Abstract
The share of renewable energy sources (RESs) in the overall power production is on the upward trend in many power systems. Especially in recent years, considerable amounts of RES type distributed generations (DGs) are being integrated in distribution systems, albeit several challenges mainly induced by the intermittent nature of power productions using such resources. Optimal planning and efficient management of such resources is therefore highly necessary to alleviate their negative impacts, which increase with the penetration level. This paper deals with the optimal allocation (i.e. size and placement) of RES type DGs in coordination with reconfiguration of distribution systems (RDS). Moreover, the paper presents quantitative analysis with regards to the impacts of RDS on the integration level of such DGs in distribution systems. To this end, a tailor-made genetic algorithm (GA) based optimization model is developed. The proposed model is tested on a 16-node network system. Numerical results show the positive contributions of network reconfiguration on increasing the level of renewable DG penetration, and improving the overall performance of the system in terms of reduced costs and losses as well as a more stabilized voltage profile.

Abstract
In this paper, a new model is developed to improve the behavior of plug-in electric vehicles (PIEVs) aggregator in the short-term and long-term time horizons. In the model, the aggregator maximizes his/her profit and optimizes PIEV owners' revenue by applying changes in the contract type to ensure their participation and attract new customers in the future. The aggregator optimizes the self-scheduling program and proposes the optimal bidding strategies to the electricity markets, considering EVs constraints. Moreover, the electricity markets are modeled as oligopoly markets, in contrast with previous works that considered perfectly competitive ones. Further, in the proposed model several uncertainties are considered, such as electricity market prices, number of connected PIEVs, the connection duration of PIEVs, amount of energy stored in the batteries, calling the aggregator by ISO for power generation and behavior of market players. The numerical results show the effectiveness of the proposed model.

Abstract
Voltage source converter (VSC) can be considered as the heart of interfacing system for the integration of Distributed Generation (DG) sources into the power grid. Several control methods have been proposed for integration of DG sources into the power grid, and injection of high quality power. However, the converter-based DG interface is subjected to the unexpected uncertainties, which highly influence performance of control loop of DG unit and operation of interfaced converter. The interfacing impedance seen by interfaced VSC may considerably vary in power grid, and the stability of interfaced converter is highly sensitive to the impacts of this impedance changes; then, DG unit cannot inject appropriate currents. To deal with the instability problem, a control method based on fractional order active sliding mode is proposed in this paper, which is less sensitive to variations of interfacing impedance. A fractional sliding surface, which demonstrates the desired dynamics of system is developed and then, the controller is designed in two phases as sliding and reaching phases to keep the control loop stable. Stability issues of the control method are discussed in details and the conditions in which the proposed model works in a stable operating mode is defined. The proposed control method takes a role to provide high quality power injection and ensures precise references tracking and fast response despite such uncertainties. Theoretical analyses and simulation results are established to confirm the performance and feasibility of the proposed control method in DG technology.

Abstract
The recent interest in smart grid vision enables several smart applications in different parts of the power grid structure, where specific importance should be given to the demand side. As a result, changes in load patterns due to demand response (DR) activities at end-user premises, such as smart households, constitute a vital point to take into account both in system planning and operation phases. In this study, the assessment of the impacts of pricing based DR strategies on smart household load pattern variations is provided. The household load data sets are acquired from a provided model of a smart household, including appliance scheduling. Then, an artificial neural network (ANN) approach based on Wavelet Transform (WT) is employed for the forecasting of responsive residential load behaviors to different pricing schemes. From the literature perspective this study contributes by considering DR impacts on load pattern forecasting, being a very useful tool for market participants such as aggregators in future pool-based market structures, or for load serving entities to discuss potential change requirements in existing DR strategies, or even to effectively plan new ones.

Abstract
Increasing environmental concerns have motivated efforts for the modernization of the power system recently. As a result, the distribution system (DS) has been given specific importance. Especially, residential end-users have been rendered active trough the introduction of several assets such as electric vehicles, energy storage systems and self-production units. Although many opportunities emerge from the spreading of these so-called "smart" households, the DS may be negatively affected if appropriate coordination techniques are not developed. At this point, the distribution system operators (DSO) that are responsible for the operation of the DS need to intervene. The smart households struggle to minimize their daily electricity procurement cost by exploiting dynamic pricing tariff schemes. On the other hand, the DSO aims to minimize the active power losses of the DS. DS reconfiguration is a core element of the daily operational practice of DSO and should be considered in the development of coordination strategies. In this study, a two-side interaction framework is proposed in order to guarantee that both the smart households and the DSO sufficiently achieve their individual goals. State-of-the art computing techniques are also used in order to render the proposed formulation tractable for real-life applications.

Abstract
The rational use of energy and energy-efficient environmental public street lighting is an important topic. In the design of new public lighting installations, national regulations containing energy-efficient guidelines are already used. Nevertheless, either in new installations or in reconstructions of existing lighting, designers do not generally consider all the available means to save energy. In installations of street lighting, energy consumption can be reduced by reducing the losses in the conductors, associated with the efficiency of the equipment, allowing better use of the available energy. The losses in the conductors must be analysed in conjunction with all the loads that contribute to the current in the sections of the installed street lighting. When opting for more efficient lamps and luminaires or lighting control systems, the current decreases in the sections covered with the most significant power loss due to proportionality with the square of the current. This decrease, often forgotten, is considered in this work in the investment analysis of efficiency and sustainable street lighting via simulation and experimental results. This analysis, combined with the features and operating parameters of the electrical installation, accounts for all the gains that can make a difference in the choice of efficient street lighting. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).