Research On Joint Optimal Scheduling Of Cascade Hydropower Stations And Their Interconnected Power Systems

Joint optimization of power generation of multi-energy interconnected power systems can adjust the energy structure of the current society and help the country build a sustainable society.Therefore,joint dispatch of multi-energy interconnected power systems has become a hot research topic in the energy field today.The implementation of regional multi-energy interconnected power system joint power generation dispatching is not only conducive to the improvement of the economic benefits of the power system,but also plays a vital role in protecting the ecological environment.Based on the research of particle swarm optimization algorithm,according to the characteristics of multi-energy interconnected power system,four types of interconnected power system scheduling models are established to explore the joint generation scheduling problem of multiple power generation power systems,and several research results can be summarized as follows:(1)In order to obtain a better single-target dispatching scheme for cascade hydropower stations,through the study of particle swarm optimization,according to its characteristics,improvements have been made in three aspects.Because the accuracy of randomly generating initial group solutions is not high,the quality is improved by the reverse learning strategy;the parameters in the algorithm evolution formula are dynamically adjusted to improve the solution quality of the algorithm;the measures of chaotic local search are adopted to avoid premature algorithm Fall into the local optimal value.Through the optimization results of seven single-objective functions,the superior performance of the improved algorithm is proved.(2)In order to smoothly solve the joint scheduling problem of cascade hydropower stations and their interconnected power systems,a processing method that can effectively deal with a large number of complex constraints in the power system is proposed.A single hydropower scheduling model and a hydropower and Dispatching model of thermal power.Through IPSO simulation solution,the results verify that the constraint processing method and IPSO solve the problem is feasible and efficient.(3)In order to improve the accuracy of solving the multi-energy interconnected power system scheduling model considering multiple objective functions,according to the operating characteristics of the multi-objective particle swarm algorithm,it is improved in the global guiding particle determination strategy to obtain a multi-objective improved particle swarm Optimized algorithm(IMOPSO),so that the algorithm can better search for the optimal position in the global range at the beginning,and in the final stage of the algorithm can search for a better solution in the local range more accurately;Finally,the algorithm is avoided by the elite learning strategy precocious.The ZTD series of functions are used to test the optimization performance of the improved algorithm.The simulation results verify that the improved algorithm is more efficient than before,and can better solve multi-objective problems.(4)In order to meet the requirements of reducing the operating cost of the power system and the emission of pollutant gas caused by burning coal in the power system,a multi-objective optimization of the hydrothermal power system considering the system operating cost and the emission of the system pollutant gas is established separately Scheduling model,wind-water-fire interconnected power system joint scheduling model,using the proposed improved multi-objective particle swarm optimization algorithm to solve,get an efficient scheduling scheme,and verify that the improved algorithm solves the multi-objective optimal scheduling problem of multi-energy interconnected power system Feasibility.