Research On Multi-Objective Optimization Based On Hybrid Neurodynamic Algorithm In Microgrid

Compared with the traditional power grid,the microgrid has the advantages of operation,low energy consumption and high performance,which can meet the different power requirements of users.Improving the utilization rate of energy and maximizing the utilization of resources is a necessary guarantee for the development of microgrid towards intelligent and interconnected.However,since the microgrid contains multiple distributed power sources,its operating characteristics are different.How to solve the reasonable allocation of resources between power sources is the primary problem in the operation of the microgrid.Multi-objective optimization refers to the optimization problem of two or more objective functions to deal with the interaction and interference problems in each objective function.In this problem it is often necessary to find a global optimal value to achieve the best performance of the entire system.The cost of power generation and pollution emissions are two important indicators for weighing the economy and environmental protection of microgrids.Effectively solving the operation optimization in the microgrid and reducing the emission of polluting gases has important research significance.Based on the above problems,this paper uses hybrid projection neural network algorithm and particle swarm optimization algorithm to find the global optimal solution of these two targets in the microgrid.The main work and specific research contents are as follows:1.Establish a microgrid model including micro gas turbine,fuel cell,diesel generator,photovoltaic power generation and load,and consider the cost function and pollution gas emission function as the target to construct a multi-objective optimization problem.2.For the multi-objective to single-object transformation problem,we use the combination of Chebyshev weighting method and projection neural network to transform the multi-objective optimization problem into a single constrained sub-problem and increase the multi-objective solution.3.Particle swarm optimization algorithm has two functions in this paper: one is to find the global optimal solution of the microgrid model;the other is to update the weight vector to avoid its subjective selectivity.The results show that the proposed algorithm has faster convergence speed and stability.