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研究生:張軒豪
研究生(外文):Hsuan-Hao Chang
論文名稱:多目標方法應用於智慧電網之隱私保護與能源管理
論文名稱(外文):Multiobjective Approach to Privacy Preservation and Energy Scheduling in Smart Grid
指導教授:邱偉育
指導教授(外文):Wei-Yu Chiu
口試委員:楊念哲蘇恆毅
口試委員(外文):Nien-Che YangHeng-Yi Su
口試日期:2017-07-14
學位類別:碩士
校院名稱:元智大學
系所名稱:電機工程學系
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:2017
畢業學年度:105
語文別:英文
論文頁數:43
中文關鍵詞:模糊系統智慧電網多點模糊預測負載預測綠色房屋能源管理系統智慧家庭帕雷托最佳化隱私保護使用者中心的多目標方法
外文關鍵詞:fuzzy systemssmart gridsmultipoint fuzzy predictionload forecastinggreen buildingsenergy management systemsmart homePareto optimalityprivacy protectionuser-centric multiobjective approach.
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一種使用模糊邏輯和大數據的新型負載預測機制被提出,稱之為多點模糊預測。多點模糊預測可與綠色建築和可再生能源相結合,以減少尖峰負載和能源消耗來源。而在負載預測的基礎上,能源管理系統可以在電價較高時對儲能裝置進行放電,並在電價較低時對其進行充電。最後透過實際的電力需求數據來說明多點模糊預測方案的有效性。再對負載需求進行預測後,本文亦探討了智能家居中搭配儲能裝置的功率調度問題。考慮到兩個目標:最小化能源成本和最大化用戶隱私的保護。一個多目標方法被發展來實現住宅用戶的這兩個目標。首先提出了一個多目標優化問題,並提出了一種混合進化演算法。通過解決多目標優化問題,可以獲得帕雷托最佳解,並進一步透過智能家居的中央控制系統來調整負載和儲能裝置的狀態。模擬結果說明了,在與現有的能源管理方法相比下,本文所提出的多目標方法能在保持用戶隱私的同時保持合理的能源成本;同時它是可擴展的一組智能家居,進而實踐一個優越的峰值因數。
A novel load forecasting mechanism that uses fuzzy logic and big data, termed multipoint fuzzy prediction (MPFP), is proposed. The MPFP can be combined with green buildings and renewable energy sources to reduce peak loads and energy consumption. On the basis of a prediction of load curves, the energy management system (EMS) can discharge energy storage devices when electricity prices are high and charge them when electricity prices are low, reducing costs. Real power demand data were employed to illustrate the validity of the proposed MPFP scheme. After having the prediction of power demand, a power scheduling in a smart home equipped with an energy storage device is investigated. Two objectives are considered: minimizing the energy costs and maximizing the privacy protection. A multiobjective approach is developed to achieve these objectives of a residential user. A multiobjective optimization problem (MOP) is first formulated, and a hybrid evolutionary algorithm is proposed. By solving the MOP, a Pareto
optimal solution can be obtained and is further used by the central control unit of the smart home to adjust the loads and storage level over time. Simulation results show that as compared to existing energy management methods, the proposed multiobjective approach can maintain a reasonable energy cost while preserving the user’s privacy at a satisfying level; and it is scalable to a group of smart homes so that a superior peak-to-average ratio (PAR) can be achieved.
書名頁 i
論文口試委員審定書 ii
中文摘要 iii
英文摘要 iv
Content v
List of Table vii
List of Figure viii
Symbol List x
1 Introduction 1
1.1 Research Motive 1
1.2 Research Background 1
1.3 Research Method 2
1.4 Research Framework 3
2 Load Forecasting for Reducing Energy Cost 4
2.1 Exciting Method of Load Forecasting 4
2.2 Proposed Method of Load Forecasting 5
2.3 System Architecture of green buildings 6
2.4 Multipoint Fuzzy Prediction Scheme 9
3 Multiobjective Approach to Privacy Preservation and Energy Cost Minimization 13
3.1 System Model of a smart home 13
3.1.1 Home Appliances 13
3.1.2 Energy Storage Device 15
3.1.3 Electricity Price 17
3.2 User-Centric Multiobjective Approach 18
4 Simulation Results 24
4.1 Simulation Results of MPFP 24
4.2 Simulation Results of User-Centric Multiobjective Approach 31
5 Conclusion 37
Reference 38
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