Abstract: To enhance the load demand response capability of the electricity demand side,and to reduce the electricity cost for household users and the peak-to-valley difference of the daily load curve,in the paper,an optimized household load scheduling strategy based on improved multi-objective particle swarm optimization is proposed.Firstly,the running characteristics of household load are analyzed and its classification model is built.Then,with power balance and energy storage charging and discharging power as constraints,a load optimization scheduling model aiming at power consumption cost and load peak-to-peak ratio is established.Secondly,adaptive inertial weights and mean Euclidean distance strategies are used to improve the multi-objective PSO,and the model is optimized and solved.Finally,the feasibility and effectiveness of the model and algorithm is verified by a simulation example,which provides a variety of reference schemes for users to participate in demand response.