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In the current paper, the optimal placement merits of distribution generations (DGs) are investigated in the economical version of a grid-parallel mode. The proposed method is constructed based on a probabilistic approach which is called Point Estimate Method (PEM) to consider the uncertainty associated with the load demand prediction error along with the variation of cost of electricity and power production of DGs. The objective functions are the total active power losses, the total emission produced and the total cost related to the grid & DGs and cost of reliability. These functions are set to be optimized that result in utilizing an interactive fuzzy approach based on improved particle swarm optimization (IPSO) to let the decision maker apply his/her preferences to the system. It is apt to mention that the application of the interactive fuzzy approach is based on the result of conflicting behaviors of the investigated objective functions. Through using the Tai-Power distribution test system the feasibility and the efficiency of the proposed method have been shown. 6 7 8 9 10 11 12 13 14

The capable incorporation of Plug-in Hybrid Electric Vehicles (PHEVs) in the upcoming transportation area presents several technical challenges to electrical distribution networks for example voltage drop and loss increase. The energy demand of these movable loads is stochastic naturally owing to the uncertainties accompanied by their location and amount of required energy. Accordingly, a new optimal stochastic reconfiguration procedure based on Gravitational Search Algorithm (GSA) is suggested that diminishes resistive loss and costs of radial distribution grids. The proposed technique is equipped with mutative operators to surpass the optimization process. Furthermore, a novel local smart charging pattern is recommended which lessens the congestion influence of PHEVs on system load curve successfully. The uncertainties of PHEV loads are modeled with Monte Carlo Simulation (MCS) and the proposed methodology is examined on Tai-power distribution network to validate its performance and robustness.

According to the standards, optimal allocation and sizing of D-STATCOM should be implemented accurately to make the maximum efficiency. Thus, this paper suggests a new approach based on social spider optimization (SSO) algorithm to deal with the optimal allocation and sizing of D-STATCOM in the distribution systems. The problem is formulated in a multi-objective framework based on scenario production and SSO algorithm to capture the uncertainties of active and reactive loads suitably. The proposed scenario based method generates a number of possible scenarios with different probabilities to model the uncertainty effects. The feasibility and satisfying performance of the proposed method are examined using an IEEE standard distribution test system.

With the high penetration of renewable power sources in the form of distributed generations (DGs), the amount of uncertainty in the power systems is increased greatly. This high uncertainty has affected most of the grid operation strategies including the optimal management of DGs. One significant source of uncertainty is the forecast error in the modeling of the future active and reactive load values. In order to deal with this problem, this paper suggests a new stochastic framework based on the scenario generation process and roulette wheel mechanism. This method converts the stochastic problem into a number of deterministic problems with different probabilities. Since the problem investigated is a complex nonlinear optimization problem, a sufficient optimization algorithm based on the bio-inspired krill herd algorithm is proposed to solve the problem effectively. The satisfying performance of the proposed method is examined on the IEEE standard test system.

In power systems, reliability evaluations mainly depend on the precise forecasting of the reliability parameters of the internal components. Nonetheless, the procedure of calculating the reliability parameters such as failure rate and repair rate parameters is based on the failure statistics and measurements which can cause much inherent uncertainty and thus affecting the reliability indices of the system. Therefore, the main purpose of this work is to suggest a sufficient and easy-implemented approach based on scenario production to capture the uncertainty of the reliability parameters including the failure rate and repair rate variables. Here, the scenario generation process is constructed using the roulette wheel mechanism along with the probability density function of the random variables. Then, the proposed scenario based approach makes use of a scenario reduction mechanism to avoid high computational burden. This aspect of the proposed stochastic framework makes it useful for the optimization applications which require iterative analysis. In order to see the feasibility and satisfying performance of the proposed framework, an IEEE standard test system is used as the case study.

This paper proposes a sufficient stochastic framework to assess the influence of charging demand of plug-in hybrid electric vehicles (PHEVs) on the operation of renewable micro-grids (MGs). In this regard, an intelligent charging approach is proposed to shift the charging demand of PHEVs to off-peak load hours. In order to reduce the total cost of the MG, battery as the storage device is incorporated in the MG. Since the problem investigated is a hard complicated optimization problem, a new optimization method called Modified Clonal Selection algorithm (MCSA) is utilized too. The proposed MCSA employs two modification techniques to improve the position of antibodies in the face of local optima. For modeling the uncertainty of parameters, 2m-point estimate method (2m-PEM) is utilized as the stochastic framework. The feasibility and appropriate performance of the proposed method are examined on a standard MG.