Optimal Operation and Services Scheduling for an Electric Vehicle Battery Swapping Station

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