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All Journal Journal of Robotics and Control (JRC) PELS (Procedia of Engineering and Life Science)
Abdulhasan, Mahmood Jamal
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Using Imperialist Competitive Algorithm Powered Optimization of Bifacial Solar Systems for Enhanced Energy Production and Storage Efficiency Hadi, Husam Ali; Kassem, Abdallah; Amoud, Hassan; Nadweh, Safwan; Ghazaly, Nouby M.; Abdulhasan, Mahmood Jamal
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22100

Abstract

Interest in renewable energy has grown due to increased environmental awareness and concern about climate change. Among the various renewable energy technologies, grid-connected bifacial PV systems are particularly important due to their higher efficiency compared to conventional systems. However, maximizing energy harvesting and storage efficiency remains a challenge for these systems, requiring the use of an efficient charge controller and an appropriate battery. The process of setting charge controller parameters and selecting the best storage technology is complex and requires a thorough study of various operating conditions. The main research contribution of this paper is the development of an efficient optimization methodology to increase the energy production and storage efficiency of the studied systems using optimization algorithms. The imperialist competitive algorithm (ICA) is used in the system design to improve performance through optimal adjustment of charge controller parameters and selection of appropriate storage technology. This decision was based on factors such as energy production from PV panels, energy consumption from loads, and energy storage in batteries. Performance is also evaluated using both the flower pollination algorithm (FPA) and Gray Wolf optimization (GWO) algorithms. The study evaluated system performance by analyzing energy production, storage efficiency, and cost effectiveness. The results showed that the ICA algorithm is effective in improving energy production and storage, resulting in higher energy output, better battery efficiency, and lower system costs. In addition, lithium-ion batteries were identified as the best storage technology. This research demonstrates the potential of the ICA approach to increase efficiency and reduce costs in the PV systems.
Artificial Intelligence for Environmental Sustainability and Circular Management of Renewable Energy Systems: A Systematic Review: Kecerdasan Buatan untuk Keberlanjutan Lingkungan dan Pengelolaan Berkelanjutan Sistem Energi Terbarukan: Tinjauan Sistematis Abdulhasan, Mahmood Jamal; Abed, Murtada Hassan; Shayyish, Yaqoub Shamal; Khader, Aya Haider
Procedia of Engineering and Life Science Vol. 8 No. 2 (2025): Proceedings of the 8th Seminar Nasional Sains 2025
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v8i2.2932

Abstract

General Background: The rapid global deployment of solar photovoltaic and wind energy systems is central to climate change mitigation but generates a growing end-of-life waste challenge. Specific Background: By 2050, cumulative waste from solar panels and wind turbine blades is projected to reach tens of millions of tons, while current linear recycling systems face technical inefficiencies and economic constraints. Knowledge Gap: There is a lack of scalable and economically viable circular management solutions capable of addressing complex composite materials and lifecycle optimization in renewable energy infrastructure. Aims: This study systematically evaluates Artificial Intelligence applications across the lifecycle of solar panels and wind turbines to assess their role in enabling circular economy strategies. Results: Based on a systematic review of 496 publications and quantitative synthesis, AI-driven solutions demonstrate 35.8% carbon emission reduction per recycled solar panel, 33% improvement in material recovery rates, 43.8% gains in disassembly efficiency, and 62.5 kg CO2 savings per logistics operation. Novelty: The study develops an integrated analytical framework linking Machine Learning, Computer Vision, Robotics, Digital Twins, and lifecycle assessment within renewable energy circularity. Implications: The findings support AI-enabled reverse logistics, Digital Product Passports, and policy-informed lifecycle management as foundational mechanisms for sustainable renewable energy systems. Keywords: Artificial Intelligence, Circular Economy, Renewable Energy Systems, Lifecycle Assessment, Waste Management Key Findings Highlights: Carbon savings of 35.8% achieved through intelligent recycling workflows Material recovery improvements reached up to 33% across PV components Logistics routing reduced transport-related CO2 by 62.5 kg per delivery
Co-Authors Abed, Murtada Hassan Amoud, Hassan Ghazaly, Nouby M. Hadi, Husam Ali Kassem, Abdallah Khader, Aya Haider Nadweh, Safwan Shayyish, Yaqoub Shamal