Abstract
Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA) method and Radial Basis Function Neural Network (RBFN). To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO) is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators.

Abstract
Microgrid (MG) is a relatively new concept for the integration of distributed generation (DG) along with the loads in a distribution system. Islanded microgrid can be considered as a weak grid that has less inertia compared with the conventional power system. This reality makes the microgrid vulnerable to contingencies. Towards a flexible, safe and secure operation of an islanded MG, researchers have introduced a hierarchical control structure comprising tertiary, secondary and primary control. The primary control plays an important role in maintaining the voltage and frequency stability by sharing the loads among the DGs. This paper reviews and categorizes various primary control methods that have been introduced to control the voltage and frequency of inverter-based microgrids. Moreover, the reviewed methods in terms of their potential advantages and disadvantages are compared. Finally, the future trends are presented.

Abstract
The non-stationary and stochastic nature of wind power reveals itself a difficult task to forecast and manage. In this context, with the continuous increment of wind farms and their capacity production in Portugal, there is an increasing need to develop new forecasting tools with enhanced capabilities. On the one hand, it is crucial to achieve higher accuracy and less uncertainty in the predictions. On the other hand, the computational burden should be kept low to enable fast operational decisions. Hence, this paper proposes a new hybrid evolutionary-adaptive methodology for wind power forecasting in the short-term, successfully combining mutual information, wavelet transform, evolutionary particle swarm optimization, and the adaptive neuro-fuzzy inference system. The strength of this paper is the integration of already existing models and algorithms, which jointly show an advancement over present state of the art. The results obtained show a significant improvement over previously reported methodologies.

Abstract
As the smart grid solutions enable active consumer participation, demand response (DR) strategies have drawn much interest in the literature recently, especially for residential areas. As a new type of consumer load in the electric power system, electric vehicles (EVs) also provide different opportunities, including the capability of utilizing EVs as a storage unit via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options instead of peak power procurement from the grid. In this paper, as the main contribution to the literature, a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies with a bi-directional utilization possibility for EV and energy storage system (ESS) is realized. A mixed-integer linear programming (MILP) framework-based modeling of a home energy management (HEM) structure is provided for this purpose. A distributed small-scale renewable energy generation system, the V2H and V2G capabilities of an EV together with two-way energy trading of ESS, and different DR strategies are all combined in a single HEM system for the first time in the literature. The impacts of different EV owner consumer preferences together with the availability of ESS and two-way energy trading capabilities on the reduction of total electricity prices are examined with case studies.

Abstract
This paper presents a multifunction control strategy for the stable operation of Distributed Generation (DG) units during grid integration. The proposed control model is based on Direct Lyapunov Control (DLC) theory and provides a stable region for the appropriate operation of DG units during grid integration. Using DLC technique in DG technology can provide the continuous injection of maximum active power in fundamental frequency from the DG source to the grid, compensating all reactive power and harmonic current components of grid-connected loads through the integration of DG link into the grid. Application of this concept can guarantee to reduce the stress on the grid during the energy demand peak. Simulation results are presented to demonstrate the proficiency and performance of the proposed DLC technique in DG technology.

Abstract
The variability of wind generation introduces uncertainty in the optimal scheduling of the system. Consequently, it is difficult for the system operator to determine the optimal amount of conventional generation that should be committed and its corresponding power production in order to reduce generation costs. Incorporation of forecasting error on the optimal unit scheduling has been extensively suggested in the literature. However, it strongly depends on the probability distribution adopted to represent wind power forecasting error. Cauchy distribution has demonstrated to be an adequate tool to represent forecasting error. In this paper, an analytical model to solve dynamic economic dispatch is presented. The proposed model is based on discretization of Cauchy distribution, so that its incorporation in the optimization problem is successfully done. This is illustrated by analyzing a representative case study and the results are compared to a Monte Carlo Simulation approach in order to show the accuracy of the proposed method.