Beyond Profit: A Multi-Objective Framework for Electric Vehicle Charging Station Operations

TL;DR

This paper presents a multi-objective framework for EV charging station operations that optimizes profit and reputation simultaneously through novel pricing and scheduling strategies, employing advanced reinforcement learning techniques.

Contribution

It introduces a joint optimization model for pricing and continuous charging rates that considers reputation, using a novel penalty and safe layer, with improved convergence methods.

Findings

Achieves 25.45%-52.20% higher JPR than baselines.

Effectively balances profit and reputation in EV charging.

Demonstrates the effectiveness of the proposed reinforcement learning approach.

Abstract

This paper explores the pricing and scheduling strategies of the electric vehicle charging stations in response to the rising demand for cleaner transportation. Most of the existing methods focus on maximizing the energy efficiency or the charging station profit, however, the reputation of EVs is also a key factor for the long-term charging station operations. To address these gaps, we propose a novel framework for jointly optimizing pricing and continuous-multiple charging rates. Our approach aims to maximize both charging station profit and reputation, considering multi-objective optimization and continuous rate control within physical constraints. Introducing a pricing fluctuating penalty for reputation modeling and a linear programming-based safe layer for constraints, we confront the complexity of continuous charging rates' action space. To enhance convergence, we explore a soft action critic framework with novel entropy temperature tunning technique. The experiments conducted with real data demonstrate that the proposed method can provide extra 25.45\%-52.20\% average JPR than the representative baselines.