Home Load Optimization Scheduling Strategy Based on Improved Binary Particle Swarm Optimization Algorithm

doi:10.11930/j.issn.1004-9649.202207079

Abstract

Abstract: In order to reduce the cost of household electricity consumption and improve the local consumption rate of residential photovoltaic power generation, a home load scheduling strategy is proposed based on real-time control of energy storage charging and discharging behavior. Firstly, the household loads are classified and a scheduling model is established with the objectives of lowest electricity cost, smallest carbon emission and largest comfort; secondly, based on the real-time photovoltaic output and peak-valley time-of-use electricity price, a scheduling strategy is proposed to meet the household load electricity demand through controlling the charging and discharging of energy storage; finally, the proposed model is simulated and solved using the scenario analysis method and hierarchical multi-strategy learning improved binary particle swarm optimization algorithm (HLSBPSO). The results show that the proposed strategy and algorithm can reduce the user's electricity bill by 49.2% and increase the comfort by 67.9%, which can provide a new theoretical support for the safe and economical operation of household photovoltaic power generation.

Key words: demand response, binary particle swarm algorithm, carbon emission, flexible load, home energy management system

ZHANG Li, LIU Qinglei, ZHANG Hongwei. Home Load Optimization Scheduling Strategy Based on Improved Binary Particle Swarm Optimization Algorithm[J]. Electric Power, 2023, 56(5): 118-128.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202207079

https://www.electricpower.com.cn/EN/Y2023/V56/I5/118

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