Planning shared energy storage systems for the spatio-temporal coordination of multi-site renewable energy sources on the power generation side
Planning shared energy storage systems for the spatio-temporal coordination of multi-site renewable energy sources on the power generation side
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DOI：
10.1016/j.energy.2023.128976
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发表时间：
2023-09
期刊：
影响因子：
9
通讯作者：
Xiaoling Song;Huqing Zhang;Lurong Fan;Zhe Zhang;F. Peña-Mora
中科院分区：
文献类型：
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作者：
Xiaoling Song;Huqing Zhang;Lurong Fan;Zhe Zhang;F. Peña-Mora
随着可再生能源使用的不断增加，共享储能服务的应用前景得到了广泛的认可。然而，考虑到多站点的时空特征，连接不同的可再生能源发电机组和确定合适的共享储能容量规模的决策过程成为一个复杂而相互关联的问题。提出了一种多站点可再生能源发电机组共享发电侧储能系统的优化规划和运行体系结构。此外，提出了一种经济环境模型，以尽量减少与能源系统基础设施相关的成本，同时保持可再生能源的高渗透率。集中式多目标模型允许可再生能源发电机组对接入共享储能站做出成本最优的规划决策，同时优化存储容量的大小，使可再生能源发电量最大化，成本最小化。采用非支配排序遗传算法- ii集中求解多目标非线性模型。数值实验证明了该系统的经济效益和环境效益。因此，强烈建议在生成端实现此方法。
The application prospects of shared energy storage services have gained widespread recognition due to the increasing use of renewable energy sources. However, the decision-making process for connecting different renewable energy generators and determining the appropriate size of the shared energy storage capacity becomes a complex and interrelated problem when considering the multi-site spatio-temporal characteristics. This paper presents an optimal planning and operation architecture for multi-site renewable energy generators that share an energy storage system on the generation side. Furthermore, an economic-environmental model is proposed to minimize the costs associated with the energy system infrastructure while maintaining a high penetration rate of renewable energy. The centralized multi-objective model allows renewable energy generators to make cost-optimal planning decisions for connecting to the shared energy storage station, while also optimizing the size of the storage capacity to maximize renewable energy generation and minimize costs. The Non-dominated Sorting Genetic Algorithm-II is employed in a centralized manner to solve the multi-objective nonlinear model. Numerical experiments are conducted to demonstrate the economic and environmental benefits of the proposed system. Therefore, this method is highly recommended for implementation on the generation side.