Weitere Kapitel dieses Buchs durch Wischen aufrufen
The development of the industry and the gradual increase of the population are the main factors for which the consumption of electricity increases. In order to establish a good exploitation of the electrical grid, it is necessary to solve technical and economic problems. This can only be done through the resolution of unit commitment problem (UCP). The decisions are which units to commit at each time period and at what level to generate power meeting the electricity demand. Therefore, in a robust unit commitment problem, first stage commitment decisions are made to anticipate the worst case realization of demand uncertainty and minimize operation cost under such scenarios. Unit Commitment Problem allows optimizing the combination of the production units’ states and determining their production planning in order to satisfy the expected consumption with minimal cost during a specified period which varies usually from 24 h to 1 week. However, each production unit has some constraints that make this problem complex, combinatorial and nonlinear. In this chapter, we have proposed two strategies applied to an IEEE electrical network 14 buses to solve the UCP in general and in particular to find the optimized combination scheduling of the produced power for each unit production. The First strategy is based on a hybrid optimization method, Gradient-Genetic algorithm, and the second one relies on a Fuzzy logic approach. Throughout these two strategies, we arrived to develop an optimized scheduling plan of the generated power allowing a better exploitation of the production cost in order to bring the total operating cost to possible minimum when it’s subjected to a series of constraints. A comparison was made to test the performances of the proposed strategies and to prove their effectiveness in solving Unit Commitment problems.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Abbassi, R., & Chebbi, S. (2012). Energy management strategy for a grid-connected wind-solar hybrid system with battery storage: Policy for optimizing conventional energy generation. International Review of Electrical Engineering, 7(2), 3979–3990.
Abbassi, R., Marrouchi, S., Moez, B. H., Chebbi, S., & Houda, J. (2012). Voltage control strategy of an electrical network by the integration of the UPFC compensator. International Review on Modelling and Simulations (I.RE.MO.S), 5(1), 380–384.
Attaviriyanupap, P., Kita, H., Tanaka, E., & Hasegawa, J. (2002). A new profit-based unit commitment considering power and reserve generating. In The 2002 IEEE-PES Winter Meeting (pp. 6–11). New York. January 27–31 2002. doi: 10.1109/PESW.2002.985227.
Azar, A. T. (2010a). Fuzzy systems. Vienna, Austria: IN-TECH. ISBN 978-953-7619-92-3. 3.
Azar, A. T. (2010b). Adaptive neuro-fuzzy systems. In A. T. Azar (Ed.), Fuzzy systems. Vienna, Austria: IN-TECH. ISBN 978-953-7619-92-3.
Cai, C. H., & Cai, Y. Y. (1997). Optimization of unit commitment by genetic algorithm. Power System Technology, 21(1), 44–47.
Cheng, C. P., Liu, C. W., & Liu, C. C. (2002). Unit commitment by annealing-genetic algorithm. Electrical Power and Energy Systems, 24(2), 149–158. CrossRef
Damousis, I. G., Bakirtzis, A. G., & Dokopoulos, P. S. (2004). A solution to the unit-commitment problem using integer-coded genetic algorithm. IEEE Transactions on Power systems, 19(2), 1165–1172. CrossRef
Dieu, V. N., & Ongsakul, W. (2007). Improved merit order and augmented lagrange hopfield network for unit commitment. IET Generation, Transmission and Distribution, 1(4), 548–556. CrossRef
Dekrajangpetch, S., Sheble, G. B., & Conejo, A. J. (1999). Auction implementation problems using lagrangian relaxation. IEEE Transactions on Power Systems, 14(1), 82–88. CrossRef
Guan, X., Luh, P. B., Yan, H., & Amalfi, J. A. (1992). An optimization-based method for unit commitment. Electric power and energy systems, 14(1), 9–17. CrossRef
Grey, A., & Sekar, A. (2008). Unified solution of security-constrained unit commitment problem using a linear programming methodology. IET Generation, Transmission and Distribution, 2(6), 856–867. CrossRef
Hong, Y. Y., & Li, C. (2002). Genetic algorithm based economic dispatch for cogeneration units considering multiplant multibuyer wheeling. IEEE Transactions on Power Systems, 17(1), 134–140. CrossRef
Juste, K. A., Kita, H., Tanaka, E., & Hasegawa, J. (1999). An evolutionary programming solution to the unit commitment problem. IEEE Transactions on Power Systems, 14(4), 1452–1459. CrossRef
Kazarlis, S. A., Bakirtzis, A. G., & Petridis, V. (1996). A genetic algorithm solution to the unit commitment problem. IEEE Transactions on Power Systems, 11(1), 83–92. CrossRef
Kohonen, T. (1998). An introduction to neural computing. Neural Network Journal, 1(1), 3–16. CrossRef
Kurban, M., & Filik, U. B. (2009). A comparative study of three different mathematical methods for solving the unit commitment problem. Mathematical Problems in Engineering, 2009(1), 1–13, (368024, Hindawi publishing corporation).
Lin, F. T., Kao, C. Y., & Hsu, C. C. (1993). Applying the genetic approach to simulated annealing in solving some NP-hard problems. IEEE Transactions on Power Systems, Man, and Cybernetics, 23(6), 1752–1767. CrossRef
Maifeld, T. T., & Sheble, G. B. (1996). Genetic-based unit commitment algorithm. IEEE Transactions on Power Systems, 11(3), 1359–1370. CrossRef
Mantawy, A. H., AbdelMagid, Y. L., & Selim, S. Z. (1998). A simulated annealing algorithm for unit commitment. IEEE Transactions on Power System, 13(1), 197–204. CrossRef
Marrouchi, S., & Chebbi, S. (2013). Combined use of genetic algorithms and gradient optimization methods for unit commitment problem resolution. Wulfenia Journal, 20(8), 357–369.
Merlin, A., & Sandrin, P. (1983). A new method for unit commitment at Electricite De France. IEEE Transactions on Power Apparatus and Systems, 102(5), 1218–1225. CrossRef
Moez, B. H., Sahbi, M., Souad, C., Houda, J., & Rabeh, A. (2011). Preventive and curative strategies based on fuzzy logic for voltage stabilization of an electrical network. International Review on Modeling and Simulation (I.RE.MO.S), 4(6), 3201–3207.
Ouyang, Z., & Shahidehpour, S. M. (1991). An intelligent dynamic programming for unit commitment application. IEEE Transactions on Power Systems, 6(3), 1203–1209. CrossRef
Padhy, N. P. (2001). Unit commitment using hybrid models: A comparative study for dynamic programming, expert systems, fuzzy system and genetic algorithms. International Journal of Electrical Power and Energy Systems, 23(8), 827–836. CrossRef
Rajan, C. C. A., & Mohan, M. R. (2004). An evolutionary programming-based Tabu search method for solving the unit commitment problem. IEEE Transactions on Power Systems, 19(1), 577–585. CrossRef
Rajan, C. C. A., Mohan, M. R., & Manivannan, K. (2002). Refined simulated annealing method for solving unit commitment problem, the 2002 neural networks, 2002. In IJCNN ‘02. Proceedings of the 2002 International Joint Conference on May 12-17 2002 (pp. 333–338). Honolulu, HI. doi: 10.1109 /IJCNN.2002.1005493.
Saber, A. Y., Senjyu, T., Yona, A., Urasaki, N., & Funabashi, T. (2007). Fuzzy unit commitment solution-A novel twofold simulated annealing approach. Electric Power Systems Research, 77(12), 1699–1712. CrossRef
Sasaki, H., Watanabe, M., Kubokawa, J., Yorino, N., & Yokoyama, R. (1992). A solution method of unit commitment by artificial neural networks. IEEE Transactions on Power Systems, 7(3), 974–981. CrossRef
Snyder, W. L., Powell, H. D., & Rayburn, J. C. (1987). Dynamic programming approach to unit commitment. IEEE Transactions on Power Systems, 2(2), 339–350. CrossRef
Sudhakaran, M., Ajay, D., & Vimal-Raj, P. (2010). Integrating genetic algorithms and tabu search for unit commitment problem. International Journal of Engineering, Science and Technology, 2(1), 57–69. CrossRef
Victoire, T. A. A., & Jeyakumar, A. E. (2005). Unit commitment by a tabu-search-based hybrid-optimization technique. IEEE Proceedings Generation Transmission and Distribution, 152(4), 563–574. CrossRef
Wei, P., & Li, N. H. (1999). Daily generation scheduling based on genetic algorithm. Automation of Electric Power Systems, 23(3), 23–27.
Wood, A. J., & Woolenberg, B. F. (1996). Power generation operation and control (2nd ed.). New York: Wiley.
Wu, Y. G., Ho, C., & Wang, D. Y. (2000). A diploid genetic approach to short-term scheduling of hydro-thermal system. IEEE Transactions on Power System, 15(4), 1268–1274. CrossRef
Yingvivatanapong, C. (2006, May). Multi-area unit commitment and economic dispatch with market operation components, PhD discussion, University of Texas, Arlington.
Zhao, B., Guo, C. X., Bai, B. R., & Cao, Y. J. (2006). An improved particle swarm optimization algorithm for unit commitment. International Journal of Electrical Power and Energy Systems, 28(7), 482–490. CrossRef
Zhuang, F., & Galiana, F. D. (1988). Towards a more rigorous and practical unit commitment by Lagrangian relaxation. IEEE Transactions on Power Systems, 3(2), 763–772. CrossRef
- Unit Commitment Optimization Using Gradient-Genetic Algorithm and Fuzzy Logic Approaches
Neuer Inhalt/© ITandMEDIA, Best Practices für die Mitarbeiter-Partizipation in der Produktentwicklung/© astrosystem | stock.adobe.com