Abstract
The optimal solution provided by metaheuristics can be viewed as a random variable, whose behavior depends on the value of the algorithm's strategic parameters and on the type of penalty function used to enforce the problem's soft constraints. This paper reports the use of parametric and non-parametric statistics to compare three different penalty functions implemented to solve the Security Constrained Optimal Power Flow (SCOPF) problem using the new enhanced metaheuristic Differential Evolutionary Particle Swarm Optimization (DEEPSO). To obtain the best performance for the three types of penalty functions, the strategic parameters of DEEPSO are optimized by using an iterative algorithm based on the two-way analysis of variance (ANOVA). The results show that the modeling of soft constraints significantly influences the best achievable performance of the optimization algorithm.

Abstract
Stationary batteries are currently seen as an interesting solution to deal with the variability of the renewable energy sources. In the same way as other types of storage, e.g. pumped-hydro units, this new type of storage equipment can improve the use of Renewable Energy Sources (RES). Additionally, the stationary batteries location in the grid is not as physically constrained as other storage systems and can be optimally selected to maximize its overall benefits. This paper proposes a new methodology to represent the unique stochastic behavior of stationary batteries while integrated into an electrical power system. This methodology includes not only the technical restrictions of this type of storage system but also how its operation strategy affects its lifetime. The methodology was tested on a small test system, which is based on the IEEE-RTS 79, using sequential Monte Carlo simulation as its core to accurately reproduce the chronology of events of stationary batteries. The results of the simulation are focused on the potential impacts of these storage devices not only in terms of renewable energy used but also in the adequacy of supply.

Abstract
The impact of large-scale Electric Vehicles (EVs) deployment in electric power systems is a current issue under study by the scientific community. The integration of this type of electric component requires robust planning solutions to mitigate possible consequences from such integration. This paper explores the quantification of the amount of renewable sources, namely wind and hydro power, which can be safely integrated into power systems in a scenario of a mass integration of EVs. The increase of renewable power in the generation portfolio is analyzed under the framework of generating system adequacy assessment considering several EVs deployment scenarios and an adequate charging strategy. The analysis is carried for the planning configurations of the Portuguese and Spanish generating systems and the results are focused on the potential benefits of EV integration in terms of hydro and wind power usage.

Abstract
This paper addresses a critical analysis of the impact of the wind ramp events with unforeseen magnitude in power systems at the very short term, modeling the response of the operational reserve against this type of phenomenon. A multi-objective approach is adopted, and the properties of the Pareto-optimal fronts are analyzed in cost versus risk, represented by a worst scenario of load curtailment. To complete this critical analysis, a study about the usage of the reserve in the event of wind power ramps is performed. A case study is used to compare the numerical results of the models based on stochastic programming and models that take a risk analysis view in the system with high level of wind power. Wind power uncertainty is represented by scenarios qualified by probabilities. The results show that the reliability reserve may not be adequate to accommodate unforeseen wind ramps and therefore the system may be at risk.

Abstract
Large-scale integration of variable Renewable Energy Sources (RES) brings significant challenges to grid operation that require new approaches and tools for distribution system management, particularly concerning voltage control. Therefore, an innovative approach for voltage control at the MV level is presented. It is based on a preventive day-ahead analysis that uses data from load/RES forecasting tools to establish a plan for operation of the different Distributed Energy Resources (DER) for the next day. The approach is formulated as a multi-temporal Optimal Power Flow (OPF) solved by a meta-heuristic, used to tackle complex multi-dimensional problems. The tuning of the meta-heuristic parameters was performed to ensure the robustness of the proposed approach and enhance the performance of the algorithm. It was tested through simulation in a large scale test network with good results.

Abstract
Combining conventional and unconventional generations with hard forecasting properties and consumption variability has made the task of fitting large amounts of wind generation into unit commitment procedures very challenging. In this context, massive integration of electric vehicles in the electric systems, is expected. This paper aims at evaluating the adequacy of a generating system taking into account the electric vehicles and high wind power integration level in its energy mix. This study was conducted on the European project MERGE framework, and the results have shown that the generating system evaluated is reliable. Nevertheless, the simulations carried out for scenarios with adverse weather conditions (dry years) have revealed specific circumstances that might jeopardize the system adequacy.