This paper presents a robust framework for active and reactive power management in distribution networks using electric vehicles (EVs). The method simultaneously minimizes the energy cost and the voltage deviation subject to network and EVs constraints. The uncertainties related to active and reactive loads, required energy to charge EV batteries, charge rate of batteries and charger capacity of EVs are modeled using deterministic uncertainty sets. Firstly, based on duality theory, the max min form of the model is converted to a max form. Secondly, Benders decomposition is employed to solve the problem. The effectiveness of the proposed method is demonstrated with a 33-bus distribution network.

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According to the environmental and economic advantages of the Electric Vehicles (EVs), it is predicted that the penetration of EVs will be increased in the near future. EVs are fast growing loads in the power systems. Several types of EVs are invented and commercialized, such as Plug-in Hybrid Electric Vehicles (PHEVs) and Hybrid Electric Vehicles (HEVs). A PHEV needs a battery charging plug. The high penetration of the PHEVs in the Grid has great impact on the network planning stages, as well as the network operation schemes. Increasing the number of PHEVs, without an appropriate management, could decrease the power quality in distribution networks. In this paper we present a reactive power management strategy, considering a high penetration of PHEVs, to tackle with the negative impact of PHEVs, namely reducing the power losses, improving the voltage profile and removing line congestion in distribution network. To do so, at the first step, we obtain a generic model for into a distribution network. Then we present a reactive power management scheme to demonstrate how PHEVs could be used to control reactive power and how it could minimize the power losses and improve the voltage profile, as well as removing congestion in the distribution networks. 33-bus distribution network is considered to perform the case study. The results are highly implicate the performance of the proposed method.

In recent two decades, the power systems have confronted with considerable changes such as the power system restructuring, growth of distributed energy sources and renewable energy sources (RESs), and emergence of smart grid concept. One of the common challenges caused by these changes is flexibility necessity of energy resources. One of the best solutions to mitigate this challenge is energy storage systems (ESSs) utilisation. The main question is how to determine size, site, and type of ESSs to maximise their benefits. This study reviews the answers to this question according to the research studies. This study first classifies the studies related to ESS expansion planning into two main categories from the viewpoint of the power system operators and the investors. Next, the first main category is divided into three subcategories: ESS expansion planning in microgrids, distribution networks, and transmission networks. The second main category is classified into two subcategories: ESS expansion planning aim to smooth RESs output power and to maximise profit. In each subcategory the modelling approaches, solving methods, and the results are evaluated. Finally, based on the existing challenges, the future research directions of ESS expansion planning are outlined.

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SUMMARY Conflicts between independent system operators (ISOs) and power producers, each preferring the annual maintenance plans based on their own objectives, have resulted from power system restructuring. Therefore, the ISO needs to specify how much the producers are free to adjust their maintenance programs on their own, without violating reliability criteria. In this article, a conflict assessment framework is described to characterize the degree of conflict among the electricity market participants' (ISO and producers) objectives. Four steps, namely, information gathering, mathematical modeling of the objectives, conflict front discovery, and finally the sensitivity analysis, were proposed as the basic steps of the framework. In this way, a multiobjective optimization problem was introduced by which a set of compromise solutions, denoted as a Pareto‐optimal front, can be found. To discover the front, the strength Pareto evolutionary algorithm was used as a robust evolutionary optimization technique. For the sensitivity analysis, two parameters, namely, the front curve relative change and its degree of linearity, were used. The proposed framework was tested on IEEE‐RTS, and its capabilities were assessed and appreciated. Copyright © 2011 John Wiley & Sons, Ltd.

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