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In this paper, a hybrid approach based on a novel binary artificial bee colony (NBABC) algorithm and local search (LS) is developed to solve the unit commitment problem (UCP). Also, the ramp rate constraints are taken into account in the solution of UCP by performing conventional economic dispatch with modifying unit generating capacities over the entire scheduling time horizon. The proposed NBABC-LS method differs from its counterparts in three main aspects: (i) it utilizes a novel strategy which measures the dissimilarity between two binary strings for generating the new binary strings for UCP; (ii) it uses an intelligent scout bee phase; (iii) A LS module is hybridized with the NBABC algorithm. These modifications result in three major advantages: (i) it avoids the problem of slow and premature convergence and thus does not fall into the local optimum solutions; (ii) it can quickly find the near global optimum solution for UCP; (iii) accuracy and robustness of the solution are achieved. The proposed approach is successfully applied to the test systems up to 100 thermal units over 24-h scheduling time horizon and the real Turkish interconnected power system consisting of eight thermal units over 8-h scheduling time horizon. The obtained results confirm the quality solution in terms of total generation cost compared with the other methods reported in the literature. [ABSTRACT FROM AUTHOR]

The Unit Commitment (UC) problem is a nonlinear and complex optimization problem used to determine the start-up and shutdown scheduling of power-generating units to meet the forecasted load demand and spinning reserve over a specified time horizon so that the target of production cost minimization is achieved while satisfying various system and generator-based constraints. The increase in computational burden with the system size requires more efficient meta-heuristic approaches for solving the UC problem. This paper proposes a modified differential evolution (DE) approach for both discrete and real part of the UC problem. The infeasibility of the solutions is also handled by incorporating some repairing mechanisms in DE, which forcefully satisfy the system constraints and speed up the search process. The experimentation is carried out on various standard and large power systems, starting from the 10-unit base case and then going up to 100 units over a 24 -h time period. The obtained results indicate the effectiveness of the proposed approach as compared to the previous work reported in the literature. [ABSTRACT FROM AUTHOR]

In this paper, a new method is proposed to solve unit commitment problem using particle swarm optimization. This method limits the search space considering demand information and the generation history of the generating units to solve the UC problem in an hourly basis. An expert system is utilized to generate the search space combinations of the generating units and a particle swarm optimization algorithm is used to optimize generation levels according to each combination. The results are more accurate and get lower operation costs in comparison with other traditional methods. [ABSTRACT FROM AUTHOR]

This paper proposes an improved local search metaheuristic using simulated annealing method with memory component (MSA) for solving the unit commitment problem (UCP) with ramp constraints. The proposed method benefits simultaneously from the advantages of a two metaheuristics: acceptance of 'bad' solutions in order to escape from local optimal configurations (SA), and prohibition for a time period of certain areas already been searched (Tabu list) as used in Tabu search method. The proposed effective MSA method is tested on several systems as the conventional ten unit test system and its multiples with 24-h scheduling horizon and the IEEE 118-bus system with 54 units. To justify the success of the MSA method, a comparison of results with those of other metaheuristic methods and hybrid methods treated by recent references is made. The results show that the proposed method obtains less total operation costs than the others with an acceptable time computing and indicate its potential for solving the UCP. [ABSTRACT FROM AUTHOR]