Each substation is critically essential to the overall operation of the electrical power system. Potential dangers include thermal stress, noise, slip, trip, fall hazards, animal waste, and nonionizing radiation. These are the causes of joint failures of cables and overhead lines, failure of one or more phases of circuit breakers, and melting of fuses or conductors in one or more phases. These kinds of failures bring about a decline in the substation’s level of dependability. On the consumer side, power cannot be received adequately because there are losses in the transmission line. To accomplish the objective of enhancing the voltage profile, the DG must be optimized. The substation’s transient analysis utilizing a variety of factors, a study of faults and transients that occur in the substation and their effects using ETAP, and an optimization of a range of parameters using artificial intelligence techniques are all used for this goal. This paper offers the complete simulation of a 500kv substation. The simulation uses advanced software Electrical Transient Analyzer Program (ETAP) with detailed load flow analysis and short circuit study of the 500 kV substation system using ETAP software. From the ETAP-generated load flow details and the short circuit details, which are studied by varying loads or other parameters, these whole simulations are carried out multiple times using real-time data from the past eighteen months. A simulation data set contains data on both standard and different faulty conditions. In the 1st step, the normal and faulty conditions are classified. In the 2nd step, the reasons for fault occurrence include line-to-line, line-to-ground, and double line-to-ground using the Artificial Intelligence technique. In both steps, Catboost performs well, followed by Support Vector Machine and Logistic Regression. In the first step, Catboost classifies normal and faulty conditions with an accuracy of 98%, SVM is 96%, and Logistic regression is 93%. Again, in the 2nd step to identify different faulty conditions, the accuracies of Catboost SVM and Logistic Regression are 97%, 95%, and 92%, respectively.

As power systems around the world shift to incorporate more renewable energy sources, particularly wind power, maintaining grid stability becomes increasingly challenging due to the inherent variability of these sources. This paper introduces a novel bi-level robust optimization framework that enhances the capabilities of adaptive Under-Frequency Load Shedding (AUFLS) in managing the uncertainties brought by high penetration of wind energy and dynamic participation of electric vehicles (EVs). Central to this framework is an innovative adaptive non-parametric Kernel Density Estimation (AAKDE) technique, which sharpens the accuracy of wind power fluctuation predictions. This method enables more precise and efficient control of load-shedding events, which is crucial for preventing frequency drops that can lead to grid instability. This research proposes a strategic shedding queue mechanism that systematically prioritizes the discharge of EVs based on their real-time state-of-charge and charging behavior. This prioritization minimizes user discomfort and taps into the potential of EVs as flexible energy resources, thus providing substantial support to grid operations. To enhance the responsiveness of our AUFLS approach, we integrate a reinforcement learning model that adjusts in real time to grid conditions, optimizing decision-making for frequency stabilization. Our extensive MATLAB/SIMULINK simulations on an upgraded IEEE 39 bus test system demonstrate a significant reduction in load shedding requirements. Compared to traditional AUFLS methods, our approach cuts load shedding by over 50%, effectively maintains system frequency within safe operational limits, and shows superior performance in scenarios of high renewable variability and EV integration. This research highlights the potential of adaptive non-parametric methods in transforming AUFLS strategies, paving the way for smarter, more resilient power systems equipped to handle the complexities of modern energy landscapes.

Reliable short-term electric load forecasting (STLF) is essential for enhancing grid stability, optimizing energy distribution, and minimizing operational costs in modern power systems. However, existing forecasting models, including statistical approaches and deep learning architectures such as multi-layer perceptron (MLP), struggle to capture complex nonlinear load variations while maintaining computational efficiency. To overcome these limitations, a self-adaptive Kolmogorov–Arnold network (SADE-KAN), an optimized forecasting framework that combines the power of Kolmogorov–Arnold networks (KAN) with self-adaptive differential evolution (SADE) is introduced to enhance both predictive accuracy and computational efficiency. Unlike conventional MLP models, KAN replaces fixed activation functions with spline-based learnable functions that offers greater flexibility in capturing temporal dependencies. However, these learnable activation functions introduce a new set of hyperparameters that require careful optimization to ensure efficient training and manage network complexity. To address this, SADE dynamically tunes these hyperparameters, ensuring an optimal balance between accuracy, complexity, and training efficiency. The proposed SADE-KAN model is validated on ISO-NE hourly load data (2019–2023, ~ 1 million observations) across multiple forecasting horizons (24, 48, 96, and 168 h). Experimental results demonstrate that SADE-KAN reduces mean absolute percentage error (MAPE) by up to 35% and root mean squared error (RMSE) by 38% compared to MLP models, while requiring 35% fewer learnable parameters. Despite a slightly higher training time, SADE-KAN significantly enhances generalization and robustness, capturing rapid load fluctuations more effectively than MLP, conventional KAN and other recently published advanced models. These findings establish SADE-KAN as a computationally efficient and highly accurate forecasting framework, offering a robust solution for real-time power system applications, demand response strategies, and energy market operations.

Power utilities rely on accurate load forecasting for making effective real-time decisions and managing system operations, which, in turn, significantly impacts their financial performance. Accurate load forecasting enables these utilities to reduce operational costs, optimize generation, and maintain system stability. However, despite extensive research efforts, the nonlinear characteristics of power system and complex nature of the load datasets make accurate load forecasting challenging. This paper presents a novel application of the new Kolmogorov-Arnold Networks (KANs) for short-term load forecasting (STLF). KANs offer a promising predictive ability compared to Multi-Layer Perceptrons by replacing their linear weights with unique learnable activation functions. These learnable activation functions enable KAN to dynamically learn and effectively capture the non-linearities and complexities in power system load data. The proposed network is applied for STLF based on the latest ISO-NE hourly sampled load data in the period 2019–2023. The results demonstrate that KANs deliver higher accuracy in forecasting load over various prediction horizons while utilizing significantly reduced number of learnable parameters. This efficiency shows not only the KAN's predictive power but also a reduced computational complexity. By leveraging the dynamic learning capabilities of KAN, power utilities can achieve more precise load forecasts, leading to optimized operations and improved financial performance.

A comprehensive review of existing Concentrated Solar (CS) technologies is carried out with a focus on identifying the most promising technologies for small and medium scale applications in the UK. The applications considered include domestic water heating, solar cooking, hydrogen production, plastic recycling and space cooling. Technical feasibility is evaluated, including factors such as site suitability, resource availability, and engineering requirements. The regulatory environment and policy framework in the UK with respect to renewable energy is also assessed to determine how Concentrating Solar can fit within this context ^[2] . Our findings suggests that the UK has moderate potential for concentrating solar applications, with low concentrating photovoltaics (LCPV) having the highest prospects. The best locations for these technologies are all situated in the southern parts of the UK. Potential barriers and challenges to the adoption of concentrated solar in the UK, such as space requirements, lack of awareness, and public acceptance are identified, and recommendations for addressing these issues are provided.