近幾年來，分散式再生能源的快速探索和開發，為各國能源政策、技術和能源市場的發展翻開了新篇章。在分散式再生能源 (DERs）大量的併入電網之際，由於其分散各處和多樣性與間歇性的發電特性在電力系統運行、穩定性、可靠性、資訊互通性及政策、技術和經濟各方面帶來了嚴苛的挑戰。此外，大量分散式再生能源的併網對傳統的集中式發電廠和調度控制中心的運行產生了重大影響。在這種情況下，分散式再生能源相關研究的創新概念正在該領域得到廣泛的討論和發展。
本論文提出一種基於物聯網的IEC 61850 XMPP雲平台分散式再生能源管理系統的新穎架構，並在台灣電力公司綜合研究所進行試驗。該架構基於歐洲CEN-CENELEC-ETSI 智慧電網協調小組（SG-CG）的智慧電網架構模型（SGAM）設計以及採用NIST IEC 62357智慧電網互通性標準及發展藍圖。此外，本論文還提出了基於工業務聯網參考架構（IIRA）的標準化 ICT技術來支援此智慧電網架構。基於IEC 61850 XMPP雲平台架構上，本文研究並建置一套機器學習方法論，通過使用長短期記憶類神經網路（LSTM）多步時間序列預測理論來預測24小時之太陽能發電。通過資料處理、模型擬合、交叉驗證、指標評估和超參數調優的機器學習過程，實驗結果顯示平均RMSE為0.512，這數據驗證本文所提出的架構與方法適合用於短期太陽能發電預測。本論文提出的研究架構與方法，可以提供給未來相關領域研究者更深入的進行發電預測相關探討，並運用於各種輔助服務模式的開發，進而強化電力系統供電品質與可靠度。

In recent years, exploration and exploitation of renewable energies are turning a new chapter toward the development of energy policy, technology and business ecosystem in all the countries. Distributed energy resources (DERs) are being largely interconnected to electrical power grids. This dispersed and intermittent generational mixes bring technical and economic challenges to the power systems in terms of operations, stability, reliability, interoperability and the policy making. In additional, DERs cause the significant impacts to the operation of traditional centralized generation power plants and the dispatch control centers. Under such circumstances, the innovative concepts of DERs related researches are being widely studied and developed in this field. In this dissertation, a novel architecture of IoT based IEC 61850 XMPP Cloud Platform for DER management system is proposed and piloted in TPRI Taiwan Power Company. This architecture is designed and intermixed based on Smart Grid Architecture Model (SGAM) from European CEN-CENELEC-ETSI Smart Grid Coordination Group (SG-CG) and IEC 62357 from NIST Framework and Roadmap for Smart Grid Interoperability Standards. Besides, standardized ICT technologies based on the Industry Internet of Things Reference Architecture (IIRA) are proposed to support this Smart Grid Architecture. Building on IEC 61850 XMPP Cloud-based Platform, an architecture of machine learning methodology to forecast one day ahead solar power generation by using LSTM multi-step time series forecasting is discussed and implemented. Through the machine learning process of data processing, model fitting, cross validation, metrics evaluation and hyperparameters tuning, the result shows that the average RMSE is 0.512 which is quite promising to inspire that the proposed methodology and architecture can best fit the short-term solar power forecasting applications.

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