地震風險下電力系統之韌性分析

Abstract

自然災害可能會對關鍵基礎設施及維生管線造成嚴重衝擊，將造成嚴重的社會成本，例如無法滿足民生需求、重要設施停擺。各國政府皆積極尋找相關的韌性提升方法，然而過去研究多忽略了韌性和脆弱度的綜合考量。因此本研究希望結合兩者的特性，應用於電力系統的網路分析，模擬災害當下與災後電網的影響及恢復程度，提出電網韌性之評估方法，並通過綜合考慮4R(Robustness, Rapidity, Redundancy, Resourcefulness)指標，找到最可行的減災和應對策略，以提高社區的抗災能力。首先，進行圖論分析，確認每個節點的中心性和其失效後的影響，以確定關鍵廠站。接著，利用地震參數、易損性曲線及復原參數，透過蒙特卡羅方法模擬地震情景下電力系統的損壞和恢復情況。研究案例將範圍選定於台北市及新北市的行政區，並採用山腳斷層規模6.6之地震事件作為災害模擬情境。研究結果顯示，本文的方法得以成功辨識關鍵節點及高風險區域，並考量社會經濟資料，進行脆弱度分析給出重要性排序。同時，衡量不同韌性提升策略之模擬改善效果，為相關決策者制定未來防、減災計畫提供參考。

Natural disasters may severely impact critical infrastructure and lifelines and even cause the shutdown of essential facilities. Governments worldwide are actively seeking resilience enhancement methods. However, previous studies have often overlooked the comprehensive consideration of resilience and vulnerability. Therefore, this research aims to integrate the characteristics of resilience and vulnerability and apply them to network analysis of an electric power system. The proposed method assesses the effects and recovery levels of the power network during and after a disaster and identifies the feasible mitigation and response strategies considering the 4R (Robustness, Rapidity, Redundancy, Resourcefulness) indicators to enhance community disaster resilience. The study first conducts graph theory analysis to evaluate the centrality of each node and its impact after failure, thereby identifying critical nodes. Then, using the seismic parameters, fragility curves, and recovery parameters, the research conducts Monte Carlo simulations to assess the damage and recovery of the power network under seismic scenarios. The study focuses on the administrative districts of Taipei City and New Taipei City, with the seismic event considered a 6.6 magnitude earthquake along the Shanchiao Fault. The results show that the proposed method can identify critical nodes and high-risk areas. The vulnerability and resilience of administrative districts are assessed through analysis, and the effects of different resilience enhancement strategies are evaluated. This research provides insights and references for decision-makers in planning disaster mitigation and resilience improvement strategies.