| The technological revolution has driven global industry forward,and with increasing industrialisation,the global demand for electricity is expanding rapidly.The burning of fossil fuels to meet electricity demand,which has caused an energy crisis and environmental pollution,is becoming more and more prominent,prompting people to focus on renewable energy sources(RES)that are clean,renewable,and less polluting.RES power generation,represented by wind power and photovoltaics,has been widely exploited.However,RES power generation is time-sensitive and volatile,and its connection to the grid may interfere with the stable operation of the grid.Battery Energy Storage Systems(BESS),a two-way energy regulator,can effectively reduce the issues caused by the grid connection of RES power generation and guarantee the grid’s safe and dependable operation.Due to the exorbitant cost of energy storage batteries,prudent planning is essential for their placement and capacity.The aim of this paper is to optimise distribution networks with wind and light power sources,by reducing average daily network losses(commonly referred to as average daily network losses).To achieve this,the capacity allocation of distributed battery energy storage systems is the focus.The main works are.(1)To introduce the composition of BESS,the functions of each component and the application scenarios of BESS,and to analyse the impact of the charging and discharging process of BESS on the distribution network loss after it is connected to the distribution network,so as to lay the foundation for the subsequent modeling.(2)Study the mathematical models of wind energy and photovoltaic output power and the mathematical model of BESS charging and discharging;consider the output power characteristics of wind power generation and photovoltaic,analyse the impact of wind power generation and photovoltaic connection to the distribution network on the grid load,and then establish the BESS capacity allocation optimization model with the average daily network loss of the distribution network as the optimization target.(3)The study of BESS capacity configuration solving methods,such as genetic algorithm and simulated annealing algorithm,proposes an improved simulated annealing genetic algorithm with an adaptive mechanism.Considering the defects of genetic algorithm,this paper improves genetic algorithm from the following two aspects: on the one hand,to address the problem that genetic algorithm is prone to fall into local optimal solutions due to the fixed crossover probability and variation probability,the adaptive mechanism and the principle of simulated annealing algorithm internal energy calculation are used to adjust the crossover and variation probability of genetic algorithm,so as to expand the search range of the algorithm and avoid the algorithm falling into local optimal solutions;on the other hand,to address the problem of slow convergence of genetic algorithm,the adaptive mechanism and the principle of simulated annealing algorithm internal energy calculation are used to adjust the crossover and variation probability of genetic algorithm.On the other hand,to address the problem of slow convergence of the genetic algorithm,the simulated annealing algorithm cooling mechanism is borrowed and improved,and the original iteration mechanism of the genetic algorithm is replaced by the improved cooling mechanism to change the iteration step of the genetic algorithm and accelerate the convergence speed of the algorithm.Finally,the simulation is carried out in the IEEE-33 node test system,and the results show the effectiveness of the proposed algorithm.(4)With the optimization objective of reducing the average daily network loss,an improved simulated annealing genetic algorithm is used to optimize the BESS capacity configuration and solve the BESS capacity configuration scheme(including the access BESS location and capacity)with the lowest average daily network loss in the distribution network connected to wind and light power sources. |
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