臺灣博碩士論文加值系統

未來一代電力系統中的智能電網具有電力流動的特點在並網模式下，但遇到的主要問題之一是不可預測的電源可再生能源（RESs）的使用。在非合作管理策略中，由於不同使用者將期望不同能量儲存的水平和對成本的考量，每個使用者在追求自己的利益時將可能在智能電網中與其他人發生部分衝突。目前，我們仍然找不到分析或計算方法有效地解決複雜的非合作能量流管理智能電網的問題，尤其是考慮了不可預期的可再生能源的電力供應。在這項研究中，我們制定了一個新的效用函數，每個管理者（用戶）可以通過它來設計他的非合作策略根據自己的考慮和其他管理者的可能影響。那麼智能電網中的非合作管理策略設計問題就可以了轉換為線性矩陣不等式（LMIs） - 條件多目標優化問題。因為LMIs約束的MOP問題不容易直接解出其Pareto最優解，因此我們提出了一種間接方法來解決這個MOP的非合作管理策略智能電網中的電力傳輸系統。此外，LMIs約束多目標基因演算法（MOEA）被開發用於有效地解決多人非合作管理策略。另一方面，在合作管理戰略的情況下，所有管理者都可以進行溝通和擁有在開始時彼此妥協了一個共同的期望目標。並且合作管理策略設計問題可以轉化為LMI約束的SOP，以保證穩健的H∞智能電網中的目標問題。最後，這兩種管理策略的模擬結果也說明了設計的程序和驗證了在智慧電網中動態能量傳遞系統的非合作與合作的策略表現。

Smart grid in the future generation of power system has the characteristic for the power flow in grid-connected mode, but one of the major issues encountered is the unpredictable power supply from renewable energy sources (RESs). In the non-cooperative management strategy, due to different desired energy storage levels and cost consideration, the microgrids involved pursue their own interests which may partly conflict with others in a smart grid network. At present, we still can not find an analytical or computational method to efficiently solve the complex non-cooperative power flow management problem for smart grid with unpredictable fluctuations of power supply from renewable energy sources. In this study, we formulate a novel utility function by which each manager (user) could design his non-cooperative strategy according to their own consideration and the possible effect of other managers’ strategies. Then the non-cooperative management strategy design problem in a smart grid network could be transformed to a linear matrix inequalities (LMIs)-constrained multi-objective optimization problem (MOP). Because the LMIs-constrained MOP problem is not easily solved directly for its Pareto optimal solutions, an indirect method is proposed to solve this MOP for the non-cooperative management strategy of power transfer system in smart grid network. Furthermore, a LMIs-constrained multi-objective evolution algorithm (MOEA) is developed to efficiently solve the multi-person non-cooperative management strategy. In the case of cooperative management strategy, all managers can communicate and have compromised a common desired target with each other at the beginning. The cooperative management strategy design problem could be transformed into a LMIs-constrained SOP to guarantee a robust H∞ target regulation problem in the smart grid. The simulation results of these two management strategies are also given to illustrate the design procedure and to validate the performance of the non-cooperative and cooperative management strategy of the dynamic energy transfer system in smart grid network.

摘要---------------------------------------------------------------------------------------i
Abstract-----------------------------------------------------------------------------------ii
致謝------------------------------------------------------------------------------------- iii
Contents--------------------------------------------------------------------------------- iv
I.Introduction------------------------------------------------------------------------2
II.System Description of Dynamic Energy Transfer System In Smart Grid Network ---------------------------------------------------------------------5
III.The Non-cooperative Management Strategy Design For Dynamic Energy Transfer System In The Smart Grid Network --------------------------------------------------------------10
IV.LMIs-Constrained MOEA Algorithm For Non-cooperative Management Strategy In Smart Grid Network --------------------------------------------------------------------16
V.The Cooperative Management Strategy Design For The Dynamic Energy Transfer System In Smart Grid Network -----------------------------------------------------------------------------18
VI.Simulation Result------------------------------------------------------20
VII.Conclusion------------------------------------------------------------------------29
Appendix A-----------------------------------------------------------------------------30
Appendix B-----------------------------------------------------------------------------32
References----------------------------------------------------------------------------------------34

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