石化燃料的耗竭以及全球氣候變遷促使各國政府著手加快能源轉型，智慧電網便是在這股改革浪潮下所產生的電力系統架構。透過整合不同的再生能源例如風力、太陽能發電以及儲能系統形成多元及分散性的電力網絡，智慧電網著重於再生能源的整合、電能的雙向傳輸、配送以及資料數據的解析、分享以達到電力資源的妥善利用。然而在智慧電網的設計與實現上往往會面臨具高複雜性且不確定性因素，計算智慧便是能夠解決這些問題的有效工具。本論文將藉由粒群演算法應用於智慧電網的兩個案例，探討以計算智慧為基礎的控制方法與最佳化設計問題。
首先由案例一揭露計算智慧在電網上的應用，提出改良型的粒群演算法以滿足電網平衡條件下的等式問題，使微電網系統得以達到最低成本的能量分配運作。另外於案例二針對社區屋頂太陽能發電系統與家用電池儲能系統的搭配，提出一套以粒群演算法最佳化模糊控制器的家用儲能系統充電法則，有效提升住戶用電的自給率並節省電能花費。最後，透過以上兩個應用案例，呈現以計算智慧為基礎的解決方案與智慧電網的有效連結，具體化人類智慧與計算機智慧在能源發展的可能。

The transition from mitigating fossil fuel to clean and sustainable energy sources, such as solar power and wind power, involves the entire upgrade and the fundamental reengineering for current electricity grids. Smart grid is introduced under the growing consciousness of this grid evolution. Many challenges arise in terms of technology, economy and regulation when developing smart grid. Considering a high level of uncertainty and dynamism of smart grid system, computational intelligence is expected to solve problems in smart grid technology which are full of variables and complex scenarios. This dissertation aims to make a link between computational intelligence and smart grid system with two different application cases and figures out the advantages and effectiveness of the computational intelligence based approaches. For the given cases, one focuses on the optimum power dispatch within a microgrid, while the other proposes an optimized fuzzy controlled charging strategy for household photovoltaic (PV)-battery systems in a community. Both cases are based on particle swarm optimization (PSO), but they reveal different aspects of issues in smart grid system and the solutions have been developed accordingly.
In the first case, to satisfy the power balance requirement which is interpreted as an equality constraint, a roulette wheel (RW) redistribution mechanism is integrated with PSO as a novel optimum power dispatch algorithm. In this way, the unbalanced power is able to be reallocated to more superior element in microgrid and the searching diversity can be preserved. On the other hand, in the second application case, it is assumed there are 74 houses installed PV-battery system individually. To cater distinct power profiles in each house within the community, a battery charging control strategy is designed to have adaptability to achieve minimum cost for houses without any meteorological or load forecasts. To sum up, it is anticipated that the introduction of the given cases can embody the computational intelligence based algorithms on the smart grid and present possibilities of a wide integration between intelligence and energy.

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