| 研究生: |
林奕臻 Lin, Yi-Zhen |
|---|---|
| 論文名稱: |
廠辦電動車停車場參與輔助服務之儲能系統排程與控制方法 ESS Scheduling and Control Approach for Factory-based EV Charging Station to Participate in Ancillary Services |
| 指導教授: |
楊宏澤
Yang, Hong-Tzer |
| 學位類別: |
碩士 Master |
| 系所名稱: |
電機資訊學院 - 電機工程學系 Department of Electrical Engineering |
| 論文出版年: | 2022 |
| 畢業學年度: | 110 |
| 語文別: | 英文 |
| 論文頁數: | 108 |
| 中文關鍵詞: | 三階段能源管理系統 、儲能系統 、電動車 、可再生能源 、契約容量 、輔助服務 、線性規劃 、模型預測控制 |
| 外文關鍵詞: | three-stage energy management system (Three-stage EMS), energy storage system (ESS), electric vehicle (EV), renewable energy sources (RESs), contract capacity, ancillary service (AS), linear programming (LP), model predictive control (MPC) |
| 相關次數: | 點閱:101 下載:5 |
| 分享至: |
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鑑於全球淨零排放浪潮與環保意識的抬頭,大量可再生能源和電動汽車加入電力系統,各國相繼擬定相關能源政策以因應電網面臨的改變與衝擊。能源管理系統能有效整合並且協調的控制分散式能源,透過合適的調控可再生能源和電動汽車,除了可以降低微電網的運營成本,更能在提升電力系統安全性和穩定性上扮演重要的角色。
本文以微電網運營商之角度,為有效將資源達到最佳效益,提出三階段優化調度策略。第一階段擬定輔助服務最佳投標策略,第二階段採用集中式最佳化架構以最小化系統運營成本為目標,而第三階段基於分散式控制架構達到每座電動汽車充電樁和儲能系統的實時控制。模擬結果中,比較傳統集中式最佳化控制和所提方法之運算時間與營運成本,以驗證所提出方法具有執行輔助服務的能力並且實時可行。其中,儲能系統採用的模型預測控制架構能有效提高系統強健性(robustness),並減少微電網需量超出契約容量的發生。
Owing to the global trend of net-zero emissions and an increase in the environmental awareness, several renewable energy sources (RESs) and electric vehicles (EVs) have been integrated into power systems. Many countries have formulated relevant energy policies to deal with changes and impacts on the power grid. The energy management system (EMS) can effectively integrate and coordinate the control of distributed energy resources (DERs). Appropriate regulation of RESs and EVs can reduce the microgrid operating costs, and it is critical in improving the safety and stability of power systems.
This thesis proposes a three-stage optimal dispatch strategy from a microgrid operator (MGO) perspective to effectively utilize resources. Stage 1 formulates the optimal bidding strategy for ancillary services (AS), Stage 2 adopts a centralized optimization architecture to minimize system operating costs, and Stage 3 adopts a decentralized control architecture to enable the real-time control of each EV charger, including the control of the energy storage system (ESS). The computation time and operating cost of the traditional centralized optimal control method and the proposed method were compared, verifying that the proposed method can execute AS and is feasible in real time. Moreover, the model predictive control (MPC) architecture adopted by the ESS can significantly improve the system robustness and reduce the occurrence of microgrid demand exceeding the contract capacity.
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