Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230600096-6.doi: 10.11896/jsjkx.230600096

• Interdiscipline & Application • Previous Articles     Next Articles

Scheduling Optimization Method for Household Electricity Consumption Based on Improved Genetic Algorithm

HUANG Fei1, LI Yongfu1, GAO Yang2, XIA Lei1, LIAO Qinglong1, DAI Jian1, XIANG Hong1   

  1. 1 Chongqing Electric Power Research Institute,Chongqing 401123,China
    2 School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Published:2024-06-06
  • About author:HUANG Fei,born in 1987,master,se-nior engineer.His main research inte-rests include smart distribution grid technology and so on.
    GAO Yang,born in 1998,postgraduate.His main research interests include application of intelligent optimization algorithms in power system optimization and so on.
  • Supported by:
    National Grid Corporation Headquarters Technology Project(5700-202141454A-0-0-00).

Abstract: In response to the problems of insufficient electricity economy and comfort at the customer side during the peak consumption period,an improved genetic algorithm based on optimization method for household electricity scheduling is proposed.The traditional genetic algorithm is improved and the electricity consumption behavior is optimized by adopting different coding methods for different types of appliances instead of the single coding of the traditional genetic algorithm,and using the fitness function with penalty function to constrain the time required for each appliance’s electricity consumption task.The results show that the proposed algorithm can effectively realize the optimization of electricity load scheduling based on time-of-use tariff,and provide customers with economical electricity concumption solutions with low complexity, it can effectively solve the problem of economic and comfort level of power consumption during the peak period of power consumption.

Key words: Microgrid scheduling, Demand response, Household electricity, Multiple constraints, Hybrid coding, Genetic algorithm

CLC Number: 

  • TP393
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