Power boosting of conventional power plants had been the core of energy research for decades. In the aftermath of the 2016 Paris climate agreement, different recent works have focused on higher dissemination of renewable and sustainable energy sources into the power grids. This was accomplished either via renewable power systems integration into existing conventional power plants, or by expanding renewable energy systems’ penetration into the electrical grids, where many projects investigated various countries’ transition to 100% renewable electricity supply by 2050. Unfortunately, as renewable resources are inherently intermittent, many challenges are developing and must be addressed to enhance the demand-supply matching and smoothen this shift to carbon neutral power systems.
At IR4TD, we have worked on projects addressing both types of renewable expansions, tackling the problems associated with each of them. For example, in the first energy project, we developed novel cycles incorporating renewable energy integrations to enhance the performance of gas turbine power plants and reduce their greenhouse emissions and NOx pollutants [1, 2]. In this project, we developed and validated a computer code to simulate an actual power plant, then utilized linear-regression and artificial neural network multi-objective optimizations (MOO) to tune the operating parameters of the proposed novel cycles. Energy  and exergy-based  MOO were employed to reduce the cost of the necessary integrations and the total exergy destruction, at the same time maximize the thermal and electric-exergy efficiencies of the system. Additionally, we showed that the proposed integrations were feasible both technically and economically; therefore, realistic for implementation.
In our second project, we investigated different routes and scenarios for the 100% renewable energy transition [4, 5] and addressed problems related to their increased penetration into the grid. For instance, one of the most challenging issues of the wide deployment of renewable systems is the mismatch between the energy supply and demand. To solve this issue, usually large installation capacities are required in addition to energy storage systems to shave off the loads at times of low supply . However, in our recent study , we showed that renewable energy systems (RES) installation locations should not be assessed based on sites of high resources but rather on demand-supply profiles matching. This led to much higher achievable RES fraction without an energy storage system. Going further, we showed that performing a multi-objective optimization to scan for and superpose multiple sites’ supply profiles, could achieve a better matching of the demand. It also led to close to 100% RES fraction without any storage requirement and at a feasible levelized cost of electricity . This latter research is vital as Lithium-ion battery storage systems come with multi-level risks; metal depletion, environmental impact, and human-health hazards. Therefore, being able to reduce Li-ion required capacity represents a significant advancement in the transition planning to 100% RES electrical grids. Currently, we proved that an alternative greener storage thermal energy storage system can feasibly replace the previously mentioned battery usage and eliminate the associated risks, at the same time provides autonomous RES grids . We have also proven the viability of producing hydrogen, the future green energy carrier, from the RES excess resulting from the supply-demand mismatch; therefore, fully exploiting the installed capacity of the renewable energy system .
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1. Darwish Ahmad, A., et al., Power boosting of a combined cycle power plant in Jordan: An integration of hybrid inlet cooling & solar systems. Energy Conversion and Management, 2020. 214: p. 112894.
2. Abubaker, A.M., et al., Multi-objective linear-regression-based optimization of a hybrid solar-gas turbine combined cycle with absorption inlet-air cooling unit. Energy Conversion and Management, 2021. 240: p. 114266.
3. Abubaker, A.M., et al., A novel solar combined cycle integration: An exergy-based optimization using artificial neural network. Renewable Energy, 2021.
4. Al-Ghussain, L., et al., An integrated photovoltaic/wind/biomass and hybrid energy storage systems towards 100% renewable energy microgrids in university campuses. Sustainable Energy Technologies and Assessments, 2021. 46: p. 101273.
5. Al-Ghussain, L., et al., 100% Renewable Energy Grid for Rural Electrification of Remote Areas: A Case Study in Jordan. Energies, 2020. 13(18): p. 4908.
6. Manaserh, Y.M.A., et al., Assessment of integrating hybrid solar-combined cycle with thermal energy storage for shaving summer peak load and improving sustainability. Sustainable Energy Technologies and Assessments, 2021. 47: p. 101505.
7. Al-Ghussain, L., et al., A Demand-Supply Matching-Based Approach for Mapping Renewable Resources Towards 100% Renewable Grids in 2050. IEEE Access, 2021. 9: p. 58634-58651.
8. Al-Ghussain, L., A.M. Abubaker, and A. Darwish Ahmad, Superposition of renewable-energy supply from multiple sites maximizes demand-matching: Towards 100% renewable grids in 2050. Applied Energy, 2021. 284: p. 116402.
9. Al-Ghussain, L., et al., Techno-economic feasibility of thermal storage systems for the transition to 100% renewable grids. Renewable Energy, 2022. 189: p. 800-812.
10. Loiy Al-Ghussain, et al., Exploring the feasibility of green hydrogen production using excess energy from a country-scale 100% solar-wind renewable energy system. International Journal of Hydrogen Energy, 2022.