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After restructuring the power systems, demand response programs form the main part of consumption management programs. This is because these programs are by nature particularly suitable for adapting to the new structure in power system management. These programs are considered as a suitable solution for solving some problems in power system restructuring. Unit commitment (UC) programs are used for power production with minimum cost. In this paper, after presenting an overview of demand response methods, a mechanism is proposed for simultaneous execution of the UC program, emergency demand response programs and interruptible/curtailable loads. The proposed mechanism, in addition to decreasing the payment costs, ensures a at load curve and increases system reliability. [ABSTRACT FROM AUTHOR]

This paper presents a new approach with a three-stage optimization algorithm for the least-cost optimal solution of the unit commitment problem. In the proposed work, the optimal schedule is obtained by optimizing the lambda operator for the states that violate the inequality constraints. The objective of the work is to minimize the fuel cost when subjected to various constraints such as load balance, minimum up/down time, ramp limit, and spinning reserve. This method of committing the units yields the least-cost solution when applied to the IEEE 10-unit systems and 7-unit Indian utility practical systems scheduled for 24 h and the results obtained are compared with the existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes an improved firefly (FF) algorithm with multiple workers for solving the unit commitment (UC) problem of power systems. The UC problem is a combinatorial optimization problem that can be posed as minimizing a quadratic objective function under system and unit constraints. Nowadays, highly developed computer systems are available in plenty, and proper utilization of these systems will reduce the time and complexity of combinatorial optimization problems with large numbers of generating units. Here, multiple workers are assigned to solve a UC problem as well as the subproblem, namely economic dispatch (ED) in distributed memory models. The proposed method incorporates a group search in a FF algorithm and thereby a global search is attained through the local search performed by the individual workers, which fine tune the search space in achieving the final solution. The execution time taken by the processor and the solution obtained with respect to the number of processors in a cluster are thoroughly discussed for different test systems. The methodology is validated on a 100 unit system, an IEEE 118 bus system, and a practical Taiwan 38 bus power system and the results are compared with the available literature. [ABSTRACT FROM AUTHOR]

The unit commitment problem (UCP) is a nonlinear, mixed-integer, constraint optimization problem and is considered a complex problem in electrical power systems. It is the combination of two interlinked subproblems, namely the generator scheduling problem and the generation allocation problem. In large systems, the UCP turns out to be increasingly complicated due to the large number of possible ON and OFF combinations of units in the power system over a scheduling time horizon. Due to the insufficiency of conventional approaches in handling large systems, numerous metaheuristic techniques are being developed for solving this problem. The genetic algorithm (GA) is one of these metaheuristic methods. In this study, an attempt is made to solve the unit commitment problem using the GA with ring crossover and swap mutation operators. One half of the initial population is intelligently generated by focusing on load curve to obtain better convergence. Lambda iteration is used to solve the generation allocation subproblem. Tests are carried out on systems with up to 100 generators over a time horizon of 24 h. Test outcomes demonstrate the proficiency of the presented scheme when compared with previously used techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time solution to the unit commitment problem by considering different constraints like ramp-up rate, unit operation emissions, next hours load, and minimum down time. In this method, an optimized trade-off between cost and emission has been taken into consideration. The effectiveness of the proposed method was verified by the significant outcomes demonstrated. [ABSTRACT FROM AUTHOR]