压缩储能与氢能驱动的虚拟电厂源-储-荷低碳经济调度

doi:10.19595/j.cnki.1000-6753.tces.250803

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摘要

该文针对虚拟电厂多能耦合利用与碳减排问题,提出了一种含绝热压缩空气和两阶段P2G-CCS协同运行的虚拟电厂优化调度模型。在分析绝热压缩空气储能热电联供原理基础上,将其与燃气机组共同接入含氢虚拟电厂,实现电热双能的灵活输出;针对系统碳排放量高、能源利用率低的问题,构建了电转气与燃气机组掺氢燃烧的两阶段氢能利用方式,并定量评估不同掺氢比例对系统经济性与碳减排效益的影响。在此基础上,引入差分进化策略以改进传统蛾焰优化算法进行求解,并通过算例验证了该文所提模型方法的有效性。研究结果表明,绝热压缩空气储能设备与两阶段P2G-CCS协同运行可以显著降低虚拟电厂的能源成本与碳排放水平。

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关键词 ：虚拟电厂, 绝热压缩空气储能, 电转气, 掺氢, 蛾焰优化算法

Abstract：

To address critical challenges in virtual power plant (VPP), including low hydrogen utilization efficiency, limited hydrogen blending applications in gas-fired units, and the narrow energy flow scope of existing energy storage systems, this paper proposes a low-carbon economic dispatch model for hydrogen-driven VPPs. The model integrates advanced adiabatic compressed air energy storage (AA-CAES) with a two-stage power-to-gas and carbon capture system (P2G-CCS), establishing a deeply coupled source-storage-load collaborative optimization framework. This framework enables unified coordination and dispatch of multiple energy carriers and devices within the VPP, aiming to significantly improve energy utilization efficiency, facilitate renewable energy integration, and substantially reduce system carbon emissions.
On the supply side, an innovative two-stage hydrogen utilization strategy is designed. In the first stage, hydrogen is produced via electrolysis using surplus renewable electricity. In the second stage, carbon dioxide captured from gas turbine emissions undergoes methanation to generate synthetic natural gas. The resulting synthetic gas mixture, rich in hydrogen, is co-fired in gas turbines and boilers. The model thoroughly considers the operational characteristics and efficiency variations of gas-fired equipment under different hydrogen blending ratios. It quantitatively analyzes the impact of hydrogen blending on overall system economics and carbon reduction benefits, verifying the rationality and technical feasibility of dynamic hydrogen blending strategies and providing scientific support for diversified hydrogen utilization in VPP.
Regarding energy storage, the cogeneration advantage of AA-CAES is fully exploited. The system recovers and stores thermal energy generated during the air compression process, which is then released during the discharge phase to meet heating demands within the VPP. This coordinated electric-thermal output enhances system dispatch flexibility, effectively mitigates the variability and uncertainty of renewable generation, reduces renewable energy curtailment risks, and improves overall energy efficiency and economic performance.
To achieve efficient coordinated operation among multiple energy carriers, a multi-energy coupled collaborative optimization dispatch model is developed. The objective is to minimize the comprehensive operating cost of the VPP, including fuel procurement expenses, carbon trading costs, carbon capture operational costs, and economic losses due to renewable energy curtailment. Considering the high nonlinearity, multiple variables, and complex constraints of the problem, a hybrid optimization algorithm combining the moth-flame optimization (MFO) algorithm with a differential evolution strategy—referred to as DE-MFO—is proposed. This algorithm enhances global search capability, accelerates convergence, and improves solution robustness, making it well-suited for large-scale complex energy system optimization.
Simulation case studies and comparative analyses validate the effectiveness and superiority of the proposed model and algorithm. Results demonstrate that the integration of AA-CAES and P2G-CCS not only significantly reduces total operating costs and carbon emissions of the VPP but also improves renewable energy accommodation and utilization efficiency. Additionally, the dynamic hydrogen blending strategy effectively supports flexible dispatch of gas turbines, achieving efficient and low-carbon hydrogen utilization. Overall, the proposed dispatch model and optimization method provide a solid theoretical foundation and practical technical pathway for multi-energy synergy and low-carbon operation in VPP, offering important guidance for the construction and operation of future clean energy systems.

Key words： Virtual power plant adiabatic compressed air energy storage power-to-gas hydrogen blending moth flame optimization algorithm

收稿日期: 2025-05-13

PACS:

TM734

基金资助:

国家重点研发计划（2021YFB2601300）和中央高校基本科研业务费专项资金（2023JC007）资助项目

通讯作者: 贾利民男,1963年生,教授,博士生导师,研究方向为交通能源融合、智能交通等。E-mail：jialm@vip.sina.com.cn

作者简介: 师瑞峰男,1977年生,教授,博士生导师,研究方向为交通能源融合、综合能源系统调度等。E-mail：Shi.ruifeng@ncepu.edu.cn

引用本文:

师瑞峰, 蔺泳琪, 张凌志, 马凯, 贾利民. 压缩储能与氢能驱动的虚拟电厂源-储-荷低碳经济调度[J]. 电工技术学报, 0, (): 258110-258110. Shi Ruifeng, Lin Yongqi, Zhang Lingzhi, Ma Kai, Jia Limin. Compressed Storage and Hydrogen-Driven Virtual Power Plant: Low-Carbon Economic Dispatch of Generation-Storage-Load. Transactions of China Electrotechnical Society, 0, (): 258110-258110.

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