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This article proposes a novel stochastic framework based on point estimate method (PEM) and firefly algorithm (FA) for the optimal operation of plug-in electric vehicles (PVs) in the distribution systems. The stochastic problem will consider the uncertainties of active and reactive loads, the number of PVs in the fleets and the departure and arrival time at charging locations. According to the high complexity and severity of the problem, a new version of FA called θ-FA is used to search the solutions in the polar space instead of the traditional Cartesian space. The objective function is to minimize the total cost of the system considering the operation and security constraints. The feasibility and high performance of the propose problem are examined on a standard IEEE test system. [ABSTRACT FROM AUTHOR]

This paper suggests a new stochastic framework based on cloud theory to model the uncertainty effects in the optimal energy management in renewable micro-grids (MGs). Cloud theory is constructed based on the fuzzy theory and probability concept to provide an intelligent model called cloud model (CM). In comparison with the popular Monte Carlo simulation (MCS) method, CM can capture more uncertainty of the problem through the cloud drops. The basic idea is to incorporate the fuzziness and randomness of qualitative concepts and afterward transform them to the quantitative model. According to the high complexity and nonlinearity of the problem, a novel optimization algorithm called modified krill herd (MKH) algorithm is suggested to explore the problem search space globally. The proposed modification method will increase both the search ability and convergence of the algorithm effectively. The feasibility and satisfying performance of the proposed intelligent stochastic framework is demonstrated though a typical MG including different types of renewable energy sources and storage device. Comparative studies reveal the reliable superiority of the proposed stochastic framework to solve the problem. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]