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研究生:林哲毅
研究生(外文):Che-ILin
論文名稱:具預測與電器排程功能之智慧家庭最佳化電能管理系統
論文名稱(外文):Home Energy Management System Considering Forecasting System and Appliance Scheduling
指導教授:楊宏澤楊宏澤引用關係
指導教授(外文):Hong-Tzer Yang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:118
中文關鍵詞:家庭電能管理系統電價制度預測系統負載最佳化管理需量反應電動車
外文關鍵詞:Home energy management systemelectricity tariffforecasting systemload optimizationdemand responseelectric vehicle
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本文提出一符合經濟效益並兼顧舒適與便利性之家庭電能管理系統,可用以自動管理家庭電器負載。本文詳細考慮即時預測系統、電價費率、家電排程最佳化及需量反應等關鍵之演算策略。此外,因應未來太陽能電池與電動車等再生能源與綠能電器可能扮演相當重要的角色,故亦將其納入系統考量。
負載排程最佳化可視為家庭電能管理系統重要一環,在滿足用戶預設條件下,可將負載排程使用於最經濟時段,因此本文開發一預測系統用以預測隔日太陽能電池發電量與估計隔日重要電器將消耗的電力及需執行的工作,為改善其預測精確度,本文提出結合並改善現有方法之預測系統,以滿足負載排程最佳化所需。依據溫濕度感測,本文利用模糊邏輯控制方法,在考慮使用者舒適度與電費容忍度下,設定冷氣機溫度;對其他可排程控制之負載,則在滿足使用者給定之限制下,以最佳化方法達到電費最小化目的。為避免未來電動車充電需求或因電力負載成長與同時使用,所可能帶來配電變壓器過載之問題,本文另提出家庭需量反應的控制方法與架構,令配電系統處於負載尖峰時可透過電能管理最佳化系統及用戶預先設定來卸除部分負載,以舒解上述問題。
根據部分國外文獻數據資料,本論文分別評估預測系統、模糊控制、電器排程及需量反應之功能,以驗證所提出的家庭電能管理系統之有效性。其中,包括以英國家用負載與日照數據驗證所提預測系統之準確性,並比較模糊控制及電器排程前後之電能費用,計算出可節省電費、驗證所建議電價結構與負載管理成效。最後,本文並模擬展示在系統接收需量反應指令時,如何依照用戶喜好和設定之家用電器限制,以執行負載控制與相關成本。
This thesis proposes a home energy management system (HEMS) with the consideration of both economic benefits and user’s comforts and conveniences to automatically control household appliances. All of the important elements, namely forecasting system, electricity tariff, appliance optimization, and demand response (DR), have all been taken into account. Moreover, renewable energy resources and green appliances such as solar power and electric vehicles (EVs) are also taken into consideration in this thesis since they are expected to play an important role in the very near future.
One of the important components of the HEMS system is to set the operation times of schedulable loads so that they can be operated in the most economical time slots while satisfying the pre-set constraints. Since predictability is of crucial for the system to know the electricity consumption of each appliance of next day and perform required actions, a high accuracy forecasting system is proposed by integrating and refining of traditional methods to enhance the performance of appliance optimization. By sensing the temperature and humidity, a fuzzy logic control method is applied on the thermostat settings of air-conditioners with the considerations of user’s comfort and electricity price. All other schedulable loads are controlled with optimally scheduled with user-defined constraints to achieve the goal of electricity payment minimization. To avoid the problem of overloading of distribution transformer causing by the EV charging demands and increase of electrical loads, a DR method and structure is proposed. With the proposed DR method, the HEMS can help residential users optimally manage the household loads according to the user pre-defined settings and relieve the problem mentioned when the total load of the distribution transformer is too high.
To examine the effectiveness of the proposed HEMS, the functions of forecasting system, fuzzy control, appliance scheduling, and demand response are separately evaluated in this thesis according to the data partly taken from foreign countries. That is, the household load and solar irradiance data of England is used to validate the accuracy of the proposed forecasting system. Then, comparisons on the electricity costs before and after the fuzzy control and appliance scheduling are made to calculate the savings. Lastly, an experiment is conducted to show how related costs are calculated and how load control is performed with user-defined preferences and constraints when a DR command is received.
摘要 i
ABSTRACT iii
誌謝 v
Table of Contents vi
List of Figures ix
List of Tables xii
Chapter 1. INTRODUCTION 1
1.1 Backgrounds and Motivations 1
1.2 Review of Literature 2
1.2.1 Forecasting Methods 3
1.2.2 Optimal Appliance Control Methods 9
1.2.3 EV and DR Methods 11
1.2.4 Electricity Tariff 14
1.3 Research Objective and Methods 19
1.4 Contributions of the Thesis 21
1.5 Organization of the Thesis 22
Chapter 2. OVERVIEW OF THE HEMS 23
2.1 Introduction 23
2.2 Overall System Structure 23
2.3 Mathematical Models of PV Module 27
2.4 Models of Home Appliances 32
2.5 Electricity Tariff of Taipower for the Load Management 39
Chapter 3. THE PROPOSED HEMS 42
3.1 Introduction 42
3.2 Proposed Forecasting Method 42
3.2.1 Input Data Selection 44
3.2.2 Wavelet Transformation 45
3.2.3 Artificial Neural Networks 46
3.2.4 Error Correcting Stage 47
3.2.5 Load Forecasting 48
3.2.6 PV Output Power Forecasting 48
3.3 Proposed Optimal Appliance Control Method 49
3.3.1 Real-Time Fuzzy Air-conditioning Control 49
3.3.2 Optimal Scheduling of Home Appliances 54
3.4 Proposed DR Method 57
3.4.1 System-Level Demand Response 58
3.4.2 Residential-Level Demand Response 61
Chapter 4. SIMULATION RESULTS 65
4.1 Introduction 65
4.2 The Data Used for TOU Tariff 66
4.3 Forecasting System Evaluation 68
4.3.1 Evaluation Indices 68
4.3.2 Test Bench of the Proposed Forecasting Method 69
4.3.3 Household Load Forecasting 77
4.3.4 PV Output Forecasting 83
4.4 Optimization Scheduling of Household Appliance 88
4.5 Demand Response 100
Chapter 5. CONCLUSION AND FUTURE PROSPECTS 109
5.1 Conclusion 109
5.2 Future Prospects 111
REFERENCES 112

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