本論文探討聯合最佳化家庭日前預先排程以及公司日前預先定價的問題。我們考慮非彈性用電和再生能源的不確定性，以及使用電池去調整家庭用電。給定已知的電價以及非彈性用電和再生能源的統計特性，而且考慮最大用電負載量以及電池用電違反的機率約束，我們要去調整彈性用電去最小化電費的期望值減去使用者的舒適度。這個問題我們針對代表半彈性用電起始時間的排程變數之集合使用鬆弛法，以及對於機率約束使用馬可夫界和樣本平均近似法得到近似解。這個問題也包含機組排程問題，也就是如果執行電器所獲得的舒適度小於執行所造成的電費，這個電器就不會執行。我們所提出的演算法在模擬中表現比貪婪法所得到的更好。接下來，給定設計好的消費者端排程策略，公司端的日前預先買電以及定價可以用微粒群演算法解決。特別是給定日前預先以及即時買電的成本函數，電力公司要決定向消費手收取的電價以及要從日前預先市場中購買多少份量的電能。微粒群演算法生成候選電價數值傳遞得消費者，再針對消費者傳回的負載計畫更新候選電價數值。針對這個部分有電腦模擬展示我們所提出演算法的效能。

Abstract i
Contents iii
1 Introduction 1
2 Day-Ahead Load Scheduling at the Consumer Side 5
2.1 Formulation of the Load Scheduling Problem with Chance Constraints . . . 5
2.2 Problem Reformulation and Relaxation . . . . . . . . . . . . . . . . . . . . . 9
2.3 Restriction and Sample Average Approximation of the Chance Constraints . 11
2.4 Mapping to Feasible Solutions . . . . . . . . . . . . . . . . . . . . . . . . . 13
3 Day-Ahead Pricing and Procurement at the Utility Company 16
3.1 Formulation of the Pricing and Procurement Problems . . . . . . . . . . . . 16
3.2 Solution based on Particle Swarm Optimization . . . . . . . . . . . . . . . . 18
4 Computer Simulations and Performance Comparisons 23
5 Conclusions 30

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