Research On Environmental Economic Dispatch With Multiple Heterogeneous Renewable Energy Sources Based On Improved Hybrid Intelligent Optimization Algorith

Under the vision of "double carbon(carbon peaking and carbon neutrality)" target,China is vigorously building a "green channel" consisting of new energy construction projects,and implementing a clean,low-carbon,safe and efficient energy system with renewable energy alternatives,which is the development trend of building a new power system with new energy as the mainstay.Due to the random,volatile,and intermittent nature of renewable energy,the uncertainty brought about by large-scale development and grid connection poses a great challenge to ensure safe and reliable operation of the power grid.Therefore,in order to consider different types of renewable energy sources and their uncertainties,it is important to construct an environmental and economic dispatch of the new power system considering multiple heterogeneous renewable energy sources as the mainstay,in order to promote the construction of a clean,low-carbon,safe and efficient energy system.In this context,this paper integrates a multi-objective environmental and economic dispatch model composed of a hybrid power system of thermal power generation,wind power,solar power and runoff small hydro power together,and also deals with the uncertainty of multiple heterogeneous renewable energy sources,and designs a solution method for the model and conducts a study.The main work is summarized as follows.(1)Gain-sharing knowledge-based algorithm(GSK),differential evolution(DE),archimedes optimization algorithm(AOA),artificial gorilla troops optimizer(GTO)and teaching-learning-based optimization(TLBO)are briefly described in terms of their background and pseudo-code flow,and the working principles of the above algorithms are A systematic description of the working principles of these algorithms is given.Preparation for the hybrid optimization algorithm designed to solve the scheduling model later.(2)GSK-DE hybrid optimization algorithm based on a two-population evolutionary framework is proposed for solving the economic dispatch problem of large-scale power systems.In this evolutionary framework,the exploration of DE and the development of GSK are well coordinated to improve the search efficiency of the hybrid algorithm.To verify the feasibility and effectiveness of the algorithm,the proposed algorithm is applied to six economic dispatching arithmetic cases.(3)A multi-objective environmental economic dispatch model for power systems containing wind power,photovoltaic,small hydropower,and thermal power is developed.The model considers various factors including unit climbing rate constraints,forbidden operation zones,valve point effects,power balance constraints,active and reactive power output constraints,and network security constraints.Meanwhile,for the uncertainties associated with the grid integration of multiple heterogeneous renewable energy sources,overestimation and underestimation mechanisms are used to describe the uncertainties.To effectively solve the proposed model,a hybrid multi-objective optimization algorithm is proposed,which combines AOA,GTO,and TLBO.in addition,chaotic opposition initialization,Morlet wavelet variation,elite memory retention techniques,and improved compromise solution selection techniques are used to improve the performance.Finally,the effectiveness of the proposed model with the hybrid optimization algorithm is demonstrated on two improved power systems.