To change an increasing trend of energy consumption, many counties have turned to solar thermal energy as a solution. Without greenhouse gas emissions, solar thermal power stations may play a vital role in the energy industry because they have a potential to produce electricity for 24 h per day. The goal of this study is to select solar thermal power stations from three regions (i.e., the United States, Spain and the other nations) throughout the world and to identify which region most efficiently produces solar thermal power energy. To measure their efficiencies, we use data envelopment analysis as a method to examine the performance of these power stations. Our empirical results show that the United States currently fields the most efficient solar thermal power stations. This study also finds that parabolic trough technology slightly outperforms the other two technologies (i.e., heliostat power tower and linear Fresnel reflector), but not at the level of statistical significance. In addition to the proposed efficiency assessment, we incorporate a new way of finding a possible existence of congestion. The phenomenon of congestion is separated into output-based and input-based occurrences. Output-based congestion implies a capacity limit (e.g., difficulties in transmission, voltage control and dispatch scheduling) in a grid network between generation and end users. Input-based congestion occurs when generators use “uncontrollable inputs” (e.g., sunlight hours). Renewable energy sources, such as solar thermal power, are indeed important for our future sustainability. However, this needs performance assessment on generation and transmission through which electricity generated by renewable energy is conveyed to end users. Such a holistic assessment, including both efficiency measurement and congestion identification, serves as a major component in evaluating and planning renewable energy generation.
Benchmarking the operational efficiency of major U.S. trucking firms using data envelopment analysis
