Abstract
With increasing environmental concerns, the electrification of transportation plays an outstanding role in the sustainable development. In this context, plug-in electric vehicle and demand response have indispensable impacts on future smart grid. Since integration of electric vehicles into the grid is a key element to achieve sustainable energy systems, this paper studies the behavior of plug-in electric vehicle parking lots in the electricity markets. Participation of these new market players, as responsive demands, is modeled in both price-based and incentive-based demand response programs. Moreover, impacts of different demand response programs on optimal operational behavior of the parking lots are investigated. In addition to uncertainties of activated reserve, uncertainties of electric vehicle class, arrival and departure times, and initial battery state-of-charge are also considered. Stochastic programming is used to address the mentioned uncertainties. Numerical studies indicate that the participation of parking lots in different demand response programs can increase their profit. © 2015 IEEE.

Abstract
Large-scale deployment of renewables in island energy systems attracts local attention of grid operators as a way of reducing fuel fossil consumption. Planning a grid based on renewable power plants poses serious challenges to the normal operation of a power system, namely on frequency and voltage stability. In past grid code compliance, wind turbines did not require services for supporting grid operation. To shift to large renewable energy integration, the island grid code should incorporate a new set of requirements in order to regulate the inclusion of these services, which is the aim of this paper. The paper also discusses additional requirements such as "virtual" wind inertia.

Abstract
Nowadays, it is a high trend to increase the electricity production using Distributed Generation (DG) technology based on renewable energy resources (RERs) such as photovoltaics and wind power. If these systems are not properly controlled, their connection to the power grid can provoke many problems on the grid side. Therefore, considerations about power generation, safe running and grid connection must be done before connecting DG units into the power grid. This paper is mainly dealing with the grid connection of DG units to the power grids and compensation of the unbalanced loads. The proposed technique provides compensation for active, reactive, unbalanced, and harmonic current components of unbalanced loads. High performance of the proposed technique is obtained through Matlab/Simulink, guaranteeing balanced overall grid currents, injection of maximum available power from DG resources, power factor increase of the utility grid, and a reduction of total harmonic distortion (THD) of grid current, under the proposed operating conditions.

Abstract
This paper describes the development of a multi-function control strategy for the stable operation of distributed generation (DG) units during the integration with the power grid. The proposed control model is based on direct Lyapunov control (DLC) theory and provides a stable region for the proper operation of DG units during the integration with the power grid. The compensation of instantaneous variations in the reference current components in ac-side and dc-voltage variations in the dc-side of the interfacing system are adequately considered in this control plan, which is the main contribution and novelty of this paper in comparison with previous control strategies. Utilization of the DLC technique in DG technology can confirm the continuous injection of maximum active power in fundamental frequency from the DG source to the power grid, compensating all the reactive power and harmonic current components of grid-connected loads through the integration of DG link into the grid. Application of this concept in smart grids system can guarantee to reduce the stress on the utility grid during the peak of energy demand. Simulation and experimental test results are presented to demonstrate the proficiency and performance of the proposed DLC technique in DG technology.

Abstract
Over the years the energy needs have increased dramatically and we have become aware that our needs were and are having implications on the environment in which we live. Increasingly people are aware to saving electricity by turning off the equipment that is not been used, or connect electrical loads just outside the on-peak hours. However, these few efforts are not enough to reduce the global energy consumption, which is increasing. Regarding the field of optimization, researchers throughout the world have been making an effort in introducing better control schemes, both in industry and domestic sectors, for all types of loads from small lamps to large motors. Much of the reduction was due to mechanical improvements; however, with the advancing of the years' new types of control arise. All these factors provide a motive in this paper for introducing a new consumption reduction method in some residential loads via the implementation of Model Predictive Control ( MPC). A single cost function is required to set the reference output near the goal, and consequently through the variation of this cost function by changing the weights, thus specific control actions have priority over the remaining actions. Therefore, it is possible to have different goals during the day, determining the possible savings for each appliance that can be made during on-peak, mid-peak, off-peak and by providing simulations upon 24 hours in the household.

Abstract
Due to increasing integration of renewable generation into the electrical framework in last decades, the mathematical techniques required for the optimal day-ahead scheduling needs to be continuously improved, specifically for modeling the variability of these sources. In this paper, a method for producing a new solution for the stochastic unit commitment (UC) problem from the analysis of each scenario is developed. The methodology described in this paper can deal with a large number of scenario sets with a reasonable computational effort, by finding the common and feasible solutions for the scenario set under analysis.