Abstract
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.
Highlights
Demand-side management (DSM) methods such as peak load shedding and valley filling allow for moving the demand of customers from peak times to off-peak times, which prevents the infrastructure costs from growing
We present the experiments carried out with the proposed Markov Decision Process (MDP)-based pricing model and Monte Carlo Tree Search (MCTS) solver described in the previous sections
It is a promising method of demand-side management, where grid operators use it to change the demand of the end-users during shortage periods
Summary
After more than a hundred years of niche use, electric vehicles (EVs) seem on the cusp of displacing internal combustion engine (ICE) vehicles in personal transportation [1,2]. Environmental friendliness, and lowering costs give EVs an edge over. To this end, the authors in [3] reported that in 2020 there was an increase of EVs from 3.5% to 11% of total new car registrations. The rise of EVs drives interest from many different actors, including governments, cities, car manufacturers, environmental groups, and electric utilities. Each is trying to prepare for the expected rise of EVs. For cities and electric utilities, the widespread use of EVs may require significant investments into infrastructure, as large numbers of EVs could increase the peak load on the grid up to threefold [4]. Demand-side management (DSM) methods such as peak load shedding and valley filling allow for moving the demand of customers from peak times (e.g., noon) to off-peak times (e.g., early morning), which prevents the infrastructure costs from growing
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