Optimal Operation and Services Scheduling

for an Electric Vehicle Battery Swapping Station

Mushfiqur R. Sarker1

Prof. Hrvoje Pandzic2

Prof. Miguel A. Ortega-Vazquez1

1University of Washington, Seattle, WA2University of Zagreb, Croatia

Presented at PES GM 2015

1) Background

2) Battery Swapping Station: Business Case

3) Optimization Model

4) Selected Results

5) Conclusion

Outline

Background and Motivation

• As Electric Vehicles (EV) penetration increases, stress on the power

system will increase

• Methods have been developed to decrease issues by the means of:

• Direct load control

• Demand response

• EV smart charging control requires energy management systems

(EMS) and charging systems to be installed

• Ultimately, causes an increase in costs to the end-user

Background

Consumers discouraged to own EV due to:

o Cost of upgrading their home to handle charging

o Wait-time for charging

o Limited public locations for charging

o Range anxiety

Background: Current Issues with EV acceptance

Motivation

Tesla Battery Swapping Technology

• Tesla Model S includes battery

swapping

• Tesla owners pay a “transport fee” and

receive a fully charged battery

• Started pilot station in California in 2014

State Grid Corporation of China

• Transport fleet, e.g. buses, is currently using swapping technology

Business Case

• BSS is a profit-seeking business entity resembling a traditional

gas station

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

battery in return

• Charges the consumer a fee for provided services

o 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

• Large demand due to battery charging occurs at BSS location

• Infrastructure upgrades minimized due to some consumers using BSS

services instead of residential charging

• Ability to provide/consume electricity when necessary

• Concentrated location with massive energy storage

• Participate in Energy Market and Ancillary Services Market

Benefits to Power System

What type of consumers benefit from BSS?

• Ones who do not want to invest in EV charging systems

• Ones who cannot install EV charging systems

• Ones who do not want to wait for charging

• Ones who want more freedom with their EVs

• Ones in an emergency

Benefits to Consumers

Optimization Model

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

Constraints include:

1. Swapping characteristics

o Binary variable dictates which battery will be swapped

2. State-of-charge (SoC) updates

o Based on efficiencies, power, and previous period SoC

3. Battery demand balance

o Total demand in each period must be met

4. Minimum/maximum SoC

5. Minimum/maximum power constraint

6. Discounts

BSS Model: constraints (cont.)

Discount given to consumer if eSoC is not 100%

Two-part discount function:

1. Reduction in total cost to consumer

2. Discount due to inconvenience of requiring a quicker battery

swap next time

BSS Model: constraints (cont.)

Extensions Degradation Management

Objective function may include cost of degrading the battery fleet. This is

modeled as:

• 𝒎𝒊 is the linear approximation of the state-of-health verses the number of

cycles remaining

• Model will optimally decide if it is economical to perform energy arbitrage

Extensions Price Uncertainty Management

Multi-band robust optimization used to hedge against market price

uncertainty

• Multiple bands (e.g. 5%, 10%) are used to manage against unforeseen

deviations

• Robustness parameter 𝜃𝑏 controls the level of protection for each band 𝑏

Extensions Battery Demand Uncertainty

Inventory robust optimization used to hedge against the uncertainty in the

number of customers who desire a battery swap

• Each battery capacity group 𝑔 (e.g. 24 kWh, 16 kWh) has a worst-case

band to hedge against uncertainty

• Robustness parameter Γ𝑔 controls the level of protection for each group 𝑔

Selected Results

1. 100 of 16 kWh batteries

2. 200 of 24 kWh batteries

3. Max power is 3.3 kW for each battery

4. Efficiency is 90%

5. SoC when replaced is random from 30% to 60%

6. Battery swap revenue (BSR) is $70

7. Value of customer dissatisfaction

is $200

Problem is a Mixed-integer linear program

solved in GAMS

Parameters

• All services, G2B, B2G, and B2B, degrade batteries in the BSS stock

• Larger capacity cost translates to larger cost of degradation accrued by the BSS

• As technology improves and capacity cost decreases, B2G and B2B services

are profitable

Selected Results: effect of battery degradation

Selected Results: effect of uncertainty

• 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: charging scheduleG2B: Charge battery energy from the grid

B2G: Discharge battery energy to the grid

B2B: Transfer energy between batteries

• G2B occurs during low-price periods and B2G during high-price periods

• B2B occurs during high-price periods

Deterministic case without uncertainty

Selected Results: charging schedule (cont.)G2B: Charge battery energy from the grid

B2G: Discharge battery energy to the grid

B2B: Transfer energy between batteries

• Uncertainty management schedules less B2G and B2B services

• Covered for any realization of prices and demand within bounds

Deterministic case with uncertainty

• Battery Swapping Stations (BSS) are beneficial to both

consumers and the power system

• BSS obtains revenue from swaps along with optimal

scheduling,

o Pre-charging during low-cost periods in G2B mode

o Discharging during high-cost periods in B2G mode

o Transferring of electricity between batteries in B2B mode

• For large scale deployment of BSSs, swapping of batteries

must be standardized

Conclusion

Acknowledgements

• NSF

• Clean Energy Institute

• Prof. Daniel S. Kirschen

• Renewable Energy Analysis Laboratory (REAL) at UW

References

• 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