Open Access

ARTICLE

Optimization Scheduling of Hydrogen-Coupled Electro-Heat-Gas Integrated Energy System Based on Generative Adversarial Imitation Learning

Baiyue Song¹, Chenxi Zhang², Wei Zhang^2,*, Leiyu Wan²

1 State Grid Heilongjiang Electric Power Co., Ltd., Electric Power Research Institute, Harbin, 150030, China
2 Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, 132012, China

* Corresponding Author: Wei Zhang. Email:

(This article belongs to the Special Issue: AI-Driven Innovations in Sustainable Energy Systems: Advances in Optimization, Storage, and Conversion)

Energy Engineering 2025, 122(12), 4919-4945. https://doi.org/10.32604/ee.2025.068971

Received 11 June 2025; Accepted 26 August 2025; Issue published 27 November 2025

Abstract

Hydrogen energy is a crucial support for China’s low-carbon energy transition. With the large-scale integration of renewable energy, the combination of hydrogen and integrated energy systems has become one of the most promising directions of development. This paper proposes an optimized scheduling model for a hydrogen-coupled electro-heat-gas integrated energy system (HCEHG-IES) using generative adversarial imitation learning (GAIL). The model aims to enhance renewable-energy absorption, reduce carbon emissions, and improve grid-regulation flexibility. First, the optimal scheduling problem of HCEHG-IES under uncertainty is modeled as a Markov decision process (MDP). To overcome the limitations of conventional deep reinforcement learning algorithms—including long optimization time, slow convergence, and subjective reward design—this study augments the PPO algorithm by incorporating a discriminator network and expert data. The newly developed algorithm, termed GAIL, enables the agent to perform imitation learning from expert data. Based on this model, dynamic scheduling decisions are made in continuous state and action spaces, generating optimal energy-allocation and management schemes. Simulation results indicate that, compared with traditional reinforcement-learning algorithms, the proposed algorithm offers better economic performance. Guided by expert data, the agent avoids blind optimization, shortens the offline training time, and improves convergence performance. In the online phase, the algorithm enables flexible energy utilization, thereby promoting renewable-energy absorption and reducing carbon emissions.

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