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.