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
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