Abstract
This paper deals with a multi-objective control strategy for integration of distributed generation (DG) sources to the power grid. The proposed control technique can provide simultaneous compensation for active and reactive power, and harmonic current components of loads through integration of DG sources to the grid, which is the main contribution of this work over the other proposed methods. A dynamic model of the proposed DG model is first formulated in the stationary reference frame and then transformed into the synchronous orthogonal reference frame. The transformed variables are used in control of voltage source converter (VSC) as the heart of an interfacing system between DG sources and utility grid. By setting appropriate reference currents in the control loop of DG, the maximum available power of DG source will be injected to the grid with fast dynamic response, thereby achieving sinusoidal grid currents in phase with load voltages, while the required power of the load is more than the maximum injected power of DG to the grid. The effectiveness of the proposed control strategy is validated with injection of maximum available power from the DG source to the grid, increased power factor of the utility grid and reduced total harmonic distortion (THD) of grid current through simulation results under dynamic and steady-state operating conditions.

Abstract
The present evolution of fuel prices together with the reduction of premiums for renewable energies make it of vital importance to improve renewable production management. This paper proposes a model of a single renewable power producer to compete more efficiently against other generators. The single unit is composed of a wind power producer and a hydro-pump storage power producer. The synergies between both renewable producers make relevant the possibility of mitigating wind power uncertainty, and due to this, the imbalances of the wind power producer will be reduced. The reduction of wind imbalances can come from deviating part of the excess of wind generation through a physical connection toward the pumping system or by increasing hydro generation to mitigate the lack of wind generation. To evaluate the problem, stochastic mixed integer linear programming is proposed to address the problem of selling the energy from the single renewable unit through a bilateral contract and in the day-ahead market, as a new contribution to earlier studies. Furthermore, a balancing market is considered to penalize the imbalances. The decision is made to maximize the profit, considering risk-hedging through the Conditional Value at Risk. The model is tested and analyzed for a case study and relevant conclusions are presented.

Abstract
This article presents the impacts of different storage technologies, their size as well as their location on the economic dispatch of a distribution system. The participation of the storage in distribution systems is evaluated over a period of 24 hours using a radial reconfiguration of the network. In order to meet this objective, a stochastic mixed integer linear problem (SMILP) is proposed, where the expected cost during the operation period is minimized. The model considers uncertainties in wind generation, load which is represented through a typical demand profile, conventional generation and storage systems. Results from a case study is presented for a distribution network with 28 buses, comprehensively describing the impacts of the location and the size of the storage system on the distribution network, as well as on the expected operation costs of the system.

Abstract
Nowadays, with the new paradigm shift in the energy sector and the advent of the smart grid, or even with the mandatory imposition for a gradual reduction of greenhouse gas emissions, the renewable producers, namely the wind power producers are faced with the competitiveness and deregulated structure that characterizes the liberalized electricity market. In a liberalized electricity market, the most important signal for all market players corresponds to the electricity prices. In this sense, accurate approaches for short-term electricity prices prediction are needed, and also for short-term wind power prediction due to the increasing share of wind generation. Hence, this paper presents a new hybrid evolutionary-adaptive approach for wind power and electricity market prices prediction, in the short-term, based on mutual information, wavelet transform, evolutionary particle swarm optimization and adaptive neuro-fuzzy inference system, tested on real case studies, proving its superiority in a comprehensive comparison with other approaches previously published in the scientific literature.

Abstract
As a recently increasing trend among different applications of smart grid vision, smart households as a new implementation area of demand response (DR) strategies have drawn more attention both in research and in engineering practice. On the other hand, optimum sizing of renewable energy based small scale hybrid systems is also a topic that is widely covered by the existing literature. In this study, the sizing of additional distributed generation and energy storage systems to be applied in smart households, which due to DR activities have a different daily demand profile compared with normal household profiles, is investigated. To the best knowledge of the authors, this is the first attempt in the literature to consider the impact of DR on sizing. The study is conducted using a mixed-integer linear programming framework for home energy management system modeling and techno-economical sizing. Also, different sensitivity analyses considering the impacts of variation of economic inputs on the provided model are realized.

Abstract
The mature bulk power system requires to meet the needs of 21th century in terms of efficient and effective utilization of electric energy, together with the capability of accommodating recently growing renewable energy resources penetration. As a new idea of modernizing the current grid structure, the smart grid issue is a widely growing area of interest with investments from developed/developing country governments. As the smart grid solutions enable active consumer participation, demand response (DR) strategies have drawn much interest as such strategies provide consumers the chance for the real-time control of their consumption to reduce their bills, while utilities can lower the peak power value to be supplied to consumers. As a new type of consumer load in the electric market, electric vehicles (EVs) also provide different opportunities, including the capability of utilizing EVs as a storage unit via vehicle-to-grid (V2G) option instead of peak power procurement from utility. This study aims to discuss the impacts of different DR strategies and EV owner consumer preferences on the reduction of total electricity prices. Different case studies are conducted to better analyze the price reduction potential of different operating strategies.