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  • 學位論文

電力資源組合適應性演化機制與應用-系統動態模擬之研究

The Adaptive Evolutionary Mechanism and Applications of a Power Resources Mix – System Dynamics Simulation Research

指導教授 : 張四立 黃書禮
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摘要


本研究引用複雜性科學(Complex Science)暨系統理論(System Theory)、演化經濟(Evolutionary Economics)與適應性控制理論(adaptive control theory)作為學理基礎,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(節約能源與負載管理)與需量反應(含需量競價),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,具備動態彈性調節電力系統供需之能力,以利確保供電可靠與安全及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,據以建構一個實證上可以進行情境模擬分析之多元代理系統動力(System Dgynamics)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之可能趨向: (1)適應性之電力需求暨需求面管理、(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和(4)適應性電力價格,以提供電力「規劃與管理」決策參考之用。 整體而言,本研究經由實驗模擬,一方面驗證了上述相關理論基礎之可行性與實用性;另方面前瞻了電力未來發展趨勢下電力經濟之深度意涵,包括: 1. 經由路徑依賴分析,充分彰顯達成低碳目標之關鍵,主要在於改變整體系統結構,從2013年(現況)高碳系統結構(傳統串聯式電力供需規劃模式)之非適應性,趨向2015年(近期)低碳系統結構(傳統串聯式電力供需規劃模式)之低適應性,及2025年(中期)低碳系統結構(並聯式電力資源整合規劃模式)之中適應性,進而朝向2035年(長期)低碳結構(網路式智慧電網結合市場之規劃模式)之高適應性。 2. 未來再生能源在低碳電力系統結構下,將適應性朝向目標調節型或S 曲線增長(或其變形)之發展趨向,非再生能源則朝向目標調節型或反S 曲線(或其變形)衰減之發展趨向。 3. 未來電力經濟特性係朝向簡單型、複合型、暫態型和混沌型下之電力資源組合的多元型態複雜路徑行為模式,並融合計畫經濟、市場經濟進而演化至協同共享經濟之適應性動態電力經濟學。 4. 面對內外在環境包括經社、能源、氣候變遷以及技術、政策、市場、經營等諸多不確定因素下,傳統電業管制下靜態式、確定性、機械化之電業經營管理模式轉型至動態式、不確定性、有機化,進而適應性之彈性實質選擇權經營模式,即所謂「適應性管理」。 5. 過去從靜態式、確定性之電力需求預測,到靜態式、集中化電源規劃、電網規劃之串聯模式,將逐步轉型為集中與分散並行及整合需求面管理之複合型電力資源組合並聯模式;進而演化成智慧電網暨自由化架構下之適應性動態電力資源組合模式,從而朝向適應性電力規劃與調度和適應性之電業經營模式。

並列摘要


By applying the theories of Complex Science, System Theory, Evolutional Economics and Adaptive Control Theory as the academic basis, this thesis aims to construct an integrated power resources mix with the core technology of smart grid, to effectively accommodate supply-side resources such as centralized power plants, distributed generation (DG) and renewable energy; demand-side resources such as demand-side management (DSM) and demand response (DR); and regulating resources such as storage system and electric vehicles (EV). Accordingly, a low-carbon, intelligent power system capable of dynamically adjusting power supply and demand will be thus formed and dedicated to assure power reliability and security, as well as to mitigate and adapt to climate change in the future. Based on the aforementioned framework, a model is established to allow further considerations, e.g., 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 takes into account the mutual relationship and interactions among the feedback mechanism. Accordingly, a dynamic multi-agent system (MAS) model for several scenarios can be executed for empirical simulations. According to the system dynamics simulation results, the trend based on power resources mix toward sustainable development are found for the various scenarios: (1) adaptive electricity demand with effective DSM, (2) adaptive self-organized non-linear path dependence for smart grid power resources mix, (3) adaptive reserve capacity for evolutionary power planning and assets management, and (4) adaptive electricity prices. The results can be used as a reference for decision-making in electricity planning and management. Through simulation experiments, this study on the one hand validates the feasibility and validation of the above-mentioned theory; on the other hand, provides insights into electricity economics for the future power development, which includes: 1. Via path dependency analysis, the result of this study demonstrates that the key to reach low-carbon target relies on changing the power system’s overall configuration. Namely, it is shown from the non-adaptive high-carbon system architecture in the year 2013 (traditional serial power supply and demand planning mode), moving forward to the low-adaptive low-carbon system architecture (traditional tandem mode power supply and demand planning) in 2015 for the short term, and the mid-adaptive low-carbon system configuration (parallel power resources integrated planning mode) in 2025 for the medium term, and finally to the high adaptive low-carbon structure (smart grid network planning model combined with the market) in 2035 for the long term. 2. Under the framework of low-carbon power system, renewable energy adaptively develops toward the direction of goal-driven growth with the S curve increasing; on the contrary, non-renewable energy develops toward the direction of goal-driven attenuation with the anti-S curve decreasing. 3. The electricity economics in the future will have basic, combined, uncertain and chaotic characteristics under power resources portfolio of multiple patterns and complex paths, and integrating planned and market economy to involve into the collaborative sharing economy of adaptive dynamic electricity economics. 4. Facing the uncertainties of internal and external environments including socioeconmic, energy, climate change, technology, policy, market and management factors, the traditional static, deterministic, certain, mechanic business model of the electricity industry will transform to dynamic, uncertain, organic and adaptive real option business model, as the so-called "adaptive management". 5. Power system planning and utilization has evolved from static, deterministic, demand-forecast mode to static, centralized power and grid planning serial mode. In the future, it will gradually transform to centralized and decentralized parallel mode and integrate demand side management under combined power resources portfolio in parallel mode. Then it will evolve into adaptive dynamic power resources portfolio mode under the framework of smart grid and liberalization, thereby towards adaptive power system planning and dispatch and adaptive management model for the electricity industry.

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