Research On Multi-objective Optimal Scheduling Strategy Of Urban Residential Household Energy

Optimal scheduling of household energy is of great significance for improving the user's electricity efficiency,maintaining the stability of the grid,and providing conditions for renewable energy grid-connected power generation.At present,in the research on optimal scheduling of household energy,single-objective optimized scheduling is difficult to meet the various needs of the power supply side and demand side of the grid.Both single-objective optimal scheduling and multi-objective optimal scheduling have the disadvantages that the types of schedulable loads are not fully considered,and it is difficult to fully realize the potential of home energy optimization scheduling.In addition,the multi-objective optimization scheduling strategy mostly requires the cooperation of distributed power generation systems,which ignores the characteristics of modern urban residential buildings with high density and unconditional installation of distributed power generation systems.In view of the above deficiencies,based on the home energy management system,considering a variety of schedulable loads,this paper studies the multi-objective optimal scheduling strategy of urban residential household energy consumption with the optimization goals of minimum user electricity cost,minimum load peak-to-average ratio and maximum user comfort.The main research work is as follows:(1)This paper studies the energy model of urban residential households.The home energy management system is analyzed,and the object of optimal scheduling is selected according to the characteristics of device and energy consumption of urban residential households.For schedulable load,a physical model reflecting the changes in their thermal or energy storage is established,and by analyzing the changes in the operating status of the device under different user wishes and power on-off decisions,the control model is established.Taking the minimum user electricity cost,the minimum load peak to average ratio and the maximum user comfort as the optimization goals,the objective functions are defined respectively.In view of the above schedulable loads and optimization goals,relevant constraints of optimal scheduling are established.(2)Aiming at the above multi-objective optimization scheduling problem,a day-ahead optimization scheduling strategy is proposed schedule household energy consumption 24 hours the next day in this paper.Based on the load shift method,a target load curve is established and used to obtain a trade-off between the minimum electricity cost and the minimum load peak-to-average ratio.The basic principles of genetic algorithm and bacterial foraging algorithm are analyzed,and an improved genetic-bacterial foraging optimization algorithm is proposed based on the global search mechanism of genetic algorithm and the local search mechanism of bacterial foraging algorithm.Based on MATLAB,the example family is optimized and scheduled.The genetic algorithm,bacterial foraging algorithm,and improved genetic-bacterial foraging optimization algorithm are applied to the solution of the optimal optimization scheduling,and the scheduling results are analyzed.(3)Aiming at the problem that the temporary changes in the user's demand may destroy the good economic benefits and smaller load peak-to-average ratio brought by the day-ahead optimized scheduling,a real-time optimization scheduling strategy that reschedules the energy within a short period of time is proposed based on the day-ahead optimized scheduling strategy.By analyzing the user's temporary load demand,the real-time scheduling problem is transformed into a 0-1 knapsack problem,and the dynamic programming equation is used to solve the knapsack problem Based on MATLAB,real-time optimization scheduling is performed on the example family,and the scheduling results are analyzed.Through research,it is found that the ratio of electricity costs and peak-to-average ratio are significantly reduced after the day-ahead optimal scheduling,and user comfort is improved.Among them,the improved genetic-bacterial foraging optimization algorithm has better performance in solving the day-ahead optimal scheduling problem.Under the time-of-use electricity prices and real-time electricity prices,the user's electricity cost reduction rates after the day-ahead optimal scheduling are 15.83% and 19.83%,the load peak-to-average ratios are 2.89 and 2.85 respectively,and the waiting time of the user is reduced by 125 minutes.The real-time optimization scheduling strategy can further improve the user's economic benefits and reduce the load peak-to-average ratio while responding the user's temporary load demand.Under the time-of-use electricity prices and real-time electricity prices,the electricity cost reduction rates after rescheduling are 17.71% and 21.58%,respectively,and the PAR is 2.76 and 2.65 respectively.