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This paper develops a new framework to solve the practical non-convex economic dispatch problem with regard to the constraints including ramp rate limits, valve loading effect, prohibited operating zone, spinning reserve and multi-fuel option. In the proposed framework, bat algorithm, as a new optimization algorithm, is employed to explore the problem search space globally. In addition, a newly introduced adaptive modification method is developed to improve the variety of the bat population when increasing the convergence speed simultaneously. This modification is constructed based on the roulette wheel mechanism in conjunction with the crossover and mutation operators from the genetic algorithm. At the end of paper, the superiority of the proposed framework is examined using the IEEE 10-unit and 40-unit test systems. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal operation management of the distribution feeder reconfiguration (DFR) considering the uncertainty effects. In contrast to the conventional objective functions, this paper considers the System Average Interruption Frequency Index (SAIFI) as a reliability index. Meanwhile, the total active power losses and the voltage deviation objective functions are considered as the other targets too. In order to make the analysis more practical, the uncertainty associated with the active and reactive load forecast errors are modeled in a stochastic framework based on 2 m Point Estimate Method (PEM). In the proposed stochastic optimization framework, an external memory called repository is defined to store the non-dominated solutions which are found during the optimization process. Also, a fuzzy based clustering technique is defined to keep the size of the repository within the predefined limits. Since the proposed problem is a nonlinear, discrete complex optimization problem, this paper proposes an intelligent self adaptive modified optimization algorithm based on θ-firefly algorithm to solve the optimal multi-objective stochastic DFR problem suitably. The proposed self-adaptive modification method consists of three sub-modification techniques which let each firefly choose the sub-modification method that best suits its situation adaptively. The feasibility and superiority of the proposed method is tested on a standard IEEE test system. [ABSTRACT FROM AUTHOR]