Research On Dynamic Environmental Economic Dispatch Method Of Power System Based On Improved Whale Optimization Algorithm

With the rapid development of social economy,the demand for power energy in China continues to increase.In recent years,although the proportion of renewable energy is increasing,but the power generation structure has not changed significantly,thermal power still occupies the dominant position in China’s power generation.At the same time,a large amount of fossil energy used for power generation not only consumes primary energy,but also causes serious pollution to the environment.Under the double pressure of environment and energy,it has become an important research content in the field of electricity system that how to allocate the units reasonably,minimize the cost of power generation and reduce environmental pollution.In this context,this paper focuses on the dynamic economic optimization dispatching problem of electricity system and the dynamic environmental economic dispatching problem of electricity system,and the main research contents are as follows.1.Aiming at the problem that constraints are difficult to handle in the optimal dispatching of electricity systems,an improved heuristic constraint processing method is proposed.The constraint processing method first adopts a threshold limiting strategy to handle inequality constraints,and then proposes a dynamic adjustment mechanism that adaptively allocates adjustment steps according to the size of the impact of unit output power changes on power generation costs and pollution emissions to handle power balance constraints,and finally realizes the continuous correction of individual unit output power.2.In order to improve the solution accuracy of the dynamic economic dispatch(DED)problem of electricity system,an improved whale optimization algorithm(MWOA)based on a mixed strategy is proposed by combining the practical advantages and disadvantages of the whale optimization algorithm.Firstly,an adaptive inertia weight is introduced to regulate the step size in the early stage of the optimization search and the population diversity in the late stage of the optimization search.Secondly,a hybrid backward learning strategy is proposed and incorporated into the original whale optimization algorithm to improve the convergence accuracy of the algorithm.Finally,a parametric nonlinear decay strategy is introduced to improve its exploration development capability and convergence speed on high-dimensional as well as complex problems.The DED model is simulated and tested using MWOA and the proposed constraint processing method,and the results indicate the superiority and effectiveness of the optimal scheduling method proposed in this paper in solving the DED problem.3.In order to improve the performance of solving the dynamic environmental economic dispatch(DEED)problem of electricity systems,an improved multi-objective whale optimization algorithm(MOWOA)is proposed.Firstly,an optimal individual selection strategy is proposed to improve the problem that the original multi-objective whale optimization algorithm has a random selection of optimal individuals,then a difference-in-variance strategy is introduced to improve the random selection phase of the original algorithm to improve the population diversity in the late iteration,Finally,a crowding distance calculation method is proposed to improve the problem that the crowding distance calculation in the original algorithm cannot fully reflect the real crowding degree of the population.MOWOA and the proposed constraint processing method are used to simulate and test the DEED model,and the results indicate that the optimal scheduling method proposed in this paper is superior and effective in solving the DEED problem.