Soft Computing-Based Optimal Operation in Power Energy System

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.

[1]  S. Bednarski,et al.  ANALYSIS AND ALGORITHM FOR A MINIMAX PROBLEM WITH THERMAL STRESS APPLICATIONS , 1973 .

[2]  Philip E. Gill,et al.  Numerical methods for constrained optimization , 1974 .

[3]  K. W. Edwin,et al.  Integer Programming Approach to the Problem of Optimal Unit Commitment with Probabilistic Reserve Determination , 1978, IEEE Transactions on Power Apparatus and Systems.

[4]  F. Albuyeh,et al.  Evaluation of Dynamic Programming Based Methods and Multiple area Representation for Thermal Unit Commitments , 1981, IEEE Transactions on Power Apparatus and Systems.

[5]  A. Merlin,et al.  A New Method for Unit Commitment at Electricite De France , 1983, IEEE Transactions on Power Apparatus and Systems.

[6]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[7]  J. Kawakami,et al.  An expert system for power generation scheduling , 1988, Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications.

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.

[10]  S. Virmani,et al.  Implementation of a Lagrangian Relaxation Based Unit Commitment Problem , 1989, IEEE Power Engineering Review.

[11]  Fred W. Glover,et al.  A user's guide to tabu search , 1993, Ann. Oper. Res..

[12]  Seiitsu Nigawara,et al.  An operation support expert system based on on-line dynamics simulation and fuzzy reasoning for startup schedule optimization in fossil power plants , 1993 .

[13]  End Use,et al.  International energy annual , 1993 .

[14]  Y. Shimakura,et al.  Short-term load forecasting using an artificial neural network , 1993, [1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems.

[15]  Gerald B. Sheblé,et al.  Unit commitment literature synopsis , 1994 .

[16]  Shigenobu Kobayashi,et al.  Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward , 1995, ICML.

[17]  S. M. Shahidehpour,et al.  Short-term generation scheduling with transmission and environmental constraints using an augmented Lagrangian relaxation , 1995 .

[18]  M. Aganagic,et al.  A practical resource scheduling with OPF constraints , 1995 .

[19]  K. Shimada,et al.  Practical approach to unit commitment problem using genetic algorithm and Lagrangian relaxation method , 1996, Proceedings of International Conference on Intelligent System Application to Power Systems.

[20]  I. Ono,et al.  A Genetic Algorithm with Characteristic Preservation for Function Optimization , 1996 .

[21]  Zbigniew Michalewicz,et al.  Boundary Operators for Constrained Parameter Optimization Problems , 1997, ICGA.

[22]  Shigenobu Kobayashi,et al.  Power plant start-up scheduling: a reinforcement learning approach combined with evolutionary computation , 1998, J. Intell. Fuzzy Syst..

[23]  Rey-Chue Hwang,et al.  Power load forecasting by neural network with a new learning process for considering overtraining problem , 1998, Proceedings of EMPD '98. 1998 International Conference on Energy Management and Power Delivery (Cat. No.98EX137).

[24]  Kensuke Kawai,et al.  Advanced automation for power-generation plants – past, present and future , 1998 .

[25]  Robert J. Thomas,et al.  Thermal unit commitment including optimal AC power flow constraints , 1998, Proceedings of the Thirty-First Hawaii International Conference on System Sciences.

[26]  Lutz Prechelt,et al.  Automatic early stopping using cross validation: quantifying the criteria , 1998, Neural Networks.

[27]  Alireza Khotanzad,et al.  ANNSTLF-Artificial Neural Network Short-Term Load Forecaster- generation three , 1998 .

[28]  Liu Jun,et al.  Short-term load forecasting based on weather information , 1998, POWERCON '98. 1998 International Conference on Power System Technology. Proceedings (Cat. No.98EX151).

[29]  Isao Ono,et al.  Adaptive-edge search for power plant start-up scheduling , 1999, IEEE Trans. Syst. Man Cybern. Part C.

[30]  P. Mastorocostas,et al.  Fuzzy modeling for short term load forecasting using the orthogonal least squares method , 1999 .

[31]  Masakazu Kato,et al.  Advanced Method for Unit Commitment Problem Using Genetic Algorithm and Mathematical Programming , 1999 .

[32]  Y. Dote,et al.  Fusion of soft computing and hard computing techniques: a review of applications , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[33]  Shigenobu Kobayashi,et al.  Advanced power plant start-up automation based on the integration of soft computing and hard computing techniques , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[34]  Chuan-Ping Cheng,et al.  Unit commitment by Lagrangian relaxation and genetic algorithms , 2000 .

[35]  Gerald B. Sheblé,et al.  A profit-based unit commitment GA for the competitive environment , 2000 .

[36]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[37]  Nima Amjady,et al.  Short-term hourly load forecasting using time-series modeling with peak load estimation capability , 2001 .

[38]  H. W. Lewis.  Intelligent hybrid load forecasting system for an electric power company , 2001, SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504).

[39]  Isao Ono,et al.  Theoretical proof of edge search strategy applied to power plant start-up scheduling , 2002, IEEE Trans. Syst. Man Cybern. Part B.