本研究引用演化經濟 (evolutionary economics) 與適應性控制理 論 (adaptive control theory) 作為基礎，據以建構以智慧電網為核心之 整合性電力供需資源組合，包括供給面之集中型電源、分散型電源、 再生能源和需求面之需求面管理與需量反應，以及調節性資源包括 儲能系統和電動車等，從而形塑一個兼具低碳和智慧化之電力系統， 具備動態彈性調節系統電力供需之能力，以利未來氣候變遷下之減 緩與調適。 其次，進一步立基於此一架構下，考量相關狀態變數（包括電 力供給之能量、容量和輔助服務等電力資源）、量測變數（包括可觀 測之能源與永續指標）、性能變數（包括可識別之價值指標與風險指 標）和控制變數（包括可控性之政策制度因素、技術因素、市場因 素和營運因素）；並考量其相互間之互動與耦合關係和回饋機制，據 以建構一個可以進行情境模擬分析之多元代理系統動力模型，進行 實證性模擬。 依據模擬結果，經由得出不同情境下各種電力資源和相關指標 之發展趨勢，從而推論永續發展下︰(1)適應性之電力需求；(2)適應 性路徑依賴之「非線性自組織」智慧電網電力資源組合；(3)電力規劃與資產管理演進下之適應性備用與備轉容量；(4)適應性電力價格之可能趨向，可以提供電力「規劃與管理」決策參考之用。

Based on evolutionary economics with adaptive control theory, this research builds up a smart grid to integrate the electricity mix of power resources, including power supply-side resources such as centralized resources and distributed generation (DG) and renewable energy, demand-side resources such as demand-side management (DSM) and demand response (DR), and regulating resources such as energy storage and electric vehicles (EV). Accordingly, the research shapes a low carbon and intelligent grid based power system, with the dynamic adjustment ability to effectively balance electricity supply and demand in response to the mitigation and adaptation to future climate change. Based on this framework, the model is established through further considerations, including underlying state variables such as energy, capacity and ancillary services of the power mix, measurement variables such as the energy index and the sustainable index, performance variables such as a value index with a risk index, and control variables such as policy, institution, market, and management factors. In addition, the model also takes into account the mutual relationship and interactions from the feedback mechanism. Accordingly, a dynamic multi-agent system (MAS) model for several scenarios can be executed for empirical simulations. Under sustainable development, system dynamic simulation results are found for various scenarios: (1) adaptive electricity demand, (2) adaptive self-organized non-linear path dependence for smart grid power resources mix, (3) adaptive reserve capacity for evolutionary power planning & assets management, and (4) adaptive electricity prices. The results can be used as a reference for decision-making in electricity planning and management.