Browsing by Author "Dr. Sobia Baig"

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Economic Feasibility Assessment of a Rural Hybrid Microgrid for Renala Khurd in Pakistan
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Misbah Sattar; FA19-REE-003; Dr. Sobia Baig; LHR TP 8473
Reliable access to electric power in rural communities is challenging for the transmission and distribution system of Pakistan. The extension of the existing power system is a challenge due to huge investment cost. One solution is to involve microgrid technology for access of electric power in such communities. Thus, the integration of renewable energy resources at the currently installed power plants is a feasible economic option for Pakistan. Moreover, for reliable operation, these microgrids are supported with battery storage. However, battery storage increases the overall capital and operational cost which in case of rural communities of developing countries are at time unaffordable and practically unsustainable. Pakistan is a country that encompasses a large canal system across the country. Besides, its prime utilization in irrigation, these canals can be beneficial for small-scale hydro power generation. For additional power generation, other renewable sources, such as solar PV with grid backup can be applied. Furthermore, while adding hydro resources, the impact on project performance parameters such as Net Present Cost (NPC), Grid unit purchased, Capital cost and Cost of Energy (COE) also need quantitative assessment with the variation in the system sizing. In this study, a canal hydro source at Renala Khurd Pakistan is used as a case study to measure the quantitative impact on sizing and performance parameters with multiple combinations of the resources. HOMER Pro software has been used to assess sizing and associated economic performance parameters. Meanwhile, for the comparison of HOMER Pro software, Genetic Algorithm (GA) based Objective Function has been developed, keeping in view the required constraints. Results show that, the inclusion of small-scale hydel power as an assisting source with solar and grid combination significantly reduces the sizing of solar PV from 4.7 MW to 0.88 MW and grid unit purchased was reduced to 47% compared to 59%. Key Words: Small-scale hydel (SSH), solar PV (SPV), Genetic Algorithm (GA), Battery storage (BATT), Hybrid Optimization Model for Electric Renewables (HOMER)
Power Potential Assessment of Wave Energy and Integration with other Renewable Energy Resources for Coastal Areas
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Muhammad Shaharyar Haider; FA21-REE-005; Dr. Sobia Baig; LHR TP 9367
Climate change is one of the most signicant challenges faced by the world. As the hu man population continues to grow, so too does the demand for energy. Fossil fuels being the primary source of energy produce greenhouse gases which adversely affect our climate. The United Nations Sustainable Development Goals (UNSDGs) emphasize the urgent need for reliable, sustainable, and clean energy. Wave energy is a promising form of renewable energy that harnesses the power of ocean waves to produce electricity. While it is under utilized and research on it is lacking in comparison to other renewable energy resources, it can contribute signicantly towards achieving the UNSDGs by providing a clean source of electricity for coastal areas. This research work assesses the potential of wave energy at a selected coastal location, aiming to evaluate its feasibility as a complementary energy source alongside other renew able energy sources. Historical wave parameter data spanning 15 years is obtained for the selected location and processed. Using Python, the dataset comprising of sea state param eters in each month of the year is then tted with probability density functions. The Monte Carlo simulation is used to generate synthetic wave scenarios. These simulations incorpo rate randomness in the wave parameters to assess the variability of wave energy generation. The MATLABbased tool for simulating wave energy converters, WEC-Sim, is used to as sess the capabilities of a converter in harvesting the resource based on the monthly average of wave parameters obtained from the simulations. In addition, a hybrid wave and solar energy system is developed using MATLAB Simulink to integrate the two renewable re sources. The analysis revealed signicant seasonal variations in wave power. At the selected location, the yearly average wave power was observed to be 376 kW/m, with the winter months from October to March showing a high availability of wave power. The month of January has the highest theoretical wave power potential of 675 kW/m. The sum mer months showed signicantly lower power potential with the month of July showing the lowest potential of 140 kW/m. This suggests seasonal variability in wave energy resource as sea conditions change and impact generation. The WEC-Sim simulations revealed that the wave energy converters only capture a fraction of the wave power. The commercially available and tested wave energy converters are analyzed and the most suitable converter is selected for use at the selected location. The research concludes that wave energy is a vi able energy source with negligible greenhouse emissions, and is capable of reducing yearly energy costs by up to 18250 USD per meter of energy capture from wavefronts. These ndings underscore the importance of integrating wave energy into the broader renewable energy portfolio to achieve the UNSDGs, especially in remote coastal communities where grid infrastructure is challenging to set up and maintain