Abstract
The development of the concentrating solar power (CSP) plant as a new dispatchable resource that can participate in the electricity markets as an independent power producer and coordinate intermittent renewables has attracted much attention recently. In this work, optimal offering strategies of a price-taker CSP plant in the day-ahead (DA) and real-time (RT) electricity markets are addressed considering non-stochastic uncertainties (NSUs) from the thermal production of the CSP plant and stochastic uncertainties (SUs) from the market prices as well as the risk attitude of the CSP plant concerned. A hybrid stochastic information gap approach (SIGA) integrating the well-established information gap decision theory with the mixed conditional value at risk (CVaR) is proposed to hedge the revenue risk against NSUs and SUs in the offering problem based on the risk preference of the decision maker. A two-stage architecture is utilized for framing the DA and RT offering problems, where the first-stage co-optimizes offering strategies in the DA and RT markets, while the second-stage determines the actual RT hourly offering strategy in a rolling horizon manner. Case studies show that the SIGA can make optimal offering strategies against the non-stochastic thermal production and stochastic market prices given the risk attitude of the CSP plant. Comparisons also demonstrate that the SIGA could be an effective tool to manage coexistent NSUs and SUs.
Highlights
During the past two decades, the development of the concentrating solar power (CSP) technology as one of the prospective solutions to environment problems and fossil energy crises has been paid much attention [1], [2]
In order to investigate the impacts of uncertainty horizon and coefficient β on the expected revenue of the CSP plant, the robust and opportunistic stochastic information gap model (SIGM) are optimized for the first-stage DA strategic offering problem using various parameters since the analyses and conclusions are the same for the second-stage RT strategic offering problem
The results show that by using the opportunistic SIGM, more information can be captured and the propitious uncertainty situations can be made the best for making favorable decisions
Summary
DA λh Predicted DA market price, in $/MW-e. Predicted RT market price, in $/MW-e. Predicted thermal production of solar field, in MW-t. Thermal power input of the power block, in MW-t. Pphs vh Charging/discharging power of TES from solar field/to power block, in MW-t. 1 means power block is in operation; 0 otherwise. 1 means power block is started up/shut down; 0 otherwise. T on/T off Minimum online/offline time of power block, in h. 1 means TES is charged/ discharged; 0 otherwise. Decision vector for the first-stage offering model, x = [(xDA)T; (xRT)]T = [PD1 A, . Decision vector for the second-stage offering model from hour h to yh:t = PRhT, PRh+T1, . Um/um Vector of actual/predicted NSUs. cm,s/cm Vector of SUs at scenario s/of predicted SUs. Pessimism coefficient of the Hurwicz’s criterion. Revenue determined by predicted NSUs, in $.
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