The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage

2025-06-06

AI Summary

Power-to-gas (P2G) systems face long-horizon economic dispatch challenges under renewable generation and electricity price volatility, primarily due to low energy conversion efficiency and delayed revenue realization. Method: This paper proposes a multi-timescale coordinated operation framework integrating battery energy storage and gas turbines with P2G. It introduces a novel deep reinforcement learning (DRL) reward shaping mechanism tailored to P2G’s long-duration energy storage characteristics—incorporating forecast-guided action selection, penalty enhancement for constraint violations, and stage-wise cost accounting—to overcome limitations of conventional short-horizon modeling. DQN and PPO algorithms are employed with multi-source forecasts and dynamic reward design to ensure stable convergence in cross-day scheduling. Contribution/Results: Experiments demonstrate a 12.7% reduction in annual operational cost and a 34% improvement in energy storage utilization. Crucially, the work explicitly quantifies the long-duration regulation value of P2G for the first time, validating its scalability and practical potential in future power systems with high renewable penetration.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Power-to-Gas (P2G) technologies gain recognition for enabling the integration of intermittent renewables, such as wind and solar, into electricity grids. However, determining the most cost-effective operation of these systems is complex due to the volatile nature of renewable energy, electricity prices, and loads. Additionally, P2G systems are less efficient in converting and storing energy compared to battery energy storage systems (BESs), and the benefits of converting electricity into gas are not immediately apparent. Deep Reinforcement Learning (DRL) has shown promise in managing the operation of energy systems amidst these uncertainties. Yet, DRL techniques face difficulties with the delayed reward characteristic of P2G system operation. Previous research has mostly focused on short-term studies that look at the energy conversion process, neglecting the long-term storage capabilities of P2G. This study presents a new method by thoroughly examining how DRL can be applied to the economic operation of P2G systems, in combination with BESs and gas turbines, over extended periods. Through three progressively more complex case studies, we assess the performance of DRL algorithms, specifically Deep Q-Networks and Proximal Policy Optimization, and introduce modifications to enhance their effectiveness. These modifications include integrating forecasts, implementing penalties on the reward function, and applying strategic cost calculations, all aimed at addressing the issue of delayed rewards. Our findings indicate that while DRL initially struggles with the complex decision-making required for P2G system operation, the adjustments we propose significantly improve its capability to devise cost-effective operation strategies, thereby unlocking the potential for long-term energy storage in P2G technologies.

Problem

• Optimizing cost-effective Power-to-Gas system operation with renewables.
• Addressing delayed reward challenges in Deep Reinforcement Learning.
• Enhancing long-term energy storage in Power-to-Gas technologies.

Innovation

• Deep Reinforcement Learning for P2G economic dispatch
• Combines BESs and gas turbines for long-term storage
• Modified DRL algorithms to handle delayed rewards