電力公司在乎如何供電使效用最大且供電成本最低，用戶則是關心如何安排須用的電工作成本最低，這些用電問題隨著不同用電管理方式產生不同結果。早期為避免尖峰用電量過高，電力公司與用戶達成協議，能直接控制用戶的部分電器，近年來，用戶的自利性及前瞻性等特性提升，因此透過需求端管理掌握自身用電需求。計價模式的選擇是決定用電成本及效用的關鍵因素之一，本研究採用新興計價模式－即時電價法(Real-time Pricing, RTP)。美國總統歐巴馬於2009年宣布將智慧電網計畫納入振興經濟的方案中後，智慧電網隨即引起世界的重視; 近年來環境意識高漲，為了保護環境不被汙染及降低相關燃料資源滅絕的速度，可再生能源成了替代能源最新來源，自然資源的特性，使發電過程較環保且不用擔心資源滅絕之問題。
本研究探討用戶能於需求端管理用電，假設電力公司則採用RTP計價模式，在智慧電網的架構下，使用分散式儲發電裝置，求解用電成本最小化問題，並證明用電成本問題在不考慮可再生能源裝置及工作期限下為NP-hard問題，提出以單一用戶為考量的電器工作排程演算法及考慮可再生能源之電池排程演算法，和多用戶排程演算法，最後，證明多用戶排程演算法在有限時間內會收斂至pure Nash equilibrium。我們的實驗結果顯示提出的演算法在執行時間上有優勢，且執行多人演算法後，每個時段的總用電量分布平均，解決了高尖峰用電的問題，且用戶的總用電成本會收斂至Nash equilibrium，儲存再生能源之電池電量也能維持在一定的水準。

In this paper, we study the problem of demand side management in smart grid with distributed power generation and storage. We assume that users have renewable power generation and storage devices and have appliances with flexible schedules. Because the cost of generating traditional power increases exponentially with demands, our goals is to lower the peak system load as well as minimizing the electricity cost for all users. In the rest of the thesis, we first show that the electricity minimization problem is NP-hard. Then, we propose a distributed demand side management algorithm to solve the problem and show that the algorithm converges to some pure Nash equilibrium in finite rounds. Experiment results showed that our proposed algorithms can help both users and the electricity company reduce electricity cost.

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