A computer‐aided method for optimizing solar power tower – the case of Syrian desert

Abstract Solar Power Tower (SPT) technology is a promising renewable energy source. Prior studies on SPT design generally treat plant capacity as a fixed parameter within the broader set of technical variables defining the solar field and power block. These technical variables are typically optimized only against a single financial metric – most commonly the Levelized Cost of Energy (LCOE). Consequently, prior optimization frameworks do not integrate technical and financial domains in a coupled manner: plant capacity remains fixed within the technical group, and multiple financial metrics are not jointly evaluated within a unified optimization exercise. Using the Syrian Desert as a case, this study develops a simplified, site‐specific estimator of solar‐field optical efficiency based on SolarPILOT‐derived correlations between receiver thermal load and field performance. This correlation enables generation of an 8760‐hour optical‐efficiency profile without repeated heliostat‐field modeling and is integrated into a MATLAB techno‐economic model that sizes all SPT components and computes annual energy output, renewable penetration, and financial metrics including LCOE, TIC, NPV, and PBP. Energy and cost differences under 5% demonstrate strong agreement between MATLAB model and SAM validation. Genetic Algorithm is then applied to optimize plant capacity, solar multiple, and storage hours, yielding renewable penetration of 90.00%–93.67% for capacities between 98 and 120 MW. Using AHP, an optimal capacity of 194 MW is selected, delivering 71.90% renewable generation, a 9.5‐year payback period, an NPV of $976 million, an LCOE of 8.91 ¢/kWh, and a TIC of 7380.30 $/kW. A key limitation is the simplified solar‐field efficiency model, which cannot fully represent real heliostat‐field geometric, atmospheric, and operational complexities, so the optical efficiencies are approximate. Applying the framework elsewhere would require re‐parameterising this model.