http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6857439 This is a companion presentation for the IEEE Transactions on Power System paper, "Optimal Operation and Services Scheduling for an Electric Vehicle Battery Swapping Station". Please make sure to reference the paper below: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6857439 M. R. Sarker, H. Pandzic and M. A. Ortega-Vazquez, "Optimal Operation and Services Scheduling for an Electric Vehicle Battery Swapping Station," IEEE Transactions on Power Systems, 2015.

1. 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, WA
2University of Zagreb, Croatia
Presented at PES GM 2015

2. 1) Background
2) Battery Swapping Station: Business Case
3) Optimization Model
4) Selected Results
5) Conclusion
Outline

3. Background and Motivation

4. • 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

5. 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

6. 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

7. Business Case

8. • 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)?

9. • 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

10. • 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

11. 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

12. Optimization Model

13. 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

14. 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.)

15. 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.)

16. 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

17. 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 𝜃 𝑏 controls the level of protection for each band 𝑏

18. 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

19. Selected Results

20. 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

21. • 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

22. 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

23. Selected Results: charging schedule
G2B: 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

24. 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

25. • 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

26. 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