Integration of PV and electric vehicles in future energy systems Pedro Nunes July 2016
1. background 2
context Sectors of energy and transport are the biggest GHG emitters in the EU (30% and 20%, respectively) EU 2050 goal: -80% to -95% GHG by 2050 (overall) This target can only be achieved by the intensive use of renewable energy sources and major alterations to the transport sector Problem? 3
context 4 Source European Environment Agency
PV background In the future it is the most attractive form of decarbonized electricity generation: ample resource available mature technology simple O&M competitiveness price trajectory (<65% by 2050) Portugal has the highest insolation levels among Europe ( 1550 kwh e /kw p ) Obstacle: PV mass adoption will likely lead to supply-demand imbalances, with middle of the day excess 5
RES management >RES DSM Power reserve Greater variability Interconnection Storage 6
EVs background EVs are seen the most promising solution to reduce emissions from vehicles: high onboard efficiency (80%) potentially much cleaner Portugal has a public recharging infrastructure and a green taxation system EVs, with their large accumulated battery capacity, may be an answer to avoid excess of energy, namely from PV 7
purpose Since both technologies have an expected parallel market uptake: What are the synergies between PV and EV? How can PV and EV be articulated to better explore their synergies? What is the best EV charging strategy? What levels of PV and EV, individually and together, are needed to comply with the EU energy-climate targets? Case study: Portugal 2050 8
2. methodology 9
model approach Demand Electricity Diesel fuel (transport) Petrol (transport) Production merit order Regulation load share Thermal flexibility Electricity demand Transport demand Wind Installed capacities Thermal power plants Wind Solar PV Large hydro Run of river Hydropump Electricity system model setup for 2011 year Run of river Solar PV Storage Hydro storage Fleet battery capacity Model setup under scenario conditions yes Outputs for 2011 year Efficiencies Thermal power plants Hydro turbine Hydropump Grid to battery Battery to grid Results as expected? tolerance: 10% Others Annual water supply Battery<->Grid connection capacity Share of parked vehicles Load diagram Curtailment Shares Fuel consumption CO 2 emissions no Model fine tuning 2011 official figures 10
January February March April May June July August September October November December Difference [%] Average power [MW] calibration 2011 2011 figures 2011 simulation Difference to reference (%) Final energy (GWh) Electricity demand 50503 50630 +0.3 Electricity production Thermal PP 27336 27300-0.1 Large hydro 4213 4210-0.1 Run of river 7638 7650 +0.2 Wind 9003 9030 +0.3 PV 262 260-0.8 RES electricity share 46.0% 45.4% -1.3 Primary Fuel (Mtoe) Natural gas 2.870 2.862-0.3 Coal 2.201 2.194-0.3 Oil 0.249 0.248-0.5 Biomass 0.380 0.379-0.2 Diesel 4.013 4.013 0.0 Gasoline 1.319 1.319 0.0 CO 2 emissions (Mt) Electricity sector 16.36 16.31-0.3 Transport sector a 15.65 15.78 +0.8 1,5 1 0,5 0-0,5-1 -1,5 0,0 0,0-0,3-0,9 0,8 0,2-0,4 0,0-0,2-0,3 0,4 Difference Average power real Average power simulation 0,0 11 6800 6500 6200 5900 5600 5300 5000
Capacity [GW] Demand [TWh] Capacity [GW] Capacity [GW] 2050 scenarios: energy 20 Solar PV 8 6 CCGT 10 4 2 0 2000 2010 2020 PV Scenario 2030 2040 2050 EPIA Paradigm shift sc. trend EPIA Accelerated sc. trend 0 1990 2010 2030 2050 CCGT scenario EU 2030 fit IEA NPS fit IEA 450 fit historical IEA NPS scenario 12 10 8 Wind 100 Electricity demand 6 50 4 2 0 1990 2010 2030 2050 Wind scenario EPIA fit PNAER fit EU 2030 fit EPIA 2012 scenario PNAER scenario 0 1990 2010 2030 2050 Central scenario Historical EPIA 2012 fit EU 2030 fit IEA CPS fit IEA NPS fit 12
parque electroprodutor 13
Car stock [M] Sales [k] 2050 scenarios: transportation Central scenario Low scenario 200 150 100 High scenario 50 0 2015 2020 2025 2030 2035 2040 2045 2050 5,0 4,5 Central scenario 4,0 3,5 3,0 2,5 2,0 1,5 1,0 0,5 0,0 2015 2020 2025 2030 2035 2040 2045 2050 Low scenario High scenario 14 ICE gasoline ICE diesel PHEV gasoline PHEV diesel PEV
Probability of travelling [%] charging strategies Non-smart day and night charging Unidirectional smart charging 7 6 5 4 3 2 1 0 0 2 4 6 8 10 12 14 16 18 20 22 daytime [h] 15
3. results 16
2011 17
2050 18
[MW] daily average power demand and production profiles 14000 0% EV Low EV Central EV High EV 100% EV 12000 10000 8000 6000 4000 2000 0 Increasing transport electrification 0 6 12 18 0 6 12 18 0 6 12 18 0 6 12 18 0 6 12 18 CEEP Total demand Total production Central PV, day charging 19
CEEP [%] CO2 emissions [Mt] day vs. night charging 100% 80% Low PV 12 10 60% Central PV 8 40% High PV 6 20% 4 0% 0 3 6 9 12 15 18 21 24 PV installed capacity [GW] 2 day charging CEEP night charging CO2 night charging CEEP day charging CO2 20
PV [GW] arrangements between EV market share and PV capacity 25 20 15 10 < -80% CO 2 CEEP > -80% CO 2 CEEP a > -80% CO 2 no CEEP 5 0 b < -80% CO 2 no CEEP 0 20 40 60 80 100 EV penetration [%] 80% CO2 reductions 0-0.01 TWh CEEP smart charging 21
3. closure 22
conclusions EV penetration leads to a reduction of excess of electricity and does not imply major changes in the load diagram day charging is better than night charging and allows to reach the EU2050 goals; at least 50% EV market share is needed to reach the EU2050 goal with day charging 23
conclusions with smart charging we need 40% EV market share to reach the EU2050 goal 0% energy excess needs a minimum of 2% of EV fleet to reach also the EU2050 target, the combination with least EV and PV is 61% and 12 GW, respectively 24
Integration of PV and electric vehicles in future energy systems Pedro Nunes July 2016