Electric Vehicles as Grid Resources

Mushfiqur R. Sarker

University of Washington, Seattle, WA

Presented for EE 500

• The global trend is aiming towards the electrification of the

transport sector using Electric Vehicles (EVs)

• In 2013, of the total greenhouse gas emissions, 27% was

due to the transportation sector

• With transportation electrification, emissions can be

reduced since renewable energy resources, e.g. wind, can

provide a part of the energy needs of EVs

Why Electric Vehicles?

Benefits of EVs

• Day-to-day operating costs lower than combustion vehicles

• Equipped with battery that can be used to provide services

to the power grid, such as:

– Energy arbitrage

– Ancillary services

Comparison between the cost of travelling 27 miles for an average vehicle (27 mpg) and an EV (0.34 kWh/mi) (Union of Concerned Scientists)

Cost to Drive 27 Miles

What’s the Problem?

• Adoption rates are low as of 2015 in the U.S.

• In 2011 State of the Union, President Obama called for 1

million EVs by 2015

• Approximately 363,000 EVs are on the road in the U.S.

(Sept. 2015)

• 1 in 4 vehicles are EVs in Norway as of 2015

• Growth rate needs to be faster to be significant for power

grid services

Current Issues with EVs

With current EVs, four issues can be identified:

• Upfront Costs

o e.g., 2016 Nissan Leaf’s price is $29,000

• Slow Charging Times

o e.g., 17 hours on Level I standard wall outlet1

o e.g., 8 hours on Level II upgraded charger1

• Lack of Public Infrastructure

o 12,922 charging stations nationwide

• Range Anxiety

1Charge times to reach from 0 to 100% charge for a Nissan Leaf with a 24 kWh battery with 90% efficiency

1. Optimal Coordination and Scheduling of Demand

Response of Residential Consumer Loads

– Provide consumers with incentives for distribution grid

services

2. Optimal Participation of an EV Aggregator in Day-Ahead

Energy and Reserve Markets

– Consumers obtain further revenue for allowing

participation of EVs in wholesale markets

3. Optimal Operation and Services Scheduling for an EV

Battery Swapping Station

– Slow charging, public infrastructure, and range anxiety

mitigated

Frameworks

Optimal Coordination and Scheduling of Demand

Response of Residential Consumer Loads

Sarker, M. R.; Ortega-Vazquez, M.A.; Kirschen, D.S., “Optimal Coordination and Scheduling of Demand Response via

Monetary Incentives," IEEE Transactions on Smart Grid, vol. 6, no. 3, pp. 1341-1352, May 2015

• Additional business-entity in the retail and/or wholesale

energy market

• Does not need to own assets, e.g. generation

• In this framework, aggregator’s roles are to:

– Maintain the power grid limits, e.g. lines

– Provide monetary incentives to consumers to motivate

demand response help offset upfront costs

– Maximizes its profit

What is an Aggregator?

• A decentralized, non-iterative approach is proposed

– Consumers optimize to minimize energy costs

– Aggregator optimizes to maximize profits

• Prices are used to control consumer demand

– Real-time pricing (RTP) motivates consumers to shift demand to

minimize energy costs

– Incentives are issued by the aggregator to consumers as a reward

for Demand Response (DR)

• Combination of price-based and incentive-based DR yields

the most benefits

Methodology

Price information flows downstream and demand information flows upstream

Framework: Information Flow

• Pre-scheduling (PS) Stage

1. Consumers optimize appliance schedules based only on RTP

2. Consumers send their power profiles to aggregator

3. Aggregator determines if grid overloads will occur

• Re-scheduling (RS) stage

4. Aggregator sends a set of monetary incentives

5. Consumers make adjustments to PS stage profiles

6. Revised profiles sent to Aggregator

7. Aggregator optimizes to determines the least-cost allocation of incentives that meet its priorities

Process

• Consumers provide their

DR capability as a

response to each

incentive, β

• Aggregator generates a

DR supply curve

• Embeds curve into

optimization model

Framework: DR Supply Curve

Model

Aggregator’s objective

Consumer’s objective

– Pre-scheduling stage

– Re-scheduling stage (rolling window)

| | 1 | | 1

RS PS market

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max

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, , , , ,

( ) ( )

min ( )

T t

h t i a h a h v h v h

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t p p p p

base , chg dsg dsg

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min

a h

h h a v h v h

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Subject to each

appliance

constraints and

consumer comfort

constraints

Power flow

constraints and

distribution line limits

• Incentives are offered to the consumer at hour 6

• Consumer optimizes and creates profiles shown above

• Aggregator receives profiles and determines if rewarding the

consumer will:

1. Mitigate grid issues, and

2. Maximize its profits

Example

No control Optimal control Feeder limit

Total demand for 100 consumers with (a) 30%, (b) 60%, and (c) 100% EV penetration under real-time tariff

30%

60%

100%

Selected Results

Final Remarks

• Consumers’ response depends on the value of monetary

incentive offered

• If some consumers do not participate, aggregator can still

maintain grid limits

• Interactions between a consumer and the aggregator is a

contractual agreement

Optimal Participation of an EV Aggregator in Day-Ahead Energy and Reserve Markets

Sarker, M. R.; Dvorkin, Y.; Ortega-Vazquez, M.A., “Optimal Participation of an Electric Vehicle Aggregator in Day-Ahead

Energy and Reserve Markets," IEEE Transactions on Power Systems, Early Access, November 2015

• Large fleets of EVs can be aggregated to provide services

in wholesale markets

– Individual EVs cannot participate due to minimum capacity

requirements, e.g. 1 MW bids in PJM

– EVs receive compensation for services offset upfront costs

• In this framework, aggregator optimally schedules these

fleets of EVs considering:

– EV transportation needs

– Competitive bidding/offering into markets

– EV battery degradation

Introduction

• Day-ahead Energy:

– Price-taker and thus cannot influence its outcome

• Day-ahead Voluntary Up Reserves:

– Price-maker

– Revenue obtained in the day-ahead as a capacity payment for being

on-stand by

– Revenue obtained in the real-time for deployment, if called by the

system operator

• Day-ahead Voluntary Down Reserves:

– Similar to the up reserve market

– No deployment payment

Which Markets?

1Regulating Reserve Service (Up & Down) in the ECOT market considered

• Use historical data from market to create Probability-Quantity-

Price (PQP) curves

• Uses probabilities to determine bidding/offer strategy in markets

– Probability of acceptance (𝜋a)

– Probability of deployment (𝜋d)

Aggregator’s Perspective

Charge

Energy Market

Energy Market Charge

(EMCHG)

Voluntary Down Reserves Market

Reserve Down

(REGDN)

Stop Discharge (STOPDSG)

Discharge

Energy Market

Energy Market

Discharge (EMDSG)

Voluntary Up Reserves Market

Reserve Up (REGUP)

Stop Charge

(STOPCHG)

EV Perspective

Action

Market

Service

Optimal scheduling of these services are necessary to maximize profits

Aggregator Model

Subject to

– State-of-charge (SoC) updates

– Minimum/maximum SoC

– Minimum/maximum power constraint

– Bidding/offering constraint in markets

– PQP curve constraints

Where, e.g.,

Selected Results

Selected Results

Can EV participation aid the power system?

– With EV participation, total system costs decrease

– Decrease in the start-up costs show less cycling of conventional

generation occurs in both the day-ahead and real-time

– Less fast-starting units on stand-by

• Bidding/offering strategy influenced by:

– Market structure

– EV battery degradation

• Up reserves is profitable due to two revenue streams

• Arbitrage is performed between markets, i.e.

– Energy is purchased during low-price periods

– Then, scheduled for up reserves

Final Remarks

Optimal Operation and Services Scheduling for

an Electric Vehicle Battery Swapping Station

Sarker, M. R.; Pandzic, H.; Ortega-Vazquez, M.A., “Optimal Operation and Services Scheduling for an Electric Vehicle Battery

Swapping Station," IEEE Transactions on Power Systems, vol. 30, no. 2, pp. 901-910, March 2015

• BSS is a profit-seeking business entity resembling a

traditional gas station

• BSS holds an inventory of EV batteries

• Provides a fully-charged battery to a consumer and receives

a battery in return

• Charges the consumer a fee for provided services

– Fee includes cost of labor, battery, and degradation

What is an EV battery swapping station (BSS)?

• Participates in electricity market by performing arbitrage,

i.e. buy energy low and sell high

• Schedules batteries to perform in three modes:

– G2B (Grid-to-Battery): Charge battery energy from the grid

– B2G (Battery-to-Grid): Discharge battery energy to the grid

– B2B (Battery-to-Battery): Transfer energy between batteries

BSS Operations

What type of consumers benefit from BSS?

– Ones who do not want to invest in charging systems

– Ones who cannot install EV charging systems

– Ones who want more freedom with their EVs

– Ones in an emergency

What are the benefits to the power system?

– Large energy storage facility that can provide grid

services

BSS Benefits

Battery swap revenue

(BSR) obtained for

each swap 𝑥𝑖,𝑡

Costs and revenue obtained

from buying and selling

energy to/from the grid

Discount given on the

BSR if swapping partially

charged batteries

Costs for being unable to

serve battery demand

Day-ahead Objective Function

The objective function is subject to the following constraints

1. Swapping characteristics

o Binary variable dictates which battery will be swapped

2. State-of-charge (SoC) updates

3. Battery demand balance

4. Minimum/maximum SoC

5. Minimum/maximum power constraint

6. Battery degradation management

BSS Model: constraints

Inventory robust optimization used to hedge against

uncertainty in the number of customers who desire a swap

– Each battery capacity group 𝑔 (e.g. 24 kWh, 16 kWh) has

worst-case band to hedge against uncertainty

– Robustness parameter Γ𝑔 controls the level of protection

Extensions Battery Demand Uncertainty

Ensures sufficient energy in stock to meet uncertain battery

demand

Demand Uncertainty – Objective Function

The model is subject to the typical BSS constraints and the

following:

Multi-band robust optimization used to hedge against market

price uncertainty

– Pre-defined multiple bands (e.g. 5%, 10%) are used to manage

against unforeseen deviations

– For example, 𝝀𝒕,𝟏𝟎%𝐦𝐚𝐱 represents 10% price deviation from 𝝀𝒕

𝐦𝐢𝐧

Extensions Price Uncertainty Management

Ensures protection against high materialization of market prices

( ) ( )b b t

b B t T

v z

Price Uncertainty Objective Function

The model is subject to the typical BSS constraints and the

following:

Effect of price uncertainty on the energy injected in B2G (a) and B2B (b) in p.u. (i.e., normalized over kWh).

Battery-to-Grid Energy Battery-to-Battery Energy

Selected Results: effect of price uncertainty

– Without price uncertainty, Battery-to-Grid is favorable

– With price uncertainty, Battery-to-Battery is favorable

• Monte Carlo was performed on various combinations of parameters

• Right-most CDFs yield the largest profits, however, there is no distinct

curve that performs the best

• If price uncertainty is ignored, i.e. 𝜃 = 0, then profits are lowered

drastically

Selected Results: effect of uncertainty

– Uncertainty management schedules less grid services for protection

– Covered for any realization of prices and demand within bounds

Deterministic case with uncertainty

G2B: Charge battery energy from the grid

B2G: Discharge battery energy to the grid

B2B: Transfer energy between batteries

Selected Results: charging schedule

• Battery Swapping Stations (BSS) are beneficial to both

consumers and the power system

• BSS obtains revenue from swaps along with optimal

scheduling

• Robust Optimization is a computationally efficient and

effective method to handle uncertainty in such applications

Final Remarks

• If exploited effectively, EVs are valuable assets to the

power grid

• Models must consider:

– Monetary compensation for services provided by EVs

– Degradation

– Uncertainty

• The developed frameworks may benefit:

– New/current business entities

– System Operators

Conclusions