隨著再生能源滲透率持續提升，儲能系統於配電系統中的併網容量與設置位 置規劃，已成為影響系統運轉效率與電壓品質的重要議題。為探討儲能系統併網 配置對配電系統之影響，本研究以 IEEE 30-Bus 配電系統為分析對象，結合 PSS/E 與 Python 建立自動化模擬平台，並採用蒙地卡羅法與粒子群演算法作為 儲能系統多點併網容量與位置配置之求解方法。 本研究首先依據儲能系統併網相關技術規範，建立全系統電壓限制、責任分 界點電壓變動率限制及線路利用率限制等三項核心約束條件，作為容量評估與最 佳化分析之基礎。其後，透過步進邏輯法求取各候選匯流排之單一最大可併網容 量，再以總系統損失與總電壓變動率作為目標函數，分別於正常運轉與事故運轉 情境下，評估儲能系統於充電與放電模式中對配電系統運轉特性之影響。 模擬結果顯示，相較於蒙地卡羅法，粒子群演算法於正常運轉與事故運轉情 境下，皆能獲得較佳之儲能系統配置結果，不僅可有效降低總系統損失，亦能改 善全系統電壓變動情形，並展現較佳之搜尋效率與收斂能力。本研究所建立之自 動化分析流程可應用於配電系統儲能併網容量與位置規劃，而粒子群演算法在處 理此類多限制條件之最佳化問題上具有良好可行性，可供後續相關研究與實務應 用參考。;With the increasing penetration of renewable energy， the planning of grid-connected capacity and installation locations for energy storage systems (ESSs) in distribution systems has become an important issue affecting system operating efficiency and voltage quality. To investigate the impacts of ESS grid-integration configurations on distribution systems， this study adopts the IEEE 30-Bus system as the test system and integrates PSS/E with Python to establish an automated simulation platform. The Monte Carlo method and particle swarm optimization (PSO) are employed to determine the capacities and locations of multiple grid-connected ESSs.
First， based on relevant technical requirements for ESS grid integration， three core constraints are established， including system-wide voltage limits， voltage variation limits at the point of common coupling， and line utilization limits. These constraints serve as the basis for capacity assessment and optimization analysis. A stepwise search method is then applied to determine the maximum allowable grid-connected capacity at each candidate bus. Subsequently， total system losses and the total voltage variation rate are adopted as the objective functions. The effects of ESS charging and discharging operations on distribution system performance are evaluated under both normal and contingency operating conditions. The simulation results demonstrate that， compared with the Monte Carlo method， PSO achieves better ESS allocation results under both normal and contingency operating conditions. The PSO-based configurations not only effectively reduce total system losses but also improve system-wide voltage variations， while exhibiting superior search efficiency and convergence performance. The automated analysis procedure developed in this study can be applied to the capacity and location planning of grid-connected ESSs in distribution systems. Furthermore， PSO demonstrates strong feasibility for solving optimization problems involving multiple operational constraints， providing a useful reference for future research and practical applications.
