Determining Candidate Renewable Energy Sources for G&TEP using Data Envelopment Analysis

Renewable energy sources (RESs) are becoming more and more dominant energy sources in many countries and are replacing conventional power plants. Furthermore, the electricity demand is rising due to sector coupling and moving toward carbon neutrality. Hence, a new framework for generation and transmission expansion planning (G&TEP) based on the intermittent output of RESs is introduced. Since G&TEP has a high computational burden, currently, expert-based or high-level approaches are used to determine the candidate units for each location. This paper proposes a systematic approach based on data envelopment analysis (DEA) to determine candidate RES combinations for G&TEP. The proposed method helps to benchmark different possible combinations of distinct PV panels and wind turbines locally. This results in reducing the number of integers in G&TEP and introduces a systematic approach to decision-making. To achieve this goal, first, the output of different PV panels and wind turbines based on meteorological data is calculated. Then, a local sizing problem for each combination, considering the transmission side constraints is solved. Finally, by applying DEA with parameters aligned with G&TEP, the relative efficiency of all combinations with respect to each other is determined. Based on the relative efficiencies, the candidate units for G&TEP can be selected. The results reveal that expert-based candidate selection can yield inefficiency. For instance, it is shown that using hybrid PV panels and wind turbine plants in Nordic countries, where solar irradiation is low, results in higher efficiency in contrast to what experts used to believe.