DocumentCode :
1810123
Title :
An evolutionary programming based simulated annealing method for unit commitment problem with cooling-banking constraints
Author :
Christober, C. ; Rajan, Asir
Author_Institution :
Dept. of Electr. & Electron. Eng., Pondicherry Eng. Coll., India
fYear :
2004
fDate :
20-22 Dec. 2004
Firstpage :
435
Lastpage :
440
Abstract :
This paper presents a new approach to solve the short-term unit commitment problem using an evolutionary programming based simulated annealing method with cooling and banking constraints. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. Evolutionary programming, which happens to be a global optimization technique for solving unit commitment problem, operates on a system, which is designed to encode each unit\´s operating schedule with regard to its minimum up/down time. In this, the unit commitment schedule is coded as a string of symbols. An initial population of parent solutions is generated at random. Here, each schedule is formed by committing all the units according to their initial status ("flat start"). Here the parents are obtained from a pre-defined set of solutions, i.e. each and every solution is adjusted to meet the requirements. Then, a random decommitment is carried out with respect to the unit\´s minimum down times. And TS improves the status by avoiding entrapment in local minima. The best population is selected by evolutionary strategy: numerical results are shown comparing the cost solutions and computation time obtained by using the evolutionary programming method and other conventional methods like dynamic programming, Lagrangian relaxation.
Keywords :
encoding; evolutionary computation; minimisation; power generation dispatch; power generation economics; power generation scheduling; random processes; simulated annealing; cooling-banking constraint; evolutionary programming; global optimization technique; minimization; operating schedule encoding; power system; predefined solution set; random generation; short-term unit commitment problem; simulated annealing method; Banking; Computational efficiency; Cooling; Costs; Design optimization; Dynamic programming; Genetic programming; Power generation; Power systems; Simulated annealing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
India Annual Conference, 2004. Proceedings of the IEEE INDICON 2004. First
Print_ISBN :
0-7803-8909-3
Type :
conf
DOI :
10.1109/INDICO.2004.1497790
Filename :
1497790
Link To Document :
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