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Öğe Design and analysis of input capacitor in DC–DC boost converter for photovoltaic-based systems(MDPI, 2023) Hayat, Aamir; Sibtain, Daud; Murtaza, Ali Faisal; Shahzad, Sulman; Jajja, Muhammad Sheheryar; Kılıç, HeybetPhotovoltaic (P.V.) systems have become an emerging field for power generation by using renewable energy (RE) sources to overcome the usage of conventional combustible fuels and the massive release of dangerous gases. The efficient operation of the PV system is vital to extracting the maximum power from the PV source. For this, a maximum power point tracking (MPPT) algorithm works with a DC–DC converter to extract maximum power from the P.V. system. Two main issues may arise with the involvement of a converter: (1) to locate M.P.P and (2) the performance of the PV model in varying weather conditions. Therefore, designing any converter gain has the utmost significance; thus, the proposed work is on non-isolated boost converters. To calculate the values of specific parameters such as input capacitor, output capacitor, and inductor, the averaging state-space modeling typically uses governing equations. In this research, the formula of the input capacitor is derived through the average state-space modeling of the boost converter, which signifies the relation between input and output capacitors. From the results, it has been proven that the input capacitor efficiently performs when the input capacitor is half of the output capacitor. At an irradiance level of 1000 W/m2, the system shows stable behavior with a fast convergence speed of 0.00745 s until the irradiance falls to a value of 400 W/m2. The system is less stable during the morning and the evening when irradiance falls are very low.Öğe Fault ride-through capability improvement in hydrogen energy-based distributed generators using STATCOM and deep-Q learning(Elsevier Ltd, 2024) Shahzad, Sulman; Alsenani, Theyab R.; Alrumayh, Ahmed Nasser; Almutairi, Abdulaziz; Kılıç, HeybetThis study explores the enhancement of Fault Ride-Through (FRT) capabilities in hydrogen energy-based distributed generators (HEDGs) by integrating Static Synchronous Compensators (STATCOM) with a novel Deep Q-Learning (DQL) control technique. Hydrogen energy systems face challenges like voltage instability during grid disturbances, which conventional Proportional-Integral (PI) controllers fail to address due to their linear operation constraints. Advanced controllers, such as Adaptive Neuro-Fuzzy Inference Systems (ANFIS), offer better adaptability but lack real-time optimization capabilities. The proposed DQL framework leverages reinforcement learning, achieving superior results by dynamically optimizing reactive power compensation and minimizing system instability. Simulation results demonstrate that the DQL-based STATCOM achieves a 35% faster settling time and reduces overshoot by 50% compared to ANFIS and PI controllers. Additionally, the DQL system maintains voltage stability within ±5% during critical faults, improving energy efficiency by 8%. This innovative approach ensures cost-effective, sustainable integration of HEDGs into modern power grids, significantly advancing intelligent control strategies for renewable energy systems. © 2024 Hydrogen Energy Publications LLCÖğe Frequency stabilization for interconnected renewable based power system using cascaded model predictive controller with fractional order PID controller(John Wiley and Sons Inc, 2023) Sibtain, Daud; Rafiq, Turab; Bhatti, Mehdi Hassan; Shahzad, Sulman; Kılıç, HeybetIn today's electrical grid, frequency cannot be ignored. The proliferation of renewable energy sources into a power system degrades its frequency; hence, the need for frequency regulation has been increased as a result. The second major factor is the perturbation in the load that originates the frequency fluctuation which needs to be suppressed in minimum time to avoid any system collapse. The designing of an optimal controller is indispensable because of increasing power system complexity. In order to maintain the stability of the power system, this paper presents the cascaded design of the model predictive controller with fractional order PID controller (MPC-FOPIDN) to mitigate the frequency oscillations due to disruption in load. The combination of the predictive capabilities of model predictive control (MPC) and fractional order control enhances control abilities, making it an optimal control strategy for load frequency control (LFC). The grasshopper optimization algorithm (GOA) is applied to obtain optimal gains values for the FOPID controller. The efficacy of the controller has effectively mitigated frequency fluctuations caused by a change in demand or any uncertainty in the power system.Öğe Optimal allocation of distributed generation in meshed power networks: A metaheuristic approach(Inst Engineering Technology-Iet, 2024) Khan, Muhammad Saad; Waheed, Danial; Shahzad, Sulman; Afzal, Suhail; Killic, HeybetThis paper introduces a novel metaheuristic technique, a COOT-based algorithm, to determine the optimal Distributed Generation (DG) allocation within a loop-configured network. This method significantly narrows the optimization gap by leveraging a COOT-based algorithm, ensuring accelerated convergence and resultant global optima. The core incentive for employing this technique is to substantially mitigate power loss, curtail voltage deviation, and bolster system stability in a loop distribution network. To attain optimal outcomes, the elaborated COOT and improved grey wolf optimization improved grey wolf optimization (IGWO) algorithms were executed on IEEE bus-33 and 69 mesh distribution networks (MDNs) under varying power factors. The derived mathematical results effectively underscore accomplishing the stipulated objectives: a marked reduction in voltage deviation and power loss coupled with an augmentation in system stability. Notably, at unity, incorporating DGs resulted in a paramount reduction in power loss, attaining a decrease of 78% and 85% for bus-33 and 69 MDNs, respectively. Moreover, an impressive decrease in power loss by 94% and 98% was observed at the optimal power factor for both MDNs. A comparative evaluation of the results accentuates that the proposed COOT and IGWO algorithms eclipse other documented research in performance, showcasing superior efficiency on a techno-economic basis. This paper introduces a novel metaheuristic technique to determine the optimal Distributed Generation (DG) allocation within a loop-configured network. This method significantly narrows the optimization gap by leveraging a COOT-based algorithm, ensuring accelerated convergence and resultant global optima. The core incentive for employing this technique is to substantially mitigate power loss, curtail voltage deviation, and bolster system stability in a loop distribution network. imageÖğe Possibilities, challenges, and future opportunities of microgrids: A review(MDPI, 2023) Shahzad, Sulman; Abbasi, Muhammed Abbas; Ali, Hassan; Iqbal, Muhammad; Munir, Rania; Kılıç, HeybetMicrogrids are an emerging technology that offers many benefits compared with traditional power grids, including increased reliability, reduced energy costs, improved energy security, environmental benefits, and increased flexibility. However, several challenges are associated with microgrid technology, including high capital costs, technical complexity, regulatory challenges, interconnection issues, maintenance, and operation requirements. Through an in-depth analysis of various research areas and technical aspects of microgrid development, this study aims to provide valuable insights into the strategies and technologies required to overcome these challenges. By assessing the current state of microgrid development in Pakistan and drawing lessons from international best practices, our research highlights the unique opportunities microgrids present for tackling energy poverty, reducing greenhouse gas emissions, and promoting sustainable economic growth. Ultimately, this research article contributes to the growing knowledge of microgrids and their role in addressing global sustainability issues. It offers practical recommendations for policymakers, industry stakeholders, and local communities in Pakistan and beyond.Öğe Short-term load forecasting models: A review of challenges, progress, and the road ahead(MDPI, 2023) Akhtar, Saima; Shahzad, Sulman; Zaheer, Asad; Ullah, Hafiz Sami; Kılıç, Heybet; Gono, Radomir; Jasiński, Michał; Leonowicz, ZbigniewShort-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class’s main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach’s advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions.Öğe Techno-economic analysis of green hydrogen integration in smart grids: Pathways to sustainable energy systems(Elsevier Ltd, 2024) Shahzad, Sulman; Alsenani, Theyab R.; Kılıç, Heybet; Wheeler, PatrickThis study conducts a comprehensive techno-economic analysis of green hydrogen integration in smart grids, highlighting its potential to transform energy systems. Green hydrogen, produced via water electrolysis using renewable sources like wind and solar, offers a carbon-neutral alternative to traditional hydrogen production. The paper evaluates electrolysis technologies' efficiency and cost dynamics, including PEM and alkaline systems, and integrates advanced smart grid technologies for real-time monitoring and energy management. Our findings reveal that while green hydrogen reduces carbon emissions and enhances grid flexibility, challenges remain in terms of infrastructure and cost reductions. Policy support through incentives, such as feed-in tariffs for green hydrogen production, and regulatory frameworks, like emission reduction targets, will be crucial to scale the adoption of green hydrogen and make it a viable component of future sustainable energy strategies. © 2024 Hydrogen Energy Publications LLC
