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As the number of electric vehicles (EVs) is steadily increasing, their aggregation can offer significant storage to improve the electric system operation in many aspects. To this end, a comprehensive stochastic optimization framework is proposed in this paper for the joint operation of a fleet of EVs with a wind power producer (WPP) in a three-settlement pool-based market. An aggregator procures enough energy for the EVs based on their daily driving patterns, and schedules the stored energy to counterbalance WPP fluctuations. Different sources of uncertainty including the market prices and WPP generation are modeled through proper scenarios, and the risk is hedged by adding a risk measure to the formulation. To obtain more accurate results, the battery degradation costs are also included in the problem formulation. A detailed case study is presented based on the Iberian electricity market data as well as the technical information of three different types of EVs. The proposed approach is benchmarked against the disjoint operation of EVs and WPP. Numerical simulations demonstrate that the proposed strategy can effectively benefit EV owners and WPP by reducing the energy costs and increasing the profits.

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Along with the advent of restructuring in power systems, considerable integration of renewable energy resources has motivated the transition of traditional distribution networks (DNs) toward new active ones. In the mean while, rapid technology advances have provided great potentials for future bulk utilization of generation units as well as the energy storage (ES) systems in the distribution section. This paper aims to present a comprehensive review of recent advancements in the operation of active distribution systems (ADSs) from the viewpoint of operational time-hierarchy.To be more specific, this time-hierarchy consists of two stages, and at the first stage of this time-hierarchy, four major economic factors, by which the operation of traditional passive DNs is evolved to new active DNs, are described.Then the second stage of the time-hierarchy refers to technical management and power quality correction of ADSs in terms of static, dynamic and transient periods. In the end, some required modeling and control developments for the optimal operation of ADSs are discussed. As opposed to previous review papers, potential applications of devices in the ADS are investigated considering their operational time-intervals. Since some of the compensating devices, storage units and generating sources may have different applications regarding the time scale of their utilization, this paper considers real scenario system operations in which components of the network are firstly scheduled for the specified period ahead; then their deviations of operating status from reference points are modified during three time-intervals covering static, dynamic and transient periods.

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It is shown that the method of lowest order constrained variational (LOCV) which is based on the cluster expansion theory is a reliable many-body technique to calculate the nuclear matter equation of state. In this respect, the state dependent correlation functions and the effective interactions which have been produced by the LOCV calculation with the Reid and -Reid soft core interactions are used to estimate the size of higher order cluster terms such as the effect of three-body cluster energy on the nuclear matter ground state energy. Finally it is shown that the LOCV normalization constraint plays a major role in the convergence of the cluster expansion and the result of LOCV calculation can be as good as more sophisticated approaches which go beyond lowest order.