研究生: |
謝明錡 Ming-Qi Xie |
---|---|
論文名稱: |
基於雲端監控之社區直流微電網能量管理策略與驗證 Energy Management Strategy and Verification of Community DC Microgrid Based on Cloud Monitoring |
指導教授: |
林長華
Chang-Hua Lin |
口試委員: |
陳貽評
Yi-Ping Chen 劉華棟 Hwa-Dong Liu 黃仲欽 Hwang-Jonq Chin |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 電機工程系 Department of Electrical Engineering |
論文出版年: | 2023 |
畢業學年度: | 111 |
語文別: | 中文 |
論文頁數: | 161 |
中文關鍵詞: | 雲端系統 、直流微電網 、儲能系統 、能量管理策略 、削峰填谷 |
外文關鍵詞: | Cloud system, DC microgrid,, Peak shaving and valley filling, Energy management strategy, Energy storage system |
相關次數: | 點閱:673 下載:0 |
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本文旨在於提出基於雲端技術的直流微電網能量管理策略,以有效管理能源在直流微電網中的流動,從而提高能源使用效率。首先,所提出的能量管理策略利用雲端監控技術,實時收集各獨立能源裝置的能源產生與相關數據,並使用神經網路預測負載需求,再加入粒子群演算法,將儲能系統電力進行合理分配,以提高能源利用率,並降低流動電費。其次,該策略基於直流微電網架構,實現了能源多元融合,包括市電供應、太陽能和儲能系統等。再者,透過在高峰時段減少能源需求,並在低峰時段儲存多餘能源,使削峰填谷策略能夠提高能源利用效率,進一步降低能源成本。
為了驗證所提出的能量管理策略的有效性,本文使用電能轉換器模擬各獨立能源裝置,再透過無線通訊技術和雲端服務組成的混合能源系統,將市電、儲能系統、再生能源和直流微電網相互連接,以建立一個完整的直流微電網系統。同時,本系統還整合軟體平台及前端服務,藉此取得儲能系統、再生能源和市電的即時狀態,並根據削峰填谷概念及再生能源發電情境,以提供各子系統相應的決策,使得各子系統達到最合適配置。最後,藉由對一實際案例在不同情境下進行降額實測,以驗證本文所提出策略的可行性。
This study proposes a cloud-based energy management strategy for a DC microgrid to reduce energy costs and optimize energy flow. The strategy utilizes cloud monitoring technology to collect real-time data. It incorporates load prediction and further integrates particle swarm algorithms to optimize power allocation in the energy storage system, thereby improving energy utilization efficiency and reducing electricity expenses. By integrating grid supply, solar power, and energy storage systems, the strategy promotes energy diversification, contributing to a greener and more reliable energy ecosystem. The proposed strategy's effectiveness is validated by implementing a hybrid system comprising power converters, battery energy storage, wireless communication, and cloud services. Real-time statuses of the microgrid's components inform decision-making processes, facilitating the optimal operation of subsystems. Finally, the strategy's feasibility is demonstrated through a comprehensive case study and rigorous experimental testing, establishing its potential for practical implementation and showcasing its potential impact on energy management in DC microgrids.
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