The usage of fossil fuel in the energy sector is the primary factor for global GHG emissions, so it is crucial to better utilize RE sources. One way to do that is to hybridize RE technologies to make up for their deficiencies while enabling a more synergistic power production. This study utilizes such an approach to hybridize the KZD-2 Geothermal Power Plant (GPP) with CST and biomass in the southwest region of Turkiye. The main motivation is to address the two main issues of GPPs—excess turbine capacities happening over the operating years and decreasing performance during hot summer months—while also increasing the flexibility of KZD-2. A topping cycle of CST–biomass is added utilizing a PTC field as the CST technology and olive residual biomass combustion as the biomass technology. The hybrid plant is simulated on TRNSYS, and the energetic data show that it is possible to generate more than 20 MW_e of additional power during sunny and clear sky conditions while also increasing the Capacity Factor (CF) from 69% to 74–76%. Moreover, the financial results show that the resulting LCOAE is 81.19 USD/MWh, and the payback period is five or nine years for using the YEKDEM incentive or the spot market prices, respectively.

Hybridizing concentrating solar power (CSP) with photovoltaics (PV) offers a pathway to combine low-cost daytime generation with dispatchable nighttime supply. This study compares two CSP–PV hybridization concepts for Midelt, Morocco, under a common tender-style design framework: (i) a co-located configuration in which PV and CSP interact at the grid level and (ii) an EH-integrated configuration in which an electric heater (EH) uses PV electricity to heat molten salt in a topping cycle. The main contribution of this study lies in the two-stage optimization workflow, in which leading candidates are selectively re-simulated at higher temporal resolution. This workflow is applied to a common design framework that compares EH-integrated and co-located concepts while considering multiple PV technologies and a broad set of interdependent sizing variables. A surrogate-assisted genetic algorithm evaluates more than 200,000 candidate designs across PV technology, inverter size, TES capacity, EH capacity, and battery energy storage system (BESS) size. The optimization minimizes the levelized cost of energy (LCOE) subject to a $200 {\mathrm{MW}}_{\mathrm{el}}$ export limit, a CAPEX ceiling, and a nighttime-delivery constraint of ${\mathrm{CF}}_{\mathrm{night}}\ge 39\%$. Candidate designs are screened at 600 s and selectively re-simulated at 120 s, showing that temporal refinement affects not only KPI values but also candidate feasibility, final ranking, and preferred component sizing. The lowest-LCOE solution is the EH-integrated bifacial configuration, achieving 64.5% overall capacity factor, ${\mathrm{CF}}_{\mathrm{night}}=39.1\%$, less than 0.1% curtailment, a specific CAPEX of $4698/kW, and an LCOE of 7.29 ¢/kWh. Pareto-front and parameter-trend analyses further show that stricter nighttime-delivery targets shift the dominant sizing levers and define a neighborhood of near-optimal solutions rather than a single fixed design.