Research On Multi-objective Economic Dispatch Of Power Grid Including Wind Power And Electric Vehicles

In essence,economic scheduling is an optimization problem,which means that the objective function of the model can be satisfied through the output of the dispatching unit under the condition of satisfying the relevant constraints.With the development of renewable energy and electric vehicles,the economic dispatching model is no longer limited to the traditional thermal power units,and the economic dispatching problem has a new development.At the same time,considering the addition of wind energy,electric vehicles and other factors,only containing a single objective in the economic dispatching model can not meet the actual requirements.Therefore,it is of great significance to study the multi-objective economic dispatching model under the background of power grid.Aiming at the multi-objective economic dispatching problem under the background of power grid,this paper analyzes the influence of wind energy and electric vehicles on the economic dispatching problem.An improved backbone particle swarm optimization algorithm based on Pareto optimization is proposed to solve the multi-objective economic dispatching model of power grid with wind power and electric vehicles.The specific work is as follows:(1)In view of the randomness,volatility,wind speed and the difficulty of wind power prediction,this paper studies the relationship between wind speed and wind turbine output and leads to the commonly used wind power prediction model.In view of the impact of electric vehicles connected to the power grid,this paper mainly studies the operation of the power grid and power quality,and gives an economic dispatching model including electric vehicles.(2)An improved backbone particle swarm optimization algorithm based on Pareto optimization is proposed.Because backbone particle swarm optimization algorithm is easy to fall into the disadvantage of local optimal,so this paper considers the two steps of crossover and mutation of genetic algorithm into backbone particle swarm optimization algorithm to improve it and solve the above problems.Then,an improved backbone particle swarm optimization algorithm based on Pareto optimization is obtained by combining it with the multi-objective solution method Pareto.Comparing this algorithm with the classical multi-objective optimization algorithm NSGA-II,it can be seen from the optimal Pareto frontier that the non-dominated solution distribution range of the proposed algorithm is wider and more uniform,and the diversity of the non-dominated solution can still remain unchanged when looking for the optimal solution.(3)A multi-objective economic scheduling model is established,which takes the cost of generating unit,pollutant emission and the owner’s ordered charge and discharge cost as the objective function.In order to better achieve the goals of cost reduction,pollutant emission reduction and coordinated scheduling,this paper establishes an economic scheduling model that includes both wind power and electric vehicles under the condition that the system can meet the power balance and the relevant constraints of daily travel of electric vehicles.On the basis of IEEE39 node system,the improved backbone particle swarm optimization algorithm based on Pareto optimization proposed in this paper is used to carry out Matlab simulation analysis on the model.Through the simulation results,it can be concluded that the research scheme proposed in this paper can reduce the power generation operation cost of the power grid and reduce environmental pollution,and well solve the economic scheduling problems of wind energy and electric vehicles.