Dynamic optimization of an integrated energy system with carbon capture and power-to-gas interconnection: A deep reinforcement learning-based scheduling strategy

This research presents an interconnected operation model that integrates carbon capture and storage (CCS) with power to gas (P2G), tackles the challenges encountered by integrated electricity-natural gas systems (IEGS) in terms of energy consumption and achieving low-carbon economic operations, and formulates a DRL-based, physically model-free energy optimization management strategy for IEGS, designed to lower operational costs and carbon emissions. Initially, the CCS-P2G interconnected IEGS system undergoes mathematical modeling. Subsequently, the system's uncertainty in optimal scheduling is formulated as a Markov decision process, with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm facilitating real-time scheduling decisions. Comparative analysis across various scenarios demonstrates that the model offers superior low-carbon economic benefits and enhanced environmental sustainability. Further analysis validates that the optimized scheduling strategy proposed herein advantages in achieving low-carbon financial objectives, convergence speed, and system learning performance, as evidenced by training the model with historical data and the comparative analysis of the DQN and DDPG algorithms. •“Integrates CCS and P2G in IEGS for optimal renewable use.”•“Reduces wind and solar curtailment effectively.”•“Enhances system's low-carbon economy significantly.”•“Utilizes surplus renewable energy for hydrogen production.”•“Demonstrates CCS-P2G synergy in minimizing carbon emissions.”