F. J. Soares, "Smart charging strategies for efficient management of the grid and generation systems," in Electric Vehicle Integration Into Modern Power Networks, DTU, Copenhagen, 2010
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F. J. Soares, "Smart charging strategies for efficient management of the grid and generation systems," in Electric Vehicle Integration Into Modern Power Networks, DTU, Copenhagen, 2010

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    F. J. Soares, "Smart charging strategies for efficient management of the grid and generation systems," in Electric Vehicle Integration Into Modern Power Networks, DTU, Copenhagen, 2010 F. J. Soares, "Smart charging strategies for efficient management of the grid and generation systems," in Electric Vehicle Integration Into Modern Power Networks, DTU, Copenhagen, 2010 Presentation Transcript

    • 24 September 2010 DTU, Copenhagen Electric Vehicle Integration Into Modern Power Networks Smart charging strategies for efficient management of the grid and generation systems F. J. Soares INESC Porto/FEUP
    • Summary 1. The Electric Mobility Paradigm a) Motives for EV adoption b) Expectable benefits c) Foreseen problems for electric power systems d) Predicted EV rollout in some EU countries 2. Conceptual Framework for EV Integration Into Electric Power Systems a) The EV supplier/aggregator b) Possible EV charging approaches 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies a) Case study A: typical Portuguese LV grid b) Case study B: typical Portuguese MV grid c) Overall conclusions 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method a) Introduction b) Case study: Flores Island network (Azores Archipelago) c) EV motion simulation d) Monte Carlo Algorithm e) Results f) Conclusions 5. Final Remarks
    • 1. The Electric Mobility Paradigm a) Motives for EV adoption  Extremely volatile oil prices with a rising trend (due to increasing demand) Source: oil-price.net
    • 1. The Electric Mobility Paradigm a) Motives for EV adoption  High concentration of GHG in the atmosphere (global problem) Source: wikipedia.org Source: wikipedia.org
    • 1. The Electric Mobility Paradigm a) Motives for EV adoption  High pollution levels in areas with high population density (local problem) Source: SMH Source: isiria.wordpress.com Source: fearsmag.com
    • 1. The Electric Mobility Paradigm b) Expectable benefits  Reduction of the fossil fuel usage in the transportations sector Immediate reduction of the local pollution levels (CO2, CO, HC, NOX, PM) Source: topnews.in  If EV deployment is properly accompanied by an increase in the exploitation of renewable endogenous resources Source: myclimatechange.net GHG global emissions will be greatly reduced  Important contribution to eradicate the global warming problematic
    • 1. The Electric Mobility Paradigm b) Expectable benefits  EV capability to inject power into the grid (V2G concept) might be used to “shape” the power demand, avoiding very high peak loads and energy losses  EV storage capability might be used to avoid wasting “clean” energy (wind/PV) in systems with a high share of renewables During the periods when renewable power available is higher than the consumption  Isolated networks might improve their robustness and safely accommodate a larger quantity of intermittent renewable energy sources If EV batteries are efficiently exploited as storage devices and used to mitigate frequency oscillations
    • 1. The Electric Mobility Paradigm c) Foreseen problems for electric power systems  Depending on the number of EV present in the grid, the increase in the power demand will lead to: • Branches overloading • Under voltage problems • Significant increase of the energy losses • Substation transformers overloading • Need to invest in new generation facilities to face increasing demand • Aggravation of the voltage imbalances between phases (for single phase EV/Grid connections)
    • 1. The Electric Mobility Paradigm d) Predicted EV rollout in some EU countries  Almost no official information available  Contradictory information from non official sources Source: Ricardo plc 2010  Difficult to make accurate network impact studies Source: Ricardo plc 2010 ACEA - European Automobile Manufacturers' Association
    • 1. The Electric Mobility Paradigm d) Predicted EV rollout in some EU countries  Types of EV available:  Plug-in Hybrid EV  use a small battery and a generator combined with an ICE  Fuel Cell EV  store energy in H2 which feeds a fuel cell that produces electricity and heat  Battery EV  powered only by electricity, which requires a large battery pack
    • 2. Conceptual Framework for EV Integration Into Electric Power Systems a) The EV supplier/aggregator  Single EV do not have enough “size” to participate in electricity markets  If grouped through an aggregator agent, EV might sell several system services in the markets  The EV suppliers/aggregators:  are completely independent from the DSO  act as an interface between EV and electricity markets  group EV, according to their owners’ willingness, to exploit business opportunities in the electricity markets  develop their activities along a large geographical area (e.g. a country)
    • 2. Conceptual Framework for EV Integration Into Electric Power Systems a) The EV supplier/aggregator MV Level  EV CVC supplier/aggregator CVC structure: Regional Aggregation Unit CVC LV Level VC EV Owner • Regional Smart Meter Aggregation Unit Microgrid Aggregation Unit Microgrid Aggregation Unit VC Smart Meter EV Owner (RAU) – located at VC Smart Meter EV Owner SUPPLIER/AGGREGATOR the HV/MV VC EV Owner substation level and VC Smart Meter EV Owner covering a region Microgrid Aggregation Unit Smart Meter (e.g. a large city) with VC Smart Meter EV Owner ~20000 clients • Microgrid MV Level Aggregation Unit CVC (MGAU) – located at CVC Regional Aggregation Unit the MV/LV substation CVC LV Level level and covering a VC EV Owner LV grid with ~400 Microgrid Aggregation Unit Microgrid Aggregation Unit Smart Meter clients VC Smart Meter EV Owner VC EV Owner Smart Meter VC EV Owner Smart Meter Microgrid Aggregation Unit VC EV Owner Smart Meter VC EV Owner Smart Meter
    • 2. Conceptual Framework for EV Integration Into Electric Power Systems a) The EV supplier/aggregator Technical Operation Market Operation CONTROL HIERARCHY PLAYERS Electric Energy Generation System GENCO Reserves Reserves Transmission System TSO Technical Validation of the Market Negotiation (for the transmission system) Control Level 1 Electricity Market Reserves DMS DSO Electric Energy Operators Electric Energy Control Distribution System Level 2 CAMC RAU Electricity Electric Energy Control Consummer Level 3 MGCC MGAU EV Supplier/Aggregator Battery Battery Parking Parking Replacement Replacement EV Parking Battery Electricity CVC VC Owner/Electricity Consumer Facilities Suppliers Consumer Controls (in normal system operation) At the level of Sell offer Technical validation of the market results Controls (in abnormal system operation/emergency mode) Communicates with Buy offer DMS – Distribution Management System CAMC – Central Autonomous Management System MGCC – MicroGrid Central Controller CVC – Cluster of Vehicles Controller VC – Vehicle Controller
    • 2. Conceptual Framework for EV Integration Into Electric Power Systems b) Possible EV charging approaches  EV as uncontrollable static loads:  EV owners define when and where EV will charge, how much power they will require from the grid and the period during which they will be connected to it  EV as controllable dynamic loads:  EV owners give the aggregator the possibility to manage their charging during the period they are connected to the grid  They only inform the aggregator about the time during which their vehicles will be connected to the grid and the batteries’ SOC they desire at the end of that same period  EV as controllable dynamic loads and storage devices:  EV are not regarded just as dynamic loads but also as dispersed energy storage devices  They can be used either to absorb energy and store it or inject electricity to grid, acting in a V2G perspective
    • 2. Conceptual Framework for EV Integration Into Electric Power Systems b) Possible EV charging approaches  Charging approaches: Charging Modes Uncontrolled Controlled Dumb Charging Multiple Prices Smart Charging Vehicle-to-Grid (DC) Tariff (MPT) (SC) (V2G)
    • 2. Conceptual Framework for EV Integration Into Electric Power Systems b) Possible EV charging approaches  Uncontrolled approaches:  Dumb charging  EV owners are completely free to charge their vehicles whenever they want; electricity price is assumed to be constant along the day  Multiple prices tariff  EV owners are completely free to charge their vehicles whenever they want; electricity price is assumed not to be constant along the day, existing some periods where its cost is lower Market Responsible for the grid technical operation DSO Aggregator Billing and Information about interruptions tariffs Power and disconnection orders in consumed case of grid problems Energy absorbed and charging period of a single EV AMM µG Charging starts when EV is plugged-in µG Storage EV Charger EV
    • 2. Conceptual Framework for EV Integration Into Electric Power Systems b) Possible EV charging approaches  Controllable approaches:  Smart charging  active management system where there is an aggregator serving as link between the electricity market and EV owners; enables congestion prevention and voltage control  V2G mode of operation  besides the charging, the aggregator controls the power that EV might inject into the grid; EV have the capability to provide peak power and to perform frequency control Responsible for the grid technical Market operation DSO Aggregator Broadcast of information related with billing, tariffs, set-points to Power adjust EV control parameters and Information about interruptions consumed SC/V2G set-points in accordance and disconnection orders in with the market negotiations Period during which a single EV will be case of grid problems connected to the grid and the required battery SOC at the end of that time AMM µG EV is plugged-in and its owner defines the disconnection hour and the required battery SOC µG Storage EV Charger EV
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies a) Case study A: typical Portuguese MV grid  Objectives:  Quantify the maximum percentage of conventional vehicles that can be replaced by EV, without compromising grid normal operation, using three different charging approaches: • Dumb charging • Dual tariff policy (= multiple prices tariff) • Smart charging  Compare grid behaviour when subjected to different percentages of EV and when different charging approaches are implemented
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies a) Case study A: typical Portuguese MV grid  Grid architecture:  Semi-urban MV network (15 kV)  Two feeding points  voltage 1.05 p.u.  Consumption during a typical weekday  271.1 MWh 18 Total 16  Peak load  16.6 MW Household Commercial 14 Industrial Consumption (MW) 12 10 8 6 4 2 0 1 5 9 13 17 21 Hour
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies a) Case study A: typical Portuguese MV grid  EV characterization and modelling:  Initially, 635 EV (~5%) were distributed through the grid proportionally to the residential load installed at each bus  12700 vehicles  Annual mileage  12800 km (35 km/day)  EV assumed charging time  4h  EV fleet considered: • Large EV  24 kWh  40% of the EV fleet • Medium EV  12 kWh  40% of the EV fleet • Plug-in Hybrid EV  6 kWh  20% of the EV fleet
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies a) Case study A: typical Portuguese MV grid  Dumb charging and dual tariff policy methodology Define, in a hourly basis, the nodal conventional load (residential, commercial and industrial) of the grid Algorithm developed to quantify the Define the initial share of conventional vehicles replaced by EV maximum number of EV that can be safely integrated into the Distribute EV through the grid proportionally to the residential power installed in each node grid with the dumb charging (without Define, in a hourly basis, the nodal EV load, if no control over charging is imposed (dumb charging mode) grid reinforcements) Calculate, in a hourly basis, the total nodal load Run a power flow for the current hour Feasible operating conditions ? Yes End of day was reached ? No No Yes Next hour Increase the share of EV in 1% Maximum share of EV was reached
    • Define, in a hourly basis, the nodal conventional load (residential, commercial and industrial) of the grid 3. Evaluation of EV Impacts in Define the initial share of conventional vehicles replaced by EV Distribution Networks – Distribute EV through the grid proportionally to the residential power installed in each node Preliminary Studies Define, in a hourly basis, the nodal EV load, if no control over charging is imposed (as in the dumb charging mode) a) Case study A: typical Portuguese Define the connection period of each EV (*) MV grid Calculate, in a hourly basis, the total nodal load Run a power flow for the current hour  Smart charging methodology No Feasible operating conditions ? Yes Any EV waiting to Voltage or resume its charging ? congestion problem ? Algorithm developed to Voltage Congestion Yes maximize the number of EV No Halt the charging Record current grid conditions Smart Charging Halt the charging of 2% of the EV that can be safely integrated of 5% of the EV connected in the connected in each node downstream Resume the charging of the first 5% of EV on the halted EV list in the grid with the smart problematic node the problematic branch Yes charging (without grid Update the list of EV whose charging was Run a power flow with the new load conditions No reinforcements) halted (**) Feasible operating conditions ? Run a power flow with the new load conditions Yes No Update the list of EV whose charging was Feasible operating conditions ? halted Yes Restore the recorded previous grid conditions Next hour No End of day was reached ? Yes (*) The EV connection period was defined according to the mobility Increase the statistical data gathered for Portugal, List of EV whose charging share of EV in Yes published in [17]. was halted is empty ? 1% (**) This list is updated and sorted each cycle, giving priority to EV who will disconnect first from the grid. No Maximum share of EV was reached
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies a) Case study A: typical Portuguese MV grid  Results regarding the maximum allowable EV integration  Dumb charging approach – 10% allowable EV integration  Dual tariff policy – 14% allowable EV integration (considering that 25% of the EV only charge during the cheaper period – valley hours)  Smart charging strategy – 52% allowable EV integration (considering that 50% of EV owners adhered to the smart charging system)
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies a) Case study A: typical Portuguese MV grid  Scenarios used to evaluate EV impacts in the network  1 power flow for each hour was performed Dumb Dual Smart Test charging tariff charging case limit limit limit Scenario 0 Scenario 1 Scenario 2 Scenario 3 Scenario 4 N.º of Vehicles 12700 12700 12700 12700 12700 EVs % 0% 5% 10% 14% 52% Hybrid Share - 20% 20% 20% 20% Medium EV Share - 40% 40% 40% 40% Large EV Share - 40% 40% 40% 40% Total Energy consumption (MWh) 277.1 283.2 294.0 301.7 388.1
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies a) Case study A: typical Portuguese MV grid  EV electricity demand with the dumb charging (52% EV penetration): Dumb Charging  was calculated taking into account mobility statistical data for Portugal Dumb Charging 35000 30000 EV load 25000 Power demand (kW) Household load Total load 20000 EV load 15000 Household load 13 17 10000 21 Total load Time (h) When people arrive 5000 home from work 0 1 5 9 13 17 21 Time (h)
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies a) Case study A: typical Portuguese MV grid  EV electricity demand with the dual tariff policy (52% EV penetration):  was calculated taking into account mobility statistical data for Portugal  was assumed that 25% of EV owners adhered to this scheme, shifting their EV Dual Tariff Policy charging to lower energy price periods Dual Tariff Policy 8 35000 8 7 30000 6 7 Electricity price 5 6 25000 EV load Power demand (kW) 4 Electricity price Household load 5 3 20000 Total load 2 Electricity price 4 EV load 15000 1 Household load 3 0 Total load 5 9 13 17 10000 21 2 Electricity price Time (h) 5000 1 0 0 1 5 9 13 17 21 When electricity is cheaper Time (h)
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies a) Case study A: typical Portuguese MV grid  EV electricity demand with the smart charging (52% EV penetration):  was assumed that 50% of EV owners adhered to this scheme, being their charging controlled by the aggregator Smart Charging Smart Charging 20000 20000 18000 18000 16000 16000 14000 14000 Power demand (kW) 12000 12000 10000 10000 EV load EV load 8000 8000 Household load Household load 6000 6000 Total load Total load 4000 4000 2000 2000 0 0 1 5 9 1 13 5 17 9 21 13 17 21 Time (h) Time (h) Avoids peak load increase
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies a) Case study A: typical Portuguese MV grid  Results  Changes in load diagrams with 52% of EV penetration 35 Without EV Dumb Charging 30 Dual Tariff Policy 25 Smart Charging Load (MW) 20 15 10 5 0 1 5 9 13 17 21 Hour
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies a) Case study A: typical Portuguese MV grid  Results  Voltages obtained for the worst bus during the peak hour 0,98 No EVs Dumb charging Dual tariff policy Smart charging 0,96 0,94 Voltage (p.u.) 0,92 0,90 0,88 0,86 0,84 0,82 No Evs 5% Evs 10% Evs 14% Evs 52% Evs
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies a) Case study A: typical Portuguese MV grid  Results  Worst branch loading obtained during the peak hour 160 No EVs Dumb charging 140 Dual tariff policy 120 Smart charging 100 Rating (%) 80 60 40 20 0 No Evs 5% Evs 10% Evs 14% Evs 52% Evs
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies a) Case study A: typical Portuguese MV grid  Results  Daily losses 30 7% 7% Losses with no EV (MWh) Dumb charging losses (MWh) 6% 6% 25 Dual tariff policy losses (MWh) Smart charging losses (MWh) Losses relative value (%) Losses relative value (% of the energy consumption) 5% 5% 20 Losses (MWh) 4% 4% 15 3% 3% 10 2% 2% 5 1% 1% 0 0% 0% Without EV 10% EV 14% EV 52% EV
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies a) Case study A: typical Portuguese MV grid  Results  Branches loading overview (peak hour), with 52% EV penetration No EV Dumb charging Dual tariff policy Smart charging
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid  Objectives:  Develop a smart charging strategy to: 1. Maximize the number of EV that can be safely connected into the grid (without reinforcing it) 2. Minimize the renewable energy wasted (in scenarios where renewable generation surplus might exist)
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid 1st Objective – Maximize the number of EV that can be safely connected into the grid (without reinforcing it)
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid  Grid architecture: 120 Total Household Commercial 100 % of the consumption  Residential LV network (400 V) 80 60  Feeding point voltage  1 p.u. 40  Feeder capacity  630 kW 20 0  250 households 1 3 5 7 9 11 13 15 17 19 21 23 Hour  9.2 MWh/day  550 kW peak load
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid  EV characterization and modelling:  Initially, 20 EV (~5%) were distributed through the grid proportionally to the residential load installed at each bus  375 vehicles  Annual mileage  12800 km (35 km/day)  EV assumed charging time  4h  EV fleet considered: • Large EV  24 kWh  40% of the EV fleet • Medium EV  12 kWh  40% of the EV fleet • Plug-in Hybrid EV  6 kWh  20% of the EV fleet
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid  Dumb charging and dual tariff policy methodology (same as in case study A) Define, in a hourly basis, the nodal conventional load (residential, commercial and industrial) of the grid Algorithm developed to quantify the Define the initial share of conventional vehicles replaced by EV maximum number of EV that can be safely integrated into the Distribute EV through the grid proportionally to the residential power installed in each node grid with the dumb charging (without Define, in a hourly basis, the nodal EV load, if no control over charging is imposed (dumb charging mode) grid reinforcements) Calculate, in a hourly basis, the total nodal load Run a power flow for the current hour Feasible operating conditions ? Yes End of day was reached ? No No Yes Next hour Increase the share of EV in 1% Maximum share of EV was reached
    • Define, in a hourly basis, the nodal conventional load (residential, commercial and industrial) of the grid 3. Evaluation of EV Impacts in Define the initial share of conventional vehicles replaced by EV Distribution Networks – Distribute EV through the grid proportionally to the residential power installed in each node Preliminary Studies Define, in a hourly basis, the nodal EV load, if no control over charging is imposed (as in the dumb charging mode) b) Case study B: typical Portuguese Define the connection period of each EV (*) LV grid Calculate, in a hourly basis, the total nodal load Run a power flow for the current hour  Smart charging methodology No Feasible operating conditions ? Yes (same as in case study A) Any EV waiting to Voltage or resume its charging ? congestion problem ? Algorithm developed to Voltage Congestion Yes maximize the number of EV No Halt the charging Record current grid conditions Smart Charging Halt the charging of 2% of the EV that can be safely integrated of 5% of the EV connected in the connected in each node downstream Resume the charging of the first 5% of EV on the halted EV list in the grid with the smart problematic node the problematic branch Yes charging (without grid Update the list of EV whose charging was Run a power flow with the new load conditions No reinforcements) halted (**) Feasible operating conditions ? Run a power flow with the new load conditions Yes No Update the list of EV whose charging was Feasible operating conditions ? halted Yes Restore the recorded previous grid conditions Next hour No End of day was reached ? Yes (*) The EV connection period was defined according to the mobility Increase the statistical data gathered for Portugal, List of EV whose charging share of EV in Yes published in [17]. was halted is empty ? 1% (**) This list is updated and sorted each cycle, giving priority to EV who will disconnect first from the grid. No Maximum share of EV was reached
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid  Results regarding the maximum allowable EV integration  Dumb charging approach – 11% allowable EV integration  Smart charging strategy – 61% allowable EV integration (considering that 50% of EV owners adhered to the smart charging system)
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid  Scenarios used to evaluate EV impacts in the network  1 three-phase power flow for each hour was performed Dumb Smart charging charging limit limit Scenario 0 Scenario 1 Scenario 1 N.º of Vehicles 375 375 375 EVs % 0% 11% 61% Hybrid Share - 20% 20% Medium EV Share - 40% 40% Large EV Share - 40% 40% Total Energy consumption (MWh) 9.17 9.81 12.74
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid  Total electricity demand with the dumb and smart charging (61% EV penetration):  The dumb charging curve was calculated taking into account mobility statistical data for Portugal  The smart charging curve obtained assuming that 50% of EV owners adhered to this scheme, being their charging controlled by the aggregator Without EVs 1000 Dumb Charging Smart charging 800 Feeder capacity 600 kW 400 200 0 1 3 5 7 9 11 13 15 17 19 21 23 Hour
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid  Results  Voltages obtained for the worst bus during the peak hour Phase R Phase S Phase T 0,97 0,96 0,95 Voltage (p.u.) 0,94 0,93 0,92 0,91 0,90 No EVs 11% - Dumb 11% - Smart 61% - Dumb 61% - Smart Charging Charging Charging Charging
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid  Results  Worst branch loading obtained during the peak hour 140 120 100 Congestion Level (%) 80 60 124 40 75 72 63 64 20 0 No EVs 11% - Dumb 11% - Smart 61% - Dumb 61% - Smart Charging Charging Charging Charging
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid  Results  Daily losses 11% EVs 61% EVs Increase in losses due to EVs consumption (%) 140 120 100 80 130 60 40 83 20 17 11 0 Dumb Smart Dumb Smart charging charging charging charging
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid  Results  Load imbalance between phases PMAX,T  PMIN ,T R,S R,S 16 LI  %   R , S ,T 100 PAVERAGE Load Imbalance in the MV/LV Transformer (%) 14 12 10 8 14,2 14,0 6 4 6,0 4,8 4,7 2 0 No EVs 11% - Dumb 11% - Smart 61% - Dumb 61% - Smart Charging Charging Charging Charging
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid 2nd Objective – Minimize the renewable energy wasted (in scenarios where renewable generation surplus exist)
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid  Selected scenario  A wet and windy day in 2011  Portuguese situation in 2011:  Around 5 GW of wind power + “must run” of the thermal units  renewable energy might be wasted (in low demand periods) Portuguese Generation Profile for a Windy Day in 2011 Installed Capacity (MW) Installed Capacity (MW) DER - Hydro Hydro - Run of River Coal Others - 52 NG Fuel Der - Thermal 9000 Hydro (with reservoir) DER - Wind Demand Wind - 5000 Hydro - 4957 8000 7000 6000 5000 P (MW) 4000 CHP - 1463 3000 2000 Thermal - 5820 1000 0 Wind energy produced - 51 GWh 1 3 5 7 9 11 13 15 17 19 21 23 Hour
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid  Demand change due to 11% of EV  Results obtained for the LV grid were transposed to the complete electric power system LV Grid Load Diagram Portuguese Generation Profile Without EVs 18000 1000 Dumb Charging DER - Hydro Hydro - Run of River Smart charging Coal NG 16000 Fuel DER - Thermal 800 Feeder capacity Hydro (with reservoir) DER - Wind Demand without EVs Demand with EVs - Smart charging 600 14000 kW Demand with EVs - Dumb charging 400 12000 Renewable Energy Wasted! 200 10000 P (MW) 0 8000 1 3 5 7 9 11 13 15 17 19 21 23 Hour 6000 Smart Charging 15 4000 Wind Dumb Charging 30 Energy 2000 Wasted No EVs 31 0 0 5 10 15 20 25 30 35 1 3 5 7 9 11 13 15 17 19 21 23 % Hour
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid  Demand change due to 61% of EV  Results obtained for the LV grid were transposed to the complete electric power system LV Grid Load Diagram National Generation Profile 18000 DER - Hydro Hydro - Run of River Without EVs Coal NG 1000 Dumb Charging Fuel DER - Thermal Smart charging 16000 Hydro (with reservoir) DER - Wind 800 Feeder capacity Demand without EVs Demand with EVs - Dumb charging 14000 Demand with EVs - Smart charging 600 kW 12000 400 10000 P (MW) 200 Large Peak Load Increase! 8000 0 1 3 5 7 9 11 13 15 17 19 21 23 6000 Hour 4000 Smart Charging 1 Wind Dumb Charging 26 2000 Energy 0 No EVs 31 1 3 5 7 9 11 13 15 17 19 21 23 Wasted Hour 0 5 10 15 20 25 30 35 %
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies b) Case study B: typical Portuguese LV grid  Daily CO2 emissions 70 60 Daily CO2 emissions (kton) 50 30 40 31 Power system emissions (including: extraction and 30 36 processing; raw material transport; and electricity 20 generation) 29 26 10 Light vehicles emissions 11 (well-to-wheel) 0 Without EVs 11% EVs* 61% EVs* *Smart charging
    • 3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies c) Overall conclusions  Losses increase as the number of EV rises  Overall GHG emissions decrease as the number of EV rises  Voltages and branches loading worsen as the number of EV increases  ~10% is the number of EV that can be integrated with the dumb charging  ~15% is the number of EV that can be integrated with the dual tariff policy  When comparing with the dumb charging and with the dual tariff policy, the smart charging allows:  decreasing grid losses and consequently GHG emissions  improving voltage profiles and branches’ congestion levels  safely integrating 50-60% of EV  avoiding the loss of renewable energy  Results are highly dependent on where and when EV will charge  A Monte Carlo simulation method should be used to obtain more accurate results
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method a) Introduction  The utilization of a Monte Carlo method to perform impact studies is more adequate  allows reducing the uncertainties by running a high number of different scenarios  This approach allows obtaining average values and confidence intervals for several system indexes, like buses voltages, branches loading and energy losses
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method b) Case study: Flores Island network (Azores Archipelago)  Grid architecture: Swing Bus 1 Thermal Power Plant Hydro Power Plant  Isolated MV network (15 kV) 2 7 8 17 41 3 9 18 42  Typical winter day consumption  47.55 4 10 19 28 35 43 Wind Farm MWh 5 11 20 29 30 31 36 44 45  2.59 MW peak load 6 12 21 32 37 (occurs at 19:30 h) 13 22 33 38  Average power factor  14 23 34 39 0.77 15 24 40  Island light vehicles fleet  2285 vehicles 16 25 24 Bus 26 Load  2 scenarios studied  Power Plant Line 25% and 50% EV 27 penetration
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method c) EV motion simulation  EV movement along one day was simulated using a discrete-time non-Markovian process to define the states of all the EV at each 30 minutes interval (48 time instants)  In each time instant, EV can be in four different states: in movement, parked in industrial area, parked in commercial area, parked in residential area  The EV state for each time instant is defined according to the probabilities specified for that time instants and according to the discrete-time non-Markovian process =1 = = In Movement = =1 In Movement → =1 → In Movement = → =1 =1 → → = → =1 =1 → → Parked in Parked in Parked in Residential Area Industrial Area Commercial Area Parked in Parked in Parked in Residential Area Industrial Area Commercial Area =1 =1 =1 Parked in Parked in Parked in Residential Area Industrial Area Commercial Area = = = = = =
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method c) EV motion simulation  The state transition probabilities applied were determined by analyzing the common traffic patterns of Portuguese drivers  It was gathered information about the number of car journeys made per each 30 minutes interval, along a typical weekday, as well as the journey purpose and its average duration  With this data, it was possible to define the probabilities of an EV reside in a given state at a given time instant
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method c) EV motion simulation  Define EV location for parked EV:  all bus loads were classified as industrial, commercial or residential  the probability of an EV be located at a specific bus was calculated with the following equations: = = =
    • 4. Evaluation of EV Impacts in Define EV initial conditions (initial state, bus, battery capacity, slow charging rated power, initial SOC, energy consumption and driver behaviour) Distribution Networks – A Monte Draw EV states and the buses where “parked” EV are located, for the next time instant Carlo Method Update EV batteries SOC d) Monte Carlo algorithm EV charge at the What is the EV driver behaviour ? end of the day or EV charge whenever is only when convenient and the it needs EV charge 1. Make the initial characterization of all the EV: Sample generation and evaluation driver has time whenever possible • initial state No No No • EV is parked in EV arrived home from the the bus they are initially located EV battery SOC < 30% ? residential area ? last journey of the day ? • battery capacity (kWh) Yes Yes • Yes slow charging rated power (kW) EV is parked in residential area ? No • initial SOC (%) Yes EV do not charge • energy consumption (kWh/km) • owners’ behaviour EV starts charging No GAUSSIAN DISTRIBUTIONS FOR INITIAL EV CHARACTERIZATION Maximum Standard Minimum Determine the new load at each bus Average value deviation value allowed allowed Battery capacity (kWh) 24.73 17.19 85.00 5.00 Power flow analysis Slow charging rated power 3.54 1.48 10.00 2.00 (kW) Energy consumption No 0.18 0.12 0.85 0.09 End of the day was reached ? (kWh/km) Initial battery SOC (%) 50.00 25.00 85.00 15.00 Yes DRIVERS’ BEHAVIOURS CONSIDERED Indexes update Update of grid technical indexes and vehicle usage indicators in a hourly and daily Percentage of the basis responses EV charge at the end of the day 33% Monte Carlo finishing criteria was met ? EV charge only when it needs 30% SOC 23% EV charge whenever possible 20% Yes Compile results: power demand, voltages, branches loading, energy losses, peak EV charge whenever is convenient and the driver has time 24% power, number of voltage and branches ratings violations
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method d) Monte Carlo algorithm 2. Samples generation: • Simulate EV movement along one typical weekday  define EV states • Attribute a bus location to parked EV • Update battery SOC for EV in movement: o if an EV was in movement in time instant t and its battery SOC went below a predefined threshold (assumed to be 15%) in time instant t+1, it was considered that the EV would make a short detour to a fast charging station for recharging purposes GAUSSIAN DISTRIBUTIONS FOR EV MOVEMENT CHARACTERIZATION Maximum Standard Minimum Average value deviation value allowed allowed Travelled distance in 9.01 4.51 27.03 0.90 common journeys (km) Travelled distance to fast 4.51 2.25 13.52 0.45 charging station (km) o the fast charging was assumed to be made during 15 minutes with a power of 40 kW o the fast charging station was considered to be installed in bus 12, as this is located near one of the more populated areas of the island, with a high number of potential clients • Compute the total amount of power required from the network, discriminated per bus and per time instant
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method d) Monte Carlo algorithm 3. Samples evaluation: • Made by running a power flow for each time instant and by gathering information about: o Voltage profiles o Power flows in the lines o Energy losses o Highest peak load 4. Terminating the Monte Carlo process  2 criteria used: • Number of iterations  10000 • Variation in the last 10 iterations of the aggregated network load variances (of each one of the 48 time instants) < 1 −4 ∆ = − −10 < 1 −4
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method e) Results  Power demand:
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method e) Results  Voltage profile of one feeder (buses 17 to 27):
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method e) Results  Network voltage profiles for the highest peak load identified:
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method e) Results  Voltage lower limit violation probability: . . = × 100 . × 48
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method e) Results  Branches loading: No EV 50% EV 25% EV
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method e) Results  Average daily energy losses:
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method e) Results  Evolution of the network load variances with the highest variation rate:
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method e) Results  Network load variances of the 48 time instants:
    • 4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method f) Conclusions  The simulation platform developed proved to be very efficient in performing a realistic evaluation of the impacts that result from a massive integration of EV in distribution networks  Allows:  evaluating the steady state operating conditions of the grid  identifying the most critical operation scenarios and the network components that are subjected to more demanding conditions and that might need to be upgraded  The island network is very robust  is capable of integrating a large number of EV without the occurrence of lines overloading and voltage limits violations (~25%)  With 50% of EV a large number of voltage violations were registered  efficient mechanisms to manage EV charging (smart charging) are required to avoid making large investments in network reinforcements  For large EV integration scenarios, losses will become a very important issue for system operator  their value grows:  58% from the scenario without EV to the one with 25% of EV  140% from the scenario without EV to the one with 50% of EV  Energy losses might be greatly reduced by using an EV smart charging strategy
    • 5. Final Remarks  EV integration in interconnected systems:  Due to the reduced energy consumption and capability of providing services to the grid, it is impossible to EV participate in the markets individually  EV suppliers/aggregators must exist for this purpose  Even under the EV supplier/aggregator management, EV might still create several problems in distribution networks  A grid monitoring mechanism must exist (independent from the aggregator and headed by the DSO), with the capability of manage EV charging, in order to avoid those problems  EV integration in small isolated systems:  As usually these systems do not have an electricity market, EV suppliers/aggregators are not needed  Only the grid monitoring mechanism controlled by the DSO must exist
    • 5. Final Remarks  EV integration limitations:  without any control actions over EV charging (dumb charging), it is impossible to integrate a large number of EV in common electricity networks  network reinforcements are required  if EV charging is controlled (smart charging), even in accordance with their owners requirements, a larger number of EV might be integrated without investments in grid reinforcements  nonetheless, if the number of EV keeps growing, there will be a moment in time where reinforcement will be inevitable, even when the smart charging is applied…
    • 5. Final Remarks  Network impacts  As the number of EV rises:  losses increase  overall GHG emissions decrease  voltages and branches loading worsen  Smart charging vs. Dumb charging:  decrease grid losses and, consequently, GHG emissions  improve voltage profiles and branches loading  allow the integration of a higher number of EV without reinforcements  allow an effective exploitation of renewable generation surplus (when such problem exists)