At present,distribution network framework planning plays a critical role in enhancing grid reliability and stability.However,the correlation between source and load outputs is often neglected,leading to a high rate of solar power curtailment,significant investment in the distribution network framework,and low line utilization rates.To address these issues,this paper proposes a framework planning method for distribution networks that considers the correlation and uncertainty of source and load output.Firstly,historical data on source and load is analyzed,ac-counting for the temporal autocorrelation of photovoltaic(PV)and load outputs.Latin hypercube sampling(LHS)and Cholesky decomposition are employed to generate source-load scenario sets.By incorporating their correlation,a load reduction method under these scenarios is designed accordingly.Secondly,the uncertainty in source and load outputs is considered to determine typical source-load scenarios and their probabilities.A multi-objective optimiza-tion planning model is developed for the distribution network.This model aims to maximize expected PV consump-tion,minimize the expected annual investment and operational costs,and minimize the expected value of line over-load probability,while considering relevant constraints.The improved NSGA-Ⅱ algorithm is used to solve this model and generate a distribution network framework planning scheme.Finally,the differences among Pareto frontier solu-tions and the impact of various parameters on the planning results are discussed,providing planners with alternative options for decision-making.
维普
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