Mihaela Albu

Politehnica University of Bucharest,Faculty of Electrical Engineering

DL IEEE Instrumentation and Measurement Society (2017-2019)

OUTLINE

• Emerging power systems

• Disruptive technologies. Game Changers ?

Energy harvesting (DG). Microgrids.

Prosumers. Energy Communities

• Control layer. Deriving models for energy transfer. Measurement context

• Data aggregation in time and in space.

• Synchronized measurements

• Smart Meters with high reporting rate.

• Back to basics. Need of definitions

Transmission network

Distribution grid

EMERGING POWER SYSTEMS

• 1882 Manhattan Pearl Street Station –(Thomas Edison)….1886 ~ 58 DC microgrids

microgrid ”integrated energy system consisting of distributed energyresources and multiple electrical loads operating as a single, autonomousgrid either in parallel to or “islanded” from the existing utility power grid” (Dr.Peter Asmus, Navigant Research)

[advanced] Prosumers

Local Energy Communities

2005http://microgrid-symposiums.org/

Source: http://smartgrid.ieee.org/ieee-smart-grid/smart-grid-conceptual-model

Source: www.microgrids.comSource: GTM Research North American Microgrids 2015

Source: Mar Martinez, PhD thesis, UPC/KUL, July 2017; http://electrifyme.org/

• IEC/TS 62898-2 Ed.1: Technical requirements for operation and control of microgrids

• IEC/TS 62898-1 Ed.1: Guidelines for general planning and design of microgrids;

• site based energy harvesting

• office appliances are DCnative loads or DC compatible;

Source: EmergeAlliance, “Strategies in Light” [ww.emergealliance.org/Portals/_default/Knowledgebase/1/150225StrategiesinLight_TheLEDShow_BTP.pdf]

[DSO] grid

Legacy Loads

PCC meter

Sub-meter Battery

PV

Neighbour

DC

ACBreaker MPPT

ProsumerResilience

zone

Resilient Loads

ProsumerClassic AC

zone

Energy Router=/=

=/=

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By designUniRCon

Mihai Sanduleac: Enabling Microgrids by Rhizomes of the EU Nobel Grid project, 2018.09.03, Bucharest

SLAM/USM

SLAM

Game changers: Prosumers

=/=

• Energy transfer is mediated by power convertersReduction in available inertia Lower time constants in control loop:

seconds millisecondsFrequency is not a system variable protection vs. control (not to be distinguished anymore: real time control

ensures protection!) impact on the sensing elements• Integration of renewables (DG / RES intermittency)

New roles for existing network operators (TSO / DSO)New stakeholders (ESCOs / end users via DSM strategies)

• Customer participation in the marketDynamic load models (estimation in real time enhanced network operation) Dynamic prices dynamic systemsState estimation algorithms Steady-state assessment

• ICT role; Smart Meters spatial & temporal data aggregation: is it really needed?!

The control layer – need of new models

THE MEASUREMENT PARADIGM IN POWER SYSTEMS. DO NOT FORGET THE GOAL!

Measurement result is meaningful only when the quality of the measurement process is quantified

measurement is a GOAL-DRIVEN PROCESS

time is a hidden variable

THE MEASUREMENT CONTEXT

• the embedded measurement information aggregator (unknown, hidden filtering module)!

• newly deployed synchronized measurement units SMUs:– high fidelity, high accuracy, high reporting rates WAMCS

• Frequency, voltage: not anymore ubiquitous information carriers!

• Unequal development of smart grids infrastructures: advanced measurement systems (and data communication) vs. [still!]traditional models for energy transfer

… specific to power system control:

… and some good news:

… and some bad news:

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THE MEASUREMENT PARADIGM IN POWER SYSTEMS. [HIDDEN] DATA COMPRESSION

information concentrators (ex.: rms values) provide data compression capabilities while keeping an analogue signal processing perspective: synchronous averaging (rms “instantaneous” values of periodic signals) data aggregation (in time / in space) with asynchronous averaging

algorithms.

MEASUREMENT PARADIGM IN POWER SYSTEMS. AGGREGATION IN TIME DOMAIN

IEC 61000-4-30 ed2.0, Electromagnetic compatibility (EMC) Part 4-30: Testing and measurement techniques - Power quality measurement methods, 2008(preserved in ed.3, 2015)

Phenomena – measurement: Signal (waveform) – [sampling] –compression – reporting (time granularity) knowledge

Example for 50 Hz system

MEASUREMENT PARADIGM IN POWER SYSTEMS. AGGREGATION IN TIME DOMAIN

IEC 61000-4-30 ed2.0, Electromagnetic compatibility (EMC) Part 4-30: Testing and measurement techniques - Power quality measurement methods, 2008 (preserved in ed.3, 2015)

Original signal f[k]

TC77/SC77A/WG9 Power Quality measurement methods new edition of IEC 61000-4-30

IEC CENELEC TC 8X/WG 07, Requirements for power frequency measurement in electrical energy supply systems, 2017-;

Example for 50 Hz system

MEASUREMENT PARADIGM IN POWER SYSTEMS. AGGREGATION IN TIME DOMAIN

AGGREGATION IN TIME DOMAIN. INFORMATION LOSS

• by averaging the measurement result, the message becomes less sensitive to measurement errors; however, there is a lack of significance of the quantity at the end of aggregation process: the decimation introduces an additional uncertainty which is associated NOT with the measurement but with the meaning of the resulting quantity; this error can be related to the “adequacy” of the information [output message] to the model (of the physical system) definitional uncertainty.

MEASUREMENTS IN POWER SYSTEMS. PMUS

Synchronized measurement technology:backbone for a “real-time” wide areamonitoring, protection and control (WAMPAC)system.PMUs make measurement data available at ahigh speed (30-120 frames per second);PMUs delivers an information concentrator!(phasor: a complex equivalent, in polar orrectangular form, of a sinusoidal wavequantity) signal model!

Optical fibers

PMUPMU

PMU

PMU

PMU

PMU

PMU

Phasor data concentrator

MicrowavePhone lines

Internet/VPN

PMU

Wide Area Network

Database server

Real time monitoring

Applications

phasor data concentrator (PDC): collects phasor data, and discrete event data from PMUs and possibly from other PDCs, and transmits datato other applications

SPATIAL AGGREGATION OF MEASUREMENT INFORMATION

MEASUREMENTS IN POWER SYSTEMS. SMART METERS.

Smart Low-cost Advanced Meter(SLAM)

nobelgrid.eu

.

Questions?

19. June 2018, 0:00-24:00 UTC

5

SMART METERS. 1S REPORTING RATE

19. June 2018, 0:00-24:00 UTC

5

SMART METERS. 1S REPORTING RATE

5 s

19. June 2018, 0:00-24:00 UTC

5

SMART METERS. 1S REPORTING RATE

4 s

microPMU (100 frames/s), PMU (50 frames/s), smart meter (1 frame/s)15.03.2017, 09:00 - 09:10 UTC

5

HOW TO CORRELATE INFORMATION OBTAINED WITH DIFFERENT REPORTING RATES?

Description PMU Micro PMU RTU/ BCU/ IED

Classic Energy meter

Unbundled Smart Meter

Synchronisationrequirements

<1 μs <1 μs 1 – 2 s 1 – 5 s ≤ 1 s

Reporting rate (typical)[frames/s]

50 100 1 1 – 0.2 > 1

Freq. resolution in steadystate conditions [mHz]

<0.01 <0.01 10… 100 10.. 100 10

Accuracy Spec. Spec. Not spec. Not spec. ≈0.2%

Measurement capabilities Dynamic state

Dynamic state

Steady state Steady state Steady state

HOW TO CORRELATE DIFFERENT REPORTING RATES?

High level of synchronization (GPS); High reporting rate (100 frames/s); can connect to PDC (C37.118)

Low level of synchronization; High reporting rate (1 frame/s)

Low level of synchronization; Low reporting rate (1 frame/h)

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APPLICATIONS. LINEAR STATE ESTIMATION

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Take advantage of voltage and current phasor measurements from PMUsIncorporate these measurements into the existing state estimator

APPLICATIONS. HYBRID STATE ESTIMATION

Back to basics. Need of definitions

time interval to assess the steady state condition: 1s

IEC 61000-4-30 ed3.0, Electromagnetic compatibility (EMC) Part 4-30: Testing and measurement techniques - Power quality measurement methods, 2015

• Measurement context • The Sampled Values (IEC 61850-9-2)

component is used to transmit high speed streams of data set samples encoded in multicast Ethernet frames.

• Sensors should communicate also the model quality Goodness of Fit concept

• Synthetic calibration

Harold Kirkham, Artis Riepnieks, Dealing with Non-Stationary Signals: Definitions, Considerations and Practical Implications, 2016 IEEE Power and Energy Society General Meeting (PESGM)

Back to basics. Need of definitions

Harold Kirkham, Artis Riepnieks, Mihaela Albu, Dealing with Non-Stationary Signals: Definitions, Considerations and Practical Implications, 2Advanced Topics in El. Engineering Symposium (ATEE), Bucharest 2017

A. J. Roscoe, A. Dyśko, B. Marshall, M. Lee, H. Kirkham and G. Rietveld, "The Case for Redefinition of Frequency and ROCOF to Account for AC Power System Phase Steps," 2017 IEEE Intern. Workshop on Applied Meas.s for Power Systems (AMPS), Liverpool, 2017, pp. 1-6.

... what is „frequency”?

Frequency in 4 locations (4 Arbiter PMUs) in Romania, during loss of 600 MW (Sunday 25th March 2018)

Example

Rocof ( Arbiter PMUs) in Romania, during loss of 600 MW (Sunday 25 March). Only the PMU on a 20 kV busbar (Galati) has registered high rocof variations – phase shift?

• [NEW] CONTROL ALGORITHMS IN SMART GRIDS REQUIRE FASTER MEASUREMENTS

• REAL-TIME DECISIONS IN MANAGEMENT OF LARGE-SCALE, COMPLEX AND SAFETY-CRITICALSYSTEMS

• NEED TO DEVELOP INFORMATION PROCESSING METHODOLOGIES TO EXTRACT MEANING ANDKNOWLEDGE OUT OF THE DATA

• DEFINITION AND ASSESSMENT OF STEADY-STATE VS. DYNAMIC STATE

• ACCURATE MODELLING/VALIDATION OF MODELS

• NEW DEFINITIONS FOR “INSTANTANEOUS” MEASUREMENTS (FREQUENCY, ROCOF)

• INCLUDE THE DEFINITIONAL UNCERTAINTY INTO THE QUALITY OF MEASUREMENT

• MERGE MEASUREMENT DATA WITH DIFFERENT REPORTING RATES

• NEW SOLUTIONS FOR DATA COMPRESSION

CONCLUSION

.

Thank you for yourattention!

Mihai Călin, Ana-Maria Dumitrescu, 2013, „Stationarity Hypothesis in Power Systems Data Aggregation. Verification Algorithm”, Proceeding of ATEE 20132013 the 8th International Symposium on Advanced Topics in Electrical Engineering, Bucharest, Romania, May, 2013, ISBN: 978-1-4673-5978-8, DOI: 10.1109/ATEE.2013.6563480, WOS:000332928500134