Real-time Monitoring of the EPFL Campus Distribution ... · C-DAX Project EC FP7-ICT-2011-8 call...

C-DAX is funded by the European Union's Seventh Framework Programme (FP7-ICT-2011-8) under grant agreement n° 318708

Real-time Monitoring of the EPFL Campus Distribution Network using PMUs

Herman Bontius – Alliander Paolo Romano – EPFL

i-PCGRID Workshop March 26th, 2015

Power Systems @ Delft TU

Substation Automation – IEC 61850

Protection, Control, Communication

SCADA – Network Control – Grid Ops

High Voltage Design & Engineering

WAMS – WAMPAC

Situational & Security Awareness

Accenture Smart Grid Services EALA – Europe, Africa, Latin America

[email protected]

ABB T&D / Automation KEMA T&D Consulting

ENECO Infra / Joulz Quanta Technology

Accenture

mailto:[email protected]�

Outline

C-DAX Project Introduction

The evolution of Active Distribution Networks (ADNs)

Alliander strategy to manage ADNs

A PMU-based approach to operate ADNs

The EPFL campus MV grid test-bench

The System Architecture • Phasor Measurement Unit (PMU)

• Phasor Data Concentrator (PDC)

• Real-time state estimator (RTSE)

Conclusions & Future Work

i-PCGRID Workshop 2015 3

C-DAX is funded by the European Union's Seventh Framework Programme (FP7-ICT-2011-8) under grant agreement n° 318708

C-DAX: A Cyber-Secure Data and Control Cloud for Power Grids

C-DAX Consortium

C-DAX Project

EC FP7-ICT-2011-8 call project • C-DAX: Cyber-secure Data And Control

Cloud for power grids Duration: 01.10.2012 – 30.09.2015 Total budget: 4.315.303 Euro EU-funding: 2.931.000 Euro

C-DAX communication middleware

• Enables power systems applications to exchange information

• Implements information-centric networking (ICN) paradigm

Targeted use cases • Real-time state estimation based

on PMU measurements • Retail Energy Transactions

Project coordination: Alcatel-Lucent iMinds / Alliander

Project website: http://www.cdax.eu

Project partners

C-DAX: A Cyber-Secure Data and Control Cloud for Power Grids 5

Encrypted Cloud Communication

MV-PMU measuring principles

http://www.cdax.eu/�

Smart Grid Communication patterns

Smart grid applications require support for diverse communication models:

• 1-to-1: e.g. control messages for specific assets

• 1-to-M: e.g. energy offers in demand response schemes

• M-to-1: e.g. energy consumption reports in demand response or smart metering

• M-to-N: e.g. multiple charging offers from different charging stations to multiple EVs

• Anycast communication: e.g. receiving an offer for voltage regulation by any suitable subset of EVs located in a certain area

• Asynchronous communication: e.g. EVs can only retrieve/deliver data while connected to the network


ICN – Information Centric Networks Point-to-point networks

• Producer of information “pushes” data to predefined consumers via explicit point-to-point connections

ICN paradigm • Consumers “pull” or “subscribe to” the data they need regardless of who

produced the information, or when, or where it is stored • Data is collected in “topics”

Advantages: • Inherent security as network and physical locations of hosts are not

exposed (publish – subscribe communication) • Overlay network takes care of managing the connections, optimal

placement of the data within the cloud, resilience • ICN allows in-network management and processing of information, e.g., in-

network caching of frequently used data, aggregation, filtering, rate adaptation, optimal traffic management based on underlying communication infrastructure


Prototype Purpose

• Validation of baseline communication functionalities and basic failure management of C-DAX

• Validation of security framework

• Validation of IEEE C37.118 protocol adaptation layer

Environment • IEEE 34 Bus as power grid

topology • PMU measurement data

provided by EPFL • Virtual Wall network test bed

provided by iMinds • RTSE application by EPFL

C-DAX: A Cyber-Secure Data and Control Cloud for Power Grids

16

RTSE LabView

PMU-Bus3 PubClient

PMU-Bus4 PubClient

PMU-Bus7 PubClient

PMU-Bus1 PubClient

PDC Adapter

SubClient

Base Station

Bus1 Bus3 Bus4 Bus7

LAN

Bus7Node Bus4Node Bus3Node

Security Server

Bus1Node

Monitor

Monitor

BaseStation Resolver

Virtual Wall

Laboratory validation


PMU PMU PMU PMU

PDC PDC

C-DAX cloud

Real-time state estimation of the targeted

electrical network

Real-time model of the electrical grid

Field Trial

Purpose • Deploy C-DAX software in an existing

distribution grid • Evaluate applicability of C-DAX under

realistic conditions Environment

• Distribution grid provided by Alliander including a solid and fast IP network

• PMUs provided by National Instruments • RTSE application by EPFL • C-DAX software

Time plan • Deployment of PMUs and C-DAX

software: late 2014 • Scheduled start of field trial 2015

Alliander’s MS Livelab

National Instruments’ PMU for MV level


Source: Alliander N.V.

Source: National Instruments Sweden

Validation results

19

Test network consisting of:

1 primary substation

16 secondary substations

5 PMU’s for full feeder

observability

Evolution of Active Distribution Networks (ADN’s)

20

0 12 24 time [hours]

• Ultra-short term volatility

63%

2 sec

HV

MV

LV

Characteristics: • Bidirectional power flows

PV o

utpu

t pow

er [

pu.]

i-PCGRID Workshop 2015

We have : Very reliable power service (CML<20min/yr) 99% underground cabled Relatively low capacity (1.3 kW/p residence),

heating/cooling is Natural Gas powered (today). Heavily regulated /owned by local government We face : High penetration private EV CP’s Residential PV is promoted Increasing of heat-pumps penetration

How to cope with increasing dynamics and unpredictable power flows in MV/LV?

Managing Alliander’s Distribution Networks

21 i-PCGRID Workshop 2015

No regret options:

Analytics : • Forecasting customer behavior and grid behavior,

Demand Side Management: • Using prosumer flexibility, local balancing, curtailment options

Measure & Control DN ADN: • ‘Distribution Automation’, Smart Metering, PMU MV

monitoring ?

And……Grid Reinforcements !!

Alliander Strategy


A PMU-based approach to operate ADNs

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= Phasor Measurement Unit (PMU)

= Feeder Monitoring, Control Unit (PDC+RTSE+control+protection)

Monitoring infrastructure components:


The EPFL campus smart-grid project

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An ideal laboratory: 40 buses MV grid (20 kV line-to-line); 30 MW peak load; 6 MW peak CHP; 2.5 MW peak PV; 1 MW peak, 0.5 MWh Li-Titanate storage system; DSM to be deployed in two buildings


The system architecture (1st equipped feeder)


“F-Class” PMUs for Distribution Networks

26

< 0.1% TVE < 0.1% Mag. error < 0.1% TVE < 0.05 Phase error 1% TVE 1% Magnitude error 1% TVE 0.573 Phase error

Design requirements :

High-accuracy • TVE << 1%

• Harmonics/dynamics rejection

High-speed • High reporting rates

• Reduced latencies


EPFL PMU prototype

27

[1] P. Romano, “Enhanced Interpolated-DFT for Synchrophasor Estimation in FPGAs: Theory, Implementation, and Validation of a PMU Prototype", Dec 2014.


Synchrophasor estimation algorithm: • IpDFT-based • Spectral interf. compensation • Sliding window DFT technique

(MSDFT)

compactRIO-based PMU prototype: • FPGA-based prototype • Extreme determinism • Low latencies (30 ms) • Reporting rates up to 10000 fps

(typically reduced to 50 fps)

PMU measurements accuracy assessment


Comments:

• TVEmax = 0.027 % • TVEavg = 0.024 %

• FEmax= 4⋅10-4 • FEavg= 9⋅10-5

• RFEmax= 6⋅10-3 • RFEavg = 1⋅10-3

Substation setup – 0.1 class sensors + PMU


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Real-time State Estimation (RTSE) of ADN’s

Real-time State Estimation (RTSE): “Process of estimating the network state (i.e., phase-to-ground node voltages) with an extremely high refresh rate (typically of several tens of frames per second) enabled by the use of synchrophasor measurements.” Advantages of adopting RTSE processes in ADNs: Implicitly reduces the number of measurement point (and the installation costs) Enables real-time network monitoring Improves measurement robustness Application fields of RTSE in ADNs: Optimal Voltage/Power control Congestion management Optimal dispatch of Distributed Energy Resources (DER) Fault detection and location Network islanding


Estimated state

31

Static (LWLS) vs. Dynamic (DKF) Linear RTSE

[2] Zanni, L.; Sarri, S.; Pignati, M.; Cherkaoui, R.; Paolone, M., "Probabilistic assessment of the process-noise covariance matrix of discrete Kalman filter state estimation of active distribution networks,” Aug. 2014

Error distributions of the estimated magnitude and phases of the network state per bus and per phase, with reference to the adopted DKF with Q matrix assessment and LWLS.


Dynamic SE

Prediction Estimation

Estimated state

PMU Measurements

Network topology

Process model Static SE PMU

Measurements

Network topology

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Phase drift due to GPS disconnection

Missing data

Integrated Bad-data Detection Process

1. Compare expected measurements with actual ones

2. Discriminate between anomalies (bad data) and faults (fast dynamics)

3. Take proper countermeasures 4. Replace bad-data with predicted ones

[3] Pignati, M.; Zanni, L.; Sarri, S.; Cherkaoui, R.; Le Boudec, J.-Y.; Paolone, M., "A pre-estimation filtering process of bad data for linear power systems state estimators using PMUs,” Aug. 2014


System performances – Latency

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PMU Telecom PDC+RTSE

Total latency: 61 ms (mean) 1.8 ms (std)

Time

Signal acquisition

Synchrophasor estimation

Data encapsulation

Network delay

Data-frame alignment

State estimation

t1 (30ms)

t2 (8ms)

t3 (≈1-15ms)

t4 (≈ 1-3ms)

t5 (≈20ms)

t6 (˂1ms)

Refresh rate: 20 ms


Conclusions and Future Work

34

We have built a novel monitoring infrastructure for ADNs and validated it in the EPFL campus MV network. The system is composed by advanced PMUs and a central unit that concentrates the data and estimates the system state.

Within the next months the same system will be transplanted in one of the MV feeder of the Alliander network in the Netherlands.

Measurements and state estimator outputs are publicly available online together with the repository of the historical data. They are accessible via smartgrid.epfl.ch.

1 2 3


http://smartgrid.epfl.ch:443/EPFL_SE.html�





Real-time Monitoring of the EPFL Campus Distribution ... · C-DAX Project EC FP7-ICT-2011-8 call...

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