Liana Cipcigan - Grid Integration of Electric Vehicles

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Liana Cipcigan - Grid Integration of Electric Vehicles

  1. 1. Grid Integration of Electric Vehicles Dr. Liana Cipcigan Lecturer Energy Institute CipciganLM@Cardiff.ac.uk Research team Panos Papadopoulos, PhD student Inaki Grau, PhD student Spyros Skarvelis-Kazakos, PhD student Joint Supervision: Prof. Nick Jenkins, Energy Institute Leader 1
  2. 2. EVs Grid Integration -What Questions are we trying to answer?Analysis• How many EV? – EV uptake scenarios, impact on generation system, impact on distribution networks• When will they charge? – temporal analysis• Where will they connect for charging? – spatial analysisEvaluation & Control• What are the infrastructure challenges of EV fleet?• What are the options for managing the spatial-temporal nature of the load?• What is the role of the Aggregator, locating the charger inside the aggregator?• Intelligent charging?• Synergies with Smart Grids?Experimental, Validation, Framework, Standards• Algorithms validation, experiment with aggregator?• Framework, standards development 2
  3. 3. Cardiff University Integrated approach of EVs integrationAutomotive Automotive Social Supplier Electricity Markets R&D Business Models R&D R&D Business Models Intelligent infrastructure / Smart Grids INTEGRATED MODEL 3
  4. 4. CAIR/CARBS BRASS JOMEC Sustainable automobility Environmental regulationsDissemination to non- New business models Waste flows, biofuels expert audience Social, economic & feedstock regulatory impacts CPLAN Transport and built environment Travel behaviour EVCE Core Team PSYCH Huw Davies, ENGIN Consumer psychology Liana Cipcigan, ENGIN Travel behaviour Paul Nieuwenhuis, CARBSLow Carbon Research Institute COMP Road Traffic Management SystemsCentre for Sustainable Places ENGIN Vehicle engineering, powertrain, safety, lightweight structures Smart grids 4
  5. 5. Electric Vehicle Centre of Excellence• EVCE is based in School of Engineering at Cardiff University.• Its purpose is the co-ordination and promotion of research activities in the EV area.• The centre draws upon skills and competencies from across the University.• Present emphasis is on energy management, structures & materials and impact assessment. Energy Management Dr. Liana Cipcigan ENGIN ELECTRIC VEHICLE CENTRE OF EXCELLENCE Structures & Materials Impact Assessment Dr. Huw Davies Dr. Paul Nieuwenhuis ENGIN CARBS 5 http://www.engin.cf.ac.uk/research/resTheme.asp?ThemeNo=5
  6. 6. Assumptions Study cases EVs penetration Analysis EVs charging regimes Impact on Impact on Uncontrolled Dual Dynamicdistribution generation tariff price system system Charging Infrastructure Control Validation Toolkit Technical Algorithms Experimental SG Scenarios constraints Standards 6
  7. 7. EV uptake projections In Europe[1] In the UK[2][1] Hacker F., et al. ―Environmental impacts and impact on the electricity market of a large scale introduction of electric cars in Europe - Critical Review of Literature’,The European Topic Centre on Air and Climate Change, 2009.[2] Department for Business Enterprise and Regulatory Reform: Department for Transport: ’Investigation into the scope for the transport sector to switch to electric 7vehicles and plug-in hybrid vehicles’, 2008.
  8. 8. EV impact on generation system• Case Study for 2030 and EV penetration levels projected by [1] for GB and Spain in collaborationwith TECNALIA, Spain EV uptake predictions in 2030 by country, level, and type of vehicleRefP. Papadopoulos, O. Akizu, L. M. Cipcigan, N. Jenkins, E. Zabala,Electricity Demand with Electric Cars: Comparing GB and Spain, Proc. IMechE Vol. 225 Part A: J. Power and Energy, pp.551-566,(2011) 8
  9. 9. Traffic distributions Uncontrolled case Nb. of commuters starting the charging processLow EV uptake High EV uptake 9
  10. 10. Electricity Demand with Electric Vehicles in 2030 British predicted energy demand for uncontrolled charging in 2030Uncontrolled EV charging regime increaseBritish winter day peak demand by 3.2 GW (3.1%) for low EV uptake case (7%)British winter day peak demand by 37GW (59.6%) for high EV uptake case (48.5%) 10
  11. 11. Selected results and conclusions 2030 P. Papadopoulos, O. Akizu, L. M. Cipcigan, N. Jenkins, E. Zabala, Electricity Demand with Electric Cars: Comparing GB and Spain, Proc. IMechE Vol. 225 Part A: J. Power and Energy, pp.551-566, (2011) Load Factor Load Factor SPAIN GB Electricity Demand (GW)Electricity Demand (GW) 120 120 67% 120 100 107.8 100 Installed Generation Installed Generation 80 80 Effective Generation 4.9 3.2 69.9 Effective Generation 60 67.5 60 without EVs without EVs Low EV Low EV Uptake Demand Uptake Demand 40 40% 32% 40 20 20 75 70.7 0 0 11
  12. 12. EV impact on Generation at National Level~ 3mil cars of ~42mil vehicle fleet(7% Low market EV penetration prediction)• Isn’t enough to make a real impact on energy demand at the nationallevel• EVs impact is expected to be at the local level• Impact on LV distribution hotspots depends on clustering 12
  13. 13. Assumptions Study cases EVs penetration Analysis EVs charging regimes Impact on Impact on Uncontrolled Dual Dynamicdistribution generation tariff price system system Charging Infrastructure Control Validation Toolkit Technical Algorithms Experimental SG Scenarios constraints Standards 13
  14. 14. Case study for 2030 33/11.5kV Source 3072 customers UK GENERIC NETWORK ~ 500 MVA 11kV/0.433kV 384 customers 96 customers Parameter Nominal Rating INPUTS FOR 2030 (PROJECTIONS PER 3,072 CUSTOMERS) Transformer loading 500 kVA Type of EV Low Medium High BEV (35kWh) 128 256 640 185mm2 cable 347A PHEV (9kWh) 256 768 1536 loading 384 1024 2176 Voltage 230V (1 phase) Total (12%) (33%) (70%)RefS. Ingram, and S Probert, ―The impact of small scale embedded generation on the operating parameters of distribution networks‖, 14 P B Power, Department of Trade and Industry (DTI), 2003.
  15. 15. Probabilistic Tool for the Evaluation of EV Impacts on LV Networks Uncertainties concerned with EV integration in residential networks Behavioural Technical (Type of EV and Equipment) • Ownership (Location) • EV Charger Ratings • Charging Time Occurrence • EV Battery Capacities • Charging Duration • EV Charger and Battery Efficiencies Outputs • Impact on Distribution Transformer and Cable Thermal Loadings • Impact on Steady State Voltage 15 • Impact on Distribution system efficiency (losses)
  16. 16. Results • Residential charging of EV batteries will overload distribution networks and modify voltage profile of feeders. • The distribution transformer was found to be overloaded for medium and high EV penetration. • The voltage limits would be violated for medium and high EV penetrations. • The 185mm2 cable was found to be overloaded for most 2030 cases. • The results from this research are used for the design of algorithms to allow the efficient management of charging infrastructureRef P. Papadopoulos, S. Skarvelis-Kazakos, I. Grau, L. M. Cipcigan, N. Jenkins, Predicting Electric Vehicle Impacts on Residential Distribution Networks with Distributed Generation, IEEE VPPC(2010). P. Papadopoulos, S. Skarvelis-Kazakos, I. Grau, B. Awad, L. M. Cipcigan, N. Jenkins, Impact of Residential Charging of Electric Vehicles on Distribution Networks, a Probabilistic Approach, UPEC, Cardiff, (2010). 16
  17. 17. Assumptions Study cases EVs penetration Analysis EVs charging regimes Impact on Impact on Uncontrolled Dual Dynamicdistribution generation tariff price system system Charging Infrastructure Control Validation Toolkit Technical Algorithms Experimental SG Scenarios constraints Standards 17
  18. 18. Collaborative Research FP7 MERGE Mobile Energy Resources in Grids of ElectricityDeliverable 2: Extend Concepts of MicroGrid by Identifying Several EV Smart Control Approaches to be embedded in the Smart Grid Concept to manage EV individually or in ClustersDeliverable3: Controls and EV Aggregation for Virtual Power Plants 18 http://www.ev-merge.eu/
  19. 19. Virtual Power Plant Virtual Power Plant (VPP) • The virtual power plant offers the opportunity to aggregate Distributed Energy Resources and create a single flexible portfolio. This way it enables their participation in the wholesale electricity and ancillary services markets. • Early VPP definitions considered only Distributed Generators. Updated definitions consider DER, which include: • DG • Controllable loads * • Energy storage • EVs ???Ref * Virtual Power Plant Concept in Electrical Networks. Juan Martí (2007) [FENIX project] 19
  20. 20. Electric Vehicle Supplier / AggregatorEV Aggregator: Entity which sells electricity to the EV owners, aggregates andmanages their load demand. EV Aggregator basic functions: Market Forecast Short Term Scheduling Decision Making Medium Term Control Long Term Monitoring Load Forecast Short Term Billing Communications Interface Medium Term Long Term Provide information for Share information with Regulators govern the future of Aggregators 21
  21. 21. Possible architectures of the EV Aggregator (EVA) Control Aggregator Centralized EV EV EV EV EV Direct Control Aggregator De-Centralised Control EV EV EV EV EV Distributed Control Aggregator Level 1 Aggregator Aggregator Aggregator Level 2 Hierarchical Agg Agg Agg Level n Control EV EV EVRef I. Grau, P. Papadopoulos, S. Skarvelis-Kazakos, L. M. Cipcigan, N. Jenkins, Virtual Power Plants with Electric Vehicles, 2nd European Conference SmartGrids and E-Mobility, Brussels, Belgium, (2010) 22
  22. 22. Interaction between the VPP Control Center and the VPP resources, DSO, TSO and market in the direct control approachRef A. F. Raab, M. Ferdowsi, E. Karfopoulos, I. Grau Unda, S. Skarvelis-Kazakos, P. Papadopoulos, E. Abbasi, L.M. Cipcigan, N. Jenkins, N. Hatziargyriou, and K. Strunz, Virtual Power Plant Control Concepts with Electric Vehicles, ISAP 2011, Crete, Greece, 2011 23
  23. 23. Interaction between the VPP control center and the VPP resources, DSO, TSO and market in the hierarchical approachRef A. F. Raab, M. Ferdowsi, E. Karfopoulos, I. Grau Unda, S. Skarvelis-Kazakos, P. Papadopoulos, E. Abbasi, L.M. Cipcigan, N. Jenkins, N. Hatziargyriou, and K. Strunz, Virtual Power Plant Control Concepts with Electric Vehicles, ISAP 2011, Crete, Greece, 2011 24
  24. 24. Interaction between the VPP control center and the VPP resources, DSO, TSO and market in the distributed control approachRef A. F. Raab, M. Ferdowsi, E. Karfopoulos, I. Grau Unda, S. Skarvelis-Kazakos, P. Papadopoulos, E. Abbasi, L.M. Cipcigan, N. Jenkins, N. Hatziargyriou, and K. Strunz, Virtual Power Plant Control Concepts with Electric Vehicles, ISAP 2011, Crete, Greece, 2011 25
  25. 25. Assumptions Study cases EVs penetration Analysis EVs charging regimes Impact on Impact on Uncontrolled Dual Dynamicdistribution generation tariff price system system Charging Infrastructure Control Validation Toolkit Technical Algorithms Experimental SG Scenarios constraints Standards 26
  26. 26. Distributed Energy Resources Research InfrastructureProject 1 –Electric Vehicle Operated Low Voltage Electricity networks with Multi- Agent Systems, TECNALIA-LAB, Spain DSO MARKET EVA CAMC agent agent CVC MGAU agent agent EV EV EV agent agent agent KEY Normal/Alert operation communications Emergency operation communications EVA Electric Vehicle Aggregator CAMC Central Autonomous Management Controller MGAU MicroGrid Aggregation Unit CVC Clusters of Vehicles Controllers 27
  27. 27. Adaptation of UK Generic Distribution Network to TECNALIA Laboratory Microgrid UK Generic Network Commercial area EV agent RAU EV agent agent EV EV 33/11.5kV agent agentGrid Supply EV agent ~ ... CVC agent EV agent500 MVA ... CAMC MGAU agent EV EV agent agent agent EV EV agent agent Residential area 28
  28. 28. Test Network in TECNALIA Laboratory Microgrid RAUNetwork configuration Agent Agent System CAMC MGAU Agent Agent CSDER/IEC 61850 EV Agent GridCommunication of MASwith Equipment Load Banks Controller KEY Two way Monitoring communication EV Avtron Millenium One way Disconnection Communication Instruction Avtron K595 DMMS300 29
  29. 29. Distributed Energy Resources Research Infrastructure Project 2 – Electric Vehicles in VPP Title: Carbon Agents for a Virtual Power Plant, in National Technical University of Athens (NTUA) and Center for Renewable Energy Sources (CRES), Greece 56 VPP Aggregator A 55 Winter Emission factor (gCO 2 /km) Summer 54 NTUA Micro-Grid CRES Micro-Grid 53 Aggregator A A Aggregator High Penetration 52 Low Penetration NTUA 51 PV A A A A 50 System 49 G G G G 48 CRES CRES CRES 0% 10% 20% 30% 40% 50% 60% 70% Micro-generation penetration level 80% 90% 100% A Agent Diesel PV Fuel G Micro-Generator Engine System Cell EV emission factor improves by increasing micro-generation penetration [Ref] The laboratory system, NTUA and CRESRefS. Skarvelis-Kazakos, P. Papadopoulos, I. Grau, A. Gerber, L.M. Cipcigan, N. Jenkins and L. Carradore, (2010), “Carbon OptimizedVirtual Power Plant with Electric Vehicles”, 45th Universities Power Engineering Conference (UPEC), Cardiff, 31 Aug – 3 Sept 2011 30
  30. 30. Smart Management of Electric Vehicles EVs load forecasting Smart Management of EVs Evaluate the performances of the algorithms through case studies Laboratory evaluation Partners: E.ON UPL Future Transport Systems Mott MacDonald (PhD student industrial placement) TECNALIA Lab, Spain WAGhttp://www.theengineer.co.uk/sectors/energy-and-environment/news/research-aims-to-deliver-ev- 31power-management-systems/1009752.article
  31. 31. Assumptions Study cases EVs penetration Analysis EVs charging regimes Impact on Impact on Uncontrolled Dual Dynamicdistribution generation tariff price system system Charging Infrastructure Control Validation Toolkit Technical Algorithms Experimental SG Scenarios constraints Standards 32
  32. 32. Lead Partner: Automotive Technology Centre (NL) 11 partners from Belgium, Germany, UK. Ireland and France CU is leading WP3 – Market Drivers and Mobility Concepts Budget €5.04 m (50% funded) Priority 1.1Project application in NW zone http://www.enevate.eu/ 33
  33. 33. WP 1: Electric WP 2:Sustainable WP 3: Market WP 4: Pilots Vehicle Energy supply drivers and Technology infrastructure mobility concepts •Analysis of existing•Supply chain •Knowledge Building •Define integrated EV Pilots in NWEanalysis sustainable e- •Transnational Mobility concepts •Implementation of•Instruments to Consultation & •Market analysis ENEVATE findingsdevelop strong Research user acceptance in regional pilotssupply chain •Scenario building •Tool Kit for future •Finalising Development & sustainable guidelines and evaluation integrated e-Mobility lessons learned concepts •Developing support instrumentsWP 5: Enabling / Innovation Accelerator- Create E-Mobility roadmap - Provide Policy Recommendations-Stimulation and active coaching of EV - Development and implementation supplychain development and innovations training programs-Facilitate acceleration of e-mobility innovation & implementation 34
  34. 34. WP 2 Sustainable Energy supply infrastructure WP2 Leader Tool Kit Development & evaluation• Vision – To develop a practical Tool Kit that can be used by developers to de-risk and optimise the effective and efficient roll out of electric vehicle infrastructure. – To create an integrated delivery process spanning from the sources of sustainable electricity through to the electric vehicle itself. – To apply, test and optimise the Tool Kit using the leading trial projects being delivered across Northern Europe.• Components of the Tool Kit – Outline of key issues – Process map – Project plan with critical path – Guidance notes – Roles & Responsibilities/Stakeholder table – Risk register – Regional variations 35
  35. 35. 36
  36. 36. Scenarios for the development of Smart Grids in the UK Partners:• Identify critical steps in the development of SGs National Grid• Identify how differences in fuel generation and sources, E.ON geography, environmental concerns, the regulatory UK Power Networks environment governing investment and market access, UPL funding complexity, and consumer values present incentives or pose barriers for the deployment of SGs IBM Nottingham Horizon Digital• Develop socio-technical scenarios for UK SG Economy deployment in the period to 2050 Durham University, LCNF project• Explore expert/stakeholder and public perceptions of Low Carbon Research Institute ,CU transition points and fully developed scenarios, highlighting social, behavioural and regulatory/market EcoTown opportunities and barriers. SustainabilityFirst FDT Fintry Development Trust USA Smart Grid Policy, Edison Electric Institute 37
  37. 37. Assumptions Study cases EVs penetration Analysis EVs charging regimes Impact on Impact on Uncontrolled Dual Dynamicdistribution generation tariff price system system Charging Infrastructure Control Validation Toolkit Technical Algorithms Experimental SG Scenarios constraints Standards 38
  38. 38. IEEE Standards AssociationWG p.2030.1, Guide for Transportation Electrification http://grouper.ieee.org/groups/scc21/2030.1/2030.1_index.html 39
  39. 39. Concluding remarks We need to understand many components• Electricity as a transportation fuel• Make charging infrastructure convenient for the EV user – strong support to EV purchase• Minimize stress upon the grid• Benefits for driver – charging as value-added service – combination with loyalty programs – discount on power for spending – automatic notification about status – web / SMS services 40
  40. 40. We need to understand many components• Complex management of large EV fleets• Integrated analysis of electricity / smart grids / transportation / market• There is an important investments in charging infrastructure• Interaction with the grid – EVs becomes an active participant in grid operations – Potential for energy storage – Ancillary services – Grid regulation• EVs synergistic with Smart Grid – Digital Communications - Information flow between vehicle and utility—on some level—is critical to maximizing value – Information Flow Control – Power Flow Control – Decision Algorithms 41
  41. 41. We need to understand many components• Pilot projects and experimental work – experiences of what works, what doesn’t and commonalities for standardization• Benefits for station providers – additional revenue streams – differentiation to competitors – holding customers for longer time – attracting customers during slow periods – promotion and special rates by SMS or – location-based services – combination with loyalty programs• Infrastructure standards are crucial• Emissions reductions and environmental image 42
  42. 42. POLAR UK’s first privately funded nationwide EV charging network• Private sector led initiative - entirely privately funded with no Government or local authority financial support.• Chargemaster Plc, the leading provider of EV charging infrastructure in Europe• POLAR - 100 towns and cities across the UK• 4,000 fully installed electric vehicle charging bays by the end of 2012• In each of the 100 towns and cities, POLAR will operate around 40 publically available charging bays• Chargemaster will work with each PiP areas• The initial rollout over the first nine months will involve 50 towns and cities: Basingstoke, Bristol, Cardiff, Bournemouth, Cheltenham, Crawley, Derby, Eastbourne, Exeter, Gloucester, Guildford, High Wycombe, Maidenhead, Maidstone, Newbury, Plymouth, Poole, Portsmouth, Reading, Rochester, Slough, Staines Southend-on-Sea, St. Albans, Southampton, Swansea, Swindon, Taunton, Telford, Warwick and Wokingham 43
  43. 43. Electric Highway 44

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