Biao Xu, Guiyuan Zhang, Ke Li, 等. Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles[J]. 现代电力系统保护与控制(英文), 2022,7(4).
Biao Xu, Guiyuan Zhang, Ke Li, et al. Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles[J]. Protection and Control of Modern Power Systems, 2022, 7(4).
Biao Xu, Guiyuan Zhang, Ke Li, 等. Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles[J]. 现代电力系统保护与控制(英文), 2022,7(4).DOI: 10.1186/s41601-022-00271-w.
Biao Xu, Guiyuan Zhang, Ke Li, et al. Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles[J]. Protection and Control of Modern Power Systems, 2022, 7(4).DOI: 10.1186/s41601-022-00271-w.
Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles
As high amounts of new energy and electric vehicle (EV) charging stations are connected to the distribution network
the voltage deviations are likely to occur
which will further affect the power quality. It is challenging to manage high quality voltage control of a distribution network only relying on the traditional reactive power control mode. If the reactive power regulation potentials of new energy and EVs can be tapped
it will greatly reduce the reactive power optimization pressure on the network. Keeping this in mind
our reasearch first adds EVs to the traditional distribution network model with new forms of energy
and then a multi-objective optimization model
with achieving the lowest line loss
voltage deviation
and the highest static voltage stability margin as its objectives
is constructed. Meanwihile
the corresponding model parameters are set under different climate and equipment conditions. Ultimately
the optimization model under specific scenarios is obtained. Furthermore
considering the supply and demand relationship of the network
an improved technique for order preference by similarity to an ideal solution decision method is proposed
which aims to judge the adaptability of different algorithms to the optimized model
so as to select a most suitable algorithm for the problem. Finally
a comparison is made between the constructed model and a model without new energy. The results reveal that the constructed model can provide a high quality reactive power regulation strategy.
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