Short-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous factors that influence the results’ precision. The purpose of this study is to integrate, additionally to the conventional factors (weather, holidays, etc.), the current aspects regarding the global COVID-19 pandemic in solving the STLF problem, using a convolutional neural network (CNN)-based model. To evaluate and validate the impact of the new variables considered in the model, the simulations are conducted using publicly available data from the Romanian power system. A comparison study is further carried out to assess the performance of the proposed model, using the multiple linear regression method and load forecasting results provided by the Romanian Transmission System Operator (TSO). In this regard, the Mean Squared Error (MSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) are used as evaluation indexes. The proposed methodology shows great potential, as the results reveal better error values compared to the TSO results, despite the limited historical data.

The optimal reactive power dispatch (ORPD) problem represents a fundamental concern in the efficient and reliable operation of power systems, based on the proper coordination of numerous devices. Therefore, the ORPD calculation is an elaborate nonlinear optimization problem that requires highly performing computational algorithms to identify the optimal solution. In this paper, the potential of metaheuristic methods is explored for solving complex optimization problems specific to power systems. In this regard, an improved salp swarm algorithm is proposed to solve the ORPD problem for the IEEE-14 and IEEE-30 bus systems, by approaching the reactive power planning as both a single- and a multi- objective problem and aiming at minimizing the real power losses and the bus voltage deviations. Multiple comparison studies are conducted based on the obtained results to assess the proposed approach performance with respect to other state-of-the-art techniques. In all cases, the results demonstrate the potential of the developed method and reflect its effectiveness in solving challenging problems.

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As disturbances due to natural disasters or man-made attacks intensify awareness regarding power systems’ resilience enhancement, the scientific community concentrates on exploring state-of-the-art technologies for emergency supply restoration strategies. Recent studies are increasingly focusing on the expanded flexibility of soft open points (SOPs) compared to conventional tie-switches to increase the restoration rate of critical loads; however, the potential of this novel technology is not limited to this aspect, with SOPs being used to improve the voltage level and increase the hosting capacity of renewable energy sources (RESs). This paper proposes a deterministic model for the optimal coordination of SOPs and distributed resources in an active distribution network (ADN) aiming at re-establishing the energy supply to critical loads after a prolonged interruption occurrence. At the same time, the support of DC microgrids with integrated RESs, embedded in SOPs, for the restoration process is explored. The efficiency of the proposed optimization model is verified based on a 24-h analysis performed on the modified IEEE 33-bus system, while considering the load and generation uncertainties as well.