李建林, 孙浩元, 梁忠豪, 姜晓霞. 多能需求场景下的综合能源系统容量优化配置[J]. 高电压技术, 2025, 51(5): 2114-2124. DOI: 10.13336/j.1003-6520.hve.20240688
引用本文: 李建林, 孙浩元, 梁忠豪, 姜晓霞. 多能需求场景下的综合能源系统容量优化配置[J]. 高电压技术, 2025, 51(5): 2114-2124. DOI: 10.13336/j.1003-6520.hve.20240688
LI Jianlin, SUN Haoyuan, LIANG Zhonghao, JIANG Xiaoxia. Optimal Capacity Allocation of Integrated Energy Systems Under Multi-energy Demand Scenarios[J]. High Voltage Engineering, 2025, 51(5): 2114-2124. DOI: 10.13336/j.1003-6520.hve.20240688
Citation: LI Jianlin, SUN Haoyuan, LIANG Zhonghao, JIANG Xiaoxia. Optimal Capacity Allocation of Integrated Energy Systems Under Multi-energy Demand Scenarios[J]. High Voltage Engineering, 2025, 51(5): 2114-2124. DOI: 10.13336/j.1003-6520.hve.20240688

多能需求场景下的综合能源系统容量优化配置

Optimal Capacity Allocation of Integrated Energy Systems Under Multi-energy Demand Scenarios

  • 摘要: 氢能等零碳能源耦合储能的综合能源系统是当下能源系统的主流,其能量管理策略的制定与容量优化配置对于综合系统的经济性与灵活性具有较大的影响。为此,提出了一种多能需求场景下的综合能源系统容量优化配置方法。首先,通过拉丁超立方抽样与欧式距离缩减法得到源荷典型场景,实现对源荷预测误差的合理刻画。其次,制定了各设备协调配合的能量管理策略。基于典型场景和能量管理策略,以系统日综合成本最小为目标函数建立容量配置模型,并通过基于自适应惯性权重的樽海鞘群算法与经济性分析得到系统最优容量配置方案。仿真结果表明:所提方案相比于缺乏蓄电池参与电能调节以及缺乏氢能余热利用的方案,日综合成本可分别降低5.82%和10.9%,同时保证了各储能设备在一个运行周期内始末容量相等,系统长期稳定运行效果更优。

     

    Abstract: The integrated energy system of zero-carbon energy coupled with energy storage, such as hydrogen energy, is the mainstream of energy systems nowadays, and the formulation of its energy management strategy and the optimal allocation of its capacity have a large impact on the economy and flexibility of the integrated system. To this end, a method for optimal capacity allocation of integrated energy systems under multi-energy demand scenarios is proposed. Firstly, a typical scenario of source-load is obtained through Latin hypercubic sampling and Euclidean distance reduction to achieve a reasonable portrayal of the source-load prediction error. Secondly, the energy management strategy with the coordination of each device is formulated. Based on the typical scenarios and energy management strategies, a capacity allocation model is established with the objective function of minimizing the daily comprehensive cost of the system, and the optimal capacity allocation scheme is obtained through the Bottle Sea Sheath swarm algorithm based on adaptive inertia weights and economic analysis. The simulation results show that the proposed scheme can reduce the daily comprehensive cost by 5.82% and 10.9%, respectively, compared with the schemes lacking the participation of storage battery in power conditioning and the use of hydrogen waste heat, and at the same time, it ensures that the capacity of each storage device is equal at the beginning and end of an operation cycle, so that the system is more effective in stable operation in the long term.

     

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