Abstract
Fossil fuels, chiefly coal, oil and natural gas, currently account for more than 60% of the primary energy used for electricity generation worldwide. This share will continue to increase steadily along with the growing global electricity demand [48]. There is therefore great demand for optimal operation of power energy systems aimed at reducing fossil fuel consumption. Such optimal operation could not only save fuel cost but also reduce CO2 emission, which is considered the main contributor to global warming.
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References
Chen C.P., Liu C.W., and Liu C.C. (2000), Unit commitment by Lagrangian relaxation and genetic algorithms, IEEE Transactions on Power Systems, 15, no. 2, pp. 707–714.
Richter, Jr., C.W. and Sheble, G.B. (2000), A profit-based unit commitment GA for the competitive environment, IEEE Transactions on Power Systems, 15, no. 2, pp. 715–721.
Takata T., Takahashi J., Yokoi H., Nakano H., Kato M., Aoyagi M., Shimada K., and Arai J. (1999), Advanced method for unit commitment problem using genetic algorithm and mathematical programming, Transactions of IEE Japan, B-119, no. 3, pp. 333–343 (in Japanese).
Kamiya A., Kawai K., Ono I., and Kobayashi S. (1999), Adaptive edge search for power plant start-up scheduling, IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 29, no. 4, pp. 518–530.
Kamiya A., Kawai K., Ono I., and Kobayashi S., Theoretical proof of edge search strategy applied to power plant start-up scheduling, IEEE Transactions on Systems, Man, and Cybernetics-Part B (Under Review).
Kamiya A., Kimura H., Yamamura M., and Kobayashi S. (1998), Power plant start-up scheduling: A reinforcement learning approach combined with evolutionary computation, Journal of Intelligent and Fuzzy Systems 6, pp. 99–115.
Hippert H.S., Pedreira C.E., and Souza R.C. (2001), Neural networks for short-term load forecasting: A review and evaluation, IEEE Transactions on Power Systems, 16, no. 1, pp. 44–45.
Dillion T.S., Edwin K.W., Kochs H.D., and Taud R.J. (1978), Integer programming approach to the problem of optimal unit commitment with probabilistic reserve determination, IEEE Transactions on Power Apparatus and Systems, PAS-97, no. 6, pp. 2154–2166.
Peng CK., Sheble G.B., and Albuyeh F. (1981), Evaluation of dynamic programming based methods and multiple area representation for thermal unit commitment, IEEE Transactions on Power Apparatus and Systems, PAS-100, no. 3, pp. 1212–1218.
Virmani S., Adrian E.C., Imhof K., and Mukherjee S. (1989), Implementation of a Lagrangian relaxation based unit commitment problem, IEEE Transactions on Power Systems, 4, no. 4, pp. 1373–1380.
Sheble G.B. and Fahd G.N. (1994), Unit commitment literature synopsis, IEEE Transactions on Power Systems, 9, no. 1, pp. 128–135.
Merlin A. and Sandrin P. (1983), A new method for unit commitment at Electricité de France, IEEE Transactions on Power Apparatus and Systems, PAS-102, no. 5, pp. 1218–1225.
Wang S.J., Shahidehpour S.M., Kirschen D.S., Mokhtari S., and Irisarri G.D. (1995), Short-term generation scheduling with transmission and environmental constraints using an augmented Lagrangian relaxation, IEEE Transactions on Power Systems, 10, no. 3, pp. 1294–1301.
Abdul-Rahman K.H., Shahidehpour S.M., Aganagic M., and Mokhtari S. (1996), A practical resource scheduling with OPF constraints, IEEE Transactions on Power Systems, 11, no. 1, pp. 254–259.
Kamiya A., Ono I., Yamamura M., and Kobayashi S. (1997), Thermal power plant start-up scheduling with evolutionary computation by using an enforcement operator and tabu strategy, Journal of Japanese Society for Artificial Intelligence, 12, pp. 100–110 (in Japanese).
Hanzalek F.J. and Ipsen P.G. (1966), Thermal stresses influence starting, loading of bigger boilers, turbines, Electrical World, 165, pp. 58–62.
Matsumoto H., Eki Y., Kaji A., Nigawara S., Tokuhira M., and Suzuki Y. (1993), An operation support expert system based on on-line dynamics simulation and fuzzy reasoning for startup schedule optimization in fossil power plants, IEEE Transaction, on Energy Conversion, 8, no. 4, pp. 674–680.
Glover F., Taillard E., and De Werra D. (1993), A user’s guide to tabu search, Annals of Operations Research, 41, pp. 3–28.
Kirtpatrick, S., Gelatt, Jr., C.D., and Vecchi M.P. (1983), Optimization by simulated annealing, Science, 220, pp. 671–680.
Amjady, N. (2001), Short-term hourly load forecasting using time-series modeling with peak load estimation capability, IEEE Transactions on Power Systems, 16, no. 3, pp. 498–505.
Papalexopoulos A.D. and Hesterberg T.C. (1990), A regression-based approach to short-term system load forecasting, IEEE Transactions on Power Systems, 5, no. 4, pp. 1535–1547.
Mastorocostas P.A., Theocharis J.B., and Bakirtzis A.G. (1999), Fuzzy modeling for short term load forecasting using the orthogonal least squares method, IEEE Transactions on Power Systems, 14, no. 1, pp.29–36.
Khotanzad A., Afkhami-Rohani R., and Maratukulam D. (1998), ANNSTLF-artificial neural network short-term load forecaster-generation three, IEEE Transactions on Power Systems, 13, no. 4, pp.1413–1422.
Prechelt L. (1998), Automatic early stopping using cross validation: Quantifying the criteria, Neural Networks 11, no. 4, pp. 761–767.
Wang F., Yu E. K., Liu Y. Q., and Yan C. S. (1998), Short-term load forecasting based on weather information, Proceedings of IEEE International Conference on Power System Technology, pp. 572–575, Beijing, China.
Shimakura Y., Fujisawa Y., Maeda Y., Ono M., Fann J.Y., and Fukusima N. (1993), Short-term load forecasting using an artificial neural network, Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems, pp. 233–238, Yokohama, Japan.
Shimakura Y., Fujisawa Y., Kishi Y., Kunugi M., Shimada K., Kawahara Y, Fann J.Y., and Ono M. (1992), Short-term load forecasting using an artificial neural network, IEE Japan Power System Technology Meeting, PE-92–11 (in Japanese).
Osaka S., Amano M., and Kawakami J. (1988), An expert system for power generation scheduling, Proceedings of International Workshop on Artificial Intelligence for Industrial Applications, pp. 557–562, Hitachi, Japan.
Rudolf A., and Bayrleithner R. (1999), A genetic algorithm solution to the unit commitment problem of a hydro-thermal power system, Proceedings of the 13th Power Systems Computations Conference, pp. 745–752, Trondheim, Norway.
Ohta T., Matsui T., Takata T., Kato M, Aoyagi M., Kunugi M., Shimada K., and Nagata J. (1996), Practical approach to unit commitment problem using genetic algorithm and Lagrangian relaxation method, Proceedings of IEEE International Conference on Intelligent Systems Applications to Power Systems, pp. 434–440, Orlando, FL.
Kawai K., Takizawa Y., and Watanabe S. (1998), Advanced automation for power-generation plants—past, present and future, Proceedings of the 7th IFAC/IFIP/IFORS/IEA Symposium in Analysis, Design and Evaluation of Man-Machine Systems, pp. 239–244, Kyoto, Japan.
Tanaka S., Ohta K., Minoura T., and Kogure Y. (1975), New concept software system for power generation plant computer control—COPOS, Proceedings of International Conference on Power Industry Computer Applications, pp. 267–275.
Kakehi A., and Hatsumi H. (1996), Development of new digital electro-hydraulic control system for steam turbine, Proceedings of the 4th International Conference on Control Automation, Robotics and Vision, pp. 2106–2110, Singapore.
Kamiya, A., Kakei A., Kawai K., and Kobayashi S. (1999), Advanced power plant start-up automation based on the integration of soft computing and hard computing Techniques, Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, pp. 1-380–1-385, Tokyo, Japan.
Kimura H., Yamamura M., and Kobayashi S (1995), Reinforcement learning by stochastic hill climbing on discounted reward, Proceedings of the 12th International Conference on Machine Learning, pp. 295–303, Lake Tahoe, CA.
Ono I., Yamamura M., and Kobayashi S. (1996), A genetic algorithm with characteristic-preserving for function optimization, Proceedings of the 4th International Conference on Soft Computing, pp. 511–514, Iizuka, Japan.
Schellstede G., and Wagner H. (1986), A software package for unit commitment and economic dispatch, Proceedings of IF AC Symposium on Power Systems and Power Plant Control, pp. 264–271, Beijing, China.
Lewis, III, H.W. (2001), Intelligent hybrid load forecasting system for an electric power company, Proceedings of the IEEE Mountain Workshop on Soft Computing in Industrial Applications, pp. 23–27, Blacksburg, VA.
H. Chen and J. Liu (1998), A weighted multi-model short-term load forecasting, Proceedings of IEEE International Conference on Power System Technology, pp. 557–560, Beijing, China.
Ovaska S.J., Dote Y., Furuhashi T., Kamiya A., and VanLandingham H.F. (1999), Fusion of soft computing and hard computing techniques: A review of applications, Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, pp. I-370-I-375, Tokyo, Japan.
Murillo-Sanchez C, and Thomas R.J. (1998), Thermal unit commitment including optimal AC power flow constraints, Proceedings of the Thirty-First Hawaii International Conference on System Sciences, 3, pp. 81–88, Koahala Coast, HI.
Karashima N., Suzuki M., Abe M., and Kogure Y. (1981), Newly developed comprehensive automation technique applied to Hirono thermal power station, Proceedings of American Power Conference 43rd Annual Meeting, pp. 153–168, Chicago, IL.
Bednarski S. and Shen C.N. (1973), Analysis and algorithm for a minimax problem with thermal stress applications, 14th Joint Automatic Control Conference, 20, no. 22, pp. 765–775.
Schoenauer M. and Michalewicz Z. (1996), Evolutionary computation at the edge of feasibility, Voigt H.-M., Ebeling W., Rechenberg I, and Schwefel H.-P. (Eds.), Proceedings of the 4th Int. Conf on Parallel Problem Solving from Nature, Springer, LNCS 1141, pp. 245–254.
Schoenauer M. and Michalewicz Z. (1997), Boundary operators for constrained parameter optimization problems, Proceedings of the 7th International Conference on Genetic Algorithms, pp. 322–329, East Lansing, ML
Hwang R.C., Huang H.C., Chen Y.J., and Hsieh J.G. (1998), Power load forecasting by neural network with a new learning process for considering overtraining problem, Proceedings of International Conference on Energy Management and Power Delivery, 1, pp. 317–322, Singapore.
Goldberg D.E. (1989), Genetic Algorithms in Search, Optimization & Machine Learning, Addison Wesley, Reading, MA.
(1999), International Energy Annual 1999, U.S. Department of Energy, Energy Information Administration (EIA), DOE/EIA-0219(99), Washington, DC.
Gill P.E., and Murray W. (Eds.) (1974), Numerical Methods for Constrained Optimization, Academic Press, Burlington, MA.
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Kamiya, A., Kato, M., Shimada, K., Kobayashi, S. (2002). Soft Computing-Based Optimal Operation in Power Energy System. In: Soft Computing in Industrial Electronics. Studies in Fuzziness and Soft Computing, vol 101. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1783-6_5
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DOI: https://doi.org/10.1007/978-3-7908-1783-6_5
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