考虑多站融合的智能变电站选址定容优化方法研究

A Study on Optimization Method for Location and Capacity of Smart Substation Considering Multi-Station Integration

由于变电站周围用电量急剧增加,造成变电站电量的运行管理存在不足,导致变电站的容量和负荷总量下降、有功损耗增加。为此,提出一种考虑多站融合的智能变电站选址定容优化方法。通过分析和研究智能变电站规划的影响因素,在多站融合条件下,以变电站、变压器和网费为优化目标,对各个阶段下变电站对应的负荷点进行约束,简化智能变电站的运行管理,构建智能变电站选址定容模型;采用改进的萤火虫算法(GFA),对所建立的模型进行求解,实现智能变电站选址定容优化。结果表明:优化后比优化前的智能变电站负荷总量最大值提升了10 MW,容量最大值提升了14 MV·A;优化后比优化前的有功损耗平均值降低了94.6 kW;优化后的最优解搜索成功率均在90%以上,最优解平均搜索时间为24 ms。所提方法不仅能够有效增加变电站容量和负荷总量,而且还能够有效降低有功损耗,以更高的成功率快速完成最优解搜索。

Due to the rapid increase in power consumption around the substation,the operation and management of substation power is insufficient,resulting in a decrease in the capacity and total load of the substation,and an increase in active power loss.Therefore,an intelligent substation location and capacity optimization method considering multi station integration is proposed in this paper.By analyzing and studying the influencing factors of intelligent substation planning,under the condition of multi station integration,taking the substation,transformer and network cost as the optimization objective,the load points corresponding to the substation in each stage are constrained,the operation management of the intelligent substation is simplified,and the location and capacity model of the intelligent substation is constructed.The improved firefly algorithm is used to solve the established model to realize the location and capacity optimization of the intelligent substation.The results show that the maximum load after optimization is increased by 10 MW and the maximum substation capacity is increased by 14 MVA.The average value of active power loss after optimization is 94.6 kW lower than that before optimization.The success rate of optimal solution search after optimization is more than 90%,and the average search time of optimal solution is 24 ms.The proposed method can not only effectively increase the substation capacity and total load,but also effectively reduce the active power loss,and quickly complete the optimal solution search with a higher success rate.