Abstract

Compressed carbon dioxide energy storage has received widespread attention as an effective solution to address the intermittency of renewable energy sources. This article proposes a novel compressed carbon dioxide energy storage system with a flexible gas warehouse compartment, which stores carbon dioxide at ambient temperature and pressure. A mathematical model is established and a parameter analysis is conducted to evaluate the thermodynamic and economic performance of the proposed system. The results indicate that increasing the mechanical efficiency of the compressor and expander can achieve lower electricity costs and higher round-trip efficiency. By reducing the pinch temperature of the heat exchanger, higher round-trip efficiency and lower electricity costs can be achieved.

Abstract

Integrated Energy Systems (IES) play a crucial role in promoting multi-energy complementarity and enhancing overall energy utilization efficiency. However, the intermittency of renewable energy sources and the stochastic nature of load demand pose significant challenges to system dispatch. To address these issues, this study proposes an economic dispatch method for renewable-integrated IES based on Deep Reinforcement Learning (DRL). The system components, including internal combustion engines, absorption chillers, and battery energy storage systems, are modeled, and the dispatch problem is formulated as a Markov Decision Process (MDP). Two DRL algorithms, Deep Q-Network (DQN) and Twin Delayed Deep Deterministic Policy Gradient (TD3), are employed to train optimal control strategies. Experimental results demonstrate that both algorithms achieve efficient system dispatch, with TD3 exhibiting superior convergence speed and overall performance. The proposed approach does not rely on explicit uncertainty modeling and can adaptively respond to fluctuations in renewable generation and load demand, thereby improving the economic efficiency and robustness of system operation.