DESIGNING ELECTRIC VEHICLES CHARGING NETWORKS

Transcription

1 DESIGNING ELECTRIC VEHICLES CHARGING NETWORKS NETWORK DESIGN CHALLENGES LISBON CASE STUDY ANGRA DO HEROÍSMO CASE STUDY DESIGNING THE NETWORK FOR AZORES Carlos Santos Silva MIT-Portugal / Instituto Superior Técnico

2 EV charging networks highlights Charging Points (CP) Slow charge (23-38 V /16-32A / kva 8 hours) Fast charge ( 5V DC / 2 A 2 minutes) Europe London, Oslo, Paris, Helsinki, Frankfurt, Lisbon, Amsterdam EV charging network is designed for: Daylight parking (in the evening cars will charge at home ) 5-1% in street parking / 9-95% parking garages Autonomy anxiety control

3 EV network design challenges Most charging will be made at home Upgrading infrastructure / power reinforcement Public network Anxiety control Visible places (main streets, shopping malls) Long distance drivers (taxi drivers, tourists) Public parking places Touristic places Integrated service with parking Commuters recharge (S. Miguel) Work place Fast charging It is all about where people park their car - Detailed mobility patterns studies

4 Early Adopters European studies Man under 35 or above 55 High, middle high class Higher education instruction Two cars

5 MOBI.E Highlights MOBI.E 11 CP in 25 municipalities 1 master+ n slaves up to 211 street parking public parking garages Municipalities choose CP location Public space electric parking have to be connected to MOBI.E

6 MOBI.E Charging Points

7 MOBI.E framework

8 Methodology to design EV charging network 1. Estimate EV penetration in the vehicle fleet Using MOBI.E study from Roland Berger Consider a service rate to define number of required CP 2. Characterize region/city/neighborhood 3. Define Criteria for location of CP 4. Use a decision-making algorithm Multi-Atribute (PROMETHEE)

9 Methodology Implementation EV projections for region/city/neighborhood Parking characterization of Region/city/neighborhood (alternatives) Decision-aid tool For each year (21 - ): 1. Evaluate alternatives 2. Compare alternatives 3. Alocate CP to alternatives Final CP allocation (alternatives) Location Criteria (Decision makers)

10 LISBON

11 1 - EV Projections assumptions

12 1 Service Degree

13 2 - Characterization of the City

14 3 - Criteria

15 4 Decision Making algorithm (PROMETHEE) 1. Initialization Alternatives A = a 1, a 2, a 3,, a n Criteria G = g 1 a i, g 2 a i,, g k a i a i A Relative weight of criteria g 1 a g 2 a g k a w 1 w 2 w k Preference function Maximize / Minimize Shape P 1 k j =1 w j = 1 p d

16 4 - Decision Making algorithm (PROMETHEE) Criteria Weight [%] Objective Preference function Parking offer on the public thoroughfare Parking offer in parks of public access Deterrent parking Parking demand pressure Occupation ratio during the day Occupation ratio during the night Percentage of still vehicles [%] Parking balance for residents Unmet parking demand for residents [%] Number charging points g 1 g 2 g 3 g 4 g 5 g 6 g 7 g 8 g 9 g d Maximize Maximize Maximize Maximize Maximize Maximize Minimize Minimize Minimize Minimize V-shape V-shape V-shape Level V-shape V-shape V-shape V-shape V-shape V-shape p=14367 p=7357 p=195 p=q=,5 p=1,31 p=1,56 p=68 p=91 p=55 p d 1 Carnide Norte ,5,67, Lumiar Norte ,97 1, Charneca ,11 1, Benfica ,17 1, Carnide sul ,5 1,33 1, Lumiar sul ,98 1, Aeroporto 3 6,92 1,8 8 Olivais ,11 1, Oriente ,93, Monsanto 164,5 1,1 1,

17 4 Decision Making algorithm (PROMETHEE) 2. Evaluation Difference table Aggregation Outranking flows d j a i, a l = g j a i g j a l k π a i, a l = P j (a i, a l )w j φ a = 1 n 1 x A j =1 π(x, a) φ + a = 1 n 1 x A π(a, x) a 1 a 2 a 3 a n a 1 π a 1, a 2 π a 1, a 3 π a 1, a n φ + a 1 a 2 π a 2, a 1 π a 2, a 3 π a 2, a n φ + a 2 a 3 π a 3, a 1 π a 3, a 2 π a 3, a n φ + a 3 a n π a n, a 1 π a n, a 2 π a n, a 3 φ + a n φ a 1 φ a 2 φ a 3 φ a n Preference ap II b iff φ a i > φ a l ai II b iff φ a i = φ a l

18 4 Example of application Example: CG Campo Grande AL Alvalade Parking offer on the public thoroughfare d(al,cg)=452 d(cg,al)=-452 P 1% Percentage of still vehicles d(al,cg)=16 d(cg,al)=-16 P 5% 1 1,28,29 d(cg,al) d(al,cg) d 55 d(cg,al) d(al,cg) d Alvalade > Campo Grande

19 4 Decision Making algorithm (PROMETHEE) Start Calculate dynamic criterion data Dynamic evaluation criteria Calculate difference tables Evaluation criteria Calculate dynamic difference tables Coalescence matrix Normalize dynamic data with preference function Dynamic preference function parameters Normalize data with preference functions Preference functions parameters Preferences aggregation Criteria weights Calculate outranking flows Update stopping criteria, dynamic preference function parameters, constrains and iteration Constrains Complete ranking Constrains No verified Update alternatives ranking No Impossible to fulfill constrains Yes Yes Update dynamic evaluation criteria List all the actions taken until this point No Stopping criteria verified Stopping criteria Stop Yes List the actions taken No Iterations completed Iterations Yes List the actions taken in all iterations Stop

20 5 Results ( CP)

21 5 Results ( CP)

22 ANGRA DO HEROISMO (TERCEIRA)

23 1 - EV Projections assumptions The ratio between Angra do Heroismo and Portuguese fleet will remain constant (15.5 k / 4.5 m,34%) Same penetration rate as in Portugal Lisbon considered that at the beginning 5% of EV will be located there and will decrease to the average In Azores, with GIP, it may be expected also higher rates The considered area (downtown represents 25% parking places in Angra)

24 Número de Veículos elétricos para Portugal Número de veículos eléctricos para Angra 1 - EV Projections results 18 Electric Vehicles Projections for Portugal and Angra do Heroismo Veículos elétricos em Portugal Veículos eléctricos em Angra

25 Service Rate In 21, 1 CP for each 2 EV (5% service rate) In 22, 1 CP for each 5 EV (2% service rate),6 Razão Postos Carregamento por VE,5,4,3 Razão Postos Carregamento por VE,2,1,

26 2 Characterization of city

27 2 Number of public parking places Lugares de estacionamento Lugares em Parques A 273 B 51 8 C 265 D 378 E 36 8 F G 337 Parque de Santa Luzia 8 Parque do Bailão 389 Parque da Praça de Touros 34

28 3 Criteria for Location Location Supply Demand Other Number of existing CP

29 Angra do Heroísmo CP network - Criteria Lugares de estacionamento Lugares em Parques Pontos de carregamento Peso dos critérios 3% 2% 5% A 273 B 51 8 C 265 D 378 E 36 8 F G 337 Parque de Santa Luzia 8 Parque do Bailão 389 Parque da Praça de Touros 34

30 Angra do Heroísmo CP network - Results A B C D E F G Santa luzia Bailão Praça de touros Total

31 Angra do Heroísmo CP network 21 (1CP) 1

32 Angra do Heroísmo CP network 215 (12CP)

33 Angra do Heroísmo CP network 22 (27CP)

34 ELECTRIC MOBILITY IN AZORES

35 TJ TJ Why EVs in Azores? Renewables Kerosene Butane Gasoline Diesel Fuel Oil Residential Public Services Commerce Industry Agriculture Electricity Production Road Transportation % of the primary energy is Azores is used on road transportation 1,38 millions of barrels of oil 85 millions [ 8$ per barrel, $=1,3 ] + refinery and transportation costs

36 Electric Mobility Impact (SM) Significant reduction in fossil fuels consumption Mild electricity consumption increase (with energy efficiency) Has to be based on renewable electricity

37 Terceira as the earlier adopter Lower energy consumption for road transportation in Terceira in average 11% of SM Local agents (municipalities, consumers) want EVs!

38 The plan for Azores (public network) All islands All municipalities? Technical vs Economic vs Social Fast charging Eventually on SM Creating value Business models Car sharing for tourists Transportation (taxi, shuttles) Parking / energy management Reconversion

39 Designing Electric Mobility for Azores

40 Case Studies Charging network Fleet Production Distribution Fleet Production

41 Patrícia Baptista FLEET ANALYSIS (FLORES)

42 Methodology Life-cycle analysis Materials and WTT: GREET, Simapro and other databases TTW: ADVISOR, Demb, Siemp, RVS

43 Flores Fleet historical data and evolution (ISP) Category Light duty vehicles - Passengers ,233 1,516 1,61 1,698 1,761 1,8 1,72 Light duty vehicles - Goods Light duty vehicles - Commercial Taxis Rent-a-car vehicles Other light duty vehicles Total LDV 971 1,379 1,683 1,914 2,18 2,16 2,181 2,213 2,49 Heavy duty vehicles - Goods Heavy duty vehicles - Passengers Other heavy duty vehicles Total HDV Tractors for agriculture Tows for agriculture Total agriculture Mopeds Motorcycles Total 2W LDV Motorization index at 498 veh per 1 inhab expected to stabilize at around 52 veh/ per 1 inhab. HDV stabilizes at around 15 veh per 1 inhab. Others Tows Machines (industrial or others) 8 Total Flores 1,353 1,695 2,18 2,225 2,34 2,429 2,55 2,527 2,349

44 Sc.1 Baseline trend Sc. 2 Policy oriented Sc. 3 Electricity powered Number of vehicles New vehicle sales shares (%) Scenario Electricity Powered (New Vehicles) 1% 9% % 1.4 7% unchanged market Gasoline LDV LDV Diesel HEV diesel 6% 5% EVs PHEV diesel HEV gasoline PHEV gasoline PHEV diesel EVs 4% 3% 2% PHEV gasoline HEV diesel HEV gasoline 1% % Alternative fuels: 1% biodiesel in 22 and 25% in 25; In 25 9% of vehicle sales will have been shifted from conventional technologies. An electricity recharging infrastructure will be available. Both PHEV and EV technologies start entering the market right away. The assumed HEV:PHEV:EV ratio for 25 was 3:5:2; and The diesel/gasoline share stabilizes at a 75/25 ratio.

45 21 Policy 21 Electricity 21 Basline 23 Policy 23 Electricity 23 Baseline 25 Policy 25 Electricity TTW Energy Consumption per technology (MJ) Scenario Electricity Powered (Energy Tank To Wheel) 1,2E+8 9,E+7 1,E+8 8,E+7 8,E+7 6,E+7 4,E+7 2,E+7,E+ EVs PHEV diesel PHEV gasoline FCV HEV FCV PHEV HEV gasoline HEV diesel LDV Diesel Gasoline LDV 7,E+7 6,E+7 5,E+7 4,E+7 3,E+7 2,E+7 1,E+7 LDV Electricity LDV biodiesel LDV diesel LDV gasoline,e+

46 André Pina ELECTRICITY PRODUCTION (FLORES)

47 Flores electricity production system The electricity system is a combination of Wind-Hydro- Diesel. To maintain frequency and voltage stability at the grid level, a flywheel energy storage system is in place The system is able to achieve 1% renewable electricity In 29, this happened for several hours of the day in at least 12 days of the year. Future investments: Hydro, 16 kw, 211. Hydro, 14 kw, 212.

48 Flores electricity production historical data Electricity production in Flores, Population of ~42 habitants Area of ~141 km 2 54% of renewable electricity in 29

49 Approach taken Main uncertainties studied: Growth rate for electricity demand Fuel prices (28 prices much higher than 27 or 29) EV penetration in fleet

50 GWh Renewable energy penetration in electricity generation Energy mix in 235 Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 BASE Diesel Wind Hydro Electricity generated from renewables 96% 94% 92% 9% 88% 86% 84% 82% 8% 78% 76% Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 BASE

51 Joel Soares GRID MANAGEMENT (FLORES)

52 Network Topology Ribeira Grande PowerPlant

53 Load diagrams

54 Scenarios characterization 5% EV penetration 1 EV: - 2% PHEV 1.5 kw rated power / 7% EV1 3 kw rated power / 1% EV2 6 kw rated power. 55 EV charging simultaneously at a charging rate of.8 C 1321 kw + 8 kw of conventional load = total load of 2121 KW Three scenarios were studied considering 1% renewable electricity: - % of adherents to the smart charging scheme ( EV) it was impossible to manage EV load for primary frequency control; - 25% of adherents to the smart charging scheme (25 EV) it was possible to manage EV load between kw for primary frequency control; - 1% of adherents to the smart charging scheme (1 EV) it was possible to manage EV load between kw for primary frequency control..

55 Phase Currents [A] Battery Current [A] Frequency [Hz] Converter Response Frequency disturbance impact on the battery charging current 5.2 Frequency Disturbance (load increase) [Hz] 5 Dead-Band Load Switch On at,4 s Time [s] 8 Droop Control Battery Charging Current [A] Battery Current Battery Current Reference Time [s] 3 Set-point value 6 A Phase currents during frequency disturbancy Time [s]

56 kw Dynamic Simulation Results Wind speed decrease Shortfall of 45 kw in the wind power production ms Scenario 3 : 1 EV; 55 EV charging simultaneously; 1% smart charging adherents