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研究生: Erica Ocampo
Erica Ocampo
論文名稱: 基於分區粒子群法並考慮太陽光電變流器種類的離網微電網容量規劃
Optimal Sizing of PV-DG-Battery Considering PV Inverter Types Using Partitioned Step Particle Swarm Optimization
指導教授: 郭政謙
Cheng-Chien Kuo
口試委員: 張宏展
Hong-Chan Chang
楊念哲
Nien-Che Yang
張建國
Chien-Kuo Chang
陳鴻誠
Hong-Cheng Chen
李俊耀
Chun-Yao Lee
學位類別: 博士
Doctor
系所名稱: 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年: 2022
畢業學年度: 110
語文別: 英文
論文頁數: 78
中文關鍵詞: Hybrid Renewable Energy SourcesInvertersOptimal SizingBatteryParticle Swarm Optimization
外文關鍵詞: Hybrid Renewable Energy Sources, Inverters, Optimal Sizing, Battery, Particle Swarm Optimization
相關次數: 點閱:186下載:6
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  • Inverters have different techno-economic specifications which contribute to the changes in system sizing. Therefore, it is essential to know which inverter is more beneficial for certain considerations. In this research, the optimal designs for a DG-PV and DG-PV-battery off-grid/ island system using the central, string, multistring, and AC module types of inverters were compared to determine the recommended inverter configuration. The problem was solved as a multi-objective optimization considering both cost and reliability. The results were matched in terms of cost, reliability, renewable energy penetration, renewable energy curtailment, and changes in battery cost. The results showed that the central inverter can be a good choice when a balance between cost and reliability is considered. The string inverter is best recommended for the least cost but is highly affected by the decrease in battery cost. The multistring can be recommended for higher renewable energy penetration. Meanwhile, the AC module can be the choice for high reliability and low curtailment. Furthermore, the Partitioned Step Particle Swarm optimization was proven effective not only in solving the sizing problem but as well as constrained and unconstrained benchmark functions compared with other algorithms.


    Inverters have different techno-economic specifications which contribute to the changes in system sizing. Therefore, it is essential to know which inverter is more beneficial for certain considerations. In this research, the optimal designs for a DG-PV and DG-PV-battery off-grid/ island system using the central, string, multistring, and AC module types of inverters were compared to determine the recommended inverter configuration. The problem was solved as a multi-objective optimization considering both cost and reliability. The results were matched in terms of cost, reliability, renewable energy penetration, renewable energy curtailment, and changes in battery cost. The results showed that the central inverter can be a good choice when a balance between cost and reliability is considered. The string inverter is best recommended for the least cost but is highly affected by the decrease in battery cost. The multistring can be recommended for higher renewable energy penetration. Meanwhile, the AC module can be the choice for high reliability and low curtailment. Furthermore, the Partitioned Step Particle Swarm optimization was proven effective not only in solving the sizing problem but as well as constrained and unconstrained benchmark functions compared with other algorithms.

    Acknowledgment iii Abstract iv Table of Contents v List of Tables vii List of Figures viii Abbreviations ix Chapter 1 Introduction 1 1.1. Background of the Study 1 1.2. Statement of the Problem 2 1.3. Research Objectives 3 1.4. Conceptual Framework 3 1.5. Significance of the Study 4 1.6. Scope and Limitations 4 Chapter 2 Literature Review 6 2.1. Optimal Sizing of Generators 6 2.2. Technical and Economic Studies on Inverters 9 2.3. Energy Management Strategy 10 2.4. Algorithms 11 Chapter 3 Methodology 15 3.1. Hybrid Renewable Energy System Design 15 3.2. Optimization Algorithm 21 Chapter 4 Results for PSPSO 28 4.1. Determination of PSPSO constants 28 4.2. Function Evaluation 29 4.3. Discussions 33 Chapter 5 HRES Design and Analysis 34 5.1. Single Objective Approach 34 5.2. Multi-Objective Approach 39 5.3. Analysis of Future Cost Reduction on Batteries 45 5.4. Discussions 48 Chapter 6 Conclusion and Recommendations 50 6.1. Conclusion 50 6.2. Recommendations and Future Works 50 References 51 Appendices 57 Appendix A. Benchmark Functions 57 Appendix B. Convergence Curves of PSO Variants 59 Appendix C. Results of Benchmark Functions 66

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