Abstract

Abstract
This paper presents an algorithm for optimal resilient allocation of Mobile Energy Storage Systems (MESSs) for an active distribution system considering the microgrids coordinated bidding process. The main contribution of this paper is that the impacts of coordinated biddings of microgrids on the allocation of MESSs in the day-ahead and real-time markets are investigated. The proposed optimization framework is another contribution of this paper that decomposes the optimization process into multiple procedures for the day-ahead and real-time optimization horizons. The active distribution system can transact active power, reactive power, spinning reserve, and regulating reserve with the microgrids in the day-ahead horizon. Further, the distribution system can transact active power, reactive power, and ramp services with microgrids on the real-time horizon. The self -healing index and coordinated gain index are introduced to assess the resiliency level and coordination gain of microgrids, respectively. The proposed algorithm was simulated for the 123-bus test system. The method reduced the average value of aggregated operating and interruption costs of the system by about 60.16% considering the coordinated bidding of microgrids for the worst-case external shock. The proposed algorithm successfully increased the self-healing index by about 49.88% for the test system.

Abstract
Accurate monthly electricity consumption forecasting (MECF) is important for electricity retailers to mitigate trading risks in the electricity market. Clustering is commonly used to improve the accuracy of MECF. However, in the existing clustering-based forecasting methods, clustering and forecasting are independently performed and lack coordination, which limits the further improvement of forecasting accuracy. To address this issue, an adaptive optimal greedy clustering-based MECF method is proposed in this article. First, a metric of predictability is defined based on the goodness of fit and the cluster's average electricity consumption. Under a predefined number of clusters, the greedy clustering algorithm achieves the optimal division of individuals with the goal of maximizing predictability. Then, an adaptive method is designed to select the optimal number of clusters from a variety of clustering scenarios according to the prediction accuracy on the validation dataset. The effectiveness and superiority of the proposed method have been verified on a real-world dataset.

Abstract
The generation mix of Portugal now contains a significant amount of variable renewable energy sources (RES) and the amount of RES is expected to grow substantially. This has led to concerns being raised regarding the security of the supply of the Portuguese electric system as well as concerns relating to system inertia. Deploying and efficiently using various flexibility options is proposed as a solution to these concerns. Among these flexibility options proposed is the use of battery energy storage systems (BESSs) as well as relaxing system inertia constraints such as the system nonsynchronous penetration (SNSP). This article proposes a stochastic mixed-integer linear programming problem formulation, which examines the effects of deploying BESS in a power system. The model is deployed on a real-world test case and results show that the optimal use of BESS can reduce system costs by as much as 10% relative to a baseline scenario and the costs are reduced further when the SNSP constraint is relaxed. The amount of RES curtailment is also reduced with the increased flexibility of the power system through the use of BESS. Thus, the efficiency of the Portuguese transmission system is greatly increased by the use of flexibility measures, primarily the use of BESS.

Abstract
Electric power systems are in state of transition as they attempt to evolve to meet new challenges provided by growing environmental concerns, increases in the penetration of distributed renewable energy sources (DRES) as well as the challenges associated with integrating new technologies to enable smart grids. New techniques to improve the electrical power system, including the distribution system, are thus needed. One such technique is dynamic distribution system reconfiguration (DNSR), which involves altering the network topology during operation, providing significant benefits regarding the increased integration of DRES. This paper lays out an improved model which aimed to optimize the system operation in a coordinated way, where DRES, energy storage systems (ESS) and DNSR are considered as well as the uncertainty of these resources. The objective function was modeled to incentivize the uptake of DRES by considering the cost of emissions to incentivize the decarbonization of the power system. Also, the switching costs were modeled to consider not only the switching, but also the cost of degradation of these mechanisms in the system operation. Two systems are used to validate the model, the IEEE 119-bus system, and a real system in Sao Miguel Island. The results of this paper show that using DNSR, DRES, and ESS can lead to a significant 59% reduction in energy demand through a 24-hour period. In addition, using these technologies results in a healthier, more efficient, and higher quality system. This shows the benefits of using a variety of smart grid technologies in a coordinated manner.

Abstract
This paper is concerned with direct lightning surge analysis in wind turbines. Portugal has one of the most ambitious goals in terms of wind power, and in 2006 was the second country in Europe with the highest wind power growth. Nevertheless, there are still very few studies in Portugal regarding lightning protection of wind turbines using the electromagnetic transients computer program, namely the most recent and restructured version EMTP-RV. A case study is presented in this paper, based on a wind turbine with an interconnecting transformer, for the analysis of direct lightning surges. Computer simulations obtained by using EMTP-RV software are presented. These computer simulations can be easily customized for parameters characterizing wind turbines or other countries. Finally, conclusions are duly drawn. © 2011 IEEE.