Distributed Photovoltaic Acceptance Capacity Calculation Based on Improved HEM under Uncertain Environments

Abstract

To address the low computational efficiency of traditional photovoltaic hosting capacity assessment methods under uncertain environments, this study proposes a rapid evaluation method based on scenario adaptation and application form optimization of the Holomorphic Embedding Method. Firstly, a node selection strategy based on the Lévy flight-improved particle swarm optimization algorithm is established. An objective model with constraint penalty functions is constructed. Candidate grid connection nodes with superior voltage regulation capability and potential for capacity enhancement are then efficiently screened. This greatly reduces the computational burden of subsequent stochastic evaluation. Secondly, Monte Carlo simulation is integrated with the optimized Holomorphic Embedding Method, combined with Latin hypercube sampling. This builds an assessment framework that considers the uncertainties of photovoltaic output and load fluctuation. The established model adopts the Holomorphic Embedding Method to solve deterministic subproblems efficiently. It then evaluates the overall adaptability and robustness of different integration schemes under various uncertain scenarios. Simulation validation on the IEEE-30 bus system demonstrates that the optimal scheme corresponds to nodes {3,19}. The maximum photovoltaic hosting capacity is 81.32 MW. This scheme obtains the minimum comprehensive flexibility score. It verifies optimal operational performance across annual stochastic scenarios. Meanwhile, the proposed method improves computational efficiency by approximately 82% compared with the conventional enumeration method. It maintains calculation accuracy. The results indicate that the application-oriented optimization of the Holomorphic Embedding Method, combined with stochastic scenario analysis and shared energy storage, can improve the photovoltaic accommodation capability and operational flexibility of distribution networks. It provides reliable theoretical and methodological support for the grid integration of high-penetration renewable energy.