極端氣候事件頻傳使電網韌性與可靠度倍受矚目，分散式電力資源的大量併網雖帶來挑戰，亦為韌性改善提供契機。本文提出整合虛擬同步發電機（Virtual Synchronous Generator, VSG）、分散式電力資源調度及機率可靠度分析之策略，以提升電網韌性與評估系統可靠度。
建構多目標最佳化與DIgSILENT動態模擬相結合之求解策略，應用基因演算法（Genetic Algorithm, GA）及柏拉圖前緣（Pareto Front）決定多組VSG的最佳控制參數，以減緩頻率震盪。其中，亦將VSG導入太陽能發電控制，並結合機器學習與風險評估方法，提出智慧功率削減模型，以降低棄光需求並提升系統穩定性。
此外，本文提出適用於微電網的複合性韌性指標，並設計三階段韌性強化策略，包含基於GA的多目標最佳化分散式能源排程，基於粒子群演算法（Particle Swarm Optimization）的系統重構與動態卸載策略。該方法考慮電源能量限制及電網穩態與暫態限制，顯著提升微電網因應高影響低機率事件之能力。然而傳統可靠度分析方法無法有效納入新型電力資源，本文提出整合需量反應（Demand Response）、儲能系統、再生能源與傳統機組的蒙地卡羅（Monte Carlo）機率可靠度分析方法，以評估新型態資源的規劃需求與可靠度影響。
由於太陽能與VSG整合更適於離島應用，本文以澎湖望安島作為模擬案例，結合VSG的太陽能控制可提升57.5%太陽能滲透率，並減少每年25 MWh的削減量。而以台灣某大型工業微電網驗證所提調度與重構策略，可提升韌性且避免全黑風險。進而以台灣電力供需情境下進行可靠度模擬分析，評估需量反應及儲能對可靠度與經濟性之影響。相關結果可供電力業者或微電網營運商參考，作為後續發展或系統規劃之決策依據。

Extreme weather events have increasingly emphasized the importance of grid resilience and reliability. While the high penetration of distributed energy resources (DERs) poses challenges, it also presents opportunities to enhance resilience. This study proposes an integrated strategy combining virtual synchronous generator (VSG) control, optimal dispatch of DERs, and probabilistic reliability analysis to improve grid resilience and evaluate system reliability.
A multi-objective optimization approach coupled with DIgSILENT dynamic simulations is developed to determine the optimal VSG control parameters for multiple synchronous generators. By applying genetic algorithm (GA) and pareto front techniques, the method effectively mitigates frequency oscillations. Furthermore, VSG control is applied to photovoltaic (PV) systems, integrating machine learning and risk assessment to propose an intelligent power curtailment model. This model reduces curtailment requirements and enhances system stability.
Additionally, this dissertation introduces composite resilience indices suitable for microgrids and designs a three-stage resilience enhancement strategy. The stages include multi-objective optimization-based DER scheduling using GA, system reconfiguration based on particle swarm optimization, and dynamic load shedding strategies.
The strategies consider energy limitations, as well as grid codes, significantly improving microgrid resilience to high-impact, low-probability events. Recognizing the limitations of traditional reliability analysis in accounting for new energy resources, the dissertation incorporates demand response (DR), energy storage systems (ESS), renewable energy, and conventional generators into a Monte Carlo-based probabilistic reliability analysis framework to evaluate the planning and reliability impacts of these emerging resources.
As VSG-integrated PV is particularly suitable for islanded applications, the proposed method was validated using a case study of Wangan Island in the Penghu Archipelago. Results demonstrated a 57.5% increase in PV penetration and a reduction of 25 MWh in annual curtailment. Furthermore, the proposed dispatch and reconfiguration strategies were validated in a large-scale industrial microgrid in Taiwan, showing significant improvements in resilience and the ability to prevent system blackout.
Finally, under Taiwan's power supply and demand scenarios, probabilistic reliability simulations assessed the contributions of DR and ESS to system reliability and economic performance. These findings provide valuable references for power utilities and microgrid operators, supporting future development and system planning decisions.

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