This paper proposes a novel hybrid ZOA-AOA optimization framework to effectively balance exploration and exploitation for solving the ORPD problem under renewable uncertainty. Unlike conventional hybrid approaches, the proposed method incorporates an adaptive role assignment where ZOA handles exploration and AOA handles exploitation. This design enhances convergence performance and solution quality. Solar and wind energy uncertainties were modeled using lognormal and Weibull probability density functions (PDFs) across different seasonal scenarios, capturing temporal variability patterns that significantly impact reactive power requirements. The methodology was implemented and tested on the actual JMB 25-bus, IEEE 118-bus, and IEEE 300-bus systems with various renewable DG penetration levels. Comprehensive comparative simulations demonstrate that ZOA-AOA consistently outperforms other metaheuristic algorithms and standalone ZOA variants, achieving active power loss reduction of up to 37.16% in the JMB 25-bus system. The hybrid approach maintained stable voltage profiles with zero under-voltage buses in most scenarios while requiring minimal reactive power compensation. In the IEEE 118-bus system, ZOA-AOA achieved a 12.5% reduction from the baseline, while in the 300-bus system, it maintained 17–18% lower losses and exhibited fast convergence. Specifically, the algorithm recorded processing times of 71.8 s, 150.7 s, and 122.8 s for the JMB 25-bus, IEEE 118-bus, and IEEE 300-bus systems, respectively. Statistical analyses over 33 independent runs confirm that the hybrid ZOA-AOA approach achieves significant improvements, including a 15.7% reduction in power loss, a 31.3% enhancement in voltage profile, and a 16.8% improvement in voltage stability, while effectively managing renewable uncertainties. The proposed method provides an effective approach to addressing constraints, nonlinear behavior, and inherent uncertainties in ORPD challenges involving DG, thereby enhancing the efficiency and reliability of modern power system operations.

The direct current (DC) microgrid’s remarkable power delivery performance has led to its development into a promising distribution network. However, creating efficient protective systems remains a major challenge for DC microgrids. Presently, the emphasis on DC microgrids is on architectural structures, control techniques, and energy management, with minimal attention given to fault analysis, detection, and isolation. Thus, the purpose of this paper is to provide researchers with a thorough grasp of DC microgrid protection by examining the current level of research in key fields and evaluating potential protection solutions. Furthermore, this study indicates key areas for future research to solve safety problems and promote the growth of the DC microgrid. Future research focuses on protection and the development of innovative protection devices that use electronic technology to provide flexible protection constraints and enhance appropriate protection schemes. Moreover, this review briefly discusses the protection challenges associated with electric vehicle (EV) integration in DC microgrids.

An all-electric ship (AES) generally employs integrated electric propulsion and coordinated onboard power supply to achieve economically efficient and environmentally sustainable maritime transportation. This article aims to evaluate the impact of wind and sea loadings on AES voyage performance and economic efficiency using a deterministic single-stage optimization model. The proposed multiobjective model optimizes total operating cost (TOC) and voyage unmet distance while satisfying navigation, propulsion, and electrical operating constraints. Wind and wave loads are modeled using analytical and semi-empirical wind-based resistance models to assess the resulting changes in propulsion power demand and attainable sailing speed. The optimization method is formulated as a mixed-integer linear programming (MILP) problem and solved using the MATLAB built-in intlinprog solver. Tradeoffs between conflicting objectives are analyzed through the Pareto front of the obtained nondominated solutions. The simulation results demonstrate that wind and wave loads significantly influence AES operation by increasing ship resistance, which in turn affects propulsion power demand.

With the growing Electric Vehicle (EV) population, proper placement of Electric Vehicle Charging Station (EVCS) is essential. This is to support widespread adoption and integration of EVs into the transportation system. Factors such as power loss, voltage stability, and grid reliability play a significant role in determining the optimal locations for EVCS. An approach to determine the optimal placement of EVCS is proposed in this research. The approach employs Hybrid Leader-Follower Multiverse Lyrebird Optimization Algorithm (HLF-MVLOA) which integrates Lyrebird Optimization Algorithm (LOA) and Multiverse Optimization Algorithm (MVO) to minimize power losses and enhance the voltage stability index. The effectiveness of the EVCS placement strategy is assessed through simulations on IEEE 33- and 69-bus network systems, with performance compared against other algorithms. Simulation results demonstrate that HLF-MVLOA achieves the lowest power losses and the most favourable voltage stability index.

Power distribution systems face increasing threats from high-impact, low-probability (HILP) events caused by extreme weather conditions such as floods, typhoons, droughts, and heatwaves. These events often lead to power outages worldwide, highlighting the need for effective strategies to mitigate their impact. This work proposes an emergency response strategy that integrates network reconfiguration (NR) with the deployment of mobile emergency generators (MEGs) to enhance system resilience. The objective is to maximize power supply availability following HILP events. To achieve this, mixed-integer quadratic constraint programming (MIQCP) is used to optimize MEG deployment and network restoration. Additionally, improved quantitative resilience metrics are introduced to assess system degradation, pre-recovery, and recovery phases, enabling continuous resilience measurement and informed decision-making. Furthermore, an optimal capacity deployment strategy (OCDS) is proposed to ensure that MEGs are deployed with suitable capacities based on the specific needs of outage-affected areas. The effectiveness of the proposed strategy is demonstrated through tests on the IEEE 118 bus system. The results show a significant improvement of up to 100% in system resilience, reducing power outages and accelerating restoration. The findings confirm that integrating NR with optimized MEG deployment enhances service restoration, providing an effective approach for utilities to manage power disruptions.

The reliability and efficiency of power system operations, especially in smart grid scenarios, depend on accurate load demand forecasting. Electrical load forecasting is crucial for power system design, fault protection and diversification as it reduces operating costs while enhancing the system’s overall reliability, stability, and efficiency from an economic and technical perspective. Previously, load forecasting analysis has frequently been limited by inadequate feature engineering and insufficient model tuning. Prediction reliability was reduced by many previous methods’ inabilities to accurately evaluate short-term variations over time and the impact of important variables. These constraints encouraged us to develop a more reliable and thorough forecasting procedure. This research proposes an enhanced short-term load forecasting framework based on a hyperparameter-tuned long short-term memory (LSTM) using a deep learning method recurrent neural network (RNN), alongside more neural network-based models such as artificial neural networks, k-nearest neighbors, and backpropagation neural networks. Hyperparameter optimization techniques (Keras Tuner, Grid SearchCV, Scikeras + Randomized SearchCV, etc.) were used to systematically tune training parameters, learning rates, and network architectures for each forecasting model to increase model accuracy. To provide a more reliable and accurate evaluation of forecasting performance, this research employs the use of an hourly load dataset (2003–2014) enhanced with historical and environmental variables. Significant statistical metrics, such as a mean absolute error of 0.0048, root mean squared error of 0.0091, coefficient of determination of R2 0.9958, and mean absolute percentage error of 1.60%, demonstrate that the hyperparameter optimized with hourly data performed better than both conventional and other deep learning models, with the highest efficiency of all tested models. In accordance with the results, accurate LSTM-RNN parameter modification significantly improves prediction accuracy.

Direct Current (DC) microgrids are emerging as a transformative solution for efficient energy distribution, especially with the growing integration of renewable energy sources and DC-native loads. However, their distinct operational characteristics, such as the absence of natural current zero-crossing, rapid fault current escalation, and bidirectional power flow, introduce significant protection challenges. Existing reviews on DC microgrid protection strategies primarily focus on conventional overcurrent and impedance-based methods, yet they often neglect AI-integrated, adaptive, and cyber-secured protection frameworks, as well as breaker-less converter coordination and solid-state transformer applications. Comparative evaluations across voltage levels and network topologies also remain limited, restricting practical implementation insights. This paper addresses these gaps by providing a comprehensive analysis of both traditional and advanced DC microgrid protection schemes, including derivative-based, AI-driven, and hybrid methods. It also explores the integration of edge intelligence, cybersecurity, and adaptive coordination, which collectively enhance fault detection speed, selectivity, and system resilience. These integrations enable faster response, secure communication, and dynamic adaptation for changing network conditions, ensuring more reliable and intelligent protection for modern DC microgrids. The findings of this study will benefit researchers, engineers, manufacturers, and policymakers seeking to develop intelligent, secure, and interoperable protection systems for next-generation DC microgrids.

This paper proposes a novel coordinated control strategy for a grid-connected Doubly Fed Induction Generator (DFIG)-based wind energy system integrated with a Static Synchronous Compensator (STATCOM) and Supercapacitor Energy Storage System (SCES). The approach enhances voltage stability, reactive power support, and transient performance under varying wind conditions by leveraging the inherent reactive power capabilities of DFIG combined with fast active power buffering from SCES. A detailed MATLAB/Simulink model validates the proposed framework, demonstrating a 150 ms voltage recovery during faults, a 62% reduction in power fluctuations, and a 52% decrease in converter losses compared to conventional systems. These results highlight the effectiveness of the coordinated control in improving grid code compliance and system reliability.

Flash floods are recognized as a major threat to power distribution systems. Thus, enhancing distribution system resilience against this catastrophic natural hazard is essential and imperative. Commonly researchers have used two-dimensional (2D) surface flow models to evaluate flood risk on power systems. Though these 2D models can provide descriptions of overland flow propagation, they fail to provide overflow locations which are crucial in flash flood modelling. Furthermore, these models are computationally expensive, hence not suitable for real-time analysis. Therefore, this study presents a probabilistic flood model that is easy to develop and can handle heavy uncertainties related to urban flash flooding. In this respect, the Monte Carlo technique is employed to predict overflow locations in a grid-based environment. Considering rainfall intensity, soil moisture, and curvature of the surface, reinforcement learning is then leveraged to trace the flow path of floodwater from these overflow locations, to identify distribution substations at the risk of inundation. The proposed flood model is applied to IEEE 33-bus and a real 23-bus distribution systems considering a hypothetical terrain and validated on a real urban area. This work will assist decision-makers and utility operators in enhancing power system resiliency to urban flash floods while overcoming the barriers of limited data and time.

This research enhances the estimation methods for renewable energy generation, particularly wind and solar power, by addressing uncertainties due to environmental factors such as wind speed and solar irradiation levels, which vary with weather, climate, and seasonal changes. Key contributions include the use of real-time, online automatic weather stations for efficient data collection, capturing weather parameters at 5-minute intervals over a year, resulting in a comprehensive dataset of 37,374 data points for wind speed and 18,993 for solar irradiation levels. The research innovatively models these uncertainties using Weibull and lognormal probability density functions (PDFs) for wind and solar energy, respectively. Results indicated a potential conversion of 69 % of wind energy into electricity using an optimally configured wind farm system comprising 200 units of 0.5 kW turbines. Similarly, a 100 kW solar PV power plant could convert up to 35 % of solar irradiation into electricity. The combined power contribution of both wind and solar PV systems to the grid was estimated at 37 kW. The research also introduced the use of 3D photogrammetry for land analysis, using aerial drones to map potential sites for wind farm and PV power plant installations, which could serve as a reference for future renewable energy projects aiming for high efficiency and reliability in electrical energy production. This approach not only contributes to better planning and installation strategies but also enhances the predictability and management of renewable energy resources.

Power transformers are essential for grid stability and efficient energy transfer, but their reliability declines due to aging insulation systems made of paper and mineral oil. Monitoring techniques such as oil testing, dissolved gas analysis (DGA), and furan compound analysis help assess degradation, with the degree of polymerization (DP) serving as a key indicator of insulation health. This study evaluates five DP estimation methods, namely Chendong, Heisler & Banzer, Vaurchex, Pahlavanpour, and De Pablo, using six statistical metrics consisting of average, standard deviation, determination coefficient (DC), correlation coefficient (CC), t-test, and p-value. The Chendong method proved most robust, achieving DC = 0.677, CC = 0.878, and the lowest standard deviation (0.81), meeting all criteria. Heisler & Banzer followed with DC = 0.529 and CC = 0.878, though its higher deviation (1.04) affected consistency. Vaurchex and Pahlavanpour showed moderate performance (DC = 0.674 and 0.435) but failed to meet t-test and p-value thresholds. De Pablo ranked lowest (DC = 0.071), meeting only one criterion. By quantifying each method’s strengths and limitations, this paper offers a benchmarking framework to improve insulation diagnostics and guide maintenance decisions which ultimately enhance transformer reliability, asset management, and power system efficiency.

For the last few years, the maritime industry has been experiencing a paradigm shift to install renewable energy resources, especially battery-associated solar photovoltaic panels as an alternative energy source of the conventional diesel generator to reduce reliance on fossil fuels. This is done to fulfill the target of the International Maritime Organization (IMO) to reduce the green-house gas (GHG) emission by 20 % by 2030. Therefore, currently maritime vessels are evolving as all-electric or multi-energy microgrids where battery energy storage (BESS), wind turbines, solar photovoltaic panels, combined cooling, heat, and power (CCHP) units, and thermal storage are equipped along with diesel generators to cater the propulsion loads and on-board service loads. These evolving ships are often called as all-electric ships (AESs) and multi-energy ships (MESs). However, optimal management of such ships is quite challenging because of the interaction between several thermal, mechanical, and electrical resources. In this regard, this article aims to provide a critical review on the present development and ongoing research works on the architecture and operation of AESs and MESs. The article first elucidates the importance of AES and MES in terms of decarbonization of the maritime sector followed by different existing power management systems, voyage planning methodologies, and optimization algorithms developed in the literature for ensuring a smooth, safe, efficient, resilient, reliable, and economic sailing. In addition, power quality issues and their resolving methodologies are discussed. At last, the future research directions are identified and illustrated to accelerate innovation in developing sustainable maritime energy system.

The assessment of grid-connected systems depends on their cost efficiency, reliability, and greenhouse gas (GHG) reduction potential. This study presents a multi-objective optimization framework for designing a grid-connected photovoltaic (PV) and battery energy storage (BES) system integrated with an electric vehicle (EV) for a household in Riyadh, Saudi Arabia. The framework aims to minimize the Cost of Energy (COE) and Loss of Power Supply Probability (LPSP) while maximizing the Renewable Energy Fraction (REF). Additionally, GHG emissions are evaluated as a result of these objectives. The EV operates in Vehicle-to-Home (V2H) mode, enhancing system flexibility and energy management. The optimization process employs two advanced metaheuristic techniques, Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Harris Hawks Optimization (MOHHO), to identify Pareto front solutions. Fuzzy logic is then applied to determine a balanced compromise among the economically optimal (minimum COE), renewable energy-oriented (maximum REF), and environmentally optimal (minimum GHG emissions) solutions. Simulation results show that the proposed system achieves a COE of USD 0.0554/kWh, a LPSP of 1.96%, and an REF of 92.55%. Although the COE is slightly higher than that of the grid, the system provides significant environmental and renewable energy benefits. This study highlights the potential of integrating dynamic EV management and advanced optimization techniques to enhance the performance of grid-connected systems. The findings demonstrate the effectiveness of combining Pareto-based optimization with fuzzy logic to achieve balanced solutions addressing economic, environmental, and renewable energy objectives, paving the way for sustainable energy systems in urban households.

Fault detection in power systems is critical for ensuring system reliability and stability. This study presents a rule-based classification approach for identifying fault types, including Single Line to Ground (SLGF), Double Line to Ground (DLGF), Line to Line (LLF), and Three-Phase to Ground (LLLGF) on IEEE 9-bus and IEEE 14-bus transmission systems. Voltage sag and current swell characteristics were extracted through simulations in PSCAD under varying fault resistances (0.01Ω to 70Ω) and fault distances. These features were analyzed in MATLAB using predefined logic rules based on amplitude thresholds and phase relationships. The classification method achieved 100 % accuracy for all fault scenarios on the IEEE 9-bus system. On the IEEE 14-bus system, the method maintained 100 % accuracy up to 50Ω fault resistance, but dropped to 73.75 % at 70Ω, particularly due to LLF cases with low fault current and voltage patterns resembling normal conditions. Overall, the classification algorithm reached $\mathbf{9 6. 2 \%}$ accuracy. The results indicate that the proposed method is effective for simpler topologies but faces challenges in more complex networks under high-resistance fault conditions. Future improvements may include signal processing enhancements or adaptive learning techniques to improve performance under these scenarios.

Optimal reactive power dispatch (ORPD) is essential for addressing power system challenges related to distributed generation (DG), particularly from renewable energy (RE) sources such as wind and solar. The intermittent nature and uncertainty of these energy sources, influenced by varying wind speeds and solar irradiation, complicate their integration into power systems. This paper proposes a solution to the ORPD problem in systems with RE-DG integration using the Archimedes Optimization Algorithm (AOA). The uncertainties of wind and solar power generation were modelled using Weibull and lognormal probability density functions (PDFs), respectively, and the optimization model was tested using a scenario-based method. The AOA was applied to the IEEE 57 bus system to minimize power loss, voltage deviation, and voltage stability index (VSI). The results demonstrated that AOA contributed to a 15.7% reduction in power loss, and an 83.9% enhancement in VSI compared to the base case. In the multi-objective optimization scenario, AOA achieved a 7.1% reduction in power loss, with an additional 11.6% improvement upon the integration of DGs. The performance of AOA was also compared with other metaheuristic algorithms, demonstrating superior results in terms of tracking accuracy and convergence speed. AOA outperformed the multi-objective ant lion optimization (MOALO) and the Levy-based Interior Search Algorithm (LISA) in terms of power loss reduction and voltage stability. AOA achieved a 1.83% lower power loss and a 29.67% lower VSI compared to MOALO. When compared to LISA, AOA achieved a 1.68% lower power loss, demonstrating its superior optimization capabilities. These findings confirm that AOA is a highly effective method for solving the ORPD problem, accounting for renewable energy uncertainties and improving overall system performance.

The integration of renewable energy sources into power systems is becoming increasingly important. Renewable energy sources (RESs) help decrease dependency on fossil-fuel-based electricity generation, making them a cleaner, more environmentally friendly option. However, incorporating distributed generations (DGs) from RESs may affect system stability, especially in terms of power loss and voltage deviation, due to the intermittent and unpredictable nature of sources like wind and solar power. Optimization techniques, such as optimal reactive power dispatch (ORPD), are crucial for enhancing stability across power system transmission lines. The ORPD problem is complex, characterized by its non-linear, non-convex, and multi-modal nature. However, existing studies often overlook the complex impact of DGs-related power fluctuations on ORPD. This study addresses the gap by implementing ORPD on the IEEE bus systems to reduce power loss, minimize voltage deviation, and improve the voltage stability index (VSI) through the optimization of control variables, such as generator voltages, transformer tap settings, and reactive power injection compensators. A comparative analysis of meta-heuristic algorithms applied to ORPD in the IEEE 57-bus system, with the integration of solar and wind power, provides a comprehensive assessment of DG impacts and algorithmic performance. Performance validation and statistical analyses reveal that algorithms inspired by physical phenomena outperform others in tracking optimal ORPD control variables, providing a statistical ranking and valuable insights for advancing renewable energy-integrated power system stability. Each meta-heuristic algorithm demonstrates distinct advantages in ORPD for renewable energy systems: GA excels in broad search capability, FOA balances exploration with exploitation, PFA achieves fast convergence, and AOA offers high precision, making these techniques valuable for managing the uncertainties of renewable energy sources.

This article discusses community service activities on Gili Iyang Island, Sumenep, Madura, which focus on implementing appropriate technology in sensing the potential of renewable energy sources. Gili Iyang Island itself is an area that actively encourages the use of renewable energy. In this activity, maintenance and monitoring of data on AirFeel are carried out, including device checks and monitoring data accuracy levels through periodic calibration. Airfeel is used as a weather station to measure the potential of renewable energy, especially wind and solar energy. The potential for wind energy is considered from the speed and direction of the wind, while the potential for solar energy is considered from the level of solar irradiation. These parameters allow Airfeel to determine the optimal location for renewable energy generation based on weather conditions. Additionally, the DJI Phantom 4 Pro RTK drone was used for 3D Mapping Photogrammetry. This technology aids in creating three-dimensional maps that are considered in the planning and development of renewable energy power plants at the location. Implementing these two technologies can contribute to future planning for developing other renewable energy sources in Gili Iyang Island.

Purpose The paper aims to design the optimum formulation of the nano-titanium dioxide (TiO2) hydrophilic coating system using the synthetic polypropylene glycol (PPG), which can create the reflection and absorption property. Design/methodology/approach TiO2 nanoparticles are used as fillers, and PPG has been blended at the proper ratio of 1PPG: 0.2TiO2. The prepared resin has been applied onto the glass substrate at different numbers of glass immersions during the dip-coating fabrication process. One-time glass immersion is labeled as T1 coating, two-time glass immersion is labeled as T2 coating and three-time glass immersion is labeled as T3 coating. All the prepared coating systems were left dry at ambient temperature. Findings T3 coating showed the lowest reading of WCA value at 40.50°, due to higher surface energy at 61.73 mN/m. The T3 coating also shows the greatest absorbance property among the prepared coating systems among the prepared coating. In terms of reflectance property, the T2 coating system has great reflectance in UV region and near-infrared region, which is 16.47% and 2.77 and 2.73%, respectively. The T2 coating also has great optical transmission about 75.00% at the visible region. Research limitations/implications The development of thermal insulation coating by studying the relationship between convection heat and reflectance at different wavelengths of incident light. Practical implications The developed coating shows high potential for glass window application. Originality/value The application of the hydrophilic coating on light absorption, reflectance and transmission at different wavelengths.