Optimal scheduling strategy of household electrical equipment based on scenario dynamic modeling

Household electricity is highly unpredictable, necessitating a deep understanding of its impact on energy scheduling for optimal resource allocation and economic gains. This issue’s complexity can lead traditional optimization methods to converge on suboptimal solutions. In this study, the dynamic Copula (DC) model is used to construct the dynamic correlation between power consumption characteristics, and the Monte Carlo (MC) method is used to generate multiple power consumption scenarios to cope with the uncertainty of user behavior. Then, the optimal scenario set is selected through the average distribution error (ADE) to participate in the subsequent scheduling. In addition, based on the established equipment operation characteristic model, the electricity cost and load peak-to-average ratio (PAR) are comprehensively considered. The Improved Dynamic search multi-objective particle swarm optimization (IDSMOPSO) is introduced to optimize the running time of the equipment. Taking the electricity consumption of a family in Xi’an as an example, the results show that the algorithm is significantly better than the other two improved algorithms in performance. Meanwhile, the electricity cost of the family was significantly reduced by 17.09 %, and the PAR value was also reduced by 31.59 %, which realized the economic operation of household electricity.