Safe and Sustainable Electric Bus Charging Scheduling with Constrained Hierarchical DRL

TL;DR

This paper introduces a novel hierarchical deep reinforcement learning framework for optimizing electric bus charging schedules, effectively balancing cost, safety, and uncertainty in real-world conditions.

Contribution

It develops a new HDRL algorithm integrating Lagrangian relaxation within a hierarchical structure for safe and efficient EB charging under multiple uncertainties.

Findings

Outperforms existing methods in cost reduction and safety compliance

Demonstrates fast convergence in real-world data experiments

Effectively manages multi-source uncertainties in charging schedules

Abstract

The integration of Electric Buses (EBs) with renewable energy sources such as photovoltaic (PV) panels is a promising approach to promote sustainable and low-carbon public transportation. However, optimizing EB charging schedules to minimize operational costs while ensuring safe operation without battery depletion remains challenging - especially under real-world conditions, where uncertainties in PV generation, dynamic electricity prices, variable travel times, and limited charging infrastructure must be accounted for. In this paper, we propose a safe Hierarchical Deep Reinforcement Learning (HDRL) framework for solving the EB Charging Scheduling Problem (EBCSP) under multi-source uncertainties. We formulate the problem as a Constrained Markov Decision Process (CMDP) with options to enable temporally abstract decision-making. We develop a novel HDRL algorithm, namely Double Actor-Critic Multi-Agent Proximal Policy Optimization Lagrangian (DAC-MAPPO-Lagrangian), which integrates Lagrangian relaxation into the Double Actor-Critic (DAC) framework. At the high level, we adopt a centralized PPO-Lagrangian algorithm to learn safe charger allocation policies. At the low level, we incorporate MAPPO-Lagrangian to learn decentralized charging power decisions under the Centralized Training and Decentralized Execution (CTDE) paradigm. Extensive experiments with real-world data demonstrate that the proposed approach outperforms existing baselines in both cost minimization and safety compliance, while maintaining fast convergence speed.