OPEX Operating Expenditures

PLC Power Line Communication

QoS Quality of Service

TCO Total Cost of Ownership

UCE Use Case Element

WAN Wide Area Network


Page 7 of 37

1. Introduction

The scope of the document is to describe the functional specification of the ICT model and methods in

relation with the planning tool developed in PGEV. Therefore, the description given in deliverable D4.2

[12] is detailed and extended to a detailed insight of a cost-benefit analysis illustrated in [1]. Major

functionalities of the ICT model and methods with special focus on economic aspects are published in

[2].

After an introduction to an overview of our tool, different functionalities in particular, Smart Grid Traffic

Modelling, Statistical Network Dimensioning and Cost Modelling, are illustrated in detail. The description

ends with a case study using the previously-introduced tooling.

1.1. ICT Model and Methods overview

The ICT model for distribution network planning is divided into two main parts according to their

functionalities, as presented in Figure 1. First, ICT Pre-Selection provides a general set of applicable

ICT technologies and high-level protocols without consideration of network and infrastructure data.

Secondly, the Infrastructure Planning considers a concrete energy network, in order to propose several

ICT infrastructure and technologies. The main part of the ICT model depends on external input defined

by the planning tool user, such as use case definition and energy network data. Finally, the outputs of

both parts are evaluated in order to provide ICT recommendations for given external input. In the

following sections, each part is described individually.

Figure 1: Overview of ICT Model for Distribution Network Planning [1]

In the following figures, subparts of this overview are mapped to their main internal functionalities, which

will be described in detail in section 2. The scenario based ICT Pre-Selection is mainly based on a use

Use Case

Groups

ICT

Requirements

High Level Protocol

Pre-Selection

Transmission Technology

Pre-Selection

Energy

NetworkControl Flow

(Communication Effort)

Evalu

ation

(Scoring)

ICT

Reco

mm

en

dati

on

s

External Input ICT Analysis Model

Infrastructure

Aggregation Topology

Infrastructure Planning (II)

Scenario based ICT Pre-Selection (I)


Page 8 of 37

case elements selection, which offers necessary input for smart grid traffic modelling (Section 2.1)

functionality.

Figure 2: Overview of Scenario based ICT Pre-Selection (I)

The infrastructure planning part is mainly based on energy network data and provides opportunities for

statistical network dimension (Section 2.3) functionality.

Figure 3: Overview of Infrastructure Planning (II)

As a conclusion of both ICT-Model subparts, evaluation is implemented within Cost Modelling (Section

2.4) functionality and directly leads to ICT recommendations. In the following section, necessary external

input is explained in advance.

Use Case

Groups

ICT

Requirements

High Level Protocol

Pre-Selection


Pre-Selection

External Input

Scenario based ICT Pre-Selection (I)

Smart GridTraffic Modelling


Pre-Selection

Energy

NetworkControl Flow

(Communication Effort)

External Input

Infrastructure

Aggregation Topology

Infrastructure Planning (II)

Statistical Network Dimensioning


Page 9 of 37

2. Functional Specification of ICT Model

As depicted before the ICT Model consists of three different functionalities displayed in Figure 4, Smart

Grid Traffic Modeling consisting of traffic and a device/connection point modelling, Network

Dimensioning and Cost Modeling based on an economic analysis. The following introduced

functionalities have been successfully published on the SmartGridComm Conference, Miami, USA [2].

Figure 4: Structure Overview of Smart Grid Communication [2]

Detailed descriptions for each functionality are given in the following subsections. Hereby, the ICT Model

considers the most appealing candidates of transmission technology solutions with regard to their

Packet Size

Frequency of

Occurence

Maximum Latency

Traffic Class (Priority-Random, Regular,

Scheduled)

Traffic Model

Monthly Data

Volume per UC

Minimum Data

Rate Requirement

per UC

Period under Study

Use Cases (UC)

(AMR, DG/DS, DA, DSM)

Connection Points

(Private, Public, Grid)

Area Expansion, Type (Urban, Suburban, Rural)

Device /

Connection Point

Model

Total Data Rate

Requirement

Total Data

Volume

Communication

Technology

Properties

Cost Factors

Network Planning

Cost Model

Total Costs of

Ownership (TCO)Net Present Value

Sm

art

Gri

d T

raff

ic

Mo

del

ing

(Sec

tio

n I

II)

Net

wo

rk

Dim

ensi

on

ing

(Sec

tio

n I

V)

Co

st M

od

elin

g

(Sec

tio

n V

)

Costs /

Device / Month

Tec

hn

olo

gy

Dep

end

ent

Ap

pli

cati

on

Dep

end

ent


Page 10 of 37

suitability for future grid applications1:

Dedicated Long-Term-Evolution (LTE) network

Combination of dedicated LTE and wired Broadband Power Line Communication (PLC) networks

Dedicated Fibre networks

Tariff network – using Tariff networks includes the use of public network structures (e.g., GPRS, UMTS, LTE). In contrast to dedicated networks, public networks should result in lower infrastructure costs.

Mixed Region – using mixed region networks considers capabilities to design different transmission technology solutions for different energy network areas (e.g., urban fibre and suburban, rural LTE network).

When necessary, technical characteristics of individual transmission technologies will be discussed

within the following sections.

2.1. ICT Model Input

The ICT model specification is dependent on external input of the planning tool user. It is divided into

three different parts, which are each explained in the following subsections. Additionally, Annex 6.1

illustrates an overview of all parameters that are needed at a minimum. In accordance with following

subsections, it is divided into traffic related parameters (detailed information given in subsection 2.1.1),

communication technology related parameters (see subsection 2.1.2), energy network data (see

subsection 2.1.3), as well as commercial and basic dimensioning parameters (see section 2.4).

2.1.1. Use Case Groups

The choice of use cases which are desired by the planning tool user is based on a predefined set of

Use Case Groups.

Automated Meter Reading (AMR)

Distributed Generation/Distributed Storage (DG/DS)

Distribution Automation (DA)

Demand Side Management (DSM)

The planning tool user is able to activate or deactivate these use case groups. If a use case group is

selected, it will be considered during ICT planning. Each use case group consists of a set of a predefined

Use Case Elements (UCE), which are explained according to their functionalities and application area

in the following subsection 2.1.1.1. The mapping of use case groups to UCE is defined in Annex 6.2. In

addition, Annex 6.2 provides values for UCE frequencies, durations, priority and data volume. These

parameters provide capabilities to calculate data rates and data volumes, which are mandatory for smart

grid traffic modelling (see section 2.2).

1 Selected technologies are an example for most suitable transmission technologies with regard to Smart Grids

and fulfill the requirements of all introduced use cases within this documentation. Nevertheless, the tool can be

extended by additional technologies.


Page 11 of 37

Remark: Additionally, a use case pre-selection by the planning tool user provides options to estimate reliable High-Level Protocols in order to fulfil requirements and demands of a use case element. This HL-preselection is not integrated in the ICT model, but several HL protocols and their application areas are discussed in deliverable D3.1 [13], task T3.2 and T3.3. 2.1.1.1 Use Case Elements

Use Case Elements and their application areas are defined as follows:

Administration and Configuration (UCE_ADMIN)

Administration and Configuration consolidates UCE options for several management and maintenance activities, such as: Device, Client and Certificate Management, Firmware Update / Upgrade, Wake-up Configuration, Monitoring (System logs) and Time Synchronization.

Alarming and Notification (UCE_ALARM)

Alarming and Notification provides UCE options for: Event/Error Reports and Alive Notification.

Controllable Local Systems (UCE_CLS)

Communication with Controllable Local System (CLS) intended for Demand-Side-Management of e.g., EVs, heat pumps or storage heater systems. These entities are either part of a smart home system or belong to small industrial companies.

Load Optimization (UCE_LO)

Load Optimization provides local load optimization capabilities by means of substation automation (distribution network level).

Distributed Energy Resources (UCE_DER)

Distributed Energy Resources (DER) includes measurement and control related communication with smaller DERs (< 1MW) as part of the distribution grid (such as wind and photovoltaic systems).

Smart Metering (UCE_SM)

Smart Metering incorporates automated Meter Readings and Communication to central systems by means of intelligent metering systems.

Electric Vehicles Supply Equipment Management (UCE_EVSE_MGMT)

Electric Vehicles Supply Equipment Management provides operations for basic charging process management of EVSEs which are not part of smart home systems, but located at public or semi-public area. This use case element provide following options: Charge Authentication, Billing, Remote Customer Support, Charge Spot Reservation and Asset Management.

Electric Vehicles Charge Management (UCE_EV_CM)

Electric Vehicles Charge Management includes charge control of EVs, which are not part of a smart home system, but located at public or semi-public Electric Vehicle Supply Equipment (EVSE). Different types of charge management are covered by appropriate UCE options: Soft / fleet focused charge management based on Time of Use tariffs, Massive charge management based on daily signals and Massive Local Charge Management based on Charge Modulation.


Page 12 of 37

2.1.2. ICT Requirements

For choosing relevant ICT components, the planning tool user defines requirements and boundaries for

ICT infrastructure. This is realized by defining technical parameters which should be met at minimum by

the eligible communication technologies and are mapped to the above introduced use case elements.

Parameters are divided into optional and mandatory parameters as listed in Table 2.

Remark: The ICT Model implementation focusses on a few communication technologies that are suitable candidates for ICT rollout for future grids. These technologies are implemented and highlighted within network dimensioning functionality of the infrastructure planning part (see section 2.3). Other technologies are listed and described within deliverable D3.1 [13].

Table 2: ICT Requirement Parameters

Ma

nd

ato

ry

Min. Availability (temporal) %

Max. Response time s

Bidirectionality y/n

IT Security Low/High

Remote Maintenance y/n

Op

tio

na

l

Min. Data Rate per unit kbit/s

Min. Availability (spatial) %

Traffic classes and QoS y/n

Maximum data rate per unit kbit/s

Technology life cycle years

Black start capability y/n

The input of mandatory parameters is necessary to filter relevant ICT technologies, and these must be

met by technologies to be considered. Optional parameters do not need to be given, and they do not

define hard exclusion criteria for transmission technologies, although a technology will be considered

less suitable if optional parameters do not meet the requirements given. In this context, the ICT model

is based on the following simple scoring procedure, which guarantees that a communication technology

which does not meet the given input will be excluded from ICT network planning.

+1 ICT requirement parameter is better than the given input

0 ICT requirement parameter meets the given input


Page 13 of 37

-1 ICT requirement parameter does not meet the given input, ICT

requirement parameter is optional

-20 ICT requirement parameter does not meet the given input, ICT

requirement parameter is mandatory

A definition of the above listed parameters follows subsequently.

Min. Availability (temporal) [%]:

The temporal availability is calculated from the ratio of the average availability of a technology

per connection based on the considered overall period, e.g. 525420 min / 525600 min =

99.9657%

Max. Response Time [s]:

The response time is the delay between sending a message and receiving an acknowledgement

(receipt of a confirmation message transmitted by the receiving station), e.g. 0.2 s.

Bidirectionality [y/n]:

This parameter defines whether a reverse channel is required or not.

IT Security [Low/High]:

This is an indicator for the fulfilment of security requirements in general.

Remote Maintenance [y/n]:

The remote maintenance parameter describes whether a technology has the opportunity of

remote access in order to resolve errors or to add communication functionality (e.g. in case of

the detection of security gaps).

Min. Availability (spatial) [%]:

The spatial availability is calculated from the ratio of a technology coverage in relation to the

desired total area, e.g. 525 km2 / 560 km2 = 93.75%.

Min. Data Rate per Unit [kbit/s]:

The min. data rate defines the data rate that is required per device to fulfil requirements of a

desired smart grid application, e.g. 10 kbit/s in GPRS.

Traffic Classes and QoS [y/n]:

Traffic classes describe the opportunity to define priority classes transmission techniques or

processes.

Maximum Data Rate per Unit [kbit/s]:


Page 14 of 37

The max. data rate defines the data rate that needs to be supported per device/application in

maximum. It is an indicator for the sustainability of a system.

Technology Life Cycle [years]:

This is an indicator for a period in years after that it is expected that a technology will be replaced

by another.

Black Start Capability [y/n]:

This parameter describes the capability of a communication technology to restart safely after a

failure without operator interaction.

2.1.3. Energy Network

In order to model the ICT infrastructure, detailed knowledge of the underlying energy network is needed.

Therefore, necessary energy network data is based on the desired energy network area, in order to

calculate e.g., coverage areas for mobile cellular networks (compare section 4.2). On the other hand

detailed information about the number of connection points (CP), located within the energy network is

needed in order to calculate the total traffic load within the traffic model (compare section 2.1).

In case of specific transmission technologies (e.g., Power line Communication, PLC), further information

about connection point positions is needed. Thus, special aggregation algorithms, in order to find

suitable number for gateways (local controller, LC) or repeaters, are introduced in subsections 2.1.3.1.

The format of the input table is shown in Table 3.

Table 3: Input for aggregation tool

ID_CP x_CP y_CP Type

… … … …

The input should consist of x- and y-coordinates, as well as an ID for every CP in the energy network.

In addition, a CP should be mapped to one of the following client types:

Private: e.g., home facilities

Public: e.g., work, universities, shop or other facilities

Grid: e.g., substations

2.1.3.1 Pre-Processing of External Network Input

The pre-processing tooling provides an aggregation algorithm to provide possible locations for local

controllers. It is used to aggregate data of several connection points to reduce traffic load in wide area

networks (WAN, compare section 4.2).

The aggregation algorithm is spatially restricted, that means only given connection point locations are

considered as possible positions for local controllers. Different parts of such aggregation algorithm are


Page 15 of 37

shortly introduced in the following subsections.

Remark: It is possible to consider not only given connection point locations as suitable location for local

controllers, but the overall energy network area. This is not recommended, due to two reasons. First,

due to a significantly higher computational effort. Second, while considering the overall energy network,

it is not guaranteed that a detected location could be used in concrete infrastructure installation, due to

environmental reasons.

2.1.3.2 NeighborFind

NeighborFind searches neighboring connection points in a given radius. Input for NeighborFind are

coordinates of every connection point and the radius for the aggregation. As an output the algorithm

returns the indices of the neighbors for each connection point.

Simple Pseudo-Code NeighborFind

INPUT: connection points = {cp1, cp2, …, cpn}, radius

OUTPUT: neighbors

for all connection points do

(1) find all connection points in radius of current point cpi

(2) add found connection points to neighbors of cpi

end for

2.1.3.3 Spatially-Restricted Aggregation

This aggregation algorithm searches suitable numbers and positions for local controllers under the

constraint that possible positions are limited to connection point locations. The output of NeighborFind

serves as the only input for this algorithm. As an output the algorithm returns the indices of connection

point locations which are suitable for local controller locations as well. This aggregation algorithm is a

greedy algorithm, thus it provides good results in a manageable amount of time, but the result may not

be the optimum.

Simple Pseudo-Code restricted Aggregation

INPUT: neighbors

OUTPUT localController

remainingNeighbors = neighbors

While remainingNeighbors not empty do

(1) find point cpi with most remainingNeighbors

(2) add cpi to localController

(3) remove neighbors of cpi from the list of remainingNeighbors

end while


Page 16 of 37

2.2. Smart Grid Traffic Modeling

Basically, the ICT Model differentiates between Use Case Groups (see 2.1.1) and Connection Points

(CP). As introduced in section 2.1, CPs are physical communication devices divided into three

categories, private, public and grid. Households are classified as private, public CPs are e.g. wide

parking areas with EV charge spots or wind / solar generation parks. Finally, grid CPs represent entities

of the energy network, such as substations. These devices are then categorized in four use case groups,

Automated Meter Reading (AMR), Distributed Generation and Storage (DG/DS), Distribution

Automation (DA) and Demand Response / Demand Side Management (DSM). According to Annex 6.2

these use cases are linked to a definition of traffic requirements, which are packet size, arrival rate,

required latency and service priority.

These requirements are used for a worst case modelling delivering minimum data rate requirement and

monthly data volume per CP. Traffic classes are not only priority and non-priority services, but a more

detailed differentiation into three categories:

priority-random (P),

regular (R),

scheduled messages (S).

Priority-random messages are high priority messages that could not be foreseen, e.g. fault alarms or

switching commands. Regular messages are periodically transmitted and are characterized by a

comparably low inter arrival time (IAT) and a maximum latency to IAT ratio of approximately 1, whereby

the IAT characterizes the message frequency. Assuming an UCE with a message frequency of 15

minutes (IAT) and maximum latency (allowed duration) of 10 minutes, the maximum latency to IAT ratio

is smaller than 1 in each case. The idea behind this requirement is that information regular messages

with a higher latency than IAT is out of date and thus not of interest. Scheduled messages are non-

priority and can either be transmitted on a regular basis or randomly. These services provide a long IAT

and small maximum latency to IAT ratio, thus enabling scheduling of messages. Detailed examples are

illustrated in the Annex.

As regular and priority random messages cannot be scheduled, these service types build a base load

regarding data rate. So, the minimum data rate requirement for every CP is composed of the sum of

minimum data rate requirements of every regular service, the sum of minimum data rate requirements

of all priority-random services and the highest minimum data rate of scheduled services, as these

services can be scheduled and thus be transmitted when general load is relatively low. Whereby we

assume a worst case in which all priority-random services are triggered simultaneously in case of

priority-random services. This results in the following equation for minimum data rate requirements:


Page 17 of 37

𝑅𝑐𝑝𝑗 = ∑ 𝑅𝑖 + max𝑛𝑠𝑗

𝑅𝑖 + ∑ 𝑅𝑖𝑛𝑝𝑗

𝑖=0

𝑛𝑟𝑗

𝑖=0 ,

where j is the index of the use case, 𝑅𝑖 marks the minimum data rate requirement of service i and r,s,p

indicate the three service groups regular, scheduled and priority-random. 𝑅𝑖 values are derived from the

traffic model, taking packet size, maximum latency and traffic class into account. The monthly data

volume per CP is based on packet size and arrival frequency of message occurrence of different use

cases.

Additionally, a Device / Connection Point Model is implemented according to the development of the

use case groups over the period under study and the Connection Points of the three types (private,

public, grid) in different areas (urban, suburban, rural). With this model, the minimum data rate

requirement and monthly data volume per CP can be used to calculate the total data rate requirement

and total data volume of the corresponding scenario. Total data rate requirement is defined by the

following equation with 𝑁𝑗 describing the number of devices in use case j:

𝑅𝑡𝑜𝑡𝑎𝑙 = ∑ 𝑁𝑗 ∙ 𝑅𝑐𝑝𝑗

𝐽

𝑗=0

2.3. Network Dimensioning

The Network Dimensioning part uses requirements calculated in the Smart Grid Traffic Model and

combines them with a database of communication technologies and their respective properties

regarding e.g. data rate and latency. Considered technologies are basically dedicated LTE

infrastructure, dedicated fibre infrastructure, PLC distribution network with LTE wide area network and

mixed network solutions (e.g., urban fibre + suburban and rural LTE network).


Page 18 of 37

Figure 5: Coverage based vs. capacity based network planning

As shown in Figure 5, network dimensioning needs to consider two major aspects:

Coverage, i.e. the spatial availability of network connectivity.

Traffic capacity, which means how much traffic load can be handled in a certain time frame.

As fibre networks innately provide a very high data rate, only coverage needs to be taken into account

for this technology. For LTE and PLC technologies, both aspects need to be taken into account.

Coverage based network planning of LTE infrastructure is premised on the Okumura-Hata propagation

model, which was extended by approaches of studying basement and indoor penetration [3]. Using this

data, it is possible to specify the maximum radii for different frequencies in Table 4. Due to different

propagation characteristics in urban, suburban and rural areas, radii are listed separately.

Table 4: LTE network dimensioning: base station radii [km] for different frequencies and area types (indoor coverage). Based on [3].

Frequency 450 MHz 800 MHz 1800 MHz 2600 MHz

Urban 2.1 1.3 0.6 0.4

Suburban 3.7 2.4 1.4 1.0

Rural 12.2 8.4 5.3 4.2

Table 4 is an example for the obtained radii in indoor coverage. Outdoor coverage can be achieved with

larger base station radii, while basement penetration requires smaller radii. Based on these data, the

required number of base stations can be derived statistically.

Regarding capacity based network planning, the number of required base stations is determined by the


Page 19 of 37

following equation:

𝑚 = max

(𝑅𝑢𝑝

𝑟𝑐𝑒𝑙𝑙𝑢𝑝

,𝑅𝑑𝑜𝑤𝑛

𝑟𝑐𝑒𝑙𝑙𝑑𝑜𝑤𝑛

)

Hereby, the number of base stations needed is determined by dividing the overall minimum data rate

requirements, individually for up and downlink, by the corresponding maximum cell data rates and

calculating the maximum among uplink and downlink.

Concerning fibre infrastructures, only coverage based dimensioning is necessary as aforementioned. A

Fibre-to-the-Building (FTTB) approach is considered here, disregarding in-house communication. The

framework provides the specification of an area to be covered by one central office (CO), so that the

area is divided into zones handled by different CO providing the required backhaul connectivity. CPs

are then attached to the CO, either in a point-to-point approach, or in a Gigabit Passive Optical Network

(GPON) architecture via intermediate splitters, using a ring topology. This approach locates splitters on

a number of rings around the CO interconnected by two additional fibre links. The splitters then

aggregate fibres from several CPs and establish the communication with the CO.

PLC network planning uses a similar approach where Local Controllers (LC) aggregate data

transmissions of nearby CPs. These LCs compromise a PLC head-end and provide backhaul

connectivity similar to the CO for fibre networks. For PLC networks there are two approaches, complete

PLC infrastructure and a combination of PLC and LTE to reduce wide area traffic load of pure mobile

cellular networks, as shown in Figure 6. When using only PLC, LCs are interconnected via repeaters

that provide distances longer than 250m and attached to the core network using one or two selected

LC. In combination with LTE, an LTE modem is located at each LC, allowing connection to the core

network. With regard to the area covered by one LC, PLC range (150 – 200m) and traffic capacities

need to be taken into account.

Figure 6: Combination of LTE and PLC networks

2.4. Cost Modelling

The results of the technological dimensioning serve as input for the cost model, examining the Total

Base Station

CP

CP

CP CP

CPCP

CP

Traffic Load

CP

CP

CPCP

CP

CP

CP

CP Connection Point

Mobile Link (e.g., LTE)

Wired Link (e.g., PLC)

WANLAN


Page 20 of 37

Cost of Ownership (TCO), the net present value and the cost per device per month for the different

network approaches made beforehand. Cost components are exposed and divided into capital

expenditures (CAPEX) and operating expenditures (OPEX), in the first tier, and network respectively

task context, in the second tier. The following subsections provide a short description for each

component and the integration into the overall model.

2.4.1. CAPEX Estimation

Access network

Costs for access network infrastructure are mainly dependent on the used technology. In case of LTE

networks this includes expenditures for base stations (eNodeB) and their installation (100-200 k€). Fibre

networks require optical splitters and central offices, but the majority of expenditures for fibre networks

are trenching (21 k€/km) and cabling (100 €/km) [4]–[6] of fibre. PLC networks are sufficiently

established with head-end units (0.5 k€) and repeaters (0.1 k€). Additionally, purchasing and installation

of user equipment is considered.

Backhaul infrastructure

All network types need a connection between access network and core network. These backhaul links

are commonly realized as fibre or directed radio connections. Both of the realizations are sufficient for

LTE and PLC, while directed radio seems inadequate for fibre connections because of lower traffic

capacity.

Technology-specific investment

In addition to the abovementioned factors, LTE networks produce costs for Evolved Packet Core

components and bandwidth-dependent license fees. The latter have been scaled down in the test

scenario in accordance with the area under study, thus preventing an inappropriate distortion of results

in comparison to other technologies. License fees have been assumed as 500 k€ for 20 MHz.

Location, buildings

Locations and buildings for network components need to be either bought or rented. The framework

allows setting up a ratio between these options.

Network planning

As a matter of course, expenditures for planning and dimensioning a new communication network need

to be considered as well.

2.4.2. OPEX Estimation

Network operation

A majority of operating expenditures are costs for actually operating the network, thus monitoring and

controlling its functioning. These costs are based on an estimated number of required employees and


Page 21 of 37

country-specific employee-level costs (60-120 k€) [5]

Network maintenance

Maintenance costs are also based on employee estimations similar to the before-mentioned factor.

Hardware replacement

In addition to employee costs for maintenance, regular costs for hardware replacement (1 %

CAPEX/year) need to be taken into consideration.

Energy costs

Network components have a certain power consumption, which has to be settled up. Costs (0.25 €/kWh)

[7] are calculated using a simplified energy model using approaches in [8], [9].

Location, building rent

See above.

Backbone network rent

It is assumed that core network capacities by existing telecommunication network operators are used

instead of setting up a completely new core network. This induces rental costs for these backbone

networks.

Management functions

Another cost factor is based on organization management, which includes costs for e.g. reporting, legal

services, accounting and management itself. These expenses are also based on estimated employee

numbers.

Communication Service Fee

The framework provides the possibility to compare setting up a new ICT solution to using existing ICT

infrastructures based on public M2M tariffs. However, using existing infrastructure produces costs in

terms of service fees. The model allows a choice between two different tariffs deduced from common

public M2M tariffs. This is the non-QoS tariff (Tariff nQoS), which employs per MB princing (0.1 €/MB)

and the Tariff QoS consisting of different data volume packages and an additional package for QoS

guarantees (5 €/device).

2.5. ICT Model Output

Based on all the aforementioned elements, the model provides the following output values for each

network deployment: TCO, which is the total expenditures for the respective network design and can be

split into CAPEX and OPEX, Net Present Value (NPV), which discounts TCO to an equivalent present

value, and costs per device and month derived from the NPV, which allow a transparent comparison

with common communication tariffs.


Page 22 of 37

CAPEX

OPEX

Net Present Value (NPV)

Costs / Month /User Equipment (UE Costs)

2.6. ICT Model Summary

Figure 7 serves to illustrate an overview of relevant ICT Model Input, Operation, as well as Output

parameter. As depicted and introduced in section 2.1, the ICT Model is mainly based on energy network

and traffic model input definitions. A detailed description of each parameter is given in Annex 6.1.

Optionally, the ICT Model offers capabilities to change configurations of below presented parameters or

traffic conditions (see section 2.2 and 2.3 for detailed parameter information). These alternative

parameterizations should only be executed by ICT professional and thus are excluded from input

parameters. Nevertheless, the test case study provides a short example of the economic impact of

varying technological, as well as traffic parameters (section 3.1).

ICT Model output parameters are introduced and defined in section 2.5.


Page 23 of 37

Figure 7: ICT Model Input/Output Overview

Inp

ut

Energy Network Data

• Connection Points [n]

• Energy Network Area

[km²]

Traffic Model Data

• Use Case Groups

• Quantity Structure

• ICT Requirements

Common Data

• Discount rate [%]

• Own Core Network

[yes/no]

• CAPEX

• OPEX

• Net Present Value (NPV)

• Costs / Month / User Equipment (UE Costs)

Ou

tpu

tIC

T M

od

el O

pe

ration

Technological Parameters, e.g.

• Frequency [MHz]

• Bandwidth [MHz]

• Network Topology

• Coverage Area

Traffic Conditions, e.g.

• Data Rate Requirement

• Data Volume

• Quantity Structure


Page 24 of 37

3. Test Case Study

The following test case study illustrates the above introduced Smart Grid ICT Modelling approach, which

is published in [2] as well.

Figure 8 presents our medium-voltage (MV) test grid with a total area of 49.4 km2 divided into Urban,

Suburban and Rural area types. CPs are randomly distributed among these area types considering

various standard-deviations. In this context, CPs are summarized into three types Private (Home

facilities), Public (Work, Shop and Other facilities) and Grid (e.g. substations).

Figure 8: Medium and Low Voltage Test Grid Structure [2]

Table 5 summarizes case study data and depicts the evolution of our considered quantity structure for

above introduced traffic use case groups, which is the multiplication factor applied per CP to obtain the

total communication traffic (zero equals, use case is not implemented). Using the example of DG/DS at

private CPs, it implies that the traffic load generated by these CPs faces a 300% increase between 2015

and 2030.

Total Area

Urban

Suburban

Rural

Suburban:

31,1 km²

Rural:

14,2 km²

Urban:

4,1 km²


Page 25 of 37

Table 5: Overview of Grid related Scenario Parameters [2]

Area [km2] Urban Suburban Rural Total

4.1 31.1 14.2 49.4

Type of Connection Point Private Public Grid LC Total

Connection

Points [n]

Total 616 223 45 217 884

Urban 217 127 14 32 358

Suburban 321 28 24 125 383

Rural 78 58 7 60 143

Traffic Use Case Groups Year Private Public Grid

AMR 2015 1 0 0

2030 1 0 0

DG /DS 2015 1 5 0

2013 3 20 0

DA 2015 0 0 1

2030 0 0 1

DSM 2015 0 1 0

2030 1 15 0

The above introduced input data leads to results presented in Figure 9, which compares several

technological variants of ICT infrastructure roll-out solutions with help of following parameters:

CAPEX

OPEX

Net Present Value (NPV)

Costs / Month /User Equipment (UE Costs)

Costs per device and month provide a direct and simple comparison between all considered

communication technologies. Thus, UE Costs values differ in a range of 48 % from 14 € (Tariff nQoS)

up to 27 € (Fibre).

In terms of NPV, results show comparatively small differences, yet a slight advance of the homogeneous

LTE- 450 MHz (5 MHz bandwidth) solution (LTE) compared to Tariff nQoS solution. Both technology

combinations - PLC aggregation network with LTE-450 MHz (5 MHz bandwidth) outdoor network for

backhaul connectivity (PLC+LTE) and urban fibre network with suburban/rural LTE infrastructure

(Fibre+LTE) – suffer the drawbacks of extra investments for different network components and

additional staff for operating two infrastructures. Also, fibre networks in both heterogeneous and

homogeneous deployments incur increased TCO due to high expenses for trenching.

It has to be taken into account that costs per device and month are comparably high due to the small

scenario. This also manifests in a relatively low CAPEX to OPEX ratio, caused by a high degree of fixed

OPEX. Large scenarios will experience better scaling, enabling further cost reductions per device. But

in general Figure 9 illustrates which capabilities the implemented ICT planning approach provides in

terms of grid planning decision making.


Page 26 of 37

Figure 9: Scenario-Specific Comparison of TCO, NPV and Costs per Device and Month for Different Communication Solutions [2]

3.1. Sensitivity Analysis

In a next step, the economic impact of different technology specific parameters (Figure 10) as well as

varying traffic load conditions (Figure 11) is analysed. Figure 10 illustrates the deviation of NPV with

regard to three different ICT reference solutions – LTE 450 MHZ frequency, 5 MHZ bandwidth; Fibre

GPON architecture, 41 % existing ducts and heterogeneous LTE and PLC network with 150 m local

controller (LC) radius. With regard to the LTE example, it is shown that the NPV rises by 119 % in case

of only 1.4 MHz bandwidth, which is caused by the considerable rise of the number of base stations

from 7 to 28, while employing 10 MHz bandwidth might even optimize the NPV slightly. The impact of

different network architectures or existing ducts for cabling is depicted within examples of Fibre as well

as PLC+LTE.

In contrast to different technical parameters, it is also possible to vary the traffic conditions, which is

implemented by, e.g., varying data rate or data volume requirements of former introduced Use Case

Groups with reference to values listed in Annex 6.2. In this context, Figure 11 illustrates risen NPV as

well as costs per device and month for both the LTE and the PLC+LTE approach, due to more strict

minimum data rate requirements (+1.3 kbps per device for use case groups DG/DS, DA, DSM). This is

a result of reduced latency for switching commands, impacting traffic related network planning. In


Page 27 of 37

comparison, this variation does not influence the costs of public telecommunication tariffs, since data

rate requirements are not part of the pricing structure. Instead of a higher minimum data rate, an

increased data volume (+6.2 MB per device per month for use case groups AMR, DG/DS) only affects

Tariff QoS, while LTE as well as LTE+PLC networks are planned for dedicated use and do not depend

on any contract resulting in higher fees for more transferred data volume.

In summary, it was shown that the presented ICT planning approach provides capabilities to compare

several communication technology solutions with regard to economic parameters. Achieved results

depend on technical parameters as well as traffic conditions and provide options to vary initial results in

order to find sufficient solutions for specific scenarios.

Figure 10: Sensitivity Analysis of Net Present Value (NPV) with Regard to Technical Parameters [2]


Page 28 of 37

Figure 11: Sensitivity Analysis of Net Present Value (NPV) with Regard to Traffic Conditions [2]


Page 29 of 37

4. References

4.1. External documents

List of reference documents used but not produced in the project:

[1] S. Böcker, F. Geth, P. Almeida, S. Rapoport, and C. Wietfeld, “Choice of ICT Infrastructures and Technologies in Smart Grid Planning,” in 23rd International Conference on Electricity Distribution, 2015.

[2] C. Dorsch, N., Böcker, S., Hägerling, C., Wietfeld, “Holistic Modelling Approach for Techno-Economic Evaluation of ICT Infrastructures for Smart Grids,” in accepted for presentation at IEEE SmartGridComm, 2015.

[3] C. Hägerling, C. Ide, and C. Wietfeld, “Coverage and Capacity Analysis of Wireless M2M Technologies for Smart Distribution Grid Services,” in Smart Grid Communications (SmartGridComm), 2014 IEEE International Conference on, 2014, pp. 368–373.

[4] C. Jiajia, L. Wosinska, C. M. Machuca, and M. Jaeger, “Cost vs. Reliability Performance Study of Fiber Access Network Architectures,” Commun. Mag. IEEE, vol. 48, no. 2, pp. 56–65, 2010.

[5] M. Mahloo, P. Monti, J. Chen, and L. Wosinska, “Cost Modeling of Backhaul for Mobile Networks,” in IEEE International Conference on Communications (ICC) Workshops, 2014, pp. 397–402.

[6] S. Kulkarni and M. El-Sayed, “FTTH-based broadband access technologies: Key parameters for cost optimized network planning,” Bell Labs Tech. J., vol. 14, no. 4, pp. 297–309, 2010.

[7] Eurostat, “Energy Price Statistics.” 2015.

[8] A. Hoikkanen, “Economics of 3G Long-Term Evolution: the Business Case for the Mobile Operator,” in Wireless and Optical Communications Networks, 2007. WOCN ’07. IFIP International Conference on, 2007, pp. 1–5.

[9] A. A. W. Ahmed, J. Markendahl, and C. Cavdar, “Interplay Between Cost, Capacity and Power Consumption in Heterogeneous Mobile Networks,” in Telecommunications (ICT), 2014 21st International Conference on, 2014, pp. 98–102.

[10] Forum Network Technology / Network Operation in the VDE (FNN), “ICT Requirements for the Operation of AMR Systems.” (in German), Berlin, 2014.

[11] S. Böcker, C. Lewandowski, C. Wietfeld, T. Schluter, and C. Rehtanz, “ICT based performance evaluation of control reserve provision using electric vehicles,” in IEEE PES Innovative Smart Grid Technologies, Europe, 2014, pp. 1–6.


Page 30 of 37

4.2. Project documents

List of reference documents produced in the project or part of the grant agreement and available on

http://www.plangridev.eu/:

[12] PlanGridEV Deliverable 4.2: Report on new methods to maximize integration of EV and DER in

distribution grids (methods for optimization under uncertainty, for storage modelling and for statistical behaviour of EV and DER), DOI: 10.13140/RG.2.1.2047.1526, (30 November 2014, Milestone 3 to 31 December 2014)

[13] PlanGridEV Deliverable 3.1: Joint network architecture model, DOI: 10.13140/RG.2.1.2050.2242, (30 June 2014)

http://www.plangridev.eu/


Page 31 of 37

5. Revisions

5.1. Track changes

Name Date

(dd.mm.jjjj) Version

Changes Subject of change pages

S. Böcker 14.08.2015 1.0 Pre-Final Version Overall

F. Geth 20.08.2015 1.1 Review WP Leader Various

E. O’Callaghan 02.10.2015 1.2 Review 1st Phase (ESB), Final Version

Various

S. Böcker 21.10.2015 1.3 Consideration of 1st review Various

E. Zabala R. Rodríguez

27.10.2015 1.4 Review Technical Coordinator Various

A. Gaul 12.11.2015 1.4 Review Project Manager Various

S. Böcker 17.11.2015 1.5 Final Version Various


Page 32 of 37

6. Annex

6.1. ICT Model Input Overview

The following table illustrates an overview of all parameters that need to be defined by the planning tool user.

Energy Network Data

# Connection Points [n] The planning tool user has to define the total

number of connection points, divided into the

three connection point types per area type:

Connection Point Types:

Private: e.g., home facilities

Public: e.g., work, universities, shop or other facilities

Grid: e.g., substations

In case of PLC, number of local controller (see section 2.1.3.1)

Area Types:

Urban

Suburban

Rural

Traffic Data

Use Case Groups The planning tool user has to define which use

case groups should be considered during ICT

planning procedure. This definition is divided into

Downlink (DL) and Uplink (UL) and results in

minimum data rate requirements per use case

group [kbps] and monthly data volume per use

case [MB].

These parameters are internally calculated on the

basis of ICT requirements (see section 2.1.2) per

UCE. Related ICT requirements per UCE and

related UCE per use case group are listed in

Annex 6.2.

Quantity Structure This input characterizes the multiplication factor


Page 33 of 37

applied per CP to obtain the total communication

traffic. It is divided into connection point type for

each use case group and the evolution between

2015 and 2030.

Assuming the example of DG/DS use case group

and private connection point type: A quantity

structure of 1 in 2015 and a quantity structure of

3 in 2030 implies that the traffic load generated by

these CPs faces a 300% increase within the

desired 15 years.

Common Parameters

Discount rate [%] The interest rate at which an eligible financial

institution may borrow funds directly from a

Federal Reserve bank.

Own Core Network [yes/no] Planning of own core network (backbone) or re-

use public ones. (Refers to all dedicated

communication technologies)


Page 34 of 37

6.2. Detailed Use Case Element Overview

The following table illustrates technical information for each use case element (UCE), which is considered within the ICT planning tool. UCEs are listed per row,

identifiable via unique ID and assigned to relevant UC groups. Technical information is given for Frequency, communication direction, priority, data volume and

date rate. Remark: Use Case Elements definition and corresponding parameter values are derived from literature [10], [11]

Use Case Element (UCE)

ID Description UC Groups

Fre

qu

ency

Main

Directio

n

Dura

tion [

Min

]

Priority

Bru

tto D

ata

Volu

me [B

yte

]

DL D

ata

Vol.

[Byte

]

UL D

ata

Vol. [B

yte

] Data rates

AMR DG/DS DA DSM min. DL-Data rate [bps]

min. UL-Data rate [bps]

UCE_ADMIN 1.1 Device management x x x x Once in 5 years

DL 1 S 5000 5000 50 666.7 6.7

UCE_ADMIN 1.2 Client management x Once in 3 years

DL 1 S 10000 10000 100 133 13.3

UCE_ADMIN 1.3 Profile management (Meter) x Once in 5 years

DL 1 S 4000 4000 40 533.3 5.3

UCE_ADMIN 1.4 Profile management (Comm.)

x x x x Once per year

DL 1 S 4000 4000 40 533.3 5.3

UCE_ADMIN 1.5 Profile management (Profile) x x x x Monthly DL 1 S 4500 4500 45 600 6

UCE_ADMIN 1.6 Key-/Certificate -Mgmt. x x x x Once in 2 years

DL, UL

1 S 4000 2000 2000 266.7 266.7

UCE_ADMIN 1.7 Firmware upgrade (replacement)

x x x x Once per year

DL 2880 S 10800000

10800000

108000

500 5


Page 35 of 37

UCE_ADMIN 1.8 Firmware update/patch x x x x Twice per year

DL 1440 S 540000 540000 5400 50 0.5

UCE_ADMIN 1.9 Wake-up configuration x Once in 3 years

DL 1 S 4200 4200 42 560 5.6

UCE_ADMIN 1.10 Monitoring (Condition Log) x x x x Once per year

UL 5 S 4000 40 4000 1.07 106.7

UCE_ADMIN 1.11 Monitoring (Calibration Log) x Once per year

UL 5 S 12000 120 12000

3.20 320

UCE_ADMIN 1.12 Monitoring (System Log) x x x x Once per year

UL 5 S 120000 100 120000

32 3200

UCE_ADMIN 1.13 Time Synchronization x x x x Once in 48 h

DL, UL

0,3 R 217 108.5 108.5 48.22 48.22

UCE_ADMIN 1.14 Firmware Download (Call) x x x x Once in 48 h

UL R 2000 20.0 2000 2.67 266.7

UCE_ADMIN 1.15 Dist. of tariff. measurement values (infrequently) - CALL ONLY

Once per year

UL 1440 S 4000 40.0 4000 0.004 0.37

UCE_ADMIN 1.16 Dist. of tariff. measurement values (frequently) - CALL ONLY

x Monthly UL 15 S 4000 40.0 4000 0.356 35.56

UCE_ADMIN 1.17 Dist. of network conditions- CALL ONLY

x x x x Once per week

UL 1 S 4000 40.0 4000 5.33 533.3

UCE_ADMIN 1.18 Wake-up x x x x Once in 48 h

DL 0,5 P 212 212.0 2.1 56.54 0.57

UCE_ALARM 2.1 Alarming - Event/Error Reports

x x x x Once in 5 years

UL 1 P 2145 21.5 2145 2.86 286.0

UCE_ALARM 2.2 Alarming - Alive Notifications x x x x Daily UL 1440 R 2000 20 2000 0.002 0.19

UCE_SM 3.1 Periodic transmission to third party (infrequently)

x Monthly UL 1440 S 2145 21.5 2145 0.002 0.2


Page 36 of 37

UCE_SM 3.2 Periodic transmission to third party (frequently)

x Every 15 Min.

UL 15 R 2145 21.5 2145 0.19 19.07

UCE_SM 3.3 Periodic transmission to third party (daily profile)

x Once per year

UL 1 S 4000 40 4000 5.33 533.3

UCE_SM 3.4 Periodic transmission to third party (network conditions)

x Twice per day

UL 1 P 4000 40 4000 5.33 533.3

UCE_SM 3.5 Spontaneous measurement value reading

x x x Twice per year

UL 0 P 12205 1.2 122.0 1.63 162.7

UCE_SM 3.6 Central tariffing x Monthly DL 1440 S 4000 4000 40 0.37 0.003

UCE_CLS 4.1 Communication third party with CLS (fast)

x x Monthly DL, UL

1 P 4000 2000 2000 266.7 266.7

UCE_CLS 4.2 Communication third party with CLS (slow)

x x Hourly DL, UL

15 P 4000 2000 2000 17.78 17.78

UCE_DER 5.1 Spontaneous measurement value reading (DER)

x Monthly UL 1 ? 4000 40 4000 5.33 533.4

UCE_DER 5.2 Communication DSO with DER-low-voltage (control)

x x Monthly DL, UL

1 P 4000 2000 2000 266.7 266.7

UCE_LO 6.1 Communication DSO with Smart Operator (control)

x Weekly DL, UL

1 P 4000 2000 2000 266.7 266.7

UCE_EVSE_MGMT

7.1 Charge Authentication x x Daily (*) DL 0.5 P 1600 1600 16 426.7 4.27

UCE_EVSE_MGMT

7.2 Billing x x Daily UL 15 S 200 2 200 0.02 1.78

UCE_EVSE_MGMT

7.3 Remote Customer Support x x x Monthly UL 30 S 1700 17 1700 0.08 7.56

UCE_EVSE_MGMT

7.4 Asset Management x x x Quarterly

UL 720 S 1700 17 1700 0.01 0.31


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UCE_EV_CM 8.1 Soft / fleet focused charge management based on Time of Use tariffs

x x Quarterly

DL, UL

15 P 1300 650 650 5.78 5.78

UCE_EV_CM 8.2 Massive charge management based on daily signals

x x Daily DL, UL

1 P 1300 650 650 86.67 86.67

UCE_EV_CM 8.3 Massive Local Charge Management based on Charge Modulation

x x Two times daily

DL, UL

0.1 P 1300 650 650 1083 1083

d4.5 prototype tool manual

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