Abstract
The integration of distributed Battery Energy Storage Systems (BESS) at the Medium Voltage (MV) and Low Voltage (LV) networks increases the distribution grid flexibility to deal with high penetration of Renewable Energy Sources (RES). In addition, it also enables the deployment of key self-healing functionalities, which allow the islanded operation of small sections of the distribution network. However, new planning and real-time operation strategies are required to allow the BESS coordinated control, as well as a cost-effective and stable operation. This paper presents new tools developed for the planning and real-time operation of distribution networks integrating BESS, particularly when operating islanding. For real-time operation, a short-term emergency operation-planning tool assesses the feasibility of islanded operation of a small section of the distribution network. The long-term impact of a BESS control strategy for islanded operation is assessed through a Life Cycle Analysis (LCA) tool. The results and implementation experience in real distribution network are also discussed.

Abstract
This paper presents two innovative Fault-Ride-Through (FRT) strategies suited for Smart-Transformers (ST) supplying hybrid AC/DC distribution grids within a microgrid environment. The first strategy is suited for ST without a local energy storage, where its Medium Voltage (MV) inverter is operated in grid-tied mode. The proposed approach relies on the voltage sensitivity of resources connected to the ST fed distribution networks aiming to limit the MV inverter current. The second strategy is suited for ST incorporating local energy storage and operating its MV inverter in grid-forming mode, thus enabling islanding operation of a MV grid section. The proposed FRT strategy aims to regulate ST's output voltage by calculating the maximum voltage drop in the coupling filter in order to control the output current. The proposed strategies are evaluated exploiting appropriated simulation models and extensive operating conditions.

Abstract
A novel ensemble algorithm based on kernel density estimation (KDE) is proposed to forecast distributed generation (DG) from renewable energy sources (RES). The proposed method relies solely on publicly available historical input variables (e.g., meteorological forecasts) and the corresponding local output (e.g., recorded power generation). Given a new case (with forecasted meteorological variables), the resulting power generation is forecasted. This is performed by calculating a KDE-based similarity index to determine a set of most similar cases from the historical dataset. Then, the outputs of the most similar cases are used to calculate an ensemble prediction. The method is tested using historical weather forecasts and recorded generation of a PV installation in Portugal. Despite only being given averaged data as input, the algorithm is shown to be capable of predicting uncertainties associated with high frequency weather variations, outperforming deterministic predictions based on solar irradiance forecasts. Moreover, the algorithm is shown to outperform a neural network (NN) in most test cases while being exceptionally faster (32 times). Given that the proposed model only relies on public locally-metered data, it is a convenient tool for DG owners/operators to effectively forecast their expected generation without depending on private/proprietary data or divulging their own.

Abstract
This paper presents an original trading strategy for electricity buyers in futures markets. The strategy applies a medium-term electricity price forecasting model to predict the monthly average spot price which is used to evaluate the Risk Premium for a physical delivery under a monthly electricity futures contract. The proposed trading strategy aims to provide an advantage relatively to the traditional strategy of electricity buyers (used as benchmark), anticipating the good/wrong decision of buying electricity in the futures market instead in the day-ahead market. The mid-term monthly average spot price forecasting model, which supports the trading strategy, uses only information available from futures and spot markets at the decision moment. Both the new trading strategy and the monthly average spot price forecasting model, proposed in this paper, have been successfully tested with historical data of the Iberian Electricity Market (MIBEL), although they could be applied to other electricity markets. © 2020 The Authors, published by EDP Sciences.

Abstract
This article presents an original predictive strategy, based on a new mid-term forecasting model, to be used for trading physical electricity futures. The forecasting model is used to predict the average spot price, which is used to estimate the Risk Premium corresponding to electricity futures trade operations with a physical delivery. A feed-forward neural network trained with the extreme learning machine algorithm is used as the initial implementation of the forecasting model. The predictive strategy and the forecasting model only need information available from electricity derivatives and spot markets at the time of negotiation. In this paper, the predictive trading strategy has been applied successfully to the Iberian Electricity Market (MIBEL). The forecasting model was applied for the six types of maturities available for monthly futures in the MIBEL, from 1 to 6 months ahead. The forecasting model was trained with MIBEL price data corresponding to 44 months and the performances of the forecasting model and of the predictive strategy were tested with data corresponding to a further 12 months. Furthermore, a simpler forecasting model and three benchmark trading strategies are also presented and evaluated using the Risk Premium in the testing period, for comparative purposes. The results prove the advantages of the predictive strategy, even using the simpler forecasting model, which showed improvements over the conventional benchmark trading strategy, evincing an interesting hedging potential for electricity futures trading.

Abstract