微電網能源管理系統優化調度基於螢火蟲移動迴歸策略

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姓名

阿古(I GEDE AGUS PURWA) 查詢紙本館藏

畢業系所

電機工程學系在職專班

論文名稱

微電網能源管理系統優化調度基於螢火蟲移動迴歸策略
(Optimal Dispatch for Microgrid Energy Management System Based on Firefly Moving Regression Strategy)

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摘要(中)

隨著電力需求的不斷增長和能源危機的出現，微電網的發展變得更加普遍. 再生能源在微電網中的利用更具優勢. 本研究探討了將分散式發電和太陽能光伏系統整合到微電網中.目標函數的主要目的是最大限度地減少太陽能光電等再生能源發電與微電網系統內的需求之間的功率差異累積.發電或從公用電網接收電力的能力取決於微電網的能源容量.在此背景下，螢火蟲演算法和移動回歸被用作解決討論中的最佳化問題的有效工具.
所提出的方法正在微電網領域應用，並且正在將實施演算法的結果與原始調度方法進行比較.在調度過程中，分配一定電量作為備用電源，以減輕停電影響，並提高微電網整體供電可靠性。在這項工作中，使用 MATLAB 模擬了所提出的方法在電力調度策略中的可行性。 Modbus通訊協定和演算法的實作通常使用C程式語言進行。一台支援程式的工業電腦已放置在國立中央大學的微電網一號中，以評估演算法的可行性以及調度方法在系統供電可靠性方面的最佳化結果。

摘要(英)

With the growing demand for electricity and the emergence of energy crises, the development of microgrids (MGs) has become more common. Renewable energy source utilization in Microgrids is more advantageous. This study examines the integration of Distributed Generation (DG) and Solar Photovoltaic (PV) systems into a Microgrid. The primary aim of the objective function is to minimize accumulation of power differences between the generation from renewable energy sources (RES), such as solar photovoltaic, and the demand within the microgrid system. The ability to produce electricity or receive power from the utility grid (UG) is dependent upon the energy capacity of the Microgrid. In this context, the Firefly Algorithm (FA) and Moving Regression (MR) are employed as effective tools for addressing the optimization problem as discussed.
The proposed methodology is being applied in the domain of Microgrid, and the outcomes of implementing the Firefly algorithm are being compared with the original dispatch methods. During the process of dispatching, a certain amount of power is allocated as backup power to mitigate the impact of power outages, hence improving the overall supply reliability of the microgrids. In this work, the viability of the proposed method in power dispatch strategy is simulated using MATLAB. The implementation of the Modbus communication protocol and algorithm is often carried out using the C programming language. A program-enabled industrial computer has been placed in Microgrid 1 at National Central University to assess the viability of the algorithm and the optimization outcome of the scheduling approach in terms of system supply reliability.

關鍵字(中)

★ 微電網調度策略
★ 螢火蟲演算法
★ 電力調度策略

關鍵字(英)

★ Microgrid (MG)
★ Firefly Algorithm (FA)
★ Power Dispatching Strategy

論文目次

TABLE OF CONTENT
TITLED i
CHINESE ABSTRACT ii
ABSTRACT iii
ACKNOWLEDGEMENT v
TABLE OF CONTENT vii
FIGURE CATALOG ix
TABLE DIRECTORY xi
I. INTRODUCTION 1
1-1 Motivation 2
1-2 Study Objective 3
1-3 Gap and Contribution 4
1-4 Battery Energy Storage System 5
1-5 Paper Organization 5
II. MICROGRID AND OBJECTIVE DESIGN 6
2-1 Microgrid System Type 6
2-1-1 Grid connection Mode 7
2-1-2 Islanded Mode 8
2-1-3 Single Microgrid System 9
III. OPTIMIZATION FORMULATION 12
3-1 Optimal Dispatch 13
3-2 Main Power 14
3-2-3 Grid operating costs 16
3-3 Solar Photovoltaic System 17
3-4 Energy Storage System 18
3-4-1 Energy Storage System Life Factor 19
3-5 Fuel Cell 22
3-6 Electrical load classification 23
3-7 Firefly Algorithm 25
3-8 Moving Regression 28
3-8-1 Moving Average 29
3-8-2 Linear Regression 29
3-8-3 Combine Moving Average and Linear Regression 30
3-9 Constraint 31
3-10 Energy Management System 32
3-10-1 Ruled Based Strategy 33
IV. SIMULATION AND IMPLEMENTATION RESULT 35
4-1 Simulink Simulation 36
4-2 Scenario One Day Data simulation 49
4-2-1 Case 1: Sunny Day on Grid Connected Mode 49
4-2-2 Case 2: Sunny Day On Islanded Mode 51
4-3 Scenario Three Day Data simulation 53
4-3-1 Case 3: Difference weather on Grid Connected Mode 53
4-3-2 Case 4: Difference weather on Islanded Mode 56
V. CONCLUSION AND FUTURE RESEARCH 64
5-1 Conclusion 64
5-2 Future Research 65
REFERENCES 66

參考文獻

REFERENCES
[1] G. O. Young, “Synthetic structure of industrial plastics,” in Plastics, 2nd ed., vol. 3, J. Peters, Ed. New York, NY, USA: McGraw-Hill, 1964, pp. 15–64.
[2] W.-K. Chen, “Linear Networks and Systems”. Belmont, CA, USA: Wadsworth, 1993, pp. 123–135.
[3] J. U. Duncombe, “Infrared navigation—Part I: An assessment of feasibility,” IEEE Trans. Electron Devices, vol. ED-11, no. 1, pp. 34–39, Jan. 1959,10.1109/TED.2016
[4] I. Alsaidan, A. Khodaei, and W. Gao, ‘‘A comprehensive battery energy storage optimal sizing model for microgrid applications,’’ IEEE Trans. Power Syst., vol. 33, no. 4, pp. 3968–3980, Jul. 2018
[5] M. Al-Saadi, M. Al-Greer, and M. Short, ‘‘Strategies for controlling microgrid networks with energy storage systems: A review,’’ Energies, vol. 14, no. 21, p. 7234, Nov. 2021.
[6] M. Faisal, M. A. Hannan, P. J. Ker, A. Hussain, M. B. Mansor, and F. Blaabjerg, ‘‘Review of energy storage system technologies in microgrid applications: Issues and challenges,’’ IEEE Access, vol. 6, pp. 35143–35164, 2018
[7] P. Singh, J. S. Lather, “Dynamic current sharing, voltage and SOC regulation for HESS based DC microgrid using CPISMC technique,” Journal of Energy Storage, vol. 30, 101509, 2020
[8] K. Dahech, M. Allouche, T. Damak, F. Tadeo, “Backstepping sliding mode control for maximum power point tracking of a photovoltaic system,” Electric Power Systems Research, vol. 143, pp. 182-188, 2017.

[9] V. Suresh, N. Pachauri, T. Vigneysh, “Decentralized control strategy for fuel cell/PV/BESS based microgrid using modified fractional order PI controller,” International Journal of Hydrogen Energy, vol. 46, pp. 4417-4436, 2021
[10] S.Vasantharaj, V.Indragandhi, V.Subramaniyaswamy,Y. Teekaraman, R. Kuppusamy, S. Nikolovski, “Efficient Control of DC Microgrid with Hybrid PV—Fuel Cell and Energy Storage Systems,” Energies, vol. 14, 3234, 2021.
[11] G. Wu, S. Ishida and H. Yin, "DC Voltage Stabilization in DC/AC Hybrid Microgrid by Cooperative Control of Multiple Energy Storages," 2019 IEEE Third International Conference on DC Microgrids (ICDCM), Matsue, Japan, 2019, pp. 1-5, doi: 10.1109/ICDCM45535.2019.9232764.
[12] N. Gurung, A. Vukojevic and H. Zheng, "Demonstration of Islanding and Grid Reconnection capability of a microgrid within distribution system," 2022 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia), Singapore, 2022, pp. 655-659, doi: 10.1109/ISGTAsia54193.2022.10003608.
[13] V. Dixit, M. Jadhwani, A. Pandey and F. Kazi, "A Hybrid Islanding Detection Scheme For Grid-tied PV Microgrid," 2021 IEEE 18th India Council International Conference (INDICON), Guwahati, India, 2021, pp. 1-6, doi: 10.1109/INDICON52576.2021.9691700.
[14] Z. Wang, X. Cheng, L. Wang, X. Zong, X. Lin and S. He, "Distributed Transaction Optimization for Multiple Microgrids in Grid-Connected and Islanded Modes," 2020 IEEE 3rd Student Conference on Electrical Machines and Systems (SCEMS), Jinan, China, 2020, pp. 398-402, doi: 10.1109/SCEMS48876.2020.9352373.
[15] 台灣電力公司.(112 C.E., January1)表燈時間電價-簡易型時間電價三段.
[16] S. Ren, J. Wang and M. Ma, "Multi-Objective Optimal Control of Micro-Grid Based on Economic Model Predictive Control," 2019 Chinese Control Conference (CCC), Guangzhou, China, 2019, pp. 2994-2999, doi: 10.23919/ChiCC.2019.8865871.
[17] Elsayed AT, Mohamed AA, Mohammed OA (2015), “DC microgrids and distribution systems: an overview”., Electr Power Syst Res 119:407–417.
[18] Bayindir R, Hossain E, Kabalci E, Billah KMM (2015), “Investigation on north American microgrid facility”., Int J Renew Energy Res 5(2):558–574.
[19] Elsayed AT, Mohamed AA, Mohammed OA (2015) “DC microgrids and distribution systems: an overview”. Electr Power Syst Res 119:407–417.
[20] L. He, Z. Wei, H. Yan, K. -Y. Xv, M. -y. Zhao and S. Cheng, "A Day-ahead Scheduling Optimization Model of Multi-Microgrid Considering Interactive Power Control," 2019 4th International Conference on Intelligent Green Building and Smart Grid (IGBSG), Hubei, China, 2019, pp. 666-669, doi: 10.1109/IGBSG.2019.8886341..
[21] N. R. Wee, J. J. Jamian, S. N. Syed Nasir and N. M. Zaid, "Enhanced Rule
Based Energy Management System for an Islanded Microgrid," 2022 IEEE International Conference on Power and Energy (PECon), Langkawi, Kedah,
Malaysia, 2022, pp. 144-148, doi: 10.1109/PECon54459.2022.9988802
[22] D. M. Minhas, J. Meiers and G. Frey, "A Rule-based Expert System for Home Power Management Incorporating Real-Life Data Sets," 2022 3rd International Conference on Smart Grid and Renewable Energy (SGRE).
[23] A. Su and G. Yerui, "Multi Objective Optimal Dispatching of Microgrid Considering Multiple Random Variables Based on Improved NSGA-III," 2021 IEEE 4th International Electrical and Energy Conference (CIEEC), Wuhan, China, 2021, pp. 1-6, doi: 10.1109/CIEEC50170.2021.9510651.
[24] A. Maulik and D. Das, "Multi-objective optimal dispatch of AC-DC Hybrid Microgrid," 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Kota Kinabalu, Malaysia, 2018, pp. 82-87, doi: 10.1109/APPEEC.2018.8566354.
[25] B. Li, J. Wang and N. Xia, "Dynamic Optimal Scheduling of Microgrid Based on ε constraint multi-objective Biogeography-based Optimization Algorithm," 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE), Dalian, China, 2020, pp. 389-393, doi: 10.1109/CACRE50138.2020.9230079.
[26] L. Cong, Z. Chuanpu, W. Kuan, S. Lizhu, W. Qingyu and Z. Wenhai, "Multi-objective Capacity Optimal Allocation Of Photovoltaic Microgrid Energy Storage System Based On Time-sharing Energy Complementarity," 2021 International Conference on Power System Technology (POWERCON), Haikou, China, 2021, pp. 1123-1129, doi: 10.1109/POWERCON53785.2021.9697536.
[27] L. Shiguang, S. Yuchen, G. Zhengzhong, L. Guangjie and X. Yujuan, "Multi-objective Optimization Scheduling of Micro-grid with Energy Storage under Time-of-Use Price Mechanism," 2021 6th International Conference on Power and Renewable Energy (ICPRE), Shanghai, China, 2021, pp. 636-641, doi: 10.1109/ICPRE52634.2021.9635286.
[28] L. Shuai-shuai, W. Hui, W. Ming-yue, L. Zi-jie and W. Di, "Multi-objective day-ahead optimal scheduling of wind-solar microgrid considering V2G technology," 2022 34th Chinese Control and Decision Conference (CCDC), Hefei, China, 2022, pp. 1091-1096, doi: 10.1109/CCDC55256.2022.10033679.
[29] Planas E, Andreu J, Gárate JI, Martínez De Alegría I, Ibarra E (2015)AC andDC technology in microgrids: a review. RenewSustainEnergyRev43:726–749.
[30] I.M Ibrahim, W.A. Omran, and A.Y.Abdelazis, “Optimal Sizing of Microgrid system using hybrid firefly and particle swarm optimization,” in Proc. 22nd Int. Middle East Power Syst. Conf.(MEPCON). Asyut, Egypt: Assiut Univ., Dec. 2021, pp.287-293
[31] P. K. Ray and A. Mohanty, ‘‘A robust firefly–swarm hybrid optimization for frequency control in wind/PV/FC based microgrid,’’ Appl. Soft Comput., vol. 85, Dec. 2019, Art. no. 105823.
[32] J. S. J. Malar, A. B. Beevi, and M. Jayaraju, ‘‘Efficient power flow management in hybrid renewable energy systems,’’ IETE J. Res., vol. 69, no. 2, pp. 1088–1100, 2020.
[33] L. Xu, X. Ruan, C. Mao, B. Zhang, and Y. Luo, ‘‘An improved optimal sizing method for wind-solar-battery hybrid power system,’’ IEEE Trans. Sustain. Energy, vol. 4, no. 3, pp. 774–785, Jul. 2013.
[34] . Mohamed, A. A. Zaki Diab, and H. Rezk, ‘‘Partial shading mitigation of PV systems via different meta-heuristic techniques,’’ Renew. Energy, vol. 130, pp. 1159–1175, Jan. 2019
[35] Xin-she yang, ”Cukoo search and Firefly Algorithm. In Studies in Computational Intelligence,” Springer link., col. 516,1989.
[36] Olivia Ferlita, “Optimasi Maximum Power Point Tracking pada Array Photovoltaic menggunakan Algoritma Particle Swarm Optimization dan Firefly Algorithm,” repository.ub.ac.id, Jul. 2018.

[37] A.Addisu, L. George, P. Courbin, and V. Sciandra, ‘‘Smoothing of renewable energy generation using Gaussian-based method with power constraints,’’ Energy Procedia, vol. 134, pp. 171–180, Oct. 2017
[38] B. Xu, A. Oudalov, J. Poland, A. Ulbig, and G. Andersson, ‘‘BESS control strategies for participating in grid frequency regulation,’’ IFAC Proc. Vols., vol. 47, no. 3, pp. 4024–4029, 2014
[39] A. A. Abdalla and M. Khalid, ‘‘Smoothing methodologies for photovoltaic power fluctuations,’’ in Proc. 8th Int. Conf. Renew. Energy Res. Appl. (ICRERA), Nov. 2019, pp. 342–346.
[40] M. E. Haque, M. N. Sakib Khan, and M. R. Islam Sheikh, ‘‘Smoothing control of wind farm output fluctuations by proposed low pass filter, and moving averages,’’ in Proc. Int. Conf. Electr. Electron. Eng. (ICEEE), Nov. 2015, pp. 121–124.
[41] V. Kumar, ‘‘Application of moving averages for PV power smoothing using battery energy storage system,’’ Int. J. Manage., Technol. Eng., vol. 8, no. 766, pp. 766–772, 2018.
[42] Miswar A. Syed and Muhammad Khalid, “Moving Regression Filtering With Battery State of Charge Feedback Control for Solar PV Firming and Ramp Rate Curtailment,” IEEE Trans. Power Syst., pp. 3052142, Jan. 2021

指導教授

陳正一(Cheng-I Chen)

審核日期

2024-1-17

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