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Charging electric vehicles in the smart grid

Charging electric vehicles in the smart grid. Kevin Mets, Arun Narayanan, Matthias Strobbe , Chris Develder Ghent University – iMinds Dept. of Information Technology – IBCN. Smart Grids. New services & business models. Fault detection? Restoration? Data processing? Privacy, security?

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Charging electric vehicles in the smart grid

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  1. Charging electric vehiclesin the smart grid Kevin Mets, Arun Narayanan, Matthias Strobbe,Chris Develder Ghent University – iMindsDept. of Information Technology – IBCN

  2. Smart Grids New services & business models Fault detection? Restoration? Data processing? Privacy, security? Pricing schemes? … Distributed generation (large scale) Green energy sources (fluctuating) ICT infrastructure PHEV charging (car parks) Demand side management Distributed generation (small scale) PHEV charging (residential) Local energy storage C. Develder, et al., "Distributed smart charging of electrical vehicles"

  3. Power grid structure Transmission network (operated by TSO) Distribution network (operated by DSO) C. Develder, et al., "Distributed smart charging of electrical vehicles"

  4. Types of electrical vehicles: Key acronyms • EV = all-electrical vehicle, aka BEV = battery electrical vehicle • HEV = hybrid EV, includes non-electrical motor (typically ICE) • PEV = plug-in EV, i.e., EV or PHEV • PHEV = plug-in hybrid EV • ICE= internal combustion engine (i.e., burning fuel) • EVSE = electrical vehicle supply equipment, i.e., charging station • V2G = vehicle-to-grid = use battery to deliver power BEV: Nissan Leaf PHEV: Toyota Prius C. Develder, et al., "Distributed smart charging of electrical vehicles"

  5. EV charging modes Slow Fast Images: httcp://en.wikipedia.org/wiki/Charging_station C. Develder, et al., "Distributed smart charging of electrical vehicles"

  6. EV charging modes Slow Fast Images: http://en.wikipedia.org/wiki/Charging_station C. Develder, et al., "Distributed smart charging of electrical vehicles"

  7. EV charging modes: Time to charge IEC 62196-2Type 2 CHAdeMO Combo C. Develder, et al., "Distributed smart charging of electrical vehicles"

  8. Communications? • To achieve external (= power grid) control of the charging process • Only in ≥ Mode 2 Figure: [K. De Craemer, 2014] C. Develder, et al., "Distributed smart charging of electrical vehicles"

  9. EV charging process Figure adapted from [K. De Craemer, 2014] C. Develder, et al., "Distributed smart charging of electrical vehicles"

  10. Alternative charging solutions? • Battery swapping • E.g., BetterPlacein Israel, Denmark – filed for bankruptcy • Tesla has proprietary system • Inductive charging • Volvo • BMW group • Fraunhofer • … • Public transportinitiatives C. Develder, et al., "Distributed smart charging of electrical vehicles"

  11. Outline • Introduction • Example 1: Peak shaving • Example 2: Wind balancing • DR algorithms for EV charging • Tools to study EV charging • Wrap-up C. Develder, et al., "Distributed smart charging of electrical vehicles"

  12. Outline • Introduction • Example 1: Peak shaving • Example 2: Wind balancing • DR algorithms for EV charging • Tools to study EV charging • Wrap-up K. Mets, R. D'hulst and C. Develder, "Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid", J. Commun. Netw., Vol. 14, No. 6, Dec. 2012, pp. 672-681. doi:10.1109/JCN.2012.00033 C. Develder, et al., "Distributed smart charging of electrical vehicles"

  13. Example case study: EV charging • Research questions: • Impact of (uncontrolled) EV charging in a residential environment? • Minimal impact on load peaks we could theoretically achieve? • How can we minimize the impact of EV charging in practice? C. Develder, et al., "Distributed smart charging of electrical vehicles"

  14. Impact of EV charging • Sample analysis for 150 homes, x% of them own a PHEV • BAU = maximally charge upon arrival at home C. Develder, et al., "Distributed smart charging of electrical vehicles"

  15. Controlling EV charging? Charging schedule • Objectives: • Reduce peak load • Flatten (total) load profile(= reduce time-variability) • Avoid voltage violations Charging Rate (W) Time Slot C. Develder, et al., "Distributed smart charging of electrical vehicles"

  16. Smart charging algorithms Quadratic Programming (QP) Multi-Agent System (MAS) Online algorithm No planning window current time slot info only(but EV bidding changes when charging deadline approaches) “Realistic” Single approach • Offline algorithm • Planning window • “Benchmark” • Three approaches: • Local • Iterative • Global Reference scenario: uncontrolled charging C. Develder, et al., "Distributed smart charging of electrical vehicles"

  17. Smart charging: QP BAU(uncontrolled) Local control (QP) Global control (QP) Market MAS C. Develder, et al., "Distributed smart charging of electrical vehicles"

  18. Market-based MAS C. Develder, et al., "Distributed smart charging of electrical vehicles"

  19. Case study • 63 Households • Randomly distributed over 3 phases • Spread over 3 feeders • Electrical vehicles • PHEV: 15 kWh battery • Full EV: 25 kWh battery • Randomized arrivals (~5pm) and departures (~6am) C. Develder, et al., "Distributed smart charging of electrical vehicles"

  20. Results (1) – Load profiles C. Develder, et al., "Distributed smart charging of electrical vehicles"

  21. Results (2) – Load peaks & variability QP1 = local QP2 = iterative QP3 = global C. Develder, et al., "Distributed smart charging of electrical vehicles"

  22. Results (3) – Voltage deviations Not solved entirely! (No explicit part of objective function!) Note: 10 slots ~ 3.4% of the time C. Develder, et al., "Distributed smart charging of electrical vehicles"

  23. Voltage deviation? C. Develder, et al., "Distributed smart charging of electrical vehicles"

  24. Outline • Introduction • Example 1: Peak shaving • Example 2: Wind balancing • DR algorithms for EV charging • Tools to study EV charging • Wrap-up C. Develder, et al., "Distributed smart charging of electrical vehicles"

  25. Distributed generation (DG) C. Develder, et al., "Distributed smart charging of electrical vehicles"

  26. Distributed generation (DG) • Motivation for DG • Use renewable energy sources (RES) ⇒ reduction of CO2 • Energy efficiency, e.g., Combined Heat and Power (CHP) • Generation close to loads • Deregulation: open access to distribution network • Subsidies for RES • … • Technologies • Wind turbines • Photovoltaic systems • CHP (based on fossil fuels or RES) • Hydropower • Biomass • … C. Develder, et al., "Distributed smart charging of electrical vehicles"

  27. Technical impact of DG? C. Develder, et al., "Distributed smart charging of electrical vehicles"

  28. Technical impact of DG? • Voltage variations • Islanding? • Feeder disconnected from grid • DG may be unsafe for people & equipment • … • Power quality • Transient voltage variations (during connection/disconnection) • Cyclic variations of generator output • … • Protection • Increase of fault currents • … C. Develder, et al., "Distributed smart charging of electrical vehicles"

  29. Wind turbines • Horizontal axis • Upwindvs downwind • Needs to be pointed into the wind • High rotational speed (10-22 rpm) • Needs a lot of space (cf. 60-90m high; blades 20-40m) • Vertical axis • Omnidirectional • No need to point to wind • Lower rotational speed • Can be closer together E.g., http://www.inflow-fp7.eu/ Darrieus Savonius C. Develder, et al., "Distributed smart charging of electrical vehicles"

  30. A typical wind profile C. Develder, et al., "Distributed smart charging of electrical vehicles"

  31. Worldwide wind power installed capacity C. Develder, et al., "Distributed smart charging of electrical vehicles"

  32. Worldwide wind power capacity & generation C. Develder, et al., "Distributed smart charging of electrical vehicles"

  33. Case Study K. Mets, F. De Turck and C. Develder, "Distributed smart charging of electric vehicles for balancing wind energy", in Proc. 3rd IEEE Int. Conf. Smart Grid Communications (SmartGridComm 2012), Tainan City, Taiwan, 5-8 Nov. 2012, pp. 133-138. doi:10.1109/SmartGridComm.2012.6485972 C. Develder, et al., "Distributed smart charging of electrical vehicles"

  34. Wind balancing • Imbalance between supply and demand • Inefficient use of renewable energy sources • Imbalance costs • High peak loads Undesirable! C. Develder, et al., "Distributed smart charging of electrical vehicles"

  35. Architecture Coordinator Coordinator Exchange of control messagesto iteratively negotiate charging plans for a specific period of time Balance Responsible Party EV EV C. Develder, et al., "Distributed smart charging of electrical vehicles"

  36. Electric vehicle model • Minimize disutility: • Charging schedule variables: xtk= charging rate for user k at time t • Spread demand over time, preferably at the “preferred charging rate” (pk), which is the maximum supported charging rate in our case • Model behavior/preferences of the subscriber (βk) • Charging schedule for a window of T time slots: minimize disutility • Respect energy Requirement: • Vehicle can only be charged between arrival time Skand departure time Tk (1) (2) (3) C. Develder, et al., "Distributed smart charging of electrical vehicles"

  37. Balance Responsible Party Model • Imbalance Costs • Minimize imbalance costs: cost penalty if supply ≠ demand • Supply: wind energy (wt) • Demand: total of all electric vehicles (dt) • Tuning parameter: α • Cost function: • For a planning window of T time slots, minimize: C. Develder, et al., "Distributed smart charging of electrical vehicles"

  38. Centralized Optimization Model Drawbacks: Privacy: sharing of cost & disutility functions, arrival/departure info, … Scalability • Based on social welfare maximization • Minimize imbalance costs • Minimize user disutility • Objective: • Global constraints: • Local constraints: • BRP: supply < limit • EV: energy & time constraints C. Develder, et al., "Distributed smart charging of electrical vehicles"

  39. Distributed optimization model • Move demand-supply constraint into objective,w/ Lagrange multiplier λt • Notice: Objective function is separable into K+1 problems that can be solved in parallel (assuming λt are given) • Iteratively update pricing vector… 1 BRP problem K subscriber problems C. Develder, et al., "Distributed smart charging of electrical vehicles"

  40. Distributed optimization model scheme: • Coordinator distributes virtual prices • BRP solves local problem • Subscribers solve local problem • Coordinator collects schedules: • BRP: • EVs: • Coordinator updates virtual prices: • Repeat until demand = supply in parallel C. Develder, et al., "Distributed smart charging of electrical vehicles"

  41. Case study: Assumptions • Wind energy supply ≈ EV energy consumption • Energy supply = 6.8 MWh • 100 Electric vehicles • Battery capacity: 10 kWh battery • Maximum charge power: 3.68 kW • Arrivals & departures: statistical model • Charging at home scenario • Time • Simulate 4 weeks • Time slots of 15 minutes • Planning window of 24 hours C. Develder, et al., "Distributed smart charging of electrical vehicles"

  42. Case study: Algorithms • Uncontrolled business as usual (BAU) • EV starts charging upon arrival • EV stops charging when state-of-charge is 100% • No control or coordination • Distributed algorithm • Executed at the start of each time slot • “Ideal world” benchmark • Offline all-knowing algorithm determines schedules for ALL sessions • No EV disutility function  maximum flexibility • Objective: min C. Develder, et al., "Distributed smart charging of electrical vehicles"

  43. Results: Uncontrolled BAU vs. Distributed C. Develder, et al., "Distributed smart charging of electrical vehicles"

  44. Results: Distributed vs. Benchmark C. Develder, et al., "Distributed smart charging of electrical vehicles"

  45. Results: Energy Mix 40% 68% 73% • Total energy consumption ≈ 6.8 MWh • Substantial increase in the use of renewable energy • Reduced CO2 emissions −45% −56% Renewables: 7.4 CO2 g/kWh Non Renewables: 351.0 CO2 g/kWh C. Develder, et al., "Distributed smart charging of electrical vehicles"

  46. Conclusions • Objective: balance wind energy supply with electric vehicle charging demand • Method: Distributed coordination algorithm in which participants exchange virtual prices and energy schedules • Performance: Distributed coordination significantly better than BAU, close to “ideal world” benchmark • Increased usage of renewable energy sources • Reduction of CO2 emissions C. Develder, et al., "Distributed smart charging of electrical vehicles"

  47. Outline • Introduction • Example 1: Peak shaving • Example 2: Wind balancing • DR algorithms for EV charging • Tools to study EV charging • Wrap-up C. Develder, et al., "Distributed smart charging of electrical vehicles"

  48. Outline • Introduction • Example 1: Peak shaving • Example 2: Wind balancing • DR algorithms for EV charging • Tools to study EV charging • Wrap-up K. De Craemer, “Ch.3: Algorithms for demand response of electric vehicles”, in: “Event-Driven Demand Response for Electric Vehicles in Multi-aggregator Distribution Grid Settings”, Ph.D. Thesis, KU Leuven, Jul. 2014 C. Develder, et al., "Distributed smart charging of electrical vehicles"

  49. Strategies for DR • Approximate DP • Stochastic programming • Iterative local search to solve (M)ILP • … • Game theory • Distributed optimization • … • Ranking, MPC • State-bin modeling • Market-based • … E.g., dual decomposition Figure adapted from [K. De Craemer, 2014] C. Develder, et al., "Distributed smart charging of electrical vehicles"

  50. Strategies for DR: Scalability vs optimality Centralized Distributed Optimality Aggregate & dispatch Cluster size Figure adapted from [K. De Craemer, 2014] C. Develder, et al., "Distributed smart charging of electrical vehicles"

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