Abstract
Large horsepower induction motors play a critical role as industrial drives in production facilities. The operational safety of distribution networks during the starting transients of these motor loads is a critical concern for the operators. In this paper, an analytical and convex optimization model is derived representing the starting transients of the induction motor in a semi-static fashion. This model is used to find the optimal energization sequence of different loads (static and motor loads) following an outage in a distribution network. The optimization problem includes the optimal control of the converter-based DGs and autotransformers that are used for the induction motor starting. These models together with the semi-static model of the induction motor are integrated into a relaxed power flow formulation resulting in a Mixed-Integer Second Order Cone Programming (SOCP) problem. This formulation represents the transient operational limits that are imposed by different protection devices both in the motor side and network side. The functionality of the proposed optimization problem is evaluated in the case of a large-scale test study and under different simulation scenarios. The feasibility and accuracy of the optimization results are validated using I) off-line time-domain simulations, and II) a Power Hardware-In-the-Loop experiment.

Abstract
With the growing concern about decreasing CO2 emissions, renewable energy sources are being vastly integrated in the energy systems worldwide. This will bring new challenges to the network operators, which will need to find sources of flexibility to cope with the variable-output nature of these technologies. Demand response and multi-energy systems are being widely studied and considered as a promising solution to mitigate possible problems that may occur in the energy systems due to the large-scale integration of renewables. In this work, an optimal model to manage the resources and loads within residential and commercial buildings was developed, considering consumers preferences, electrical network restrictions and CO2 emissions. The flexibility that these buildings can provide was analyzed and quantified. Additionally, it was shown how this model can be used to solve technical problems in electrical networks, comparing the performance of two scenarios of flexibility provision: flexibility obtained only from electrical loads vs. flexibility obtained from multi-energy loads. It was proved that multi-energy systems bring more options of flexibility, as they can rely on non-electrical resources to supply the same energy needs and thus relieve the electrical network. It was also found that commercial buildings can offer more flexibility during the day, while residential buildings can offer more during the morning and evening. Nonetheless, Multi-Energy System (MES) buildings end up having higher CO2 emissions due to a higher consumption of natural gas.

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.