In order to improve the adaptive control effect of the multiemergency power supply networking system connected to new energy (NE), this paper studies the adaptive control strategy (ACS) of the multiemergency power supply networking system connected to NE by combining intelligent algorithms and determines the appropriate installation node of the distributed power supply according to the voltage stability index. Moreover, this paper proposes a method to optimize the distribution network feeder reorganization and distributed power configuration problems based on the fireworks algorithm to reduce the network loss and improve the voltage distribution. In addition, distribution network reorganization and distributed power configuration optimization can more effectively minimize network losses and improve voltage distribution. Through the experimental research, it can be seen that the ACS of the multiemergency power supply network system connected to NE proposed in this paper has the established effect.

The wide application of clean energy has promoted the development of microgrids. For direct current (DC) microgrids, power fluctuations are inevitable, because photovoltaics, wind power and other clean energy sources are very unstable, which will bring great trouble to DC microgrids. Hybrid energy storage systems (HESS) are an effective solution to suppress power fluctuations. For a HESS composed of battery and supercapacitor (SC), a low-pass filter is usually used to allocate energy and power requirements to each energy storage unit, but the filter constant of the filter cannot be dynamically adjusted to adapt to different situations. The low-pass filter compensation coefficient used in this paper will change with the state of charge and discharge, and dynamically change and allocate different power requirements to the battery and SC according to the actual operating conditions of the HESS. The objective function of DC bus power fluctuation is established, and the optimized particle swarm algorithm (PSO) is used to obtain the output power coefficient of each energy storage unit. The output coefficient of the HESS control strategy has the characteristics of fast convergence speed and high computational efficiency in the optimization process, and it will not fall into the local optimal solution. Through the design experiment, the SC can withstand more high-frequency components, fully utilize the advantages of SC with fast response to power fluctuations, improve the performance of HESS, and have great engineering application value.

No abstract

No abstract