Research On Multi-objective Optimal Scheduling Of Building Loads Based On Grey Wolf Optimization Algorithm

Nowadays,the energy crisis is becoming more and more serious,and the power industry is facing an imminent supply and demand problem.The current proportion of residential electricity consumption is increasing year by year.With the corresponding demand,smart grids,smart buildings,Internet of Things,distributed energy and other technologies have developed rapidly Power management and load scheduling will become the focus of current research.The main research content of this paper is the load scheduling of building residents.Based on the analysis and modeling of building power system,the intelligent optimization algorithm is improved to solve the model.The main research work done in this article is as follows:(1)Analyze the research status of demand response and intelligent optimization algorithms,and establish a building load optimization scheduling model.Summarizing and analyzing the research on demand response and intelligent optimization in recent years,the main content of this paper is brought out based on the current research.By analyzing the building load system,the basic structure of the building load system is created,and the scheduling model to reduce the cost of electricity and the level of user discomfort,and to cut peaks and fill valleys is established to establish a load optimization scheduling model.(2)The grey wolf optimization algorithm that adjusts the convergence factor and improves the dynamic update of the location is proposed to optimize the controllable load of single-household buildings.Firstly,the grey wolf optimization algorithm is selected according to the multi-constraint,high latitude and nonlinear characteristics of the optimization model.Secondly,due to the weak global search ability of the grey wolf optimization algorithm,it is easy to fall into the local optimum at the later stage of the iteration,resulting in slow convergence and other shortcomings.Make some adjustments to the change form of the grey wolf optimization algorithm's convergence factor and position update formula to make it change with the algorithm optimization iteration process is adapted to improve the algorithm's global and local search capabilities and improve its search accuracy.In addition,the building microgrid load scheduling model established in this paper has the characteristics of high dimensionality,multi-constraints,non-linearity,etc.The binary processing of the grey wolf optimization algorithm is to adapt the algorithm to the model and improve the solution efficiency.Finally,simulation proves that the improved algorithm can get a better scheduling plan.(3)A multi-objective grey wolf optimization algorithm combined with a smart contract reward mechanism is proposed for the multi-residential electricity dispatching optimization scheme of a building.Firstly,the improved multi-target grey wolf optimization algorithm is adopted for the characteristics of huge electricity consumption data of multi-residential buildings;for the shortcomings of demand response based only on time-of-use electricity prices,a smart contract reward mechanism is proposed;the two are used in the building load scheduling model,The results obtained by the simulation are excellent,proving that the multi-objective grey wolf optimization algorithm and smart contract reward mechanism combine to optimize the superiority of multi-residential electricity dispatching in buildings.