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The increasing number of renewable energy sources in power systems contributes to overloads of power lines in emergency situations. Lines made with relatively small cross-section cables, which in the past were designed for an operating temperature of 40 °C, are particularly exposed to overloads. Currently, they constitute the so-called "bottlenecks" in network capacity. This is manifested in the fact that when carrying out expert opinions aimed at examining the impact of a source on the network, computational analyses show overloads of its elements. This article proposes a methodology for eliminating these overloads. It involves the use of two methods at the same time, namely optimal network reconfiguration combined with minimisation of the total power curtailment in RE sources. The search for the optimal network configuration will also allow for minimising power curtailment in renewable energy sources, and thus reduce the costs of this type of operation. With such a tool, network operators will be able to achieve the effect of relieving the line load with the lowest possible cost of redistribution. Based on the IEEE 118 bus test network, calculations were performed that confirmed the effectiveness of the proposed approach. The operation of the proposed methodology is presented with the example of two selected network failure states. The novelty of the proposed solution lies in the simultaneous use of two methods of eliminating line overloads. This streamlines the entire process and improves its effectiveness. [ABSTRACT FROM AUTHOR]

The rapid expansion of solar photovoltaic (PV) generation has established its pivotal role in the shift toward sustainable energy systems. This study conducts an in-depth analysis of solar generation data from 2015 to 2018 in Spain, with a specific emphasis on temporal patterns, excluding weather data. Employing the powerful eXtreme gradient boosting (XGBoost) algorithm for modeling and forecasting, our research underscores its exceptional efficacy in capturing solar generation trends, as evidenced by a remarkable root mean squared error (RMSE) of 11.042, a mean absolute error (MAE) of 5.621, an R-squared (R²) of 0.999, and a minimal mean absolute percentage error (MAPE) of 0.046. These insights hold substantial implications for grid management, energy planning, and policy development, reaffirming solar energy's promise as a dependable and sustainable contributor to the electrical power system's evolution. This research contributes to the growing body of knowledge aimed at optimizing renewable energy integration and enhancing energy sustainability for future generations. [ABSTRACT FROM AUTHOR]