Research On Multi Energy Microgrid Optimization Based On Improved Whale Optimization Algorithm

With the support of national policies and continuous innovation in clean energy technologies,microgrid technology is becomimg a hot topic gradually.In order to achieve low-carbon mode and solve energy security issues,the optimization of microgrid operation is one of the most important means to optimize the energy industry.Various renewable energy sources are integrated into a Microgrid that have diverse and flexible power generation methods and can bring substantial benefits to the traditional power industry.However,the instability caused by the randomness of its energy supply,the high generation cost of some controllable power sources,and the complexity of operation control still need to be addressed,so its control optimization is one of the key issues that must be solved in microgrid operation.Firstly,this article elaborates on the operating characteristics and working mechanisms of distributed power sources such as photovoltaics,wind turbines,batteries,micro gas turbines,and diesel generators in microgrid systems,and analyzes the output models and power generation models of various power sources.It also analyzes the uncertainty factors in microgrid operation,providing a theoretical basis for the optimization of microgrid operation.Secondly,this paper studies and establishes a microgrid operation optimization model.Constraints are established considering the actual working state and application conditions of multiple power sources,including power balance condition constraints,output power constraints of various types of power sources,temporal energy storage constraints of energy storage devices,power climbing constraints,pollutant emission constraints,etc.And the objective function containing the power generation cost and pollution control cost of multi-energy type microgrid is established for the main problems of microgrid operation.Thirdly,this paper analyzes the defects of the whale algorithm itself,carefully analyzes the problems of the whale algorithm and gives improvement suggestions,and proposes an improved whale algorithm.The uniformity of the initial population is improved by Tent mapping,which increases the uniformity of the coverage of the initial population for the solution space and speeds up the optimization progress of the algorithm.A perturbed elite backward learning method is established to make full use of iterative experience to shift the contemporary optimal solution to the worst solution of the previous generation,and to perturb this solution according to the iteration progress to improve the ability of the algorithm to jump out of the local optimal solution and enhance the performance of the algorithm in the face of multi-local optimal solution problems.The selection of the optimization strategy of the whale algorithm is regulated by adaptive parameter optimization,making full use of its strong convergence capability,focusing on the selection of the global search strategy in the first and middle stages,and improving the selection probability of the local search strategy in the later stages.The Archimedean spiral is selected to replace the original logarithmic spiral contraction method to strengthen the local search ability and enhance the information collection ability of the solution space.And the multi-type benchmark functions in the original literature of the whale algorithm are used to compare with the original algorithm to verify the improvement of the algorithm’s solution performance in the face of various problems.Finally,based on the established microgrid optimization model,this article analyzes the two working states of microgrids,grid-connected and off-grid.The improved whale optimization algorithm is used to optimize the output of each distributed power source,and its performance is compared with particle swarm optimization,grey wolf optimization algorithm,and the original whale optimization algorithm using MATLAB simulation software for a one-day optimization cycle.The results show that the improved whale optimization algorithm has good performance,fast convergence speed,can quickly locate the global optimal solution,and has strong ability to escape from local optimal solutions and powerful optimization ability.It can effectively reduce the operating cost and pollution level of microgrids,improve energy utilization efficiency,and prove that this method has practical significance and reference value for the optimization operation of microgrids.