Abdullah Hamed Al-Badi

The Government of Oman has initiated a fuel diversification policy to increase the share of renewable energy in the country’s total electricity generation to approximately 30% by 2030 (Sultan and F...

The primary aim of this research is to assess the wave energy potential along the coast of Oman especially coasts facing Arabian Sea and Indian ocean by analyzing the wave energy distribution and time series of wave heights, obtained through numerical modeling over a three-years period. The study focuses on evaluating the spatial, seasonal, monthly, and directional variability of wave power and energy at multiple coastal locations. The spatial analysis reveals a clear trend of increasing wave power in the southeastern coast, toward the open Indian Ocean, where stronger wind conditions prevail. The monthly analysis indicates that mean wave power peaks during the summer months (June to August), coinciding with the southwest Indian monsoon season, which significantly enhances wave activity along the southern coastline. To simulate and analyze wave characteristics, wave data were obtained from the Global Ocean Waves Analysis and Forecast product provided by Copernicus Marine, which is based on the MFWAM (a third-generation wave model) developed by Météo-France. This dataset enabled the generation of high-resolution data on wave height, period, and direction, providing a comprehensive understanding of wave energy dynamics across the study area. Results indicate that the majority of the annual wave energy is contributed by significant wave heights ranging from 1 to 4 m, suggesting that waves in this range contribute most of the annual wave energy resource in the study area. These findings provide a characterization of the wave energy resource along the coast of Oman and identify the locations and seasons with relatively higher wave energy potential. The results can support future device-specific feasibility studies and technology selection for wave energy development in the region.

Traditional load-forecasting methods rely heavily on extensive historical data to predict future values, thus limiting their applicability in long-term forecasts where historical data may be unavailable or irrelevant. While several forecasting techniques, such as regression models and machine learning/deep learning approaches, have been tested, the inherent uncertainty in long-horizon predictions remains a major challenge. To address this gap, an interval-based modeling framework grounded in granular computing is proposed. Specifically, justifiable granules are constructed to define interpretable lower and upper bounds around the central tendency of load values, optimized through a justification criterion that balances coverage and specificity. This approach enables the quantification of uncertainty without assuming specific distributional forms. These granules are generated for multiple temporal resolutions (daily, weekly, and monthly) using historical load data from 2020 to 2022 and evaluated against unseen data from 2023. A detailed overlap analysis is performed to assess the alignment between combined and year-specific intervals, providing insights into generalizability and robustness. Visualizations further demonstrate the interpretability of the proposed intervals, offering a practical solution for long-term load modeling under uncertainty.

The presence of non-linear, time-variant loads in power networks introduces harmonics and other power quality issues in voltage and current waveforms. With the growing prevalence of power electronics, managing harmonic distortion has become a significant challenge in modern power distribution systems. This study aims to assess and analyze power quality at various locations within an academic institution (Sultan Qaboos University). The study encompasses the outputs of PV converters, uninterruptible power supplies (UPS), chillers with variable-speed motors, 11 kV/415 V transformers at both the College of Engineering and the Center of Information Systems, as well as two main substations supplying power to the university (33 kV/11 kV). Data collected—such as harmonic content in current and voltage waveforms and Total Harmonic Distortion (THD)—were meticulously analyzed to identify the factors affecting waveform quality. Measurements were conducted using advanced power quality instruments (Fluke 435-II), and the data were analyzed using MATLAB software. The study reveals that most results adhered to both the Oman grid code and international standards. The findings and methodology presented in this paper provide a critical benchmark for guiding standards compliance as Sultan Qaboos University, and other academic institution, undertakes the modernization of aging equipment and the expansion of new high-tech facilities.

Al Hallaniyat Island is located in the southern part of Oman. The island has a low number of population and is currently fed by a number of diesel generators with a capacity of 1568 kW. Decreasing the emission of greenhouse gases is one of the main goals for the country’s vision of 2040 as well as Oman’s commitment to net-zero emissions by 2050, in line with the Paris Agreement of limiting global warming to 1.5°C. The purpose of this paper is to study the feasibility of replacing diesel generators with 100% renewable energy sources. The proposed energy source for the Island will be a hybrid system consisting of the wave energy converter, PV, wind and battery storage system. Real load data was used in the model with measured wind speed and solar radiation. The Hybrid Optimization Model for Electric Renewables software is used in this study. Several systems were proposed but the most economical one is PV-Wind-Wave-Battery system that resulted in levelized cost of energy of US$/kWh 0.1586 and Net Present Cost (NPC) of US$ 5,010,594. Having PV-Wave-Battery will result in levelized cost of energy of US$/kWh 0.174 whereas having Wind-Wave-Battery will increase the energy cost to US$/kWh 0.42.

This study aimed to optimize the incorporation of static var compensators (SVCs) into Oman’s main interconnected system (MIS) using real 2023 MIS data. Leveraging the particle swarm optimization (PSO) algorithm within MATLAB, substantial enhancements were achieved in voltage profiles, with associated losses reducing by roughly 2%. A multi-objective strategy effectively managed costs while preserving improved voltage profiles and controlled losses. Validation through DigSILENT showcased the dynamic advantages of optimal SVC placement through consistently elevating voltage profiles and mitigating losses, notably within the Muscat region. Analyses encompassing harmonics, transient stability, and load distribution indicated that harmonics remained within acceptable thresholds, and overall system stability was enhanced. Optimal SVC deployment expedited the attainment of steady-state conditions, as illustrated via the QV curve, demonstrating increased stability as the buses loaded from 18% to 96%. These findings underscore the robust and efficient nature of SVC integration as a viable solution within Oman’s MIS system, addressing voltage profile enhancement, loss minimization, and fortified system stability.

Appropriate renewable distributed generation (RDG) placement is one of the most significant issues for the efficient operation of current power systems. Since the inverter-interfaced RDG lacks rotating mass to sustain the system’s inertia, microgrids have low total system inertia, which impairs frequency stability and can yield significant frequency and voltage instability in severe disruptions. The virtual synchronous generator (VSG), which uses concepts that regulate the inverter to simulate a conventional synchronous generator, is one of the most promising solutions to address these challenges. Hence, this research proposes a unique technique of simultaneous optimal solution for RDG and VSG sizing and placement in distribution networks using a recent metaheuristic technique called the Multi-objective Salp Swarm Optimization Algorithm (MOSSA). The objective function was to minimize the frequency deviation and maximize the total annual energy savings and operational costs of the RDG and VSG units. Moreover, this study assesses the IEEE 33-bus, 69-bus distribution network, and practical Masirah Island network as the test systems. Furthermore, the proposed method’s Pareto fronts are superior to two recent metaheuristics employed in this research domain: Multi-objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II). According to the findings, the MOSSA optimal Pareto solution candidates (PSC) for the three test systems satisfied the frequency constraints and cost objectives. In addition, all PSCs accurately prevented voltage limit infringements, and the overall energy losses and the active power losses during each optimization period were significantly reduced.