Sustainable Electric Mobility: What are the Challenges ?

1. Sustainable Electric Mobility: What are the Challenges? Claudio CañizaresDepartment of Electrical & Computer EngineeringPower & Energy Systems (www.power.uwaterloo.ca) WISE (www.wise.uwaterloo.ca) With slides from Dr. Amir Hajimiragha and PhD student Isha Sharma. 1

2. Outline • Sponsors • Motivation • Objectives • Generation and Transmission Studies: • Deterministic model • Probabilistic model • Plug-in hybrid vehicles (PHEVs) • Ontario studies • Distribution Systems: • Optimization Model • Test System • Case studies • Conclusions

3. Supporting Organizations

4. Motivations • Energy: • Global demand for energy is projected to increase by 50% over the next 25 years. • This significant demand increase along with the dwindling supply of oil has raised concerns over the security of the energy supply as well as the environment.

5. Motivations • Transportation sector: • It’s one of the largest and fastest growing contributors to both energy demand and greenhouse gases. • In Canada, represents almost 35% of the total energy demand and is the highest source of greenhouse gas emissions. • In view of these issues and the problems with the supply of oil, the subject of alternative fuels for meeting the future energy demand of the transport sector has gained much attention.

6. Motivations • Optimal utilization of existing infrastructure: • It’s desirable for both economic and environmental reasons. • The electricity infrastructure is designed to meet the highest expected demand, which only occurs a few hundred hours per year (at most 5% of the time in Ontario). • This system is underutilized for the remainder of the time, in particular during off-peak hours (namely, 12 to 7 am). • During these time intervals, the system could generate and deliver a substantial amount of energy to other sectors such as transport. • Surplus generation capacity of the electricity network during off-peak periods could be utilized for charging the batteries in Plug-in Vehicles (PEVs).

7. Motivations • If the electricity supply mix presents a low GHG footprint, enabling PEVs and displacement of gasoline by electricity would be a major contribution to GHG reduction from the transport sector. For example, in Ontario: Source: OPA Source: OPA

8. Motivations • Opportunity also exists to dovetail into smart grid development (quite significant developments and plans in Ontario).

9. Motivations • Major auto manufacturers are committed to bringing PEVs to the market. In the US, government, universities, utilities and the automotive industry are actively partnering to make PEVs a reality in the next decade or so. • PEV energy storage capacity presents unique opportunities to better integrate “intermittent” energy resources such as solar and wind power. • A significant penetration of these vehicles will have important positive ramifications for power systems by introducing energy storage capacity for the grid. • The economics and technical considerations appear promising, but R&D is needed to address specific technical and system questions and identify policy instruments. • Penetration of PEVs is contingent on availability, cost and consumer acceptance. • Integration into existing power systems needs to be understood and addressed from infrastructure, planning and regulatory perspectives.

10. Objectives • Investigate the technical and economic feasibility of improving the utilization of the power grid during off-peak periods. • Reduce the demand on fossil fuels for transportation. • Consider the existing electricity infrastructure and future plans to derive the maximum level of PHEV penetration into the transport sector, with minimum impact on the grid. • This work has concentrated in the Ontario case.

11. Generation and Transmission (G&T) Deterministic Model • In order to find the maximum level of extra load in the form of PHEVs that can be added to an electricity network in a given period of time: • A multi-interval DCOPF model with loss factor considerations was developed based on a zonal model of the electricity system during base-load time intervals. • PHEVs are treated as additional discrete zonal loads that depends on number of vehicles. • Based on a piecewise linearization of power losses, the resulting optimization model is a Mixed Integer Linear Programming (MILP) problem.

12. G&T Deterministic Model • The objective function in this optimization model consists of electricity generation and imported/exported power cost and revenue components from 12 am to 7 am, as well as CO2 credit components which are assigned to each PHEV that can be added to the transport sector: Zone Year Emission credit Penetration levels Zonalgeneration, imported and exported power Hourly Energy Price Number of PHEVs

13. G&T Deterministic Model • CO2 credit of PHEVs: • The net GHG emission reduction is influenced by the share of fossil fuel in the marginal generation mix. • PHEVs result in lower emissions compared to gasoline vehicles even for regions with high CO2 levels from electric generation. • Based on base-load generation mix in and typical social cost values of CO2 emission, a CO2 credit can be assigned to each PHEV: • Based on 30 km/day all electric operation of a PHEV, total CO2 cut by one PHEV in a populated area in Ontario was found to be 1.5 ton/year. • Maximum penetration levels are achieved for credit values larger than 101.18 CAD. • These values do not necessarily represent an actual CO2 emission credit to be traded in the market.

14. G&T Probabilistic Model • The uncertainty in some parameters such as electricity prices and Light Duty Vehicle (LDV) growth affect the optimal planning. • These uncertainties were studied using: • Monte Carlo simulations to evaluate and rank the effect of parameters in the model. • Robust optimization to determine optimal plan for a given uncertainty “budget” (no interval solutions).

15. G&T Probabilistic Model • Robust optimization: • Uncertain MILP model: • It can be solved using the robust formulation:

16. G&T Probabilistic Model • This model can be transformed into a “standard” MILP problem:

17. G&T Probabilistic Model • ¡i2 [0,Ji] represents the “budget” of uncertainty, with ¡i= 0 corresponding to no “protection” against uncertainty, and ¡i= n(Ji≤ n) yielding a very conservative solution (all uncertain parameters in constraint e taking their worst value).

18. PHEVs • LDVs consist of passenger vehicles and light trucks with gross weight below 4.5 tons. • Based on the Canadian Vehicle Survey: • More than 95% of the vehicles on Canadian roads fall into the LDV category. • Number of LDVs by vehicle type for Canada (2005 base): • Compact sedan: 29% • Mid-size sedan: 29% • Mid-size SUV: 4% • Full-size SUV: 4% • Van: 16% • Pickup truck: 18%

19. PHEVs • Per capita number of vehicles in Ontario (0.55) is assumed to be valid during the planning period of 2008-2025. • The total number of light vehicles in each zone of Ontario between 2008 and 2025 was found based on Ontario’s zonal population in this period and per capita number of vehicles. • The objective is then to find the maximum percentages of these light vehicles that can plug into the power grid for some or all of their energy needs.

20. PHEVs • Maximum numbers of PHEVs in Ontario’s transport sector considering 2 different adoption rates (transitions) and based on ideal penetration by 2025 (100%):

21. PHEVs • Main assumptions: • Average daily drive per LDV: ~50 km • All electric daily trip: 30 km (referred to as PHEV30; it covers at least 60% of the average daily drive per light vehicle in Ontario) • Maximum allowable depth of discharge: 70% • Connection power level (120 V/15 A): 1.4 kW • Charging efficiency: 85%

22. PHEVs • Battery charging requirements for different types of PHEV30 vehicles: • Charging times fit in the 8 hours of off-peak time periods in Ontario from 12 am to 7 am.

23. PHEVs • Hourly power requirements (kW) for different types of PHEV30 vehicles at 1.4 kW connection power level: • Average hourly power consumptions for each type of vehicles were considered in the model.

24. Ontario System Model • Electricity network model: • Based on zonal representation of Ontario’s network, a 10-bus simplified model for this network was developed. • This is mostly a 500 kV network, with a 230 kV interconnection between northern zones. • Transmission capacity enhancements were considered based on the existing projects and future developments published by the Ontario Power Authority (OPA).

25. Ontario System Model • Electricity generation model: • A zonal pattern of generation capacity procurement from 2008 to 2025 contributing to base-load energy in Ontario was developed. • This is based on the Ontario Integrated Power System Plan (IPSP) and the general information published by the OPA and IESO. • The model specifies the total effective generation capacity which is available in each zone during base-load time periods from 2008 to 2025.

26. Ontario Results • Maximum power requirement of PHEVs in Ontario considering the assumed transition curves and based on ideal penetration by 2025 (100%):

27. Ontario Results • Assuming different penetrations for the various zones based on population density for Transition 1:

28. Ontario Results • For Transition 2:

29. Ontario Results • In spite of somewhat higher penetrations by 2025 based on Transition 1, the total costs for the system during the planning span is the same as for Transition 2.

30. Ontario Results • Maximum grid and electricity market potential: Max Penetration (PHEV 30km “valley filling”) Optimal Yearly Penetration (PHEV 30km off-peak charging)

31. Ontario Results • Parameter uncertainty ranking (54 parameters):

32. Ontario Results • Comparison of deterministic (DM) and robust (RM) optimization results (no generator emission constraints):

33. Distribution System Model • Conductors/cables, transformers, LTCs and switches are modeled using ABCD parameters and are constants except for LTC: • A and D matrices for LTC (continuous variable): ,

34. Distribution System Model • Loads: • All loads are modelled as constant impedance: • PEV load: • Constant current load: • Battery capacity constraint: • Socket constraint: Level 1 charging: Level 2 charging:

35. Distribution System Model • Minimize J • Case 1: Total energy drawn from the substation = • Case 2: Total losses in the system = • Case 3: Total cost of charging PEVs =

36. Distribution System Model • Equality constraints: • Feeder. • Transformers. • LTCs. • Capacitors. • Inequality constraints: • Bus voltage limits. • Feeder current limits. • Charging socket limits.

37. PHEVs • All the case studies have been carried out considering PHEV30 mid-size sedan (8.14 kWh battery capacity). • Due to life cycle considerations, SOC at the start of charging is 20% and is charged to 90% of its full capacity. • PEV is not available from 7A.M. to 5 P.M. for charging. • Level 2 charging: MaxW=4.8 kW(208-240V/40-100A). • Level 1 charging: MaxW=1.4 kW(120V/15A). • 85% charging efficiency.

38. PHEVs • 100% penetration implies that every house has one PHEV. • Number of PHEVs added at each load bus L are integers: where: x = penetration level in p.u. PDL = kW load at bus L AVHL = Average hourly kW load

39. Test System

40. Case Studies • Minimizing the total energy drawn by the substation at different penetration levels:

41. Case Studies(Min. Energy 90% Penetration)

42. Case Studies(Min. Energy 90% Penetration) • Phase voltages for buses with lowest voltage magnitude at peak load:

43. Case Studies • Minimizing the total cost of charging PHEVs at different penetration levels:

44. Case Studies(Min. Cost 90% Penetration)

45. Case Studies(Min. Cost 90% Penetration) • Phase voltages for buses with lowest voltage magnitude at peak load:

46. Case Studies • Maximize PHEV charging: • The quadratic term adds a high penalty value to the objective function at no-integer solutions, controlled by the parameter K. • MINLP problem is converted into an NLP.

47. Case Studies(Max. PHEV Charging)

48. Case Studies(Max. PHEV Charging) • Phase voltages for buses with lowest voltage magnitude at peak load:

49. Case Studies

50. Conclusions • It will take anywhere from 5 to 10 years for PEVs to begin to assume any noteworthy share of the market and longer for a critical mass to emerge. • For the first 5 years, charging needs can be managed with existing options without significant disruption. • Beyond this time period, the planning process, informed by emerging data on consumer acceptance, would be expected to address future needs. • Detailed assessments of market potential will be required and coordination of activities amongst planning agencies, utilities and auto manufacturers will be necessary to ensure the requisite infrastructure is in place when needed.