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With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 1
Integrated cyber-physical solutions for intelligent distribution grids with high penetration of renewables
Final Event
Online, June 14, 2021
Demonstrations at St. Julien, St‐Jean‐de‐Maurienne (France• Multi‐agent based self‐healing scheme in distribution networks
• Advanced solar PV production forecast
Tran‐The Hoang and Quoc Tuan TC
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 4
About usProf. Quoc Tuan TRANScientific [email protected]
Dr. Tran-The HOANGResearch & Development [email protected]
50 avenue du Lac Léman73375 Le Bourget-du-Lac, France
Tel. +33 4 79 79 28 51
UC01: Advanced Photovoltaic
Forecast
UC02: Advanced Consumption
Forecast
UC03: Congestion Forecast
UC04: Congestion Management with
M.A.S.
UC08: Dynamic State-Estimation Based Protection
UC07: Distributed State Estimation
UC06: Advanced Measurement
(Smart Sensors)
UC12: Advanced Voltage control
(isolated)
UC11: Advanced Voltage Control
(DER)
UC01_OUT1
UC02_OUT1
UC03_OUT1
UC06_OUT1
UC08_OUT1
UC09_OUT1
UC07_OUT1UC07_OUT3
UC07_OUT2
UC09: Self-Healing Solution
UC06_OUT2
Advanced functionalities for distribution grids operation
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 5
Developed by CEA
External Grid 1Bus_1_Livraison_ST_Julien
M. Enrobes
Fontagneux
Epine
Plantees/Gare
Clos
SCM
SDa
utes
Lequet
Post
e Le
quet
St. Antoine Claret Pitavie Rauz D’en Haut Gymnase
Post
de
Gym
nase
Riondaz
Avenue de la Gare
Croix Blanche
Chef Lieu/Bourg
St Pierre
Gran
d Ch
amps
Pre
de P
aque
s
Argerel
Post
Pre
de
Paqu
es
St BarthelemyVillarclement
Exte
rnal
Grid
2
Entrale st Julien de Post
Cent
rale
st Ju
lien
Prise
d’e
au
Description of demonstration site (SOREA network)
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 6
Three LV networks will be considered in detail: Pre de Paques ΣPV: 161 kWp, in
which 89 kWp canopies and 72 kWp in rooftop
A lequet: single-phase PV with ΣPV: 43.36 kWP
Gymnase: sport complex with ΣPV: 234 kWp
St. Julien Montdenis 20 kV network: Two PVs, one at Villarclement &
one at Ruaz D’en Haut, 250 kWpeach
3.2 MW hydroelectric power plant
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 7
PV: 161 kWp
PV: 43 kWp
SOREA’s 20 kV grid
PV: 234 kWpBat: 36 kWh
Real demonstration at SOREASOREA is a DSO company, 3MW PV, 10MW Hyd.
Scenarios for demonstration (SOREA)
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 8
Simulation in PowerFactory (SOREA)Pre de Pâques; PV: 161 kWp
A Lequet; PV: 43 kWpSOREA’s 20 kV grid
GymnasePV: 234 kWpBat: 36 kWh
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 9
• Quartier A Lequet from the SOREA electrical network (0.1 km2 area)
• Low voltage network: 400V• Number of loads: 77 (houses)
• Sum of subscribed powers: 549 kVA• Type of loads: residential• 9 PV * plants with 43.4 kWp installed• Current PV percentage: 11%
• Apparent power of transformer: 400 kVA
Actual scenario
A Lequet grid 0.4kV
Simulation in PowerFactory(SOREA)
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 10
Simulation in PowerFactory(SOREA)
Multi‐agent based Self‐healing System
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 11
Apply the MAS-based self-healing solution for 20 kV St. Julien Montdenis distribution network
External Grid 1Bus_1_Livraison_ST_Julien
M. Enrobes
Fontagneux
Epine
Plantees/Gare
Clos
SCM
SDa
utes
Lequet
Post
e Le
quet
St. Antoine Claret Pitavie Rauz D’en Haut Gymnase
Post
de
Gym
nase
Riondaz
Avenue de la Gare
Croix Blanche
Chef Lieu/Bourg
St Pierre
Gran
d Ch
amps
Pre
de P
aque
s
Argerel
Post
Pre
de
Paqu
es
St BarthelemyVillarclement
Exte
rnal
Grid
2
Centrale st Julien de Post
Cent
rale
st Ju
lien
Prise
d’e
au
Agent Controlled Switch
Manual Switch
Circuit Breaker
Determine the optimal location of RCS
Objective function: minimize ENS (Energy Not Supplied)
Multi‐agent based Self‐healing System
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 12
► subscribing GOOSE messages published by feeder IED (FIED)► counting number of fault passing► identifying fault direction using proposed direction method► exchanging required data with neighbors and FIED► processing the obtained data for detecting fault location & making decision locally► informing FIED about the its switch operation for reconnecting affected the feeder► running optimization algorithm for restoring supply to the affected consumers
Agent functionalities:
Remote control of switches is replaced with local control by MAS systems
Multi‐agent based Self‐healing System
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 13
► Validation based on co-simulation approach
for simulating the grid dynamic behavior and for performing signal processing
for exchanging measurement & control signals between Python & PowerFactory
For modeling agents and their communication protocol
► Objectives: to develop and evaluate the communication mechanism
between controllers (agents and IEDs) in real time.
to validate the coordinated operation betweeninvolved control elements
to estimate the total system operation time
► PowerFactory: for simulating the grid dynamic behavior
for performing signal processing
► OPC server: for exchanging measurement & control signals
between Python & PowerFactory
► Python: for modeling agents and their communication protocol
Multi‐agent based Self‐healing System
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 14
► Validation based on co-simulation approach Key results: Operation times for different fault locations
fault
fault direction
fault location system operation
A1|A2|A3 A1|A2|A3 action sequences time, s
F1 U|U|U I|E|E(1) CB1 opens(2) S1 opens(3) CB1 recloses
12.3
F2 F|U|U I|I|E(1) CB1 opens(2) S1 & S2 open (3) CB1 recloses
13.5
F3 F|F|U E|I|I(1) CB1 opens(2) S2 & S3 open (3) CB1 recloses
12.7
F4 F|F|F E|E|I(1) CB1 opens(2) S3 & S4 open (3) CB1 recloses
12.9
U & F – unknown & forward I & E – internal & external
Multi‐agent based Self‐healing System
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 15
ABB REF 615
► Validation based on CHIL testbed Key results: Validation of GOOSE package publishedby REF 615 captured by WireShark
Multi‐agent based Self‐healing System
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 16
ABB REF 615
► Validation based on CHIL testbed
fault operation sequences Time, s
F1 CB-1 opens RREC operates & locks out CB opens 12.03
F2CB-1 opens RREC operates & locks out CB opens S1 & S2 open CB recloses
13.68
F3CB-1 opens RREC operates & locks out CB opens S2 & S3 open CB recloses
13.52
F4CB-1 opens RREC operates & locks out CB opens S3 open CB recloses
13.64
Key results: Operation times for different fault locations
Benefits
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 17
Dealing with bidirectional fault currents in distribution networks which can be foreseen in the context of high share of PV systems
Reduce the fault location and isolation fault
Improve system reliability indices such as SAIDI and CAIDI
PV plant
data
Plant administrator
PV plant
data
PV plant
data
D+1 meteorological forecastD-1 measures
D+1 forecast
Meteorological forecast providers
CEA / INES
D-1 measures
D-1 measures
D+1 forecast
D+1 forecast
Plant administrator
Plant administrator
Renewable forecasting
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 18
Schematic representation of the information exchange as part of the PV production forecasting tool
Measures data
Learning
Meteorological forecast
Local weather conditions
Plant simulation
Production forecast
Time interpolation
Database
Models
fore
cast
Renewable forecasting
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 19
System description
Name of the plant VILLARCLEMENT RUAZ GYMNASE Siege EDF SiegeHangar
Latitude 45.263960 45.257472 45.256715Longitude 6.390184 6.414569 6.409742Peak power 250,8 kWc 250,8 kWc 100 kWcAzimuth +30° +20° +40°Tilt 30° 30° 20°Technology PV PV PVTrackers No No NoBrand of modules Canadian Solar Canadian Solar Astronergy
Installation of the PV forecasting module(The SW‐04 sky imager)
Configuration of the forecasting module communication
Renewable forecasting
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 20
Web‐based visualization
Villard Clément
RuazSiege EDF
Siege Hangar
Combrière Gymnase
European Project, United Grid: PV forecasting at SOREA
Renewable forecasting
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 21
PV plant
data
Plant administrator
PV plant
data
PV plant
data
D+1 meteorological forecastD-1 measures
D+1 forecast
Meteorological forecast providers
CEA / INES
D-1 measures
D-1 measures
D+1 forecast
D+1 forecast
Plant administrator
Plant administrator
Day-ahead forecasting based on meteorological data
1
Short-term forecasting based on satellite images (hourly)
2
Very short-term forecasting based on sky camera (a few minutes)3
Key results
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 22
Day-ahead forecasting based on meteorological data1
Time horizon: 24 h
Key results
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 23
Short-term forecasting based on satellite images (hourly)2
Time horizon: 1 h
Key results
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 24
Very short-term forecasting based on sky camera (a few minutes)3
Time horizon: 1 min
Avg 7.698 %
Key results
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 25
Very short-term forecasting based on sky camera (a few minutes)3
Time horizon: 1 min
Avg 12.079 %
Time horizon: 1 min
Demo showcase
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 26
Time horizon: 1 minTime horizon: 1 min
Benefits for DSOs
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 27
DSOs
Design optimal trading strategies Trading
1
1 Day-ahead forecasting 2 Short term forecasting
3 Very short term forecasting
Optimal maintenance
planningMaintenance
1
Reduce the storage capacity associated with the reduction
of renewable energy uncertainty
Storage capacity
1 Predict the risk of grid congestion during the high PV production actions to reduce the PV curtailment.
PV curtailment2
Decrease of the cost for system balancing
Bala
ncin
g co
st
1
Manage different sources more effectively reduce the cost of MWh produced
Generation cost
2
Improve the frequency stability and voltage controlControl
3
Q&A Discussion
Thank you
With funding from the European Community's Horizon 2020 Framework Programme under grant agreement 773717 29
