Performance enhancement of parabolic trough collector solar thermal power plants with thermal energy storage capability

This paper proposes a fuzzy non-linear programming based optimisation approach using Genetic algorithm to enhance the performance of commercial parabolic trough collector concentrated solar thermal power plants with thermal energy storage capability. The proposed optimisation approach, unlike conventional methods, considers three plant performance parameters: capacity factor, plant efficiency, and levelized cost of energy for arriving at optimal plant performance. By properly sizing the PTC plant with respect to plant efficiency, thermal energy storage capacity, capacity factor, and levelized cost of electricity, even better performance levels can be achieved. For analysis purpose and to validate the efficiency of the proposed approach, three representative locations in Saudi Arabia: Yanbu, Abha, and Dawadmi were considered-wherein the design of a 100 MW commercial parabolic trough collector solar thermal power plant is optimised. The results showed that the proposed optimization approach when applied to commercial parabolic trough collector plants with thermal energy storage capability can achieve superior plant efficiency of the order of 16.53% and 17.42% and with a capacity factor greater than 60%. The findings of the study has been validated with the help of operational parameters of the commercially operated Andasol-1, La Florida, and Aste 1A parabolic trough collector concentrated solar power plants. The results confirm the efficacy of the proposed optimization approach as it achieved superior plant efficiency. This will put decision makers in better position while choosing the ideal solar thermal technology for a particular location. This research work will help augment the competitiveness and efficacy of parabolic trough collector concentrated solar power technology and prompt policymakers to increase their share in the global renewable energy mix. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/).