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计及分布式电源的城市重要用户应急电源优化配置 被引量:3

Optimal Allocation of Emergency Power Supply of Urban Important Loads Considering Distributed Generation
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摘要 在城市高可靠性供电方式下,城市重要用户专门配置的应急电源利用率低下,资源浪费严重。随着未来电网的发展,分布式电源在城市日趋普及,将其兼做应急电源是解决传统应急电源闲置问题、进一步发挥分布式电源作用的有益尝试。提出了一种计及分布式电源的城市重要用户应急电源优化配置方法,以城市分布式电源的类型与特点确定其应急工作方式,利用多人决策AHP方法量化用户重要性,然后基于区域电网结构,以等效重要负荷最大为最优目标,以有功功率平衡为约束,通过遗传算法求解来划分应急孤岛,对进入计划孤岛的重要用户依据允许断电时间进行传统电源配置的容量调整。算例表明,此配置方法能有效利用分布式电源就地应急的能力减少传统备用电源的配置容量,分布式电源利用率受其与重要用户相对位置的影响。 In the mode of urban high-reliability power supply, the utilization of the emergency power supply allocated for the important loads is low, and the waste of resources is serious. With the development of power grid in the future, distributed generations(DGs) are becoming more and more popular in the city, and they can also play a role of the emergency power supply so as to further exert the function of DG. An optimization configuration method of emergency power supply is proposed in the paper for urban important loads, which takes into account the DGs. The emergency working mode is determined by the type and characteristics DGs, and the multi-person decision AHP method is used to quantify user importance. Then, based on the structure of regional power grid, the optimal target is maximizing the important load, and the constraint is the active power balance. The emergency isolated island is divided by the genetic algorithm. The capacity of the traditional power supply configuration is adjusted according to the allowable power outage time for the important users entering the planned isolated island. The case study shows that this configuration method can effectively utilize the ability of local emergency response of DGs to reduce the configuration capacity of traditional backup power supply. The utilization of DG is affected by its relative position with important users.
出处 《全球能源互联网》 2018年第5期581-588,共8页 Journal of Global Energy Interconnection
关键词 分布式电源 重要用户 层次分析法 应急电源配置 遗传算法 distributed generation important loads analytic hierarchy process emergency power configuration genetic algorithm
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