以粒子群最佳化負載調度策略於家庭耗能之優化

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姓名

范德瑞(Dery Elfando) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

以粒子群最佳化負載調度策略於家庭耗能之優化
(Optimizing Household Energy Consumption with Particle Swarm Optimization (PSO)-Based Load Scheduling Strategy)

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至系統瀏覽論文 ( 永不開放)

摘要(中)

由於技術進步帶來的現代文明對電力的持續需求，有效的能源管理至關重要。這項研究旨在透過策略性負載調度來優化家庭能源消耗，特別強調減少夏季和冬季的尖峰負載和電力成本。為了提高能源效率和永續性，本研究使用粒子群優化（PSO）和遺傳演算法（GA）技術將光伏（PV）系統結合在微電網框架內。國立中央大學的白色能源之家提供了有關家庭能源使用和光伏發電的實際數據，這些數據是在一周內收集的。為了確定消耗趨勢和尖峰負載特徵，需要對資料進行評估。 PSO 和 GA 演算法均在 MATLAB 中實現，以優化 11 種家用電器的調度，同時考慮使用時間 (ToU) 率、最大需求限制和運行持續時間等限制。比較分析是本研究的核心組成部分，評估使用 PSO 和 GA 優化前後的負載調度效能。結果表明，優化後夏季和冬季場景的尖峰負荷和電力成本均顯著降低。在夏季場景中，使用 PSO 後，尖峰負載從 4.08 kWh 降低到 3.48 kWh，電費從 92.49 新台幣降低到 52.60 新台幣。同樣，在冬季場景中，使用 PSO 後，尖峰負載從 4.48 kWh 降低到 3.63 kWh，電費從 115.16 新台幣降低到 81.41 新台幣。 GA演算法也有所改進：在夏季場景中，尖峰負載降低至3.48kWh，成本降低至新台幣68.42；而在冬季場景中，尖峰負載降低至3.73kWh，成本降低至新台幣90.89。對比研究表明，PSO 在兩個季節都比 GA 更有效地降低了高峰需求和電力成本。 PSO 顯示出更顯著的成本降低，以及高能量設備與光伏高產量和電價較低時期的更好協調。這些發現證明了 PSO 和 GA 在實現具有成本效益和可持續能源消耗方面的有效性，其中 PSO 被證明是更好的方法。這項研究為家庭負載調度提供了實用且高效的解決方案，展示了 PSO 和 GA 在能源管理方面的潛力，並促進了再生能源在住宅環境中的更廣泛採用。

摘要(英)

Effective energy management is essential because of the constant need for power in modern civilization brought about by technological improvements. This research aims to optimize household energy consumption through strategic load scheduling, with a particular emphasis on reducing peak loads and electricity cost in the summer and winter. In order to improve energy efficiency and sustainability, this study combines photovoltaic (PV) systems inside a microgrid framework using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) techniques. To determine consumption trends and peak load characteristics, the data is evaluated. Both PSO and GA algorithms are implemented in MATLAB to optimize the scheduling of 11 household appliances, considering constraints such as Time of Use (ToU) rates, maximum demand limits, and operational durations. Comparative analysis is a core component of this study, evaluating the performance of load scheduling before and after optimization using PSO and GA. The results indicate significant reductions in peak loads and electricity costs for both summer and winter scenarios post-optimization. In the summer scenario, peak load decreased from 4.08 kWh to 3.48 kWh, and electricity costs reduced from NTD 92.49 to NTD 52.60 using PSO. Similarly, in the winter scenario, the peak load decreased from 4.48 kWh to 3.63 kWh, and electricity costs reduced from NTD 115.16 to NTD 81.41 using PSO. The GA algorithm also showed improvements: in the summer scenario, peak load decreased to 3.48 kWh and costs to NTD 68.42, while in the winter scenario, peak load decreased to 3.73 kWh and costs to NTD 90.89. The comparison research shows that PSO reduces peak demand and power costs more effectively than GA in both seasons. PSO showed a more significant reduction in costs and better alignment of high-energy appliances with periods of high PV production and lower electricity rates. These findings demonstrate the effectiveness of PSO and GA in achieving cost-effective and sustainable energy consumption, with PSO proving to be the superior method. This research provides a practical and efficient solution for household load scheduling, showcasing the potential of PSO and GA in energy management and promoting the broader adoption of renewable energy sources in residential settings.

關鍵字(中)

★ 負載調度
★ 再生能源
★ 光電系統
★ 微電網
★ 家庭能源消耗
★ 粒子群優化（PSO）
★ 遺傳演算法（GA）

關鍵字(英)

★ Load Scheduling
★ Renewable Energy
★ Photovoltaic System
★ Microgrid
★ Household Energy Consumption
★ Particle Swarm Optimization (PSO)
★ Genetic Algorithm (GA)

論文目次

論文摘要 I
Abstract II
Acknowledgement III
Table of Contents IV
List of Figure VII
List of Table IX
I. Introduction 1
1.1 Research Background 1
1.2 Motivation 3
1.3 Problem and Study Objective 5
1.4 Gap and Contribution 5
1.5 Paper Organization 6
II. Microgrid and Demand Side Management (DSM) 8
2.1 Microgrid System 8
2.2 Renewable Energy Sources (RES) 10
2.2.1 Photovoltaic (PV) 10
2.2.1.1 Constraints 13
2.3 Demand Side Management (DSM) 14
2.3.1 Demand Response (DR) 17
2.3.2 Electric Appliance 20
2.3.3 Energy Consumption Model 21
2.3.3.1 Load Model 22
2.3.3.2 Pricing Model 22
III. Research Methodology (Optimization) 25
3.1 Research Approach 25
3.2 Research Location and Timeframe 25
3.3 Research Method 26
3.3.1 Optimization 27
3.3.2 PSO Algorithm 27
3.3.3 Objective Function 33
3.3.4 Genetic Algorithm (GA) 33
3.4 Load Scheduling 36
3.4.1 Constraints 39
3.5 Research Flowchart 40
3.6 Technique for Analyzing Research Flowchart 41
IV. Result and analysis 43
4.1 White Energy House Data 44
4.1.1 Summer Season (June 19th to June 25th, 2023) 44
4.1.1.1 PV Production per week 45
4.1.1.2 Energy Load Consumption per week 46
4.1.2 Winter Season (December 25th to December 31st, 2023) 47
4.1.2.1. PV Production per week 48
4.1.2.1. Energy Load Consumption per week 49
4.2 Household Load Scheduling Strategy 49
4.2.1 Scenario 1. Summer Season 50
4.2.1.1 Case 1. Baseline Analysis (without Optimization Algorithm) 53
A. Peak Load Consumption 53
B. Electricity Cost 54
4.2.1.2 Case 2. Optimized Scheduling with PSO Algorithm 55
A. Peak Load 55
B. Electricity Cost 56
C. PSO Convergence 57
4.2.1.3 Case 3. Optimized Scheduling with GA Algorithm 58
A. Peak Load 58
B. Electricity Cost 59
C. Convergence of Penalty Values in GA Optimization 60
4.2.2 Scenario 2. Winter Season 61
4.2.2.1. Case 1. Baseline Analysis (without Optimization Algorithm) 63
A. Peak Load Consumption 63
B. Electricity Cost 64
4.2.2.2. Case 2. Optimized Scheduling with PSO Algorithm 65
A. Peak Load Consumption 65
B. Electricity Cost 66
C. PSO Convergence 67
4.2.2.3 Optimized Scheduling with GA Algorithm 68
A. Peak Load 68
B. Electricity Cost 69
C. Convergence of Penalty Values in GA Optimization 69
4.3 Evaluation of Load Scheduling Strategies: Comparative Analysis of PSO and GA Algorithms 70
4.3.1 Comparative Impact of Optimization Algorithms on Seasonal Peak Load Reduction 70
4.3.1.1 Comparative Analysis of Seasonal Peak Load Calculations 70
4.3.1.2 Comparative Analysis of Seasonal Peak Load Calculations 72
V. CONCLUSION 74
REFERENCES 75

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指導教授

陳正一(Cheng-I Chen)

審核日期

2024-8-20

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