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研究生： Dimas Prasetyo
Dimas Prasetyo
論文名稱： 發電問題的確定性和元啟發式算法的實現
Implementation on Deterministic and Metaheuristic Algorithm for Electricity Generation Problem
指導教授： 周碩彥
Shuo-Yan Chou
口試委員: 喻奉天
Vincent F. Yu
郭伯勳
Po-Hsun Kuo
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2019
畢業學年度： 107
語文別： 英文
論文頁數： 47
中文關鍵詞： Electricity Generation 、Unit Commitment 、Economic Dispatch 、Dynamic Programming 、Particle Swarm Optimization
外文關鍵詞： Electricity Generation, Unit Commitment, Economic Dispatch, Dynamic Programming, Particle Swarm Optimization
相關次數： 點閱：272  下載：3 
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    • In solving the electricity generation, a common objective for all power system operators is to ensure that sufficient generation is available for hours and days ahead of the operation time. The on-off states of the generation units or the “commitment decision” provides the first step toward the optimal solution. In power generation scheduling, the unit commitment decision indicates, for each point in time over the scheduling horizon, what generating units are to be used. Then, the most economic dispatch, i.e. the distribution of load across generating units for each point in time, is then determined to meet system load and reserve requirements. Various approaches to the solution of the UC problem have been proposed where they ranged from simple to complicated methods. The electricity generation problem belongs to the complex combinational optimization problems. Several mathematical programming techniques have been proposed to solve this time-dependent problem. Recent mathematical developments and advances in computing technologies made the problem readily solvable. The application of hybrid systems in power system problems has been advanced in recent literature, and it still represents a future trend in power systems research. This research initially want to collaborate the deterministic and metaheuristic to make an improvement in computational for solving electricity generation. The specific algorithm that will be used are dynamic programming and particle swarm optimization.

    In solving the electricity generation, a common objective for all power system operators is to ensure that sufficient generation is available for hours and days ahead of the operation time. The on-off states of the generation units or the “commitment decision” provides the first step toward the optimal solution. In power generation scheduling, the unit commitment decision indicates, for each point in time over the scheduling horizon, what generating units are to be used. Then, the most economic dispatch, i.e. the distribution of load across generating units for each point in time, is then determined to meet system load and reserve requirements. Various approaches to the solution of the UC problem have been proposed where they ranged from simple to complicated methods. The electricity generation problem belongs to the complex combinational optimization problems. Several mathematical programming techniques have been proposed to solve this time-dependent problem. Recent mathematical developments and advances in computing technologies made the problem readily solvable. The application of hybrid systems in power system problems has been advanced in recent literature, and it still represents a future trend in power systems research. This research initially want to collaborate the deterministic and metaheuristic to make an improvement in computational for solving electricity generation. The specific algorithm that will be used are dynamic programming and particle swarm optimization.

    Table of Contents CHAPTER 1 : INTRODUCTON v 1.1 Introduction & Research Background 1.2 The Context of The Research and Proposed Elements of Novelty 1.3 Aims and objectives of the thesis 1.4 Significance of The Study 1.5 Outline Report 1.6 Scope and Limitation CHAPTER 2 : BASIC KNOWLEDGE AND LITERATURE REVIEW 2.1 A global View of Unit Commitment and Economic Dispatch 2.1.1 Thermal Units 2.1.2 Objective Function 2.2 Optimization Techniques from Classical to Hybrid Metaheuristic CHAPTER 3 : PROBLEM FORMULATION AND METHODOLOGY 3.1 Mathematical Model 3.2 Problem Constraints 3.2.1 Power balance constraint 3.2.2 Generator Limits 3.2.3 Minimum UP Time and Minimum Down Time Constraint 3.2.4 Spinning reserves constraint 3.3 The Proposed Approach 3.3.1 Dynamic Programming 2.3.2 Particle Swarm Optimization CHAPTER 4 : COMPUTATIONAL AND NUMERICAL RESULT 4.1 Data Collection 4.2 Variables and Parameter Setting 4.2 Result of DP-PSO CHAPTER 5 : CONCLUSIONS 5.1 Algorithm 5.2 Recommendation and Future Work REFERENCE

    [1] B. Zhao, C.X. Guo, B.R. Bai and Y.J. Cao. An improved particle swarm optimization algorithm for unit commitment. Electrical Power and Energy Systems 28 (2006) 482–490
    [2] Abedi S, Riahy GH, Hosseinian SH, Alimardani A. Risk-constrained unit commitment of power System incorporating PV and wind farms. ISRN Renew Energy 2011;2011.
    [3] Ting T-O, Rao M, Loo CK, Ngu S. Solving unit commitment problem using hybrid particle swarm optimization. J Heuristics 2003;9:507–20.
    [4] Ting TO, Rao MVC, Loo CK. A novel approach for unit commitment problem via an effective hybrid particle swarm optimization. IEEE Trans Power Syst 2006;21:411–8.
    [5] Le, K. D., Jackups, R. R., Feinstein, J., & Griffith, J. S. (1990). Operational aspects of generation cycling. IEEE Transactions on Power Systems, 5(4), 1194–1203.
    [6] Ting T-O, Rao M, Loo CK, Ngu S. Solving unit commitment problem using hybrid particle swarm optimization. J Heuristics 2003;9:507–20.
    [7] Sen S, Kothari DP. Optimal thermal generating unit commitment: a review. Int J Electr Power Energy Syst 1998;20:443–51.
    [8] Belede L, Jain A, Gaddam RR. Unit commitment with nature and biologically inspired computing. Nature & Biologically Inspired Computing, 2009 NaBIC 2009 World Congress on2009. p. 824–9
    [9] Farhat I, El-Hawary M. Optimization methods applied for solving the short-term hydrothermal coordination problem. Electr Power Syst Res 2009;79:1308–20.
    [10] Hobbs BF, Rothkopf MH, O'Neill RP. Chao H-p. The next generation of electric power unit commitment models. Springer Science & Business Media; 2006.
    [11] Rao SS, Rao S. Engineering optimization: theory and practice. John Wiley & Sons; 2009.
    [12] Bansal R. Optimization methods for electric power systems: an overview. Int J Emerg Electr Power Syst 2005:2.
    [13] Senjyu T, Miyagi T, Saber AY, Urasaki N, Funabashi T. Emerging solution of large scale unit commitment problem by stochastic priority list. Electr Power Syst Res 2006;76:283–92.
    [14] Senjyu T, Shimabukuro K, Uezato K, Funabashi T. A fast technique for unit commitment problem by extended priority list. IEEE Trans Power Syst 2003;18:882–8.
    [15] Quan R, Jian J, Yang L. An improved priority list and neighborhood search method for unit commitment. Int J Electr Power Energy Syst 2015;67:278–85.
    [16] Rong A, Hakonen H, Lahdelma R. A variant of the dynamic programming algorithm for unit commitment of combined heat and power systems. Eur J Oper Res 2008;190:741–55.
    [17] Singhal PK, Sharma RN. Dynamic programming approach for solving power generating unit commitment problem. Computer and Communication Technology (ICCCT) In: Proceedings of the 2nd International Conference on 2011. p. 298–303.
    [18] Xiaohong G, Qiaozhu Z, Papalexopoulos A. Optimization based methods for unit commitment: Lagrangian relaxation versus general mixed integer programming. Power Engineering Society General Meeting, IEEE2003. p. 1100Vol. 2.
    [19] Sawa T, Sato Y, Tsurugai M, Onishi T. Security Constrained Integrated Unit Commitment Using Quadratic Programming. Power Tech IEEE Lausanne2007. p. 1858–63.
    [20] Tao L, Shahidehpour M. Price-based unit commitment: a case of Lagrangian relaxation versus mixed integer programming. IEEE Trans Power Syst 2005;20:2015–25.
    [21] Cohen AI, Yoshimura M A branch-and-bound algorithm for unit commitment. Power Engineering Review, IEEE. 1983;PER-3:34–5.
    [22] Dudek G. Adaptive simulated annealing schedule to the unit commitment problem. Electr Power Syst Res 2010;80:465–72.
    [23] Garlík B, Křivan M. Renewable energy unit commitment, with different acceptance of balanced power, solved by simulated annealing. Energy Build 2013;67:392–402.
    [24] Mantawy AH, Abdel-Magid YL, Selim SZ. A simulated annealing algorithm for unit commitment. Power Syst, IEEE Trans on 1998;13:197–204.
    [25] Kazarlis SA, Bakirtzis AG, Petridis V. A genetic algorithm solution to the unit commitment problem. IEEE Trans Power Syst 1996;11:83–92.
    [26] Rudolf A, Bayrleithner R. A genetic algorithm for solving the unit commitment problem of a hydro-thermal power system. IEEE Trans Power Syst 1999;14:1460–8.
    [27] Mori H, Usami T. Unit commitment using Tabu search with restricted neighborhood. Intelligent Systems Applications to Power Systems, 1996 Proceedings, ISAP '96, International Conference on. p. 422-7; 1996.
    [28] Borghetti A, Frangioni A, Lacalandra F, Lodi A, Martello S, Nucci C, et al. Lagrangian relaxation and tabu search approaches for the unit commitment problem. Power Tech Proceedings, 2001 IEEE Porto: IEEE; 2001. p. 7 pp. vol. 3.
    [29] Selvi V, Umarani DR. Comparative analysis of ant colony and particle swarm optimization techniques. Int J Comput Appl 2010:5.
    [30] Sum-Im T, Ongsakul W. Ant colony search algorithm for unit commitment. In: Proceedings of the IEEE International Conference on 2003 Industrial Technology. p. 72–7 vol.1.
    [31] Clerc M, Kennedy J. The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evolut Comput 2002;6:58–73.
    [32] Kennedy J, Eberhart RC. A discrete binary version of the particle swarm algorithm. Systems, Man, and Cybernetics, 1997 Computational Cybernetics and Simulation, 1997 IEEE International Conference on.1997 p. 4104–8. vol. 5.
    [33] Logenthiran T, Srinivasan D. Particle Swarm Optimization for unit commitment problem. Probabilistic Methods Applied to Power Systems (PMAPS), 2010 IEEE In: Proceedings of the 11th International Conference on. 2010p. 642–7.
    [34] Saneifard S, Prasad NR, Smolleck HA. A fuzzy logic approach to unit commitment. IEEE Trans Power Syst 1997;12:988–95.
    [35] Kadam DP, Sonwane PM, Dhote VP, Kushare BE. Fuzzy logic algorithm for Unit Commitment Problem. Control, Automation, Communication and Energy Conservation, 2009 INCACEC 2009 International Conference on2009. p. 1–4.
    [36] R. Nieva, A. Inda, and J. Frausto, “CHT: a digital computer package for solving short term hydro-thermal coordination and unit commitment problems,” IEEE Trans. Power App. Syst., vol. PAS-1, pp. 168–174, Aug. 1986.
    [37] W. L. Snyder, Jr., H. D. Powell, Jr., and J. C. Rayburn, “Dynamic programming approach to unit commitment,” IEEE Trans. Power Syst., vol. 2, pp. 339–347, May 1987.
    [38] F. N. Lee, J. Huang, and R. Adapa, “Multi-area unit commitment via sequential method and a dc power flow network model,” IEEE Trans.Power Syst., vol. 9, pp. 279–287, Feb. 1994.
    [39] D.N. Simopoulos, S.D. Kavatza, C.D. Vournas, Unit commitment by an enhanced simulated annealing algorithm, IEEE Trans. Power Syst. 21 (February (1)) (2006) 193–201.
    [40] B. Zhao, C.X. Guo, B.R. Bai, Y.J. Cao, An improved particle swarm optimization algorithm for unit commitment, Elect. Power Energy Syst. 28 (7) (2006) 482–490.
    [41] Y.-W. Jeong, J.-B. Park, J.-R. Shin, K.Y. Lee, A thermal unit commitment approach using an improved quantum evolutionary algorithm, Elect. Power Compon. Syst. 37 (7) (2009) 770–786.
    [42] F.Barani, M.Mirhoseini, Unit commitment by an improved binary quantum GSA, Computers and Electrical Engineering, pp. 1-18, 2017
    [43] K. Srikanth, L. Panwar, B, Panigrahi and E. Herrera-Viedma, Metaheuristic framework: quantum inspired binary grey wolf optimizer for unit commitment problem, Computers and Electrical Engineering, pp. 1-18, 2017

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