Research On The Planning And Operation Of Distributed Photovoltaic Access To Distribution Networks Considering Energy Storage And Its Solution Methods

In recent years,in order to achieve the “dual carbon” goal of “carbon peak and carbon neutrality”,the National Energy Administration has promoted the pilot work of rooftop distributed photovoltaic development within the entire county(city,district)scope.The large-scale access of distributed photovoltaics to the distribution network is an important measure to help achieve the “dual carbon” goal.However,the large-scale access of new energy sources has brought a series of challenges to the safety and economy of the distribution network,and scientific planning for the development of distributed photovoltaics is required.Due to the randomness and instability of photovoltaic output,if demand-side management is considered while accessing energy storage,it can reduce the uncertainty of photovoltaic output to a certain extent.Therefore,it is of great practical value to study how to plan and operate the access of distributed photovoltaics and energy storage to the distribution network.This article first introduces the photovoltaic power generation and energy storage models in the distribution network,and analyzes the impact of large-scale photovoltaic access on the distribution network.Through simulation examples,it verifies the conclusion that different locations and capacities of distributed photovoltaic access have different impacts.In terms of the uncertainty of distributed photovoltaic output,the scenario analysis method is used and the typical scenario curve of distributed photovoltaic output is obtained based on historical data.Secondly,to address the issue of planning and layout for the large-scale integration of distributed photovoltaic power generation into distribution networks,a multiobjective planning model is constructed to optimize distribution network losses,photovoltaic investment costs,and system voltage enhancement indicators.To effectively solve this model,an improved algorithm that avoids local optima and has excellent convergence capabilities is proposed for use in photovoltaic optimization configuration problems.Due to the limitation of particle swarm algorithms in stagnating at local optima,the niche particle swarm algorithm is improved by introducing reverse learning factors and mutation and crossover strategies.The algorithm’s superiority is verified through comparative analysis with sparrow search algorithms and artificial hummingbird algorithms.The model is solved using the IEEE33-node system as an example.Simulation results show that the improved particle swarm algorithm has fast convergence speed,improved system voltage enhancement indicators and network losses,and has good application significance in solving optimization configuration problems.Finally,a two-layer model for the planning and operation of photovoltaic joint storage in the distribution network was constructed using the distribution network cluster division and the two-layer research framework.The planning layer takes the annual comprehensive cost of the distribution network as the objective function,and the operation layer takes the voltage deviation of the distribution network,network loss,and photovoltaic absorption ratio as the objective function.In terms of solving complex variable two-layer models,the improved particle swarm algorithm and multi-objective particle swarm algorithm with adaptive grid in Chapter 3 are used to solve the twolayer model and compared with the hybrid algorithm of improved particle swarm and second-order cone.Using the IEEE33 node system example,four different scenarios are constructed for simulation comparison analysis.From the aspects of economic cost,voltage quality,peak shaving and valley filling of the distribution network,the effectiveness and rationality of the model proposed in this paper and the algorithm for solving the two-layer model are verified.It also provides certain theoretical support for distributed photovoltaic grid-connected planning and storage capacity configuration.