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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]

Abstract: This paper proposes a new probabilistic framework based on 2m Point Estimate Method (2m PEM) to consider the uncertainties in the optimal energy management of the Micro Girds (MGs) including different renewable power sources like Photovoltaics (PVs), Wind Turbine (WT), Micro Turbine (MT), Fuel Cell (FC) as well as storage devices. The proposed probabilistic framework requires 2m runs of the deterministic framework to consider the uncertainty of m uncertain variables in the terms of the first three moments of the relevant probability density functions. Therefore, the uncertainty regarding the load demand forecasting error, grid bid changes and WT and PV output power variations are considered concurrently. Investigating the MG problem with uncertainty in a 24 h time interval with several equality and inequality constraints requires a powerful optimization technique which could escape from the local optima as well as premature convergence. Consequently, a novel self adaptive optimization algorithm based on θ-Particle Swarm Optimization (θ-PSO) algorithm is proposed to explore the total search space globally. The θ-PSO algorithm uses the phase angle vectors to update the velocity/position of particles such that faster and more stable convergence is achieved. In addition, the proposed self adaptive modification method consists of three sub-modification methods which will let the particles choosel the modification method which best fits their current situation. The feasibility and satisfying performance of the proposed method is tested on a typical grid-connected MG as the case study. [Copyright &y& Elsevier]

This paper suggests a new self-adaptive modification method using firefly algorithm (FA) to investigate the multi-objective probabilistic distribution feeder reconfiguration problem. In this regard, the idea of phase angle vector is employed to replace the traditional Cartesian framework in the FA and thus called θ-FA. Also, a new modification method based on an adaptive mechanism is suggested that will allow each firefly to choose the appropriate modification technique during the optimization suitably. As regards the objective functions, the main focus of this paper is to assess the effect of the reconfiguration on the reliability indices including active power losses, voltage deviation, and system average interruption frequency index. In order to handle the uncertainty effects, a sufficient framework based on 2 m + 1 point estimate method is proposed too. The satisfying performance of the proposed method is checked using IEEE 32-bus radial distribution system. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]