Loiy Al-Ghussain

Bio: Loiy Al-Ghussain is an academic researcher from University of Kentucky. The author has contributed to research in topics: Renewable energy & Photovoltaic system. The author has an hindex of 11, co-authored 25 publications receiving 423 citations. Previous affiliations of Loiy Al-Ghussain include Middle East Technical University & German-Jordanian University.

Global warming: review on driving forces and mitigation

Abstract: Global warming is one of the major consequences of the human activities where the overuse of fossil fuels as energy resources caused the increase in the concentration of the greenhouse gases (GHGs), such as CO2, CH4, N2O, and water vapor, in the atmosphere causing the increase in the average surface temperature of the earth. This article reviews the driving forces of global warming and highlights the major contributors to this phenomenon and presents some of the mitigation techniques. Water vapor is responsible for two-third of the global warming; however, CO2 is considered as the controlling factor of the global warming. In other words, if the concentration of CO2 did not increase, global warming would not have happened. Scientists claim that doubling or halving the CO2 in the atmosphere causes the change in the average surface temperature of the earth by +3.8 C or −3.6 C, respectively. However, this amount of change depends on the change in the humidity of the air which in return depends on the air’s temperature. Conversely, even though the other GHGs such as CH4 and N2O have stronger ability to absorb the radiation, their contribution in the global warming is insignificant because of their low concentration in the atmosphere compared with CO2. The adoption of the mitigation and adaptation strategies at the same time is the most effective economic and technical solution for the global warming issue. © 2018 American Institute of Chemical Engineers Environ Prog, 2018

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles' Charging Demand

Abstract: Renewable microgrids are new solutions for enhanced security, improved reliability and boosted power quality and operation in power systems. By deploying different sources of renewables such as solar panels and wind units, renewable microgrids can enhance reducing the greenhouse gasses and improve the efficiency. This paper proposes a machine learning based approach for energy management in renewable microgrids considering a reconfigurable structure based on remote switching of tie and sectionalizing. The suggested method considers the advanced support vector machine for modeling and estimating the charging demand of hybrid electric vehicles (HEVs). In order to mitigate the charging effects of HEVs on the system, two different scenarios are deployed; one coordinated and the other one intelligent charging. Due to the complex structure of the problem formulation, a new modified optimization method based on dragonfly is suggested. Moreover, a self-adaptive modification is suggested, which helps the solutions pick the modification method that best fits their situation. Simulation results on an IEEE microgrid test system show its appropriate and efficient quality in both scenarios. According to the prediction results for the total charging demand of the HEVs, the mean absolute percentage error is 0.978, which is very low. Moreover, the results show a 2.5% reduction in the total operation cost of the microgrid in the intelligent charging compared to the coordinated scheme.

Climate change 2014 - Mitigation of climate change

Abstract: The talk with present the key results of the IPCC Working Group III 5th assessment report. Concluding four years of intense scientific collaboration by hundreds of authors from around the world, the report responds to the request of the world's governments for a comprehensive, objective and policy neutral assessment of the current scientific knowledge on mitigating climate change. The report has been extensively reviewed by experts and governments to ensure quality and comprehensiveness.

Multifunctional applications of biochar beyond carbon storage

Abstract: Biochar is produced as a charred material with high surface area and abundant functional groups by pyrolysis, which refers to the process of thermochemical decomposition of organic material at elev...

Robust multi-objective optimal design of islanded hybrid system with renewable and diesel sources/stationary and mobile energy storage systems

Abstract: Planning of an islanded hybrid system (IHS) with different sources and storages to supply clean, flexible, and highly reliable energy at consumption sites is of high importance. To this end, this paper presents the design of an IHS with a wind turbine, photovoltaic, diesel generator, and stationary (battery) and mobile (electrical vehicles) energy storage systems (ESS). The proposed method includes a multi-objective optimization to minimize the total cost of construction, maintenance, and operation of sources and ESSs within the IHS and the emission level of the system using two separate objective functions. The problem is subject to operational and planning constraints of sources and ESSs and power. Employing the Pareto optimization technique based on the e-constraint method forms a single-objective optimization problem for the proposed design. The problem involves uncertainties of load, renewable energy, and energy demand of mobile ESSs and has a nonlinear form. Adaptive robust optimization based on a hybrid meta-heuristic algorithm that utilizes a combination of the sine-cosine algorithm (SCA) and crow search algorithm (CSA) is proposed to achieve an optimal robust structure for the suggested scheme. In this scheme, operation model of the mobile storage systems in the IHS considering the uncertainties prediction errors and its model using HMA-based ARO besides adopting the HMA to achieve a unique optimal solution are among the novelties of this research. Eventually, considering the climate data and energy consumption of a region in Rafsanjan, Iran, capabilities of the method in extracting a robust IHS for sources and ESSs are validated depending on optimal economic and environmental conditions. The HMA succeeds to reach an optimal solution with an SD of 0.92% in the final response and this underlines its capability in achieving approximate conditions of unique responsiveness. The proposed scheme with proper planning and operation of sources and storages in the form of a HIS finds optimal values for economic and environmental conditions so that the difference between pollution and cost values from its minimum values at the compromise point is roughly 22%. For 17% uncertainty parameters prediction errors, the scheme obtains a robust structure for the IHS.