Multi-target differential grey wolf algorithm-based reactive power optimization method of power distribution network

Abstract

The invention relates to a multi-target differential grey wolf algorithm-based reactive power optimization method of a power distribution network. Photovoltaic and load time sequence fluctuation is considered, a DSTATCOM is introduced and used as a compensation connected to an active power distribution network, segmentation is performed by taking hour as a time segment, the dynamic reactive powerof the DSTATCOM is smoothly changed according to change of an equivalent load after photovoltaic and load, fluctuating according to a time sequence, connected to the power distribution network, and the active network loss and the voltage deviation are reduced to the maximum extent under the condition that the minimum reactive compensation capacity is output. In order to solve the problem of multiple targets in a reactive power optimization model, an original grey wolf algorithm is improved, variation and cross in a differential algorithm are introduced, and multiple targets are processed by rapid non-domination sequencing, congestion distance and fuzzy subjection function. By the multi-target differential grey wolf algorithm-based reactive power optimization method, the influence on systemnetwork loss and voltage after time sequence photovoltaic and load connected to the power distribution network is effectively solved; and with the adoption of the multi-target differential grey wolfalgorithm, the problem of multi-target non-linear reactive power optimization is processed, and the global and local searching capability is balanced.

Description

technical field

[0001] The invention relates to the field of active distribution network reactive power optimization, in particular to a distribution network reactive power optimization method based on a multi-objective differential gray wolf algorithm. Background technique

[0002] In recent years, the rapid development of renewable energy represented by distributed power generation has achieved remarkable results in energy conservation, environmental protection, and mitigation of energy crises. Among them, distributed photovoltaics are growing rapidly, and the proportion of distributed power generation has increased from less than 1% in 2011 to 46.9% in 2017. Distributed photovoltaics are greatly affected by temperature and light, and their output power is highly random. Large-scale access to the distribution network brings three major challenges: (1) Excessive output power causes two-way power flow, and the power of the distribution network is difficult to real-time Bala...

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