Capacity Optimal Configuration For Multi-energy Complementary System

With the increasing shortage of energy and the continuous growth of energy demand,China’s energy structure has an imbalance between supply and demand.Aiming at the complementary coupling characteristics and uncertainties of various types of energy resources at multiple temporal and spatial scales,studying how to reasonably allocate the capacity allocation of multi-energy power generation systems is an important way to structure a high proportion of renewable energy,which is of great significance for economic,environmental protection and reliable operation.Therefore,in order to realize the high proportion of renewable energy application,based on the complementary characteristics and set loads of wind,solar and water in Northwest China,this paper studies the optimal configuration of multi-energy complementary power generation system combining wind,solar water and storage,and uses two methods to solve them separately to ensure the optimal configuration results.The specific work of this paper is as follows:Firstly,the development and research status of energy complementary and complementary system capacity allocation are introduced,the model of wind-solar-hydrostorage multi-energy complementary power generation system is established,the power generation principle of each power generation part involved in the system is analyzed and the corresponding output model is built,which lays a good theoretical foundation for the smooth development of subsequent research.Secondly,in response to the problems of low reliability and severe resource waste in multi energy complementary systems,a configuration optimization model for multi energy complementary system based on reliability and economy was constructed.Taking cost of system life cycle and cost of energy as the objective function,shortage of power supply probability,renewable energy utilization rate,excess of power supply probability as the reliability evaluation index,power balance,output constraint of each power generation unit,grid transaction,battery charge and discharge constraint and power supply reliability constraint.This means ensuring the lowest operating costs on the basis of reliability.Then,an improved simulated annealing particle swarm optimization algorithm is proposed to address the problem of basic algorithm being prone to falling into local optima.Propose improvement measures for the three parameters of the particle swarm update iteration formula: the process control weight coefficient of the "S-shaped" growth curve using the“Softsign” function,and cognitive coefficient through linear control.And utilizing the ability of simulated annealing to jump out of local optima and converge to global optima,the metropolis criterion is introduced into the process of particle swarm optimization.The verification shows that the improved simulated annealing particle swarm optimization algorithm indeed has more efficiency,higher quality,and faster convergence speed than traditional algorithms.Collect relevant resource data and load conditions in the research area,determine the basic equipment parameters of each power generation unit,and use the improved algorithm to solve the capacity configuration optimization model of the wind,solar,and water storage multi energy complementary system.The results show that the algorithm has improved in terms of economy and reliability.Finally,a energy scheduling strategy for independent/grid connected operation mode is proposed based on four typical scenarios of wind,solar,and load demand,combined with the complementary characteristics of wind,solar,and water,and HOMER’s scheduling strategy.Build a model of a multi energy complementary system for wind,solar,water and storage in HOMER software,simulate and solve the optimal configuration results of the economically optimal multi energy complementary power generation system,and analyze the power production and distribution of each equipment under the optimal configuration results,as well as the energy regulation curve for typical days of the four seasons.By comparing the two optimal results,the differences between the two methods for studying the planning and design of multi energy complementary system were summarized.