Abstract
This paper presents an algorithm for integrating and managing electrochemical energy storage systems (ESSs) on unit commitment (UC) problem. As some elements required for integrating electrochemical ESS, such as the power converter, have non-linear characteristics, its corresponding linear modeling could be difficult to be developed and included on the UC problem, which could lead to unfeasible solutions or unexpected results. In order to incorporate full models of ESS and its interface with the power system, in this paper an algorithm to incorporate electrochemical ESS management on the UC problem is presented. An insular power system of 10-units is analyzed, and conclusions are duly drawn.

Abstract
This paper presents a new way to formulate and solve the distribution system optimal reconfiguration problem. The system equations representing the network topology, power flow equation and objective function equation are transformed into an artificial dynamic model formulated by using only differential equations, on the basis of an error function defined into a Lyapunov space domain. Possible inequality constraints are handled by using additional slack variables to transform them into equality constraints. The solution of the optimal reconfiguration problem is obtained at the convergence of the artificial dynamics and is based on dynamic optimal power flow formulation. Starting from a time domain continuous problem, a mixed continuous integer solution in Lyapunov space domain is obtained. The results obtained on a test system are shown and discussed.

Abstract
The introduction of plug-in electric vehicles (PEVs) in the electrical system is bringing various challenges. The main issue is incorporating the PEV owner's preferences in the models. One of the main attributes representing the preference of the owners is their travel purposes, impacting on the traffic flow pattern. The PEVs' traffic pattern defines the required charging schedule of the PEVs, and consequently, characterizes the operation of the charging facilities such as PEV parking lots (PLs). The deployment of resources such as PEV PL requires a detailed modeling of the factors affecting their operation. In this regard, this paper aims to model the power flow of the PEVs based on their traffic flow. Different travel types and purposes are considered for the PEVs traffic modeling. Two types of charging infrastructure (i.e., PLs and individual charging stations) are considered. The study is performed on a distribution network categorized based on the consumption patterns of the zones.

Abstract
This paper analyses the impact of the high penetration of electric vehicles (EVs) charging loads on the thermal ageing of distribution transformers of an isolated electric grid in a Portuguese Island. In this paper, a transformer thermal model is used to estimate the hot-spot temperature (theta(h)) given the load ratio. Real data are used for the main inputs of the model, i.e. residential load, transformer parameters, time-of-use rates and electric vehicle parameters. Conclusions are duly drawn.

Abstract
Optimal operation of power systems with high integration of renewable power sources has become difficult as a consequence of the random nature of some sources like wind energy and photovoltaic energy. Nowadays this problem is solved using the Monte Carlo Simulation (MCS) approach, which allows the consideration of important statistical characteristics of wind and solar power production, such as the correlation between consecutive observations, the diurnal profile of the forecasted power production, and the forecasting error. In this paper, a new model of the unit scheduling of power systems with significant renewable power generation based on the scenario generation/reduction method combined with the priority list (PL) method is proposed that finds the probability distribution function (PDF) of a determined generator be committed or not. This approach allows the recognition of the role of each generation unit on the day-ahead unit commitment (UC) problem with a probabilistic point of view, which is important for acquiring a cost-effective and reliable solution. The capabilities and performance of the proposed approach are illustrated through the analysis of a study case, where the spinning reserve requirements are probabilistically verified with success.

Abstract
Wind power is expected to deliver a significant part of power generation in future smart grid. However, many economic challenges have arisen from the intermittent nature of wind power. In this paper, a multi-stage stochastic model is proposed for self-scheduling problem of Wind Power Producers (WPPs) in competitive electricity markets. The proposed model includes three trading levels namely; forward, day-ahead, and balancing sessions. The problem uncertainties, such as wind power, market prices and quantity of activated reserve by ISO are considered by the Monte Carlo method. Moreover, Conditional Value-at-Risk (CVaR) is employed in the model as an appropriate risk measuring technique. The proposed model yields the optimal behavior of WPPs to participate in day-ahead energy and ancillary services markets (i.e. spinning reserve and regulation). Simulation results indicate that simultaneous participation of the WPPs in the mentioned markets not only augments their profit but also can significantly decrease the associated risks.