基于多目标优化的源网荷储协调调度求解OA
Solution for coordinated scheduling of source-grid-load-storage based on multi-objective optimization
针对当前源网荷储协调调度过程中单目标优化算法导致新能源消纳能力较差的问题,结合多目标优化算法,提出源网荷储协调调度问题求解优化方法.以最小调度成本和最大可再生能源消纳量为目标,定义源网荷储协调调度多目标优化函数;从能源元件、主网能源购买、柔性负荷响应、储能装置4方面,分别设置合理的约束条件,在粗糙集理论的辅助下,确定每个调度优化目标函数的权重系数,并引入非线性权重的改进鲸鱼优化算法求解多目标优化函数,得出最优协调调度方案.实验结果表明:依托于所提方法生成的调度优化方案应用后,主动配电网的新能源消纳百分比达到97.25%,极大提升了电力系统的新能源消纳能力.
Aiming to address the issue of poor new energy accommodation capacity caused by single-objective optimization algorithms in the current coordinated dispatch process of source-grid-load-storage,a multi-objective optimization algorithm is combined to propose an optimized solution method for the coordinated dispatch problem of source-grid-load-storage.With the goals of minimizing dispatch costs and maximizing renewable energy accommodation,a multi-objective optimization function for coordinated dispatch of source-grid-load-storage is defined.Reasonable constraints are set from four aspects:energy components,main grid energy procurement,flexible load response,and energy storage devices.With the assistance of rough set theory,the weight coefficients of each dispatch optimization objective function are determined.An improved whale optimization algorithm with nonlinear weights is introduced to solve the multi-objective optimization function and derive the optimal coordinated dispatch plan.Experimental results show that after applying the dispatch optimization plan generated by the proposed method,the new energy accommodation percentage of the active distribution network reaches 97.25%,significantly enhancing the new energy accommodation capacity of the power system.
宋明曙;苏常胜;吴茂乾;宋炜;何凯
国网新疆电力有限公司,新疆 乌鲁木齐 830017国网新疆电力有限公司,新疆 乌鲁木齐 830017国网新疆电力有限公司,新疆 乌鲁木齐 830017北京清能互联科技有限公司,北京 100080北京清能互联科技有限公司,北京 100080
动力与电气工程
多目标优化算法源网荷储协调调度新能源消纳约束改进鲸鱼优化算法
multi-objective optimization algorithmsource-grid-load-storagecoordinated dispatchnew energy accommodationconstraintsimproved whale optimization algorithm
《太赫兹科学与电子信息学报》 2025 (4)
416-422,7
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