Research on Rolling Optimization Strategy Considering the Dynamic Capacity Increase Limitation of Photovoltaic Reverse Overload Distribution Areas
With the promotion of the dual-carbon goals,photovoltaic development across entire counties in China has surged.Previous designs of the low-voltage distribution network did not anticipate the integration of a large number of distributed energy sources,leading to problems of power inversion and even reverse overload.However,the energy stor-age investment cost is high,the configuration capacity is often limited,and the problem of reverse overload in the area cannot be completely solved.Consequently,we investigate the influence mechanism of overload on transformer life,pro-pose an optimization strategy for managing reverse overload under capacity enhancement limitation,and compare the proposed strategy with the existing strategies of maximum power output and constant power uniform output in energy storage.Secondly,a rolling time window selection method considering the overall characteristics of the reverse power severe period is proposed,and a suitable fixed-variable window combined rolling scheduling strategy is formulated.Fi-nally,an improved particle swarm optimization algorithm with chaotic mapping and elite reverse learning is used to solve the overload optimization governance model.The data in actual area are analyzed to verify the effectiveness of the over-load control and rolling optimization strategy in this paper.
distribution areareverse overloadtransformer lifespanrolling schedulingelitist inverse learning
万方数据
维普
