Evaluation of alternative renewable energy combinations: Multi-objective optimization approach for long-term planning

This study proposes a multi-objective mixed-integer optimization model aimed at integrating renewable energy sources into long-term production planning. The model presents a decision support framework that simultaneously addresses the objectives of reducing total system costs, lowering CO 2 emissions, and supporting social acceptability by locating power plants further away from city centers. During the solution process, a Pareto-optimal solution set was generated using the ɛ ɛ constraint method, and seven portfolio alternatives were comparatively evaluated for the Turkish electricity system for the period 2024–2050. The findings show that different trade-off profiles exist between cost, emissions, and spatial targets depending on the portfolio composition. In the Pareto solution set, total cost ranges from 1 . 3766 × 1 0 12 to 2 . 9234 × 1 0 12 $, while the total distance indicator to city centers ranges from 1 . 3925 × 1 0 8 to 6 . 5674 × 1 0 9 km. In terms of relative performance, the geothermal+biomass combination provides the highest cost reduction (1.31%) and CO 2 reduction (0.008%) compared to the reference solution; the solution with the most significant spatial target improvement is the geothermal+wind combination, with a total distance increase of 3.08%. The geothermal+wind+biomass combination increases distance (by approximately 2.28%) but shows more limited gains in terms of cost reduction. The Pareto solutions obtained provide a quantitative basis for a comparative discussion of the relative performance of hybrid portfolios, under the assumptions considered and depending on policy priorities.