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.

Distribution networks play a vital role in bridging transmission systems and end users, offering enhanced flexibility, decentralization, and the capacity to integrate distributed generation. However, with nations worldwide actively pursuing carbon neutrality and emission peak goals, sustainable energy sources such as solar and wind are increasingly penetrating distribution networks, posing significant challenges to conventional fault detection, classification, and localization techniques due to bidirectional power flows, dynamic fault currents, and rising network complexity. These challenges manifest as reduced sensitivity of protection systems in distribution networks, increased difficulty in identifying high impedance faults, and frequent misclassification or mislocation of faults under dynamic network conditions. To address these limitations, this paper presents a comprehensive review of artificial intelligence-driven approaches and emerging technologies that are specifically tailored for fault analysis in distribution networks, to enhance diagnostic accuracy, adaptability, and real-time decision-making efficiency. Following the chronological development of artificial intelligence, the review systematically investigates smart fault detection methods applied to fault scenarios in distribution networks, with a particular emphasis on presenting fault type classification and fault localization separately to facilitate a logically structured understanding. In addition, common types of distribution network faults are examined, and the impact of distributed generation on fault behavior, electrical characteristics, and protection coordination is critically assessed. The review further distinguishes between artificial intelligence-based smart approaches that directly process raw distribution networks signal data and those that rely on advanced feature extraction techniques to enhance functional performance. This review also explores the emerging potential of large language models to enhance the explainability of diagnostics, support multi-agent coordination, and enable natural language-based fault reasoning. The insights offered herein are expected to provide practical guidance for engineers and researchers for selecting and deploying intelligent fault diagnosis strategies in future distribution networks with high distributed generation penetration.

This study introduces a new deep learning-based methodology for classifying single-label and multilabel faults in power transformers. The proposed approach leverages a deep twin network architecture to effectively analyze dissolved gases (DGs) without relying on conventionally used synthetic data techniques. Initially, dissolved gas analysis (DGA) data in ppm format are log normalized, followed by predicting transformer age using a deep bidirectional long short-term memory (BiLSTM) network. The method has another novelty of generating a large number of features from the DG data through feature engineering. Feature selection is then performed using neighborhood component analysis (NCAs), followed by semi-supervised dimension reduction using t-SNE. To address multilabeled scenarios, combined fault labels are created and classified using a deep twin network. The performance of the proposed method is evaluated through testing and comparison using the Silhouette Index (SI). Results demonstrate that the proposed methodology achieves high accuracy in classifying multiple faults, showcasing better cluster separation than other state-of-the-art methods.

This study presents an optimization framework for grid-connected solar photovoltaic (PV) systems using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS). With the growing demand for sustainable energy solutions, the research addresses the dual objective of maximizing power output while minimizing capital costs under architectural, financial, and energy constraints. A detailed case study involving real-world building parameters and energy consumption data was conducted, evaluating 17 PV system configurations. The F-TOPSIS method was applied to rank these alternatives based on fuzzy-weighted criteria of power generation and budget limitations. Results indicate that the optimal configuration—utilizing 230 panels arranged across 10 MPPTs—achieves a balanced trade-off between performance and cost, delivering 129.6 kWp at a capital cost of RM334,025. The study demonstrates the effectiveness of the fuzzy decision-making approach in handling uncertainties in multi-criteria PV system design and provides a systematic methodology for selecting the most viable solar PV configuration.

The Pancharatnam-Berry (PB) metasurface has gained significant attention for its exceptional phase control capabilities. Various optimization techniques have been introduced, yielding diverse results even with the same unit cell design. We explored methods such as array pattern synthesis (APS) with particle swarm optimization (PSO), random sequence, specialized 0-1 coding, a genetic algorithm with a nonlinear fitting method, and PSO combined with far-field scattering and the annealing algorithm. Among these, the annealing algorithm demonstrated the most effective radar cross-section (RCS) reduction. The proposed design operates over a frequency range of 10-21.4 GHz, with a center frequency of 17 GHz. The 10 dB far-field RCS reduction bandwidth extends from 10.5 to 19.5 GHz, achieving a relative bandwidth of 52.94 % at normal incidence under linearly polarized normal incidence waves. For oblique incidence linear polarization, the metasurface maintains effective RCS reduction up to an incidence angle of 30(degrees). The design utilizes unit cells with dimensions of 0.34 lambda x 0.34 lambda and a thickness of 0.145 lambda at the operating frequency. Experimental results closely match the simulated outcomes, validating the accuracy of the proposed design. Furthermore, compared to previously proposed methods, this approach demonstrates superior performance in bandwidth and RCS reduction. The coding metasurface comprises varying numbers of unit cells, with optimized configurations leading to low backscattering through mechanisms like polarization conversion, scattering and reflection. While the annealing algorithm demonstrates consistent RCS reduction (>10 dB) across the entire frequency spectrum, other coding strategies achieve < 10 dB reduction in specific bands. These findings illustrate the immense potential of the annealing algorithm for electromagnetic wave manipulation, with applications in stealth technology.

This research proposes a real-time protection scheme for distribution networks with distributed generation, offering critical insights for benchmarking protection levels against established guidelines. The project aims to (1) propose optimal protection coordination, (2) update settings for network changes in distribution systems, and (3) integrate the protection scheme with an active management system. The first objective involves using a detailed model and optimization algorithms to design a protection coordination scheme that minimizes fault clearance times while ensuring selectivity and preventing unnecessary outages. This model also allows seamless updates to accommodate system changes, ensuring continued effectiveness in a dynamic environment. The third objective emphasizes using the model to bridge the real-time protection scheme with the active management system, enabling proactive interventions and preventive measures. Overall, this research not only introduces a novel real-time protection scheme but also integrates it within a larger framework for proactive risk management and dynamic adaptation, enhancing the reliability and resilience of distribution networks.

Maintaining a reliable power supply despite uncertainties from failures, renewable fluctuations, and load growth is vital for electricity infrastructure. Assessing reliability helps identify operational weaknesses, yet evolving system variability demands advanced analytical tools. This study presents a comprehensive performance evaluation of a 1179 kWp grid-tied car park solar photovoltaic (PV) installation in Abqaiq, Saudi Arabia, designed to meet daytime consumption for industrial buildings under harsh desert-coastal environmental conditions. Using PVsyst simulation software, real-time monitoring data, and Monte Carlo simulation techniques, the research investigates discrepancies between modelled and actual system performance during the plant’s first operational year. The actual annual energy yield was measured at 1506.17 MWh approximately 24.4 % below the PVsyst predicted P50 value of 1992 MWh. Key performance indicators, including Performance Ratio (PR) and Capacity Utilization Factor (CUF), were also significantly lower than expected, at 64.5 % and 14.5 %, respectively, compared to simulated values of 81.64 % and 18.9 %. Monte Carlo analysis, using 10,000 iterations, confirmed a low probability of the actual output occurring under the simulation’s standard variability assumptions, indicating severe underperformance. The study identifies real-world challenges such as excessive dust accumulation, elevated ambient temperatures, and system degradation as primary contributors to the energy shortfall. The integration of deterministic simulation with probabilistic modelling provides a robust methodology for yield prediction and risk assessment. The findings underscore the need for improved forecasting accuracy, routine performance diagnostics, and adaptive system design tailored to extreme climates. The integration of confidence interval analysis and probabilistic modelling provides a deeper understanding of yield deviations and system underperformance, offering a replicable methodology for performance validation in extreme environments. This approach supports improved planning, investment evaluation, and maintenance strategies for large-scale PV systems exposed to challenging climatic conditions.

A bidirectional wireless power transfer (BD-WPT) system with phase shift pulsewidth modulation (PWM) control is proposed for electric vehicle (EV) applications, incorporating a wireless communication method for data exchange between the primary and pickup-side converters. The system enables bidirectional power flow while ensuring stable and efficient power regulation. Digital clock frequency synchronization between the digital signal processor (DSP) processors on both sides is achieved via a wireless transceiver, eliminating the need for analog synchronization circuits and maintaining smooth power transfer. Power regulation is controlled by adjusting the phase shift on both sides using a digital proportional-integral (PI) controller and phase shift PWM control. The operating principle of the BD-WPT system and the series-series (SS) compensation topology are investigated, and the optimal PI controller parameters are derived using the Bode plot analysis of the system's transfer function. A 2-kW SS-compensated BD-WPT prototype with constant-power (CP) control is developed and experimentally validated. To evaluate power regulation performance, the system is tested under coil misalignment in the x-axis direction while maintaining a constant 2-kW dc output. Experimental results confirm the effectiveness and robustness of the proposed closed-loop control scheme, achieving an overall system efficiency of 94.86% at 2-kW output power.

Due to the intermittent nature of renewable energy sources (RESs) such as wind farms or solar farms, integrating these RESs into power systems can result in instability, affecting the power system's quality and stability. To address this issue, various energy storage systems (ESSs) used to compensate for power fluctuations or different flexible AC transmission systems (FACTS) used to control voltage magnitude and active/reactive power injections were proposed to improve the stability of the connected power systems. Some effective supplementary damping controllers (SDCs) need to be designed by using suitable control theory for the ESSs or the FACTS devices to achieve the goal of stability improvement. A static synchronous compensator (STATCOM) joined with a vanadium redox flow battery (VRFB)-based ESS is proposed to suppress subsynchronous resonance (SSR) occurring in a hybrid steam-turbine generator (STG)/offshore wind farm (OWF) system fed to an infinite bus through a series-capacitor compensated line. The OWF is based on a doubly-fed induction generator, and the d-q axis equivalent-circuit model of the studied system under three-phase balanced loading conditions is derived to establish the complete system model. An SDC of the STATCOM is designed using modal control theory to improve the damping of the dominant modes of the studied system. Small-signal stability and dynamic simulation results of the studied system are systematically performed to demonstrate that the STATCOM joined with the proposed VRFB-ESS with the designed SDC effectively suppresses the studied system's SSR.

Ensuring the reliability and sustainability of power systems is essential for maintaining efficient and uninterrupted operations, especially under varying load conditions and potential faults. This study tackles the critical task of contingency ranking by evaluating the severity of disturbances caused by transmission line disconnections. Such evaluations enable power system operators to make informed and strategic decisions during real-time scenarios. A novel approach utilizing the Modified Sine Cosine Algorithm (MSCA), a nature-inspired metaheuristic optimization technique, is proposed to resolve (N-1) contingency rankings efficiently. The MSCA method is validated using the IEEE 30-bus test case, focusing on optimal parameter tuning for population size, iterations, and key variables. Results demonstrate that MSCA achieves a high capture ratio of 96.67%, explores only 8.33 × 10??% of the search space, and requires a processing time of 3.69 seconds. Compared with established methods such as Ant Colony Optimization (ACO) and Genetic Algorithm (GA), MSCA exhibits superior computational efficiency while maintaining competitive accuracy. These findings underline the potential of MSCA in real-time applications where speed and precision are critical. By closely matching manual contingency rankings, the proposed method integrates reliability assessment and optimization techniques, offering practical value for improving system resilience and reducing risks associated with disruptions. This research advances state-of-the-art power system reliability assessment and optimization approaches, providing operators and planners with a robust tool for addressing complex contingency challenges. ABSTRAK: Memastikan keandalan dan kelestarian sistem tenaga elektrik adalah penting untuk mengekalkan operasi yang cekap dan tidak terganggu, terutamanya dalam menghadapi keadaan beban yang berubah-ubah dan kemungkinan kerosakan. Kajian ini menangani tugas kritikal dalam perangkingan kontingensi dengan menilai tahap keparahan gangguan yang disebabkan oleh pemutusan talian penghantaran. Penilaian sebegini membolehkan pengendali sistem tenaga membuat keputusan yang berinformasi dan strategik dalam senario masa nyata. Pendekatan baharu yang menggunakan Modified Sine Cosine Algorithm (MSCA), satu teknik pengoptimuman metaheuristik yang diilhamkan oleh alam, dicadangkan untuk menyelesaikan perangkingan kontingensi (N-1) dengan cekap. Kaedah MSCA ini disahkan menggunakan kes ujian IEEE 30-bus dengan memberi tumpuan kepada penalaan parameter optimum untuk saiz populasi, iterasi, dan pemboleh ubah utama. Keputusan menunjukkan bahawa MSCA mencapai nisbah tangkapan yang tinggi sebanyak 96.67%, hanya meneroka 8.33 × 10??% daripada ruang pencarian, dan memerlukan masa pemprosesan sebanyak 3.69 saat. Berbanding dengan kaedah sedia ada seperti Ant Colony Optimization (ACO) dan Genetic Algorithm (GA), MSCA menunjukkan kecekapan pengiraan yang unggul sambil mengekalkan ketepatan yang kompetitif. Penemuan ini menekankan potensi MSCA dalam aplikasi masa nyata di mana kelajuan dan ketepatan adalah kritikal. Dengan hasil yang hampir menyamai perangkingan kontingensi manual, kaedah yang dicadangkan ini mengintegrasikan penilaian keandalan dan teknik pengoptimuman, memberikan nilai praktikal untuk meningkatkan daya tahan sistem dan mengurangkan risiko yang berkaitan dengan gangguan. Penyelidikan ini memajukan pendekatan terkini dalam penilaian keandalan sistem tenaga dan pengoptimuman, menyediakan pengendali dan perancang dengan alat yang kukuh untuk menangani cabaran kontingensi yang kompleks.

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.

Electricity consumption in residential and public buildings has become a concern recently. Ambient temperature and humidity levels play a crucial role in enhancing the efficacy of tasks performed within an occupancy area. Due to the discomfort caused by hot weather conditions in tropical regions, the use of cooling systems is common practice to help normalize the environmental temperature for the comfort of the occupants. However, inefficient air conditioner usage, especially when rooms are unoccupied, leads to excessive energy consumption and high electricity costs. To address this issue, this paper proposes an Arduino-based solar-powered smart monitoring system for occupancy and energy use in lecture rooms. The system consists of two solar-powered Arduino boards (Wemos D1 and AI-Thinker camera). The Wemos D1 integrates a temperature sensor and a PIR motion sensor to monitor environmental conditions, while the AIThinker camera provides image-based occupancy verification via Telegram. Real-time data transmission is enabled through Blynk and Telegram applications, allowing remote monitoring of room conditions. Results show that the system achieved 41.12 % weekly energy savings, effectively reducing carbon footprints and operational costs. The prototype conforms with SDGs climate action and clean energy initiatives, demonstrating its potential for sustainable energy management in lecture rooms.

Effective energy management in microgrids with renewable energy sources is crucial for maintaining system stability while minimizing operational costs. However, traditional Reinforcement Learning (RL) controllers often encounter challenges, including long training time and instability during the training process. This study introduces a novel approach that integrates Transfer Learning (TL) techniques with RL controllers to address these issues. By using synthetic datasets generated by advanced forecasting models, such as ResNet18+BiLSTM, the proposed method pre-trains RL agents, embedding domain knowledge to enhance performance. The results, based on one year of operational data, show that TL-enhanced RL controllers significantly reduce cumulative operation costs and system imbalance, achieving up to a 62.63% reduction in costs and an 80% improvement in balance compared to baseline models. Furthermore, the proposed method improves initial performance and shortens the training duration needed to reach operational thresholds. This approach demonstrates the potential of combining TL with RL to develop efficient, cost-effective solutions for real-time energy management in complex power systems.

This study presents the design, simulation, and optimization of a hybrid grid-connected solar photovoltaic (PV) system integrated with battery energy storage to provide reliable and sustainable energy for common spaces in high-rise residential buildings. The solar PV system was modelled using Maximum Power Point Tracking (MPPT) with optimized tilt and azimuth angles to maximize the PV power output under varying irradiance and temperature conditions. The battery energy storage system was evaluated for charging and discharging performance within a safe state-of-charge (SOC) range of 20% to 80%. Grid integration was achieved through droop control to enable decentralized power sharing. Particle Swarm Optimization (PSO) was employed to optimize energy flow management, prioritizing renewable energy utilization, and leveraging time-of-use tariffs to minimize the grid's imported power and costs. Results demonstrate enhanced energy efficiency, reduced grid dependency, and a cost-effective, reliable power delivery solution tailored to urban residential energy demands.

As power distribution systems evolve from conventional radial structures to more complex ring networks, driven by the widespread integration of distributed generation (DG), the challenge of ensuring reliable and effective protection becomes increasingly critical. Directional Overcurrent Relays (DOCRs) are pivotal in this context, protect the network through precise coordination between primary and backup protection schemes. However, optimizing these settings is a daunting task due to the nonlinear nature of the problem and the myriad constraints involved. This paper embarks on a comprehensive exploration of cutting-edge optimization techniques deployed to tackle DOCR coordination challenges. The paper delves into Nature-Inspired Algorithms (NIAs), metaheuristic approaches, mathematical formulations, and artificial intelligence methods, critically assessing their capabilities in navigating the complex landscape of relay coordination. While NIAs excel in exploring vast search spaces, their slower convergence can be a drawback. Metaheuristics offer robust solutions but at the cost of high computational demand, and traditional mathematical methods, though precise, may fall short in dynamic environments. Pushing the boundaries of current methodologies, the paper investigates hybrid optimization techniques that fuse the strengths of multiple approaches. These hybrids show promise in transcending the limitations of individual methods, offering innovative solutions for optimizing relay coordination in increasingly intricate network configurations. Moreover, this paper sheds light on the profound impact of emerging energy technologies on existing protection frameworks, emphasizing the urgent need for adaptive strategies to ensure stable and reliable power delivery. Through this investigation, actionable insights and forward-thinking solutions are proposed to bolster the safety and resilience of modern distribution networks, positioning them to meet the demands of a rapidly transforming energy landscape.

This study conducts an in-depth analysis of the energy performance and economic feasibility of utilizing five cutting-edge PV systems on the rooftop of an industrial building spanning approximately 29,000 m2 in Abqaiq City, Saudi Arabia. The primary focus lies in evaluating the performance of these systems within the local grid’s conditions, with an average monthly electrical consumption of 534,000 kWh/month. Using PVsyst software, simulations are carried out for five configurations representing advanced PV technologies. The objective is to determine the most efficient technology and scenario under the project’s environmental conditions. Installation costs, maintenance expenses, revenue estimates, and potential revenue sources are also factored in, considering local regulations and policies. By meticulously simulating and comparing five advanced PV technologies across various configurations, the research identifies the N-type Mono BIFACIAL module in the third configuration scenario as the most effective. This configuration demonstrated an impressive annual energy yield of 5,568 MWh, a high-performance ratio of 94.2 %, and an economically favourable Levelized Cost of Energy (LCOE) of $0.014/kWh. Additionally, it achieved a significant Net Present Value (NPV) of $5,084,516, a robust Return on Investment (ROI) of 431.9 %, and a short payback period (PBP) of 4.7 years. The overarching goal of this study is to provide informed investment decisions aimed at selecting the most suitable PV technology and configuration for the harsh coastal climate conditions prevalent in the region. Special consideration is given to meeting daytime electrical consumption needs while taking into account subsidies on electrical tariffs for the industrial sector in Saudi Arabia.

Integrating distributed generation (DG) while maintaining grid efficiency and stability presents a critical challenge in modern power systems. The strategic placement and sizing of DG units are paramount, as suboptimal allocation can exacerbate power losses and voltage instability. This study evaluates optimal DG siting and sizing in radial distribution systems using metaheuristic algorithms to balance power loss reduction and voltage stability enhancement. Four algorithms, representing both evolutionary and swarm intelligence approaches, are evaluated under various weighting factor combinations (WFCs) to tackle this multi-objective optimization problem. Results exhibit that PSO consistently outperforms other algorithms across all WFCs, achieving excellent loss reduction while preserving voltage stability. Among WFCs, the loss-prioritized scheme yields the most balanced performance, effectively optimizing system efficiency without compromising voltage security. In contrast, stability-focused configurations exhibit very high losses, leading to suboptimal performance. For sensitivity analysis, PSO shows its robustness across WFCs for the power loss case, while FA shows similar characteristics for the voltage stability case. These findings approve PSO with lossprioritized WFCs for reliable DG optimization, helping utilities to enhance efficiency while avoiding reverse power flow risks.

In the past three decades, there has been a sweeping trend in Western and developed countries worldwide to transform the vertically integrated electricity supply chain into competitive electricity markets to diversify investment in the system and ultimately drive down operation costs. Nonetheless, due to some geopolitical and economic reasons, many developing countries adopted a modestly liberalized version of the power market (imperfect market). With the trend of privatization, specifically at the generation level, to leverage the hypothetical competitiveness, countries that did not adopt a full-fledged market structure face a dilemma. The system operators of incumbent imperfect market models find it increasingly difficult to deal with multiple private ownership of Independent Power Producers who are unwilling to share their detailed operational parameters for long-term generation scheduling (lasting for years). In this paper, Blockchain (BC) is being advocated as a platform that simulates a virtual market environment to address such issues. The proposed BC-based structure allows generators to participate in the short-term scheduling mechanism (such as day-ahead) in a trust-free environment without sharing their vital data yet achieving efficient, market-grade solutions. The feasibility of this new proposition is demonstrated through three different application scenarios, utilizing real-world load and renewable generation profiles sourced from the respective Grid System Operators databases. Python library (PYPSA) and Ethereum Testnet are being used for grid simulation and BC platform implementation respectively. The results of BC-assisted generation scheduling are presented and compared with the imperfect market model to highlight the viability of the proposed new approach.

This paper proposes an optimized Energy Management System (EMS) for grid-connected-residential solar photovoltaic (PV) systems, integrating Demand Response (DR) program to maximize user benefits while ensuring system reliability and stability. The proposed EMS comprises of solar PV array and Battery Energy Storage System (BESS), with the residential loads modelled and simulated using MATLAB. The EMS prioritizes renewable energy utilization while minimizes grid power consumption, incorporating automatic load control for enhanced efficiency. Variability of residential loads is addressed through DR program with Interruptible Load (IL) control, optimized using Particle Swarm Optimization (PSO) and Crayfish Optimization Algorithm (COA). The optimization targets maximum user comfort as well as peak load and cost reduction. Comparative analysis reveals that COA outperforms PSO, leading to its adoption in the optimized EMS. Real-world evaluation using daily residential data demonstrates that the optimized EMS achieves lower electricity costs, higher user satisfaction, and improved system stability compared to a conventional EMS.

This study presents a comprehensive analysis of the energy performance and economic feasibility of optimal power generation systems, including an electrical network and a grid-connected PV/battery system, designed to meet the electricity demands of an industrial building in Saudi 'Arabia's Eastern region. The building, located in Abqaiq City, spans approximately 29,000 m2 and has an average annual electrical consumption of 6,500 MWh. The primary focus is on assessing the economic feasibility of these systems under local grid conditions. Simulations using PVsyst software were conducted for three configurations: grid-connected PV and PV with battery. The findings indicate that bifacial PV modules increase energy harvesting in coastal arid climate regions, with specific production ranging from 2,235 to 2,158 kWh/kWp/year and a PR between 95.9 % and 92.6 %. The grid- connected PV system is more feasible under industrial electricity tariffs, with a levelized cost of energy of $0.016/kWh, an NPV of $4,233,274, an ROI of 426.5 %, and a payback period of 4.7 years. These systems achieve energy savings of approximately 66 % for grid-connected PV and 89 % for hybrid PV/battery systems. Additionally, commercial and global tariffs significantly enhance the NPV and ROI of both systems, reducing the payback period and improving economic feasibility. Under commercial tariffs, the grid-connected PV system excels in ROI and payback period, while the hybrid system outperforms in NPV under global tariffs. By addressing economic and climate challenges in harsh coastal environments, this work provides valuable insights for sustainable investment decisions in an industrial city context.