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Abstract

Multi energy systems can flexibly adjust different energy supply ratios to meet real-time energy demand changes, optimize the demand side response of multi energy systems, and improve energy utilization efficiency. Therefore, a power demand side active response efficiency optimization method based on discrete particle swarm optimization algorithm is proposed. This method deeply analyzes the inherent characteristics of residential demand response and industrial demand response, and designs targeted optimization strategies in combination with the active response structure on the power demand side. Its core goal is to minimize the total cost of electricity demand side response and maximize its demand response capability, thereby achieving a dual improvement in economy and efficiency. In order to ensure the effectiveness and feasibility of the optimization process, a series of constraints have been set to ensure the applicability of the solution in practical operation. In the solving process, an improved discrete particle swarm optimization algorithm was adopted, which not only inherits the global search ability of traditional particle swarm optimization algorithms, but also makes it more suitable for solving power demand side response problems through discretization processing. The innovation of this method is mainly reflected in the following aspects: firstly, a targeted objective function is designed based on the actual characteristics of the power demand side; Secondly, an improved discrete particle swarm optimization algorithm was introduced to improve the efficiency and accuracy of optimization solutions; Finally, by comprehensively considering the total cost and maximum demand response capability, the comprehensive optimization of power demand side response efficiency was achieved. The experimental results show that after optimization, the demand side can actively and timely participate in the peak shaving of the distribution network, achieving load reduction; Improved the electrical, thermal, and gas responses of the power system; The loss results are all less than 0.41 MW; The annual response cost is less than 2.52 million yuan; The voltage deviation index of each node is less than 5.56%, which is at least 4% lower than other methods.

Keywords

discrete particle swarm power demand side active response efficiency optimization response mechanism load reduction

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Optimization of Active Response Efficiency of Power Demand Side Based on Discrete Particle Swarm Optimization

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