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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Electrical Power & Energy Systems, Volume 23, Issue 8, November 2001, Pages 827–836
Hybrid models for solving unit commitment problem have been proposed in this paper. To incorporate the changes due to the addition of new constraints automatically, an expert system (ES) has been proposed. The ES combines both schedules of units to be committed based on any classical or traditional algorithms and the knowledge of experienced power system operators. A solution database, i.e. information contained in the previous schedule is used to facilitate the current solution process. The proposed ES receives the input, i.e. the unit commitment solutions from a fuzzy-neural network. The unit commitment solutions from the artificial neural network cannot offer good performance if the load patterns are dissimilar to those of the trained data. Hence, the load demands, i.e. the input to the fuzzy-neural network is considered as fuzzy variables. To take into account the uncertainty in load demands, a fuzzy decision making approach has also been developed to solve the unit commitment problem and to train the artificial neural network. Due to the mathematical complexity of traditional techniques for solving unit commitment problem and also to facilitate comparison genetic algorithm, a non-traditional optimization technique has also been proposed. To demonstrate the effectiveness of the models proposed, extensive studies have been performed for different power systems consisting of 10, 26 and 34 generating units. The generation cost obtained and the computational time required by the proposed model has been compared with the existing traditional techniques such as dynamic programming (DP), ES, fuzzy system (FS) and genetic algorithms (GA).
In most of the interconnected power systems, the power requirement is principally met by thermal power generation. Several operating strategies are possible to meet the required power demand, which varies from hour to hour over the day. It is preferable to use an optimum or sub-optimum operating strategy based on economic criteria. In other words, an important criterion in power system operation is to meet the power demand at minimum fuel cost using an optimal mix of different power plants. Moreover, in order to supply high quality electric power to customers in a secured and economic manner, thermal unit commitment is considered to be one of best available options (refer Appendix A). It is thus recognized that the optimal unit commitment of thermal systems results in a great saving for electric utilities.
نتیجه گیری انگلیسی
This paper presented a heuristic technique for the unit commitment problem that is free from the computational difficulties faced by complex power systems. The heuristic technique is computationally fast and quite amenable for on-line implementations. The convergence is reached within a very short time and the results are reasonably accurate. The proposed fuzzy decision system model is faster, and requires low core memory. The mathematical complexity in the DP approach and other heuristic approaches is completely eliminated and the proposed model does not suffer from the problem of dimensionality. The proposed algorithm provides a decision for a unit commitment problem, which will satisfy the minimum cost, meet the required demand and maintain appropriate reserve margin level and other constraints. At the same time, the major advantage of the genetic algorithm model is that it can accept complex mathematics more efficiently. The genetic algorithm model evaluates the priority of the units dynamically, considering the system parameters at each time period in the scheduling horizon. The results obtained from the hybrid models are highly impressive and encouraging, for real time implementation in any power system. The main observation from hybrid model is that combination of two or more AI methods performs better than individual methods for solving unit commitment problems. In the case of hybrid models, one method is normally used to compensate the demerit of the other method, so hybrid models are considered to be the best, with very few limitations.