This article presents a new method of metaheuristic optimization, the algorithm of the innovative gunner (AIG), inspired by the choice of artillery parameters that sends a shot precisely to a target. The applied correction of the cannon’s setting parameters differs from that defined by classic artillery theory. The innovativeness of the AIG results from the fact that at each step of the iterative process, the previous solution is corrected by means of specially selected multiplication factors. This is a major difference from most methods in which, at each successive step, a corrective element is added to the previous solution. The AIG is characterized by high efficiency and speed in solving various optimization problems having nothing to do with ballistics. It is extremely simple in description and implementation. The algorithm was tested on several known test-optimization tasks. The results are noticeably better than those achieved by other methods and prove the practical usefulness of the AIG.

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.

Electric power system is characterized by relatively high demand for lagging reactive power. From the economic viewpoint, reactive power sources should be installed close to its demand. Optimal compensation should ensure minimal costs of the reactive power generation and transmission within the considered system. The optimization of activities related to reactive power compensation concerns the location and power of compensation devices. This is to optimize voltage levels and reactive power flows in the system. The article presents methods of simulated annealing and particle swarm applied to solve an optimization task of the reactive power flow. It has been assumed that active power losses in a power system are the objective function.

The challenges currently faced by network operators are difficult and complex. Presently, various types of energy sources with random generation, energy storage units operating in charging or discharging mode and consumers with different operating characteristics are connected to the power grid. The network is being expanded and modernised. This contributes to the occurrence of various types of network operating states in practice. The appearance of a significant number of objects with random generation in the power system complicates the process of planning and controlling the operation of the power system. It is therefore necessary to constantly search for new methods and algorithms that allow operators to adapt to the changing operating conditions of the power grid. There are many different types of method in the literature, with varying effectiveness, that have been or are used in practice. So far, however, no one ideal, universal method or methodology has been invented that would enable (with equal effectiveness) all problems faced by the power system to be solved. This article presents an overview and a short description of research works available in the literature in which the authors have used modern methods to solve various problems in the field of power engineering. The article is an introduction to the special issue entitled Advances in the Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering. It is an overview of various current problems and the various methods used to solve them, which are used to cope with difficult situations. The authors also pointed out potential research gaps that can be treated as areas for further research.

The article presents the concept of voltage control in a medium-voltage network, using the classical control of a HV/MV (High Voltage/Medium Voltage) transformer and active participation of distributed generation sources. The proposed solution is based on the results of the optimization process. The objective function is considered as a single-criterion—the voltage quality indicator or value of power losses in the network, and optionally as two-criteria (voltage quality and losses combined, with appropriately selected weight factors). The analysis carried out for random selection of independent variables, using the original heuristic algorithm, indicated a very high efficiency of the proposed control process, compared to the traditional approach. A significant improvement in the voltage quality index and reduction of losses was found, which justifies the advisability of looking for new solutions in the field of voltage control in MV networks, taking into account active participation of distributed generation.