Electricity, Volume 6, Issue 4 (December 2025) – 21 articles

Wide-area damping controls, like the wide-synchronization control (WSC), are crucial for power system stability but are vulnerable to communication latencies. This article presents a comprehensive theoretical characterization of the impact of time delays on the WSC. The formal analysis derives mathematical models for both differential and common modes. Two distinct scenarios are investigated: a symmetric condition, where the WSC is applied to both coupled areas, and an asymmetric condition, where it is applied to only one area. A formal stability assessment is conducted to determine stability boundaries and critical delay-induced crossings into unstable regions. Key findings show that under symmetric conditions, the system remains stable for all delays, as latencies only affect the common mode. Conversely, the asymmetric condition introduces a coupling between modes, making the system susceptible to delay-induced instability, especially at high control gains. The work validates the theoretical findings through numerical experiments and evaluates the accuracy of various linear Padé approximant models for representing delays, highlighting how low-order models can fail to predict instabilities, requiring high-order approximants to guarantee adequate accuracy in the analysis. Full article

Enhancing the efficiency of mechanized oil production remains a critical objective in the industry. This paper presents a comparative analysis of existing methods aimed at improving the energy efficiency of oil extraction systems, outlining their respective advantages and limitations. A novel approach is proposed, based on the use of a submersible compensator of reactive power to optimize the performance of electric submersible pumps (ESPs). A mathematical model of the ESP’s electrical system is developed to support the proposed method. Theoretical findings are validated by the experimental studies conducted on operational oil wells. Test results demonstrate a reduction in current consumption by 14.5–20% and an improvement in the power factor from 0.62 to 0.96. These outcomes confirm the effectiveness of the proposed solution in enhancing energy efficiency and reducing electrical losses in oil production processes. Full article

Energy management systems (EMSs) are essential for enabling the integration and operation of multiple interconnected microgrids within a microgrid system, especially when the penetration of renewable energy resources is high. As global energy demands rise and the need for sustainable solutions intensifies, microgrids offer a promising path toward enhancing grid resilience and efficiency. This review delves into the state of the art of EMSs in microgrid systems, highlighting the predominant use of optimization algorithms, and artificial intelligence (AI) techniques as the most commonly used strategies in energy management. Despite the advancements in these areas, there is a notable gap in the exploration of bio-inspired strategies that do not rely on traditional optimization approaches. Bio-inspired methods, which mimic natural processes and behaviors, have shown potential in various fields but remain underrepresented in EMS research. This paper provides a comprehensive overview of existing strategies and their applicability to energy management in microgrid systems. The findings suggest that while optimization algorithms and AI techniques dominate the landscape, their combination and integration with other techniques, such as multi-agent systems, are also gaining attention. The document explores how bio-inspired algorithms not only improve the efficiency of existing EMS methods but also enable new paradigms for managing energy in interconnected multi-microgrid systems. Additionally, applications such as vehicle-to-grid (V2G) and the integration of renewable resources are considered in the optimization of operational costs. Bio-inspired approaches could offer innovative solutions for enhancing the performance and sustainability of microgrid systems by defining the interactions between microgrids in a way that mirrors how communities interact; however, bibliometric analysis reveals that those techniques remain under reported, even though they could improve performance and resilience in multi-microgrid systems. This review underscores the need for further investigation into bio-inspired strategies to diversify and improve EMSs in microgrid systems. Full article

This paper proposes a risk-based, multi-objective approach to identify a solution, referred to as the fairness improvement plan, that enhances the fairness of photovoltaic (PV) curtailment, primarily applied to mitigate overvoltage issues in both balanced and unbalanced low-voltage distribution networks with high PV penetration. The proposed approach considers the uncertainty of loads, PV generation, and slack bus voltage. Relative Distance Measure (RDM) interval arithmetic is employed to represent these uncertainties while accounting for correlations among uncertain quantities, and the Pareto Simulated Annealing (PSA) method is used to generate a set of efficient fairness improvement plans. The Hurwicz criterion for measuring risk, which accounts for a decision maker’s risk preference, is incorporated in the interval TOPSIS technique to identify the fairness improvement plan, selected from a set of efficient plans, that minimizes the risk of financial losses and the risk of unfairness of PV’s active power curtailment. The numerical results obtained show that the proposed approach improves the insight and the understanding of the fairness improvement planning under uncertainty. They also highlight the effectiveness of incorporating decision makers’ risk preferences and their trade-off preferences between fairness and cost in developing the optimal fairness improvement plan under uncertainty in low-voltage distribution networks with high PV penetration. Full article

Modern power systems face growing challenges in stability assessment due to large-scale renewable energy integration and rapidly changing operating conditions. Data-driven approaches have emerged as promising solutions for real-time stability assessment, yet their performance often degrades under network topology reconfigurations. To address this limitation, the Spatiotemporal Contrastive Graph Convolutional Network (STCGCN) is proposed for the joint task prediction of voltage and transient stability across known and unknown topologies. The framework integrates a graph convolutional network (GCN) encoder to capture spatial dependencies and a temporal convolutional network to model electromechanical dynamics. It also employs supervised contrastive learning to extract discriminative features due to the grid topology variation, enhance stability class separability, and mitigate class imbalance under varying operating conditions, such as fluctuating loads and renewable integration. Case studies on the IEEE 39-bus system demonstrate that STCGCN achieves 89.66% accuracy on in-sample datasets from known topologies and 87.73% on out-of-sample datasets from unknown topologies, outperforming single-task learning approaches. These results highlight the method’s robustness to topology variations and its strong generalization across configurations, providing a topology-aware and resilient solution for real-time joint voltage and transient stability assessment in power systems. Full article

Accurate prediction of wind turbine power output is essential for optimizing renewable energy generation, enhancing grid integration, and improving the efficiency of wind farms. However, the inherent non-linearities of wind speed–power relationships, combined with abrupt cut-in, rated, and cut-out effects, pose a significant modeling challenge. In this study, we investigate the use of artificial neural networks (ANNs) to model the power curve of a $1\mathrm{kW}$ wind turbine, using an open-access dataset of real operational measurements recorded at 10 min intervals over the course of 2011. In particular, we compare a conventional multilayer perceptron (MLP) trained on raw wind speed inputs with a Fourier-feature-encoded MLP designed to mitigate spectral bias—the tendency of neural networks to favor smooth, low-frequency patterns over sharp, high-frequency variations. Experimental results show that the Fourier-enhanced MLP substantially improves predictive performance, reducing the mean absolute error (MAE) by more than $65\%$ and achieving an $R^2$ score of $0.999$. The proposed approach demonstrates that Fourier feature encoding enables neural networks to capture sharp non-linearities in wind-turbine power curves, representing one of the first applications of this technique to wind-turbine power-curve modeling. Full article

The increasing integration of renewable energy sources is reshaping power systems from centralized, synchronous generator-based architectures to more inverter-dominated and decentralized architectures. This transition, however, results in a significant reduction in system inertia, posing challenges to frequency stability. To address this issue, various control strategies have been proposed to emulate the inertial response of traditional synchronous generators—commonly known as virtual inertia. This study reviews inverter-based virtual inertia and related control strategies that replicate or extend synchronous generator dynamics, covering five main approaches: droop control, synchronverters, virtual synchronous generators (VSGs), the swing equation-based approach, and data-driven grid-forming (GFM) methods. While all approaches enhance frequency nadir and RoCoF, they differ in complexity, robustness, and adaptability. Droop control offers simplicity but lacks true inertia support, whereas synchronverter and swing equation-based controls provide closer emulation of synchronous behavior for grid-forming or islanded systems. VSG offers a more practical grid-following solution, and data-driven GFM introduces adaptability through learning-based mechanisms. Overall, this study contributes to a comprehensive understanding of how these control strategies can be implemented through inverter control to maintain frequency stability in renewable-dominated power systems. Full article

This paper presents an economic–environmental power dispatch approach for a grid-connected microgrid (MG) with photovoltaic (PV) generation and battery energy storage systems (BESSs). The problem was formulated as a multiobjective optimization problem with functions such as minimizing fixed and variable generation costs, power losses, and CO₂ emissions. This study addresses the problem of intelligent energy management in microgrids with PV generation and BESSs to optimize their performance based on multiple criteria. This study focuses on optimizing the Energy Management System (EMS) with metaheuristic algorithms to achieve practical implementation with simpler algorithms to solve a complex optimization problem. This study employs four multiobjective optimization algorithms: Nondominated Sorting Genetic Algorithm II (NSGA-II), Harris Hawks Optimization (HHO), multiverse optimizer (MVO), and Salp Swarm Algorithm (SSA), which are classified as robust techniques for obtaining Pareto fronts. The computational resources employed to simulate the problem are presented. The optimal dispatch obtained from the Pareto front achieved reductions of 0.067% in fixed costs, 0.288% in variable costs, 3.930% in power losses, and 0.067% in CO₂ emissions, demonstrating the effectiveness of the proposed approach in optimizing both economic and environmental performance. The SSA stood out for its stability and computational efficiency, establishing itself as a promising method for energy management in urban and rural microgrids (MGs) and providing a solid framework for optimization in alternating current systems. Full article

To address the critical issue of low reliability caused by fault impacts in large-scale wind farms transmitting power over long distances via flexible DC transmission systems, this study proposes a collaborative solution. First, a new protection scheme integrating variable quantity differential protection, steady-state quantity differential protection and zero-sequence differential protection is proposed. By establishing a refined model of a wind farm with a flexible DC system, the adaptability of the differential protection for the outgoing lines is checked. Simulation results show that the sensitivity of metallic faults within the protection zone is better than 3.0, and the protection reliably remains inactive for faults outside the protection zone. Second, an innovative fault ride-through strategy combining self-regulating resistor circuits with wind farm MPPT load reduction is proposed. During faults on the receiving grid, the DC voltage fluctuation is controlled within 1.05 p.u. through graded switching of resistor modules and dynamic power regulation. This solution offers both rapid response and smooth fault ride-through characteristics, significantly improving the feasibility and economic viability of wind farm integration via flexible DC transmission. Full article

Maritime power systems must reduce fuel use and emissions while improving resilience. We study a shipboard PV–battery subsystem interfaced with a DC–DC converter running maximum power point tracking (MPPT) and curve-scanning GMPPT to manage partial shading. Dynamic average-value models capture irradiance steps and show GMPPT sustains operation near the global MPP without local peak trapping. We compare converter options—conventional single-port stages, high-gain bidirectional dual-PWM converters, and three-level three-port topologies—provide sizing rules for passives, and note soft-switching in order to limit loss. A Fourier framework links the switching ripple to power quality metrics: as irradiance falls, the current THD rises while the PCC voltage distortion remains constant on a stiff bus. We make the loss relation explicit via $I_{rms}^{2}R$ scaling with THDi and propose a simple reactive power policy, assigning VAR ranges to active power bins. For AC-coupled cases, a hybrid EMT plus transient stability workflow estimates ride-through margins and critical clearing times, providing a practical path from modeling to monitoring. Full article

This paper presents the implementation of the Grid-Forming (GFM) control technique in renewable energy source inverters to synchronize with the grid and provide frequency support. Specifically, the GFM Droop Control technique, based on the Power–Frequency relationship, is employed. The proposed model was developed and validated in the Matlab-Simulink environment. By using electromagnetic transient (EMT) simulations, we were able to precisely monitor and analyze voltage and current waveforms, thereby confirming the approach’s effectiveness in enhancing grid stability and power quality. The implementation of the GFM control technique in islanded mode demonstrated high system frequency stability. In response to sudden load changes up to 5 MW (equivalent to over 30% of the total load), a maximum frequency deviation of 0.04 Hz and a maximum Rate of Change of Frequency (RoCoF) of 4 Hz/s were observed. The system ensured the frequency’s return to its nominal value of 60 Hz, thanks to the virtual inertia and frequency regulation provided by the GFM. The total harmonic distortion (THD) of current and voltage in steady-state operation consistently remained below 1%, thus complying with IEEE 1547 standards. In tests with the GFM interconnected to the grid, the droop+LPF control provided dynamic support to the external system, effectively mitigating both frequency deviations and RoCoF. The GFM contributes to the grid’s frequency stability by providing virtual inertia. The power quality at the point of common coupling (PCC) was excellent, as the voltage distortion was maintained below 0.5%, confirming that the injection of harmonic currents does not violate established limits. Full article

Modern power distribution systems are increasingly stressed as they operate closer to their voltage stability limits, driven by growing electricity demand, complex load behaviors, and the evolving structure of power networks. Radial distribution systems, in particular, are highly susceptible to voltage instability under critical loading conditions, where even minor load increases can trigger voltage collapse. Such events threaten the continuity and quality of power supply and can cause damage to infrastructure and sensitive equipment. While large-scale cascading failures are typically associated with transmission systems, localized cascading effects such as sequential voltage drops, feeder outages, and protective device operations can still occur in distribution networks, especially under high loading. Therefore, reliable and timely voltage stability assessment is essential to maintain system reliability and prevent disruptions. This study presents a comprehensive comparative analysis of four voltage stability indices designed for radial distribution networks. The performance of these indices is evaluated on the IEEE 33-bus and 69-bus test systems under various critical loading conditions and multiple static load models, including Constant Power Load (CPL), Constant Current Load (CIL), Constant Impedance Load (CZL), Composite Load (COML), and Exponential Load (EXL). The analysis investigates each index’s effectiveness in identifying voltage collapse points, estimating critical load levels, and calculating load margins, while also evaluating their robustness across diverse operating scenarios. The findings offer practical insights and serve as a valuable benchmark for selecting suitable voltage stability indicators to support monitoring and planning in modern distribution networks. Full article

The design of battery modules for Electric Vehicles (EVs) and stationary Energy Storage Systems (ESSs) plays a pivotal role in advancing sustainable energy technologies. This paper presents a comprehensive overview of the critical considerations in battery module design, including system requirements, cell selection, mechanical integration, thermal management, and safety components such as the Battery Disconnect Unit (BDU) and Battery Management System (BMS). We discuss the distinct demands of EV and ESS applications, highlighting trade-offs in cell chemistry, form factor, and architectural configurations to optimize performance, safety, and cost. Integrating advanced cooling strategies and robust electrical connections ensures thermal stability and operational reliability. Additionally, the paper describes a prototype battery module, a BDU, and the hardware and software architectures of a prototype BMS designed for a Hardware/Model-in-the-Loop framework for the real-time monitoring, protection, and control of battery packs. This work aims to provide a detailed framework and practical insights to support the development of high-performance, safe, and scalable battery systems essential for transportation electrification and grid energy storage. Full article

The maximum demand payment component (MDPC) of the electricity bill, which reflects the highest level of power demand during a billing period, is a well-recognized barrier to the feasibility of electric vehicle fast-charging facilities (EVFCFs). While several studies have explored control strategies to mitigate demand peaks, they primarily focus on slow-charging facilities and fail to account for maximum demand prices. On the other hand, the few existing EVFCF-particular strategies overlook the diminished user-desired quality of service caused by the additional charging time needed for demand management. Moreover, their implementations under real-world conditions also remain unexplored. To address these issues, this work proposes a managed charging solution that explicitly considers the impact of maximum demand prices while maintaining user-desired quality of service, and implements it under real-world conditions in three different metropolitan areas in the United States. Simulation results indicate that the proposed solution can increase an EVFCF’s operational profits by 5–26% compared with conventional charging methods. The findings also highlight that the outcomes of the proposed solution are significantly influenced by the EVFCF utilization rate, the time between consecutive EV arrivals, the incumbent electric utility-specified maximum demand prices, and the user preferences of selecting the various “alternative fuel-grade options” offered at an EVFCF. These findings could pave the way for a more informed deployment of managed charging solutions at EVFCFs, thereby accelerating equitable transition to transportation electrification. Full article

Electricity expansion planning is inherently subject to uncertainty, shaped by climatic, regulatory, and economic risks. In Brazil, this challenge is compounded by recurrent crises that have repeatedly reduced electricity demand. This study proposes a complementary decision-support approach to make planning more resilient to such crises. Using Brazil’s official optimization models (NEWAVE), we introduce two analytical elements: (i) a regret-minimization screen for choosing between conservative and optimistic demand trajectories and (ii) a flexibility stress test that evaluates the cost impact of compulsory-dispatch shares in generation portfolios. Key findings show that conservative demand projections systematically minimize consumer-cost regret when crises occur, while portfolios with lower compulsory-dispatch shares reduce total system cost and improve adaptability across 2000 hydro inflow scenarios. These results highlight that crisis-robust planning requires combining cautious demand assumptions with flexible supply portfolios. Although grounded in the Brazilian context, the methodological contributions are generalizable and provide practical guidance for other electricity markets facing deep and recurrent uncertainty. Full article

The global transition toward sustainable energy has significantly accelerated the deployment of solar power systems. Yet, the inherent variability of solar energy continues to present considerable challenges in ensuring its stable and efficient integration into modern power grids. As the demand for clean and dependable energy sources intensifies, the integration of artificial intelligence (AI) with solar systems, particularly those coupled with energy storage, has emerged as a promising and increasingly vital solution. It explores the practical applications of machine learning (ML), deep learning (DL), fuzzy logic, and emerging generative AI models, focusing on their roles in areas such as solar irradiance forecasting, energy management, fault detection, and overall operational optimisation. Alongside these advancements, the review also addresses persistent challenges, including data limitations, difficulties in model generalization, and the integration of AI in real-time control scenarios. We included peer-reviewed journal articles published between 2015 and 2025 that apply AI methods to PV + ESS, with empirical evaluation. We excluded studies lacking evaluation against baselines or those focusing solely on PV or ESS in isolation. We searched IEEE Xplore, Scopus, Web of Science, and Google Scholar up to 1 July 2025. Two reviewers independently screened titles/abstracts and full texts; disagreements were resolved via discussion. Risk of bias was assessed with a custom tool evaluating validation method, dataset partitioning, baseline comparison, overfitting risk, and reporting clarity. Results were synthesized narratively by grouping AI techniques (forecasting, MPPT/control, dispatch, data augmentation). We screened 412 records and included 67 studies published between 2018 and 2025, following a documented PRISMA process. The review revealed that AI-driven techniques significantly enhance performance in solar + battery energy storage system (BESS) applications. In solar irradiance and PV output forecasting, deep learning models in particular, long short-term memory (LSTM) and hybrid convolutional neural network–LSTM (CNN–LSTM) architectures repeatedly outperform conventional statistical methods, obtaining significantly lower Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and higher R-squared. Smarter energy dispatch and market-based storage decisions are made possible by reinforcement learning and deep reinforcement learning frameworks, which increase economic returns and lower curtailment risks. Furthermore, hybrid metaheuristic–AI optimisation improves control tuning and system sizing with increased efficiency and convergence. In conclusion, AI enables transformative gains in forecasting, dispatch, and optimisation for solar-BESSs. Future efforts should focus on explainable, robust AI models, standardized benchmark datasets, and real-world pilot deployments to ensure scalability, reliability, and stakeholder trust. Full article

Microgrids have extended their use when connected to or isolated from the grid, where decentralized control architectures are increasingly being used due to their inherent advantages. Among controllers, the non-communicated type allows the problems introduced by the use of communication systems to be avoided; however, these type of controllers are generally limited to performing first-level control actions, precisely due to the lack of information caused by the absence of a communication network. This work proposes an algorithm for a non-communicated controller to (a) identify which of the power sources are connected to a microgrid and (b) calculate the load power; both of these actions only require local measurements and allow the microgrid performance to be improved. The proposal aims at identifying the power sources by analyzing the electromechanical oscillations that occur in microgrids that are fed by inertia-enabled inverters and synchronous generators using droop controllers. This is used to automatically adjust the power sharing ratio between sources based on the generation capacity and load of a microgrid. Numerical simulations that clearly show the advantages are used to support the effectiveness of the proposal. Full article

This scoping review analyzes the role of inertia in converter-dominated microgrids, with an emphasis on hybrid AC/DC architectures. Following the PRISMA-ScR methodology, 54 studies published between 2015 and 2025 were identified, screened, and synthesized. The review addresses two key aspects, inertia estimation methods and control strategies for emulating inertia via power converters, emphasizing the role of the interlinking converter (ILC) as a bidirectional interface for inertia support between the AC and DC subsystems. This work addresses several limitations of prior reviews: their narrow scope, often overlooking advanced data-driven approaches such as machine learning; the lack of systematic classifications, hindering a comprehensive overview of existing methods; and the absence of practical guidance on selecting appropriate techniques for specific conditions. The findings show that conventional estimation methods are insufficient for low-inertia grids, necessitating adaptive and data-driven approaches. Virtual inertia emulation strategies—such as Virtual Synchronous Machines, Virtual Synchronous Generators, Synchronverters, and ILC-based controls—offer strong potential to enhance frequency stability but remain challenged by scalability, adaptability, and robustness. The review highlights critical research gaps and future directions to guide the development of resilient hybrid microgrid control strategies. Full article

Peer-to-peer (P2P) energy trading has emerged as a novel approach to enhancing the coordination and utilization of distributed energy resources (DERs) within modern power distribution networks. This study presents a techno-economic analysis of different DER characteristics, focusing on the integration of photovoltaic (PV) systems and energy storage systems (ESS) within a community-based P2P energy trading framework in Aswan, Egypt, under a time-of-use (ToU) electricity tariff. Eight distinct cases are evaluated to assess the impact of different DER characteristics on P2P energy trading performance and an unbalanced low-voltage (LV) distribution network by varying the PV capacity, ESS capacity, and ESS charging power. To the best of the authors’ knowledge, this is the first study to comprehensively examine the effects of different DER characteristics on P2P energy trading and the associated impacts on an unbalanced distribution network. The findings demonstrate that integrating PV and ESS can substantially reduce operational costs—by 37.19% to 68.22% across the analyzed cases—while enabling more effective energy exchanges among peers and with the distribution system operator (DSO). Moreover, DER integration reduced grid energy imports by 30.09% to 63.21% and improved self-sufficiency, with 30.10% to 63.21% of energy demand covered by community DERs. However, the analysis also reveals that specific DER characteristics—particularly those with low PV capacity (1.5 kWp) and high ESS charging rates (e.g., ESS 13.5 kWh with 2.5 kW inverter)—can significantly increase transformer and line loading, reaching up to 19.90% and 58.91%, respectively, in Case 2. These setups also lead to voltage quality issues, such as increased voltage unbalance factors (VUFs), peaking at 1.261%, and notable phase voltage deviations, with the minimum Vb dropping to 0.972 pu and maximum Vb reaching 1.083 pu. These findings highlight the importance of optimal DER sizing and characteristics to balance economic benefits with technical constraints in P2P energy trading frameworks. Full article

Electrical energy supply services are characterised by unpredictable risks that affect both distribution network operators (DSOs) and electricity consumers. This paper presents an innovative cooperative game theory (GT) framework to enhance electric energy supply reliability and demand-side flexibility by aligning the interest of DSOs and consumers. The research investigates the performance of the proposed GT model under different distribution network (DN) topologies and fault intensities, explicitly considering outage durations and restoration times. A cooperation mechanism based on penalty compensation is introduced to simulate realistic interactions between DSOs and consumers. Simulation results confirm that adaptive cooperation under this framework yields significant reliability improvements of up to 70% in some DN configurations. The GT-based approach supports informed investment decisions, improved stakeholder satisfaction, and reduced risk of service disruptions. Findings suggest that integrated GT planning mechanisms can lead to more resilient and consumer-centred electricity distribution systems. Full article

Microgrids are becoming an innovative piece of modern energy systems as they provide locally sourced and resilient energy opportunities and enable efficient energy sourcing. However, microgrid operations can be greatly affected by sudden environmental changes, deviating demand, and unexpected outages. In particular, extreme climatic events expose the vulnerability of microgrid infrastructure and resilience, often leading to increased risk of system-wide outages. Thus, successful microgrid operation relies on timely and accurate outage predictions. This research proposes a data-driven machine learning framework for the optimized operation of a microgrid and predictive outage detection using a Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM) architecture that reflects inherent temporal modeling methods. A time-aware embedding and masking strategy is employed to handle categorical and sparse temporal features, while mutual information-based feature selection ensures only the most relevant and interpretable inputs are retained for prediction. Moreover, the model addresses the challenges of experiencing rapid power fluctuations by looking at long-term learning dependency aspects within historical and real-time data observation streams. Two datasets are utilized: a locally developed real-time dataset collected from a 5 MW microgrid of Maple Cement Factory in Mianwali and a 15-year national power outage dataset obtained from Kaggle. Both datasets went through intensive preprocessing, normalization, and tokenization to transform raw readings into machine-readable sequences. The suggested approach attained an accuracy of 86.52% on the real-time dataset and 84.19% on the Kaggle dataset, outperforming conventional models in detecting sequential outage patterns. It also achieved a precision of 86%, a recall of 86.20%, and an F1-score of 86.12%, surpassing the performance of other models such as CNN, XGBoost, SVM, and various static classifiers. In contrast to these traditional approaches, the RNN-LSTM’s ability to leverage temporal context makes it a more effective and intelligent choice for real-time outage prediction and microgrid optimization. Full article