Risk-Constrained Stochastic Optimization of a Concentrating Solar Power Plant

• Published in: IEEE transactions on sustainable energy Vol. 11; no. 3; pp. 1464 - 1472
• Main Authors: Yu, Dongmin, Ebadi, Abdol Ghaffar, Jermsittiparsert, Kittisak, Jabarullah, Noor H., Vasiljeva, Marina Vladimirovna, Nojavan, Sayyad
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Concentrating solar power (CSP) plant; Constraints; Downside risk constraints (DRC); Electric power generation; Electric power systems; Electricity; Electricity pricing; Electricity supply industry; Energy sources; Energy storage; Integer programming; Irradiation; Linear programming; Optimization; Power generation; Power plants; Power systems; Risk; Risk aversion; Risk-in-profit (RIP); Solar energy; Solar heating; Solar power; Solar radiation; Solar thermal storage unit; Stochastic optimization; Stochastic processes; Stochasticity; Storage units; Thermal energy; Thermal storage; Uncertainty
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2019.2927735

A central concentrating solar power (CSP) plant is increasing in the power systems as novel technology in the solar energy sources. Also, solar thermal storage unit is combined with the CSP plant to increase flexibility and decrease the dependence on the instantaneous solar radiation. Furthermore, the CSP plant can be obtained the optimal offering strategies to submit to the electricity market in order to sell the produced power and increase the expected profit. In this paper, the uncertainty modeling of solar irradiation and electricity market price is a big challenge for a CSP plant. Therefore, the scenario-based stochastic framework is proposed for optimal scheduling of a CSP plant in the presence of uncertainties to obtain optimal offering curves in order to sell to power market. Also, the risk related to uncertainties is considered via the downside risk constraints (DRC), which leads to obtain risk-constrained stochastic optimization of a CSP plant. The proposed model is formulated as mixed-integer linear programming, which is solved via CPLEX solver under GAMS optimization software. Risk-averse strategy is introduced in comparison with risk-neutral strategy to investigate the impacts of DRC implementation, which leads to decrease the expected profit while the expected risk-in-profit reduced.