Abstract

We construct a fair residential critical-peak price (CPP) optimization model based on a penalty mechanism to address the peaking pressure on the grid and the cost of new energy abandonment caused by the yearly increase in residential electricity load and the significant increase in new energy capacity. According to the electricity consumption habits of different residential consumers, we use the K-means clustering method to identify and classify high-electricity consumers (HECs), low-electricity consumers (LECs), and normal-electricity consumers (NECs) during the critical peak and valley load hours respectively, and design a penalty mechanism of electricity price for HECs (PMEP-H) that penalizes HECs during the critical peak load hours, and compensates the total penalty amount to LECs during the critical peak load hours and HECs during the valley load hours, then calculate the residential electricity load after implementation of PMEP-H based on consumer psychology. The CPP optimization model is constructed by considering the constraints of consumer satisfaction with electricity, tariff penalty, compensation balance, penalty and compensation price, power balance and new energy-generating units output power, and the objective function of high comprehensive system efficiency represented by the difference between peak cost of thermal generation, the benefit of new energy consumption and benefit of the grid side. The model is then solved using an improved particle swarm optimization algorithm for a real residential area in a populated area in northern China. The results show that the developed model can connect the generation, grid and user sides through the electricity consumption tariff, which generates considerable benefits in both peak cutting and new energy consumption targets and can enhance the comprehensive benefits of the generation, grid and user sides system to achieve the supply-grid-load linkage, providing a reference for the improvement of the CPP mechanism in China.

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