Techno-economic and Environmental Multi-Objective Optimization of Concentrated Solar Power Hybridization with both Wind and Gas Turbines in Arid Region Climate

This work assesses a novel hybridization of concentrated solar power (CSP) with both wind and gas turbines for a remote arid region application. The CSP component is based on a solar power tower (SPT) which is considered as the main component of the proposed plant. The assessment is based on different ranges of Wind Turbine (WT) capacities, Natural Gas Combined Cycle (NGCC) capacitates and CSP’s key design parameters such as Thermal Energy Storage (TES) and Solar Multiple (SM). The proposed plant performance assessment has been achieved through an observation of the key performance indicators of the Levelized Cost of Energy (LCOE), Capacity Factor (CF), CO2 emissions and water consumption.
Prior to the entire proposed plant performance assessment, this work assesses the greatest two threats to the techno-economic feasibility of the main component of the plant (SPT) in arid regions, i.e. aerosols density and water scarcity. The aerosols effect on the SPT has been first assessed by an assembly of a site adapted typical aerosols year (TAY). Based on the assembled TAY, the performance of the SPT has been observed based on three different aerosols scenarios: no-aerosols, daily and yearly averaged aerosols representative values. Regarding the water consumption issue in arid regions, the work has assessed four different scenarios of SPT condenser, i.e. wet-cooled, air-cooled and two hybrid scenarios where trade-offs between the highest energy generation and water consumption have been assessed. The entire standalone SPT assessment has been carried out in the System Advisor Model (SAM) simulation tool.
Then, both the WT and the NGCC have been proposed as hybridization options with the SPT. Here, both these technologies’ ability to enhance the performance of the SPT has been assessed in detail with regards to the above mentioned performance indicators. While both the solar and wind components have been separately simulated in the SAM, the performance of the NGCC has been simulated in Aspen Plus. Then, all the proposed hybrid plant’s components have been imported into the SAM in order to simulate the performance of the entire proposed system with the assistance of an in-house developed algorithm. This has been carried out based on two different scenarios: a Carbon Capture and Storage (CCS) unit inclusion scenario in addition to the conventional scenario without a CCS. The novelty of this work emerges from the integration of the aerosols affected SPT with both WT and NGCC, i.e. a methodology that provides accurate SPT assessment and uses both renewable and fossil fuel technologies to enhance the overall techno-economic performance of the plant.
Since these performance indicators are of a conflictive nature, the optimal configuration of the proposed hybrid system is elected through a multi-objective optimization technique where the previously assessed key performance indicators are assigned as objective functions for the optimization problem. In addition to the important performance indicators of LCOE and CF, this work prioritizes water consumption and assigns it as an objective function in the optimization problem; a typically left out metric in the literature. Also, the proposed methodology assesses the CO2 for the entire lifetime of the proposed system through carrying out a full-scale life cycle assessment (LCA), then assigns the CO2 emissions as an objective function among the other performance indicators which is essential as it considers all techno-economic and environmental aspects of a proposed plant. The work exposes both the advantages and the limitations of each component inclusion and eventually proposes sets of optimal configurations elected by an elitist evolutionary algorithm; the Genetic Algorithm (GA) with regards to the objective functions. Finally, a sensitivity study is carried out by assigning different weights to the objective functions of the elected set of optimal solutions according to their importance. This is achieved with the assistance of a multi criteria decision making tool (here the TOPSIS) which also enables the ranking of the optimal solutions from best to worst.