Sustainable Electric Mobility: What are the

Challenges?

1

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.

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

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Supporting Organizations

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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.

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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.

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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).

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49% 47% 45% 47% 49% 49% 48% 49% 44% 44% 44% 43% 43% 44% 47% 49% 49% 49% 48% 47%

22% 24% 24% 25%25% 25% 25% 28%

30% 30% 30% 31% 33% 32%31% 30% 30% 30% 30% 30%

2% 3% 5%7%

7% 8% 9%11% 12% 12% 13% 13%

13% 13%13% 13% 13% 14% 14% 15%

9% 10% 10%10%

10% 10% 10%

12% 14% 14% 12% 13% 11% 11% 8% 7% 7% 7% 8% 9%17% 17% 16% 12% 9% 8% 8%

0%

10%

20%

30%

40%

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80%

90%

100%

2008 2010 2012 2014 2016 2018 2020 2022 2024 2026

% o

f TW

h

0%

10%

20%

30%

40%

50%

60%

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90%

100%

Nuclear Renewables Conservation Gas Coal

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

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Gree

nhou

se G

as E

mission

s (M

egat

onne

s)

Range of Results for Scenarios Case A Case BSource: OPA

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Motivations– Opportunity also exists to dovetail into smart grid

development (quite significant developments and plans in Ontario).

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.

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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.

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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.

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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:

Number of PHEVs

Penetration levels

Emission credit

Hourly Energy PriceZonal generation, imported and exported power

8 365 iy iy iy i PHEV iyy i

Pg Pim Pex HEP N EC

ZoneYear

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.

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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).

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G&T Probabilistic Model• Robust optimization:

– Uncertain MILP model:

– It can be solved using the robust formulation:

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minx2X

cT xs.t. ~aT

i x ¸ bi 8i (all constraints)~ai = [[ai1 ¡ ¢ ai1;ai1 + ¢ ai1]¢¢¢[ai j ¡ ¢ ai j ;ai j + ¢ ai j ]¢¢¢[ain ¡ ¢ ain ;ain + ¢ ain]]T

minx2X

cT x

s.t. aT x + minsi 2S i

nX

j =1¢ ai j xi si j ¸ bi 8i

Si =(

si = [si1 : : :sin]¯̄¯̄j̄si j j =

¯̄¯̄~ai j ¡ ai j

¢ ai j

¯̄¯̄ · 18i;

nX

i=1jsi j j · ¡ i

)

G&T Probabilistic Model– This model can be transformed into a “standard” MILP

problem:

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minx2X

cT x

s.t. aT x ¡ ¡ i pi +J iX

j =1qi j ¸ bi 8i ^j = 1;2;: : :; J i

pi + qi j ¸ ¢ ai j rj¡ rj · xj · rjpi ¸ 0qi j ¸ 0rj ¸ 0

9=; ! aux. variables

G&T Probabilistic Model

– ¡i 2 [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).

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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%

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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.

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PHEVs• Maximum numbers of

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

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%

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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.

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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.

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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).

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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.

Ontario Results• Maximum power

requirement of PHEVs in Ontario considering the assumed transition curves and based on ideal penetration by 2025 (100%):

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Ontario Results• Assuming different penetrations for the various zones

based on population density for Transition 1:

Ontario Results• For Transition 2:

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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.

300 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

-50

0

50

100

150

200

250

July-2008

HO

EP ($

/MW

h)Ontario Results

• Maximum grid and electricity market potential:

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 20250

20

40

60

80

100

Staggered penetration Max. penetration

% S

hare

Max Penetration (PHEV 30km “valley filling”)

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 202505

101520253035

Pen

etra

tion

[%]

Optimal Yearly Penetration (PHEV 30km off-peak charging)

Ontario Results• Parameter uncertainty ranking (54 parameters):

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Ontario Results• Comparison of deterministic (DM) and robust

(RM) optimization results (no generator emission constraints):

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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):[𝑉 𝑠

𝐼 𝑠 ]=[ 𝐴 𝐵𝐶 𝐷] [𝑉 𝑟

𝐼 𝑟 ]

,

𝑡𝑎𝑝𝑎=𝑡𝑎𝑝𝑏=𝑡𝑎𝑝𝑐

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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:

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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 =

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Distribution System Model• Equality constraints:

– Feeder.– Transformers.– LTCs.– Capacitors.

• Inequality constraints:– Bus voltage limits.– Feeder current limits.– Charging socket limits.

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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.

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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 LAVHL = Average hourly kW load

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NP E VL = °oorµ

x °oorµ P DL

AVH L¶¶

Test System

1 3 5 7 9 11 13 15 17 19 21 23

0200400600800

100012001400

Base system Load profile

abc

Time (hour)

Tota

l Dem

and

(kW

)

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Case Studies

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• Minimizing the total energy drawn by the substation at different penetration levels:

Penetration Levels

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Energy (MWh)

62.97 64.01 65.13 66.26 67.39 68.56 69.69 70.83 71.97 73.13 74.36

Ploss (MW)

1.52 1.56 1.60 1.65 1.72 1.78

1.84 1.90 1.97 2.04 2.1

Cost($)

0 62.95 129.87 195.20 265.44 341.43

405.09 456.97 527.90 593.47 677.89

Case Studies(Min. Energy 90% Penetration)

1 3 5 7 9 11 13 15 17 19 21 23

0100200300400500600700800

Feeder (Bus 650-632) current

abcImax

Time (hour)

A

1 3 5 7 9 11 13 15 17 19 21 23

0

100

200

300

400

500

600

700

Demand at Bus 675 with & w/o PEV

a w/o PEVb w/o PEVc w/o PEVa with PEVb with PEVc with PEV

Time (hour)

kW

1 3 5 7 9 11 13 15 17 19 21 23

0200400600800

1000120014001600

System load with & w/o PEV

a w/o PEVb w/o PEVc w/o PEVa with PEVb with PEVc with PEV

Time (hour)

kW

1 3 5 7 9 11 13 15 17 19 21 23

050

100150200250300

Feeder (Bus 692-675) current

abcImax

Time (hour)

A

41

Case Studies(Min. Energy 90% Penetration)

• Phase voltages for buses with lowest voltage magnitude at peak load:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0.920.930.940.950.960.970.980.99

11.01

Bus voltage

634-a646-b675-c

Time (hour)

per u

nit

42

Case Studies

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• Minimizing the total cost of charging PHEVs at different penetration levels: Penetration Levels

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Energy(MWh)

67.14 70.25 71.49 71.79 74.38 73.27 75.38 76.60 77.13 79.56

Ploss (MW)

1.91 1.99 2.03 1.90 2.06 1.96 2.18 2.18 2.34 2.40

Cost ($) 62.95 128.27 193.99 258.52 325.82 389.95 454.48 520.20 585.52 654.8

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Case Studies(Min. Cost 90% Penetration)

1 3 5 7 9 11 13 15 17 19 21 230

200400600800

10001200140016001800

System load with & w/o PEV

a w/o PEVb w/o PEVc w/o PEVa with PEVb with PEVc with PEV

Time (hour)

kW

1 3 5 7 9 11 13 15 17 19 21 23

0

200

400

600

800

Feeder (Bus 650-632) current

abcImax

Time (hour)

A

1 3 5 7 9 11 13 15 17 19 21 23

0100200300400500600700

Demand at Bus 675 with & w/o PEV

a w/o PEVb w/o PEVc w/o PEVa with PEVb with PEVc with PEV

Time (hour)

kW

1 3 5 7 9 11 13 15 17 19 21 23

0

50

100

150

200

250

300

Feeder (Bus 692-675) current

abcImax

Time (hour)

A

Case Studies(Min. Cost 90% Penetration)

• Phase voltages for buses with lowest voltage magnitude at peak load:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0.9

0.92

0.94

0.96

0.98

1

1.02

1.04

Bus voltage

652-a675-c

Time (hour)

per u

nit

45

Case Studies

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• 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.

J =X

h

X

LNP H E V ¡ K [NP H E V ¡ round(NP H E V )]2

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Case Studies(Max. PHEV Charging)

1 3 5 7 9 11 13 15 17 19 21 23

0100200300400500600700800

Feeder (Bus 650 - 632) current

abcImax

Time (hour)

A

1 3 5 7 9 11 13 15 17 19 21 23

0100200300400500600700800900

1000

Demand at Bus 671 with & w/o PEV

a=b=c w/o PEVa with PEVb with PEVc with PEV

Time (hour)

kW

1 3 5 7 9 11 13 15 17 19 21 23

0

400

800

1200

1600

System load with & w/o PEV

a w/o PEVb w/o PEVc w/oPEVa with PEVb with PEVc with PEV

Time (hour)

kW

1 3 5 7 9 11 13 15 17 19 21 23

0100200300400500600700800

Feeder (Bus 632-671) current

abcImax

Time (hour)

A

Case Studies(Max. PHEV Charging)

• Phase voltages for buses with lowest voltage magnitude at peak load:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0.9

0.92

0.94

0.96

0.98

1

1.02

Bus voltage

634-a611-c

Time (hour)

per u

nit

48

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Case Studies  No PEV Case 1

(Min Energy)Case 2

(Min losses)Case 3

(Min cost)Case 4

(Max PEV charging)For 90% penetration

Total energy drawn (kWh)

62966.59 (Min energy)

73126.207(16.13%)

73322.576(16.45%)

77130.64(22.49%)

101114.633 (60.58%)

Total losses (kW)

1512.405 (Min losses)

2040.638(34.93%)

1962.685(29.77%)

2344.773(55.04%)

3606.481 (138.46%)

Total cost for charging PEVs

($/day)

0 593.47(1.36%)

597.01(1.96%)

585.52(0%)

2,010.93(243.44%)

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.

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Conclusions• Charging levels:

– Level 1 overnight charging requires no significant installation and operation challenges and costs, and it’s already taking place.

– Level 2 upgrades to home garage charger could be provided either at a small cost (or as an incentive) to the first adopters.

– Level 3 fast charging capability will be necessary for those customers who opt for it, but as a premium service option.

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Conclusions• Workplace charge stations:

– Necessary to develop consumer acceptance of PEVs. – The cost of installation and electricity use can be recovered in

a number of ways such as payroll deductions or within the employee’s benefits package.

• Public charge stations:– Need to be installed in high traffic zones providing all three

options for charging but at different prices. – The public installations could be led by either a utility-

municipality partnership or private sector entity investment.– Pilot projects are being developed (e.g. Toronto Hydro).

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Conclusions• Region-specific or neighborhood specific

“maps” of vehicle purchases and demand for charging stations need to be developed.

• The utility should install override controls (with customer agreement) and “entice” the customer to charge at times when it is best from a utility operations perspective (e.g. Peak Saver DR program).

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Conclusions• Charging of PEVs during on-peak hours will have a significant effect

on the grid that will have to be planned for, especially in highly populated areas such as the GTA due to clustering.

• Significant PEV penetration levels:– Will definitely impact the grid and its associated electricity market.– Even if all charging takes place at night, there will be upward pressure on

electricity prices. – Prices should be conveyed in a “smart” manner to make optimal charging

decisions.• To mitigate and manage the impact that high penetration and

concentration of PEVs, “smart charging” strategies and technologies will have to be developed and deployed in the context of smart grid technologies.

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Conclusions• Vehicle-to-Grid (V2G) as well as Vehicle-to-House (V2H)

technologies:– Present many potential advantages to the grid such as regulation and

energy storage for wind and solar power. – These technologies will not be economically feasible until several of the

issues with batteries are resolved. – Since technologies will likely improve in the long-term, R&D is needed now

to be ready for deployment. • Standards for PEV charging devices:

– Installations and communications are currently under development by a variety of institutions (e.g. SAE, UL, CSA).

– These communication standards will be very much dependent on the standards finally adopted for smart grid applications (e.g. ZigBee and WiFi).

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Conclusions• A “champion” agency should be identified and

empowered to ensure the policy goals can be attained.

• This lead agency would work with all stakeholders to develop a clear set of regional plans for implementing electric mobility initiatives.

• To promote sustainable mobility, the planning efforts must also address social science issues such as urban land use and others.

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Further Reading• I. Sharma, C. A Cañizares, and K. Bhattacharya, “Modeling and Impacts of Smart

Charging PEVs in Residential Distribution Systems,” Proc. IEEE-PES General Meeting, July 2012, 8 pages.

• A. Hajimiragha, C. A. Cañizares, M. W. Fowler, S. Moazeni, and A. Elkamel, “A Robust Optimization Approach for Planning the Transition to Plug-in Hybrid Electric Vehicles,” IEEE Transactions on Power Systems, vol. 26, no. 4, November 2011, pp. 2264-2274.

• A. Hajimiragha, C. A. Cañizares, M. Fowler, and A. Elkamel, “Optimal Transition to Plug-in Hybrid Vehicles in Ontario-Canada Considering the Electricity Grid Limitations,” IEEE Transactions on Industrial Electronics, special issue on “Plug-in Hybrid Electric Vehicles”, vol. 57, no. 2, February 2010, pp. 690-701.

• C. A. Cañizares, J. Nathwani, K. Bhattacharya, M. Fowler, M. Kazerani, R. Fraser, I. Rowlands, and H. Gabbar, “Towards an Ontario Action Plan For Plug-In-Electric Vehicles (PEVs),” Waterloo Institute for Sustainable Energy (WISE), University of Waterloo, Report, May 2010, 165 pages. Available at: http://www.plugndriveontario.ca/

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