臺灣博碩士論文加值系統

本論文建構了一個應用智慧充電和網路重組的程序，該技術的目標在於轉移因大量電動車的出現對配電系統運轉成本造成的衝擊。
智慧充電將運用價格取向的需量反應，電動車的充電情形將取決於時間電價與充電器的使用中情況決定，本論文將同時考慮日前市場、時間電價預測與負載曲線進行分析。每小時的電動車變化等級的計算與最小化充電成本的目標將以最佳線性規劃模型計算並達成，完成最佳充電排程後，將總充電負載加入配電系統的負載曲線中。而網路重組考慮了切換成本，藉由減少電力的傳輸損失和切換操作達到減少配電系統的操作成本的目標，而本論文採用基因演算法決定每小時的網路結構。本論文將以智慧電網的骨架修改IEEE系統進行案例測試，模擬結果將驗證本方法的效用。

A procedure for implementing smart charging and network reconfiguration is formulated in this thesis. This technique aims to mitigate the negative impact in the operating cost that may occur due to the presence of large number of electric vehicles connected into power distribution systems.
Price-based demand response is implemented for smart charging, where the electric vehicles are charged based on their availability and the energy price. A day-ahead market is considered, from where the forecasted energy price and initial load profile is collected. Linear programming optimization model is used to determine the hourly charging level of each electric vehicle with the goal of minimizing the charging costs.
After the smart charging is carried out, the total scheduled charging load is added to the forecasted initial load profile of the distribution system. Network reconfiguration considering the switching cost is then performed to reduce the operating cost of the distribution system resulting from power losses and the switching operation. Genetic algorithm technique is adopted to determine the hourly configuration of the system.
The proposed procedure is tested in a modified IEEE system under smart grid framework. Simulations results are provided to validate the effectiveness of the proposed method.

Thesis/dissertation verification letter in Chinese………………………………………...i
Thesis/dissertation verification letter in English………………………………………...ii
Acknowledgement………………………………………………………………………iii
Abstract in Chinese……………………………………………………………………...iv
Abstract in English………………………………………………………………………v
CHAPTER 1: Introduction
1.1 Introduction…………………………………………..…………………………….1
1.2 Objective……………………………………………………………………..……..2
1.3 Thesis Outline………………………………………………………………...……..3
CHAPTER 2: Basic Concepts
2.1 Power Distribution Systems………………………………………………………..4
2.1.1 Distribution System Operation…………………………………………………….5
2.1.2 Distribution Load Flow……………………………………………………………6
2.2 Smart Grid………………………………………………………………………….8
2.2.1 Energy Market……………………………………………………………………10
2.2.2 Day-ahead Energy Market…………………………………………………...…..11
2.2.3 Demand Response………………………………………………………………..12
2.3 Electric Vehicles…………………………………………………………………...13
2.3.1 Main Types of Electric Vehicles…………………………………………………13
2.3.2 Electric Vehicle Charging………………………………………………………..14
2.3.3 Battery Capacity………………………………………………………………….14
2.3.4 Initial Charge……………………………………………………………………..14
vii
CHAPTER 3: Electric Vehicle Charging Model
3.1 General Description……………………………………………………………….15
3.2Car Park Model…………………………………………………………………....16
3.2.1 Electric Vehicle Specification……………………………………………………17
3.2.2 Arrival and Departure Table……………………………………………………...18
3.2.3 Initial Charge Table………………………………………………………………19
3.2.4 Energy to be Supplied……………………………………………………………20
3.3 Smart Charging Problem…………………………………………………………21
3.3.1 Optimization Method……………………………………………………………..22
3.3.2 Solution Representation and Procedure…………………………………………..23
3.3.3 Smart Charging Procedure………………………………………………………..24
CHAPTER 4: Distribution System Operating Cost
4.1 General Description……………………………………………………………….25
4.2 Definition of Network Reconfiguration………………………………………….27
4.2.1 Mathematical Model of Network Reconfiguration………………………………27
4.3 Operating Cost Reduction Problem……………………………………………...29
4.3.1 Switching Cost……………………………………………………………………30
4.3.2 Optimization Method……………………………………………………………..31
4.3.3 Genetic Algorithm………………………………………………………………..32
4.3.4 Flow Chart of Genetic Algorithm………………………………………………...34
4.3.5 Solution Representation…………………………………………………………..35
4.3.6 Proposed Procedure for Operating Cost Reduction………………………………36
viii
CHAPTER 5: Simulation Results
5.1 Simulation Model………………………………………………………………….37
5.1.1 Distribution System Description…………………………………………………37
5.1.2 Line Data…………………………………………………………………………38
5.1.3 Forecasted Energy Price and Initial Load Profile………………………………...39
5.1.4 Car Park Data……………………………………………………………………..40
5.1.5 Car Park Occupancy……………………………………………………………...41
5.2 Study Cases………………………………………………………………………..42
5.2.1 Scenario 1: Smart Charging………………………………………………………42
5.2.2 Scenario 2: Network Reconfiguration……………………………………………48
5.2.3 Scenario 3: Proposed Method…………………………………………………….50
5.2. Results Summary…………….…………………………………………………….52
CHAPTER 6: Conclusion and Future Work
6.1 Conclusion………………………………………………………………………….53
6.2 Future Work………………………………………………………………………...54
References……………………………………………………………………………...55

[1] Hadi Saadat, “Power System Analysis”, Mc Graw Hill, Second Edition 2002.
[2] William H. Kersting, “Distribution System Modelling and Analysis, Second Edition.
[3] J. Zhu, “Optimization of Power System Operation”, 2009.
[4] Ali Keyhani, “Design of Smart Power Grid Renewable Energy Systems”, John Wiley & Sons.
[5] Jen-Hao Teng, “A direct approach for distribution system load flow solutions,” IEEE Transactions on Power Delivery, vol. 18, no. 3, pp. 882–887, 2003.
[6] Energy Department of the USA, Technology Development, Smart Grid at site: http://energy.gov/oe/services/technology-development/smart-grid
[7] William W. Hogan, “Independent System Operator (ISO) for a Competitive Electricity Market”, Center for Business and Government, Harvard University, 1998.
[8] W. Liu, Q. Wu, F. Wen et al. “Day-Ahead Congestion Management in Distribution Systems Through Household Demand Response and Distribution Congestion Prices”, IEEE Transactions on Smart Grid, Vol. 5, Issue 6, Pages 2739-2747, 2014.
[9] S. Huang, Q. Wu, Z. Liu, and A. H. Nielsen, “Review of congestion management methods for distribution networks with high penetration of distributed energy resources,” in IEEE PES Innovative Smart Grid Technologies, Europe. IEEE, Oct. 2014, pp. 1–6.
[10] Alharbi, Abdulelah Yousef, "Impact of plug in electric vehicle battery charging on a distribution system based on real-time digital simulator" (2013). Theses and Dissertations.
[11] M. Alonso, H. Amaris and J. Germain et al. “Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms”, Energies, Vol.7, Issue 4, Pages 2449-2475, 2014.
- 56 -
[12] D. Recalde Melo, H. Gooi and T. Massier, “Charging of Electric Vehicles and Demand Response Management in a Singaporean Car Park”, 2014 49th International Universities Power Engineering Conference (UPEC).
[13] Matlab, Optimization Toolbox User’s Guide.
[14] P. Ravibabu, K. Venkatesh, and C. S. Kumar, “Implementation of genetic algorithm for optimal network reconfiguration in distribution systems for load balancing,” 2008 IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering, pp. 124–128, 2008.
[15] Natanael Acencio Rijo, “Capacitor Allocation Study Accounting For Load and Distributed Solar Generation Uncertainty with the Use of 2PEM Probabilistic Load Flow”, Master Thesis, 2015.
[16] Matlab R2016a – Global Optimization Toolbox User’s Guide.
[17] Wen-Jye Shyr, “Parameters Determination for Optimum Design by Evolutionary Algorithm”, Convergence and Hybrid Information Technologies, Marius Crisan (Ed.), ISBN: 978-953-307-068-1, 2010.
[18] C. Power and L. Company, “Distribution feeder reconfiguration for loss reductions.”, vol. 3, no. 3, pp. 1217–1223, 1988.