This paper addresses the challenges in forecasting electrical energy in the current era of renewable energy integration. It reviews advanced adaptive forecasting methodologies while also analyzing the evolution of research in this field through bibliometric analysis. The review highlights the key contributions and limitations of current models with an emphasis on the challenges of traditional methods. The analysis reveals that Long Short-Term Memory (LSTM) networks, optimization techniques, and deep learning have the potential to model the dynamic nature of energy consumption, but they also have higher computational demands and data requirements. This review aims to offer a balanced view of current advancements and challenges in forecasting methods, guiding researchers, policymakers, and industry experts. It advocates for collaborative innovation in adaptive methodologies to enhance forecasting accuracy and support the development of resilient, sustainable energy systems.

An electrical power system (EPS) is subject to unexpected events that might cause the outage of elements such as transformers, generators, and transmission lines. For this reason, the EPS should be able to withstand the failure of one of these elements without changing its operational characteristics; this operativity functionality is called $N-1$ contingency. This paper proposes a methodology for the optimal location and sizing of a parallel static Var compensator (SVC) in an EPS to reestablish the stability conditions of the system before $N-1$ contingencies take place. The system’s stability is analyzed using the fast voltage stability index (FVSI) criterion, and the optimal SVC is determined by also considering the lowest possible cost. This research considers $N-1$ contingencies involving the disconnection of transmission lines. Then, the methodology analyzes every scenario in which a transmission line is disconnected. For every one of them, the algorithm finds the weakest transmission line by comparing FVSI values (the higher the FVSI, the closer the transmission line is to instability); afterward, when the weakest line is selected, by brute force, an SVC with values of 5 Mvar to 100 Mvars in steps of 5 Mvar is applied to the sending bus bar of this transmission line. Then, the SVC value capable of reestablishing each line’s FVSI to its pre-contingency value while also reestablishing each bus-bar’s voltage profile and having the lowest cost is selected as the optimal solution. The proposed methodology was tested on IEEE 14, 30, and 118 bus bars as case studies and was capable of reestablishing the FVSI in each contingency to its value prior to the outage, which indicates that the algorithm performs with 100% accuracy. Additionally, voltage profiles were also reestablished to their pre-contingency values, and in some cases, they were even higher than the original values. Finally, these results were achieved with a single solution for a unique SVC located in one bus bar that is capable of reestablishing operational conditions under all possible contingency scenarios.

In this research, an alternative methodology is proposed for the location of Static VAR Compensators (SVC) in power systems, considering the reconfiguration of reactive power flows through the optimal switching of the transmission stage, which resembles the contingency restriction N-1 usually considered in transmission expansion planning. Based on this methodology, the contingency index was determined, which made it possible to determine which is the contingency that generates the greatest voltage degradation in the system. For the quantification of reactive flows, optimal AC power flows were used, which minimize the operating costs of the power system subject to transmission line switching restrictions, line charge-ability, voltages and node angles. To determine the node in which the compensation should be placed, the contingency index criterion was used, verifying the voltage profile in the nodes. The proposed methodology was tested in the IEEE test systems of 9, 14 nodes and large-scale systems of 200, 500 and 2000 bus-bars; to verify that the proposed methodology is adequate, the stability of the EPS was verified. Finally, the model allows satisfactorily to determine the node in which the SVC is implemented and its compensation value.

Electric power systems (EPS) are exposed to disconnections of their elements, such as transmission lines and generation units, due to meteorological factors or electrical failures. Thus, this research proposes a smart methodology for the re-entry of elements that have been disconnected from the EPS due to unforeseen events. This methodology is based on optimal AC power flows (OPF-AC) which allow verifying the state of variables such as voltage, angular deviation, and power (these variables are monitored in normal and fault conditions). The proposed study considers contingencies N-2, N-3, N-4, and N-5, for which the disconnection of transmission lines and generation units are carried out randomly. The analysis of the EPS after the disconnections of the elements is carried out by means of the contingency index, with which the impact that the disconnections of the elements have on the EPS is verified. In this way, the optimal route is generated to restore the elements that went out of operation, verifying that when the elements re-enter the acceptable limits, voltage and voltage angle are not exceeded. According to the results of the methodology used, it was found that NM contingencies can be applied in the proposed model, in addition to considering stability restrictions, modeled as restrictions on acceptable voltage limits, and a new restriction for the voltage angle between the differences of the bars.

This paper attempts to elucidate the transformative integration of computational techniques within power systems, underscoring their critical role in enhancing system modeling, control, and the efficient integration of renewable energy. It breaks down the two-sided nature of technological progress, highlighting both gains in operational efficiency and new challenges such as real-time processing, data management, and cybersecurity. Through meticulous analysis of query-based research patterns and mathematical frameworks, this study delves into the balancing act between specificity and breadth in scholarly inquiries while evaluating the impact and evolution of research trends through citation analysis. The convergence of interests and transient research trends is evident, particularly in Artificial Intelligence and optimization. This comprehensive narrative anticipates a sophisticated trajectory for power systems, advocating for continuous innovation and strategic research to foster sustainable, resilient, and intelligent energy networks.

This research proposes a methodology for the optimal location and sizing of reactive compensation in an electrical transmission system through a deep neural network (DNN) by considering the smallest cost for compensation. An electrical power system (EPS) is subjected to unexpected increases in loads which are physically translated as an increment of users in the EPS. This phenomenon decreases voltage profiles in the whole system which also decreases the EPS’s reliability. One strategy to face this problem is reactive compensation; however, finding the optimal location and sizing of this compensation is not an easy task. Different algorithms and techniques such as genetic algorithms and non-linear programming have been used to find an optimal solution for this problem; however, these techniques generally need big processing power and the processing time is usually considerable. That being stated, this paper’s methodology aims to improve the voltage profile in the whole transmission system under scenarios in which a PQ load is randomly connected to any busbar of the system. The optimal location of sizing of reactive compensation will be found through a DNN which is capable of a relatively small processing time. The methodology is tested in three case studies, IEEE 14, 30 and 118 busbar transmission systems. In each of these systems, a brute force algorithm (BFA) is implemented by connecting a PQ load composed of 80% active power and 20% reactive power (which varies from 1 MW to 100 MW) to every busbar, for each scenario, reactive compensation (which varies from 10 Mvar to 300 Mvar) is connected to every busbar. Then power flows are generated for each case and by selecting the scenario which is closest to 90% of the original voltage profiles, the optimal scenario is selected and overcompensation (which would increase cost) is avoided. Through the BFA, the DNN is trained by selecting 70% of the generated data as training data and the other 30% is used as test data. Finally, the DNN is capable of achieving a 100% accuracy for location (in all three case studies when compared with BFA) and objective deviation has a difference of 3.18%, 7.43% and 0% for the IEEE 14, 30 and 118 busbar systems, respectively (when compared with the BFA). With this methodology, it is possible to find the optimal location and sizing of reactive compensation for any transmission system under any PQ load increment, with almost no processing time (with the DNN trained, the algorithm takes seconds to find the optimal solution).