Linear: Background and Figures Preparation - Linear team.pdf · 10 different clusters, e.g.: W....
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Transcript of Linear: Background and Figures Preparation - Linear team.pdf · 10 different clusters, e.g.: W....
Linear: Background and Figures
Ronnie Belmans ‐ EnergyVille
Evolutions in the grid
• Increased share of intermittent renewables• Stabilization/decrease controllable production• Electrification of transport and heating
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Linear situated⇒ Energy excesses and shortages⇒ Grid capacity issues
⇒ storage⇒ “consumption follows production” (i.e., demand response)
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Classical control paradigm“production follows consumption”
no longer holds
Linear focuses onResidential Demand Response
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How much flexibility is when available at residential premises? For what and when can we best use this flexibility?
How much flexibility is when available at residential premises? For what and when can we best use this flexibility?
Research PartnersResearch Partners
Industrial PartnersIndustrial Partners
Additional MembersAdditional Members
Partners
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Linear acknowledges theFlemish Government for its support
Smart Grid in Flanders
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Residential demand response,deployed and tested in the field
by a multi‐stakeholder consortium
Residential demand response,deployed and tested in the field
by a multi‐stakeholder consortium
Started in 2009, finishes end 2014
9,5M€ by the Flemish government30M€ in kind by the industrial partners
Started in 2009, finishes end 2014
9,5M€ by the Flemish government30M€ in kind by the industrial partners
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240 Families
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Mix of dispersed and concentrated families participate to Linear
Mix of dispersed and concentrated families participate to Linear
Brussels,Belgium
445 Smart WhitegoodAppliances
445 Smart WhitegoodAppliances
Smart Appliances
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15 Smart DHW Buffers15 Smart DHW Buffers
7 Electrical Vehicles7 Electrical Vehicles
In Figures
Present Today
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3E, ABB, Accenture, Actility Benelux, Agoria, Alliander, ALSTOM Thermal Power Belgium, Anode, Antea Group,
BAM Techniek, Cbt‐Center, CECED, CG Power Systems, CGI, COGEN Vlaanderen, Connect Group, De
Watergroep, Eandis, écoconso, Econogy, EDF Luminus, EDSO for Smart Grids, Eindhoven University of
Technology, Electrabel GDF‐SUEZ, Elia System Operator, Elster, EmBet, Eneco, EnergyICT, EnergyVille, eni gas &
power, Essent Belgium, Febeg, Febeliec, Federal Council for Sustainable Development, fifthplay, Fortech, fractie
Groen ‐ Vlaams Parlement, GEN, Ghent University Researchgroup Lemcko, hYFOPACK, IBM, Ichec Brussels
Management School, imec, iMinds, IncubaThor NV, Infrax, Ingenium, Intellisol, Itho Daalderop, ITRON contigea,
IWT, Joule Assets Inc, Kabinet Annemie Turtelboom, KBC Group, KU Leuven, Laborelec GDF‐SUEZ, Landis+Gyr,
Lava, LOGINCO, Mandu, Matthys, Methis Consulting, Miele, Miele België, MOBISTAR, MR, Nethys Energy,
Nobeco, ODE Vlaanderen, ORES, P&V – RKW, Participatie Maatschappij Vlaanderen, Programme Office Electric
Vehicles, Provincie Vlaams‐Brabant, RESA sa, Schneider Electric ‐ Energy and Sustainability Services, Simac
3Services, Smart Media Publishing, Sony Techsoft, Stad Antwerpen, Stad Genk, STEM Platform,
stroomtarieven.be, Sustesco, Tecnolec, Th!nk E, Thomas More Kempen, Trilations, UC Leuven‐Limburg,
Universiteit Gent, University of Antwerp, Vanparijs Engineers, Vasco Group, VITO, VREG, VRT, Wolters Kluwer
Team
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Reinhart Appels, Dimitris Athanasiadis, Vasiliki Balafouti, Frank Behaegel, Nathalie Belmans, Ronnie Belmans, Bart Beusen, Luc Beyaert, Bart Boesmans, Aline Bogaerts, Lut Bollen, Isabelle Borremans, Ralf Bosch, Bert Callens, Ann Canière, Wim Cardinaels, Koen Casier, Yonghua Cheng, Parvathy Chittur Ramaswamy, Bert Claessens, Sven Claessens, Peter Coenen, Erwin Cornelis, Freek Couttenier, Cathy Crunelle, Theo Daems, Kevin Daems, Jo Das, Sven De Breucker, Pieter De Bruycker, Bart De Caesemaeker, Klaas De Craemer, Jean‐Marie De Hoe, Michael de Koster, Lieven De Marez, An De Moor, Fjo De Ridder, Simon De Rijcke, Ian De Ruyver, Tom De Rybel, Lily De Schryver, Erik De Schutter, Johan De Smet, Eef De Vos, Laurent De Vroey, Bert De Vuyst, Johan De Winter, Kevin Daems, Ludo Deckers, Stefan Decoene, Geert Deconinck, Lieven Degroote, Emmanuel Dejaeger, Cedric Dejonghe, Johannes Deleu, Annelies Delnooz, Bram Delvaux, Piet Demeester, Wim Deprez, Frederick Depuydt, Johan Desmedt, Dimitri De Swert, Dirk De Taey, Chris Develder, Kristof Devos, Johan Dewinter, Bram Dewispelaere, Annick Dexters, William D'haeseleer, Reinhilde D'Hulst, Koen Dierckx, Pol Dockx, Johan Driesen, Olivier Ducarme, Benjamin Dupont, Mario Dzamaria, Nikolaos Efkarpidis, Alexander Eisenberg, Hakan Ergun, Jordi Everts, Ben Flamaing, Wim Foubert, Gert Fransen, Egide Gaublomme, Frederik Geth, Jürgen Geuns, Davy Geysen, Jozef Ghijselen, M. Ghijsen, Maarten Gijsbers, Virginia Gomez Onate, Carlos Gonzalez de Miguel, Jan Gordebeke, Bert Gysen, Liesbeth Haelterman, Wouter Haerick, Jean Hart, Laurence Hauttekeete, Matthias Hendrickx, Anton Heyman, Frederik Hindryckx, Maarten Hommelberg, Jan Hoogmartens, Hanspeter Hoschle, Tom Hostyn, Sandro Iacovella, Geert Jacobs, Rafaël Jahn, Mark Jansen, Johannes Jargstorf, Luc Jespers, Ben Juez‐Ponce, Kris Kessels, Wouter Labeeuw, Eric Laermans, Kristof Lamont, Bart Lannoo, Malinee Lebegge, Filip Leemans, Niels Leemput, Chris Lefrere, Kris Lemkens, Joris Lemmens, Jo Lenaers, Heidi Lenaerts, Jos Liebens, Ilse Lievens, Kim Loffens, Marijn Maenhoudt, Philip Marivoet, Carolien Martens, Muhajir Mekonnen ‐ Tadesse, Stijn Melis, Pieter Merckx, Kevin Mets, Gianni Manderioli, Guy Meynen, Carlo Mol, Jonathan Moreel, Grietus Mulder, Jelle Nelis, Eric Nens, Jan Neyens, Marleen Nijsten, Thomas Nuytten, Kristof Paredis Kristof, EefjePeeters, Johny Plessers, Eddy Poncelet, Raf Ponnette, Jef Poortman, Ronald Poosen, Roland Reekmans, Sabrina Remy, Kurt Reynders, Patrick Reyniers, Patrick Robijns, Johan Roef, Frederik Ruelens, Duan Rui, Claude Ruyssinck, Hans Rymenants, Wendy Sannen, Kristof Scheepers, Ness Schelkens, Wim Schoofs, Dimitri Schuurman, J. Schwab, Daan Six, Tom Sneyers, Maarten Steenhuyse, Ben Steurs, Marcel Stevens, Randy Stiens, Bart Stiers, Steve Stoffels, Jeroen Stragier, Matthias Strobbe, Catherine Stuckens, Tuur Swimberghe, Muhajir
Thank You!
Simulations versus real‐life tests
Pieter Vingerhoets ‐ EnergyVille
© Eandis
Business Cases
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Line Voltage ControlCan demand response help to reduce overvoltages and undervoltages in the
distribution grid?
Line Voltage ControlCan demand response help to reduce overvoltages and undervoltages in the
distribution grid?
Transformer AgeingCan we lengthen the lifespan of the distribution grid transformers with demand response? Can we postpone investments in bigger transformers?
Transformer AgeingCan we lengthen the lifespan of the distribution grid transformers with demand response? Can we postpone investments in bigger transformers?
Portfolio Management
Can demand response shift the energy use of families according to the day‐ahead markets?
Portfolio Management
Can demand response shift the energy use of families according to the day‐ahead markets?
Intraday (Wind) BalancingCan the energy supplier use demand responseto correct intraday imbalances in its portfolio, caused by differences between predicted and
actually generated wind energy?
Intraday (Wind) BalancingCan the energy supplier use demand responseto correct intraday imbalances in its portfolio, caused by differences between predicted and
actually generated wind energy?
Load profiles
• Statistical clustering based on questionnaire + measurement campaign• Load profile generator
Average day consumer
Large day-consumer
10 different clusters, e.g.:
W. Labeeuw et al., Residential Electrical Load Model based on Model Clustering and Markov Models
Load profiles
• Increasing stress on grid infrastructure• Coordination strategies can be used to spread the peak in time
N. Leemput et al. : A Case Study of Coordinated Electric Vehicle Charging for Peak Shaving on a Low Voltage Grid
Transformer lifetime
• Gain in effective lifetime only significant when peak load ~ 150% of transformer rating• Business case behind transformer lifetime improvement appears to be limited
J. Jargstorf et al, Effect of Demand Response on Transformer Lifetime Expectation
Transformer aging
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Can we lengthen the lifespan of the distribution grid transformers with demand response? Can we postpone investments in bigger transformers?
Can we lengthen the lifespan of the distribution grid transformers with demand response? Can we postpone investments in bigger transformers?
DSO focusedDSO focused
Control parameter:Toil & I
Control parameter:Toil & I
J. Jargstorf et al, Effect of Demand Response on Transformer Lifetime Expectation
The grids
Rural
City
Semi-Urban
Urban
Representative feeders from Infrax and Eandis
Simulations to study voltage problems
‐ Increasing load and PV penetration can cause voltage problems‐ Results are extremely dependent on exact feeder configuration and load profile
C. Gonzalez et al, LV Distribution Network Feeders in Belgium and Power Quality Issues due to Increasing PV Penetration Levels
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-A (V
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-B (V
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Weeks of the year0 10 20 30 40 50 60
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-C (V
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Weeks of the year
x1 PVx2,5 PVx5 PVx10 PVx20 PV
Simulations to study voltage problems
‐ Results are extremely dependent on exact feeder configuration and load profile
C. Gonzalez et al, LV Distribution Network Feeders in Belgium and Power Quality Issues due to Increasing PV Penetration Levels
Battery storage
Battery Energy Storage System
Voltage regulation objective:
Peak shaving objective:
With
the total complex power consumption in phase p
Battery Storage
Potential of battery storage: Techno-economic analysis
- Voltage improvement of 11V- Peak reduction of 7kVA- 5000€/y
- Voltage improvement of 5V- Peak reduction of 2kVA- 500€/y
J. Tant et al, Multi-Objective Battery Storage to Improve PV Integration in Residential Distribution Grids
Line Voltage Control
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DSO focussedDSO
focussed
Control parameter:
V
Control parameter:
V
Can demand response help to reduce overvoltages and
undervoltages in the distribution grid?
Can demand response help to reduce overvoltages and
undervoltages in the distribution grid?
Results extremely dependent on feeder topology, load profiles and amount of flexibility Reduction of typically 20-30% of voltage problems
Voltage control: simulations Field test feeder Field test scenario Whitegood 6 boilers
S. Iacovella, R. D’Hulst et al, Standalone LV distribution network control mechanism
Portfolio management
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BRP focussedBRP
focussed
Can demand response shift the energy use of families according to the day‐
ahead markets?
Can demand response shift the energy use of families according to the day‐
ahead markets?
Control parameter:
€
Control parameter:
€
Portfolio management: Simulations
S. Iacovella et al.: Double-Layered Control Methodology Combining Price Objective and Grid Constraints
Time of Use: Simulations
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Scenario 1 Scenario 2 Scenario 3
# Voltage problems/HH/day
Base case
DSO objective
Consumerobjective
Consumer/DSOobjective
Genk LS07 feeder 38/38 smart Households 9 days All WG Deterministic
Scenario 1: 25% EVScenario 2: 50% EVScenario 3: 50% EV
+50% boiler
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1 2 3
Yearly Profit/HH relative to base case
DSO objective
Consumer objective
Consumer/DSOobjective
• Significant potential benefits for the consumer that is shifting consumption in time• It is theoretically possible to combine Portfolio management and voltage control
Intraday (wind) balancing
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Can the energy supplier use demand response to correct intraday
imbalances in its portfolio, caused by differences between predicted and actually generated wind energy?
Can the energy supplier use demand response to correct intraday
imbalances in its portfolio, caused by differences between predicted and actually generated wind energy?
BRP focussedBRP
focussed
Control parameter:
ΔP
Control parameter:
ΔP
Wind balancing
• Flexibility can be used by the BRP as an alternative for gas power plants• The exact economic benefit is dependent on the amount of flexible consumption
K. Kessels et al.: Feasibility of employing Domestic Active Demand For Balancing Wind Power Generation
Other research results
• Battery storage systems as an alternative to demand response
• Electric Vehicle charging coordination schemes
• Combined Heat and Power as a bridge between electricity and gas grids
N. Leemput et al. : A Case Study of Coordinated Electric Vehicle Charging for Peak Shaving on a Low Voltage Grid
J. Tant et al, Multi-Objective Battery Storage to Improve PV Integration in Residential Distribution Grids
F. Ruelens et al. : Stochastic Portfolio Management of an Electric Vehicles Aggregator Under Price Uncertainty
F. Geth et al. : Voltage Droop Charging of Electric Vehicles in a Residential Distribution Feeder
J. Vandewalle et al. : The Role of Thermal Storage and Natural Gas in a Smart Energy System
Conclusions
• A realistic simulation environment was created to test the potential of different smart grid solutions
• Transformer ageing can only be improved by demand response if the peak load is sufficiently high compared to the rated load
• Improvement in the line voltage is possible with demand response and batterystorage
• Results are highly feeder-dependent
• Significant benefits can be achieved for consumers who can shift their consumptionin time, mainly for the large consumers such as EV and DHW
• Combination of portfolio management and voltage control does not significantlyimpact the business case
• Significant benefits can be achieved for the BRP when using demand response forbalancing its portfolio
Electric vehicles
Case study using coordinated charging 100% integration of Electric Vehicles obtained!
Technical Developments
Koen Vanthournout ‐ EnergyVille
The flexibility challenge
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How to harvest flexibility at residenceswithout comfort impact?
Linear focused on smart appliancesLinear focused on smart appliances
The Linear Smart Appliances
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Domestic Hot Water Buffer
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Electric vehicles
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Home Energy Management
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Smart Meters
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2 remuneration models
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185 families185 families 55 families55 families
Capacity feeCapacity fee Dynamic tariffDynamic tariff
Capacity fee
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1€/40 hours1€/40 hours
Dynamic tariff
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A technically challenging projectA technically challenging project
Integration crucial
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Labs and friendly usersLabs and friendly users
SCRUMSCRUM
Good PracticesGood Practices
Separate test backendSeparate test backend
Core Development TeamCore Development Team
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ICT ARCHITECTURE
Matthias Strobbe ‐ iMinds
Linear Control architecture
• Support of balancing and technical business cases
• One business case at the time• Support for # actors (DSOs,
BRPs, Aggregators, ESPs) with # control technology
• Generic interfaces for smart devices and 2 types of smart meters
• Real‐time collection of consumption, production and flexibility data on household and appliance level
• Central collection of data for in depth analyses of business cases and user participation
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Data
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240 M household and applianceconsumption and production records
24 M smart meter power andvoltage measurements
1.9 M whitegoodflex records
571 M records500 GB
27 M DHW flex records
265 M status records on gateway, plugs and smart appliances
13 M transformermeasurements
Need for an efficient data management infrastructure, data processing and visualization tools
Calculation of control signals for the four defined
business cases
Calculation of control signals for the four defined
business cases
Complex queries on alldata, validation & aggregations scripts
Complex queries on alldata, validation & aggregations scripts
Live server replication & backup, development during phase of reference measurements,
analysis tools
Live server replication & backup, development during phase of reference measurements,
analysis tools
Real‐time measurements, control signals, bonus
information
Real‐time measurements, control signals, bonus
information
Backend Requirements: • Guaranteed availability so that no data is
lost• Secure communication, storage and
access to the data • Tools for efficient follow‐up of the
experiments and analysis of the data
Backend Requirements: • Guaranteed availability so that no data is
lost• Secure communication, storage and
access to the data • Tools for efficient follow‐up of the
experiments and analysis of the data
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In‐home Infrastructure
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Internet connection via home networkHome
gateway
Smart Meters
Household Consumption
PV
Heat Pump
Smart appliances
Smart appliances
Garage
Example home setup
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Total consumption
measurement
Laundry Room
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Smart grid ready tumble dryer
Non-smart washing machine
Externalcontroller for WM
Home GatewayTD gateway
SubmetersWM & TD
Zigbee communication
Living Room
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Regular Internet cable modem
Example home setup
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LaundryRoom
Living Room
Garage
PLC communication
Dashboard tool
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Main challenges backend architecture
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Scalability• Need for different servers
increases probability of incidents• Basic MySQL technology for
backend DBs near its limits• Retrieval of data in real‐time
required some adaptations at the energy service provider level
More scalables solutions neededfor full scale roll‐out: cloud & big data services, and efficientcommunication platforms
Interoperability• Interfaces and device abstraction• Well handled in Linear
Scalability• Need for different servers
increases probability of incidents• Basic MySQL technology for
backend DBs near its limits• Retrieval of data in real‐time
required some adaptations at the energy service provider level
More scalables solutions neededfor full scale roll‐out: cloud & big data services, and efficientcommunication platforms
Interoperability• Interfaces and device abstraction• Well handled in Linear
Test Families
Wim Cardinaels ‐ EnergyVille
Time schedule
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Phase 1: Reference Measurements
Field Test Concl
Field Test
2011 2012 2013 2014
besluitValidation of Linear technology Field Test
Ph 2: Reference Measurements
Ph 3: Reference Measurements
30 families: employees of partners
100 families, living scattered in Flanders, no smart meter
110 families, concentrated on a few transformers, smart meter
Conclusions
In real‐life – step 1
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2010: test energy monitoring system with ‘friendly users’
2011: start recruitment in Flanders via new papers and media2012: installation HEMS ‘phase 2’ (systems without smart meter)
In real‐life – step 2
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Systeem 2012
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2012: start recruitment‘ phase 3’ (system integrated with smart meter)2013: installation ‘phase 3’
April> activation of Demand Response Control System
In real‐life – step 3
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Systeem 2013
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No smart meter available
Smart meter integration
Installation examples
Some are more complex
Every house is a lab on its own
Every house is a lab on its own
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Support team
Help Desk – Info meetings – Newsletters www.linear‐smartgrid.be
Help Desk – Info meetings – Newsletters www.linear‐smartgrid.be
Support tickets
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#sup
port tickets
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feb mrt apr mei jun jul aug sep okt nov jan feb mrt apr mei jun jul aug sep okt nov
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(blank)vervangingverplaatsingupdatetellerstandstoringSmart Startserviceresetplug naamswijzigingophalingOfflinemetinglogininterventieinterfaceinstallatieInfoherstartGezinssituatiefoutmeldingEnergieverbruikdefectDATAcontractcommunicatiealarmen1st line
In house communication
Components in outlying corners of the house(cellars, attics, garages, etc. )
Wired: not when retro‐fitting
PLC: issues when longer or multiple parallel PLC systems
Wireless (WIFI & ZigBee): heavy attenuation
concrete, stone and ironaccess points next to or behind appliance metal encasings
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In house communication
• No plug&play solution that works for the majority of the households
• Must be addressed before a full scale roll‐out
Linear forwarded and speeded the product development of
its partners
Linear forwarded and speeded the product development of
its partners
Questions
