Smart Pricing for Smart Charging: A Deep Reinforcement Learning Framework for Residential EV Infrastructure

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Abstract
The growing use of electric vehicles in the residential building sector presents new challenges in the management of the charging infrastructure, especially in deciding how to best price the use of it to balance operator revenue and user satisfaction with grid stability. Traditional pricing methods like fixed pricing rates and time-of-use tariffs cannot accommodate the dynamic nature of charging demand, which fluctuates depending on temporal patterns and weather conditions as well as user behavior. This limitation means that the resources are not used optimally and the revenue opportunities are lost during periods of high demand. To overcome this issue, we propose a reinforcement learning framework for dynamic pricing for residential electric vehicle charging stations. The framework models the pricing problem as a Markov Decision Process and uses Proximal Policy Optimization to learn a policy for setting optimal prices of private and shared charging stations according to real-time conditions. The state representation is done using ten features such as temporal indicators, current loading on the grid, grid status, traffic volume, and weather data. A multi-objective reward function is an approach to balance four objectives - revenue maximization, station utilization, grid stability, and user satisfaction. The system is trained on actual charging data from a residential complex in Trondheim, Norway. 6878 charging sessions during a 13-month period are used for training. We compare the learned policy with three baseline technologies: fixed pricing, time-of-use pricing and rule-based pricing. Experimental results show that the proposed approach reaches an overall score of 0.569, which is 32.9% and 48.9% improvements in comparison to fixed pricing and time-of-use pricing, respectively. The learned policy is able to successfully adjust the prices based on different conditions and sustain a balanced performance for all the goals. The main contributions include a custom reinforcement learning environment for residential EV charging pricing, a multi-objective reward formulation, and empirical evidence that learned policies outperform traditional pricing approaches.

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