This paper presents a new approach to solve the short-term unit commitment problem using An Evolutionary Programming Based tabu search method with cooling and banking constraints. 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.
This paper presents a MATLAB-SIMULINK based Dynamic model of a combined cycle stand alone gas power plant the transfer function model of load frequency and temperature control loop has been developed. The objective of this proposed work is to regulate the frequency and temperature in order to maintain the system stability of the power plant. Since most of the loads are induction type and the induction motor speed is directly proportional to frequency, the frequency has to be maintained within the limits. If temperature is low, it cause low efficiency of the heat recovery boiler and maintaining temperature higher than allowed will reduce life of the equipment. Hence the temperature has to be regulated to have safe operation of the power plant. Considering these backgrounds, it is important to study dynamic behavior of combined cycle plants. Hence it has been developed the dynamic model for a single shaft combined cycle plant. In this paper the analysis of power plant response to electrical load and frequency transients with the effects of SMES is presented.
This paper presents a new approach to solve short-term unit commitment problem (UCP) using Neural Based Tabu Search (NBTS) 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 next H hours. A 7-unit utility power system in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different IEEE test systems consist of 10, 26 and 34 units. Numerical results are shown to compare the superiority of the cost solutions obtained using the Tabu Search (TS) method, Dynamic Programming (DP) and Lagrangian Relaxation (LR) methods in reaching proper unit commitment
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