Dynamic Pricing for Charging of EVs with Monte Carlo Tree Search

As electric vehicles (EVs) are slowly becoming a common occurrence on roads, commercial EV charging is becoming a standard commercial service. With this development, charging station operators are looking for ways to make their charging services more profitable or allocate the available resources op...

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Bibliographic Details
Published in:Smart cities (Basel) 2022-03, Vol.5 (1), p.223-240
Main Authors: Mrkos, Jan, Basmadjian, Robert
Format: Article
Language:English
Subjects:
Air fares
dynamic pricing
electric mobility
Electric rates
Electric utilities
Electric vehicle charging
Electric vehicles
Electricity distribution
EV charging
Game theory
Heuristic
Hotels & motels
Infrastructure
Linear programming
Load
Markov analysis
Markov Decision Process
Markov processes
Methods
Monte Carlo simulation
Monte Carlo Tree Search
Operators
Optimization
Prices
Pricing
Pricing policies
Profits
Queuing theory
reservations
Revenue
Search algorithms
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Summary:As electric vehicles (EVs) are slowly becoming a common occurrence on roads, commercial EV charging is becoming a standard commercial service. With this development, charging station operators are looking for ways to make their charging services more profitable or allocate the available resources optimally. Dynamic pricing is a proven technique to increase revenue in markets with heterogeneous demand. This paper proposes a Markov Decision Process (MDP)-based approach to revenue- or utilization- maximizing dynamic pricing for charging station operators. We implement the method using a Monte Carlo Tree Search (MCTS) algorithm and evaluate it in simulation using a range of problem instances based on a real-world dataset of EV charging sessions. We show that our approach provides near-optimal pricing decisions in milliseconds for large-scale problems, significantly increasing revenue or utilization over the flat-rate baseline under a range of parameters.
ISSN:2624-6511
2624-6511
DOI:10.3390/smartcities5010014