A Mixed CVaR-Based Stochastic Information Gap Approach for Building Optimal Offering Strategies of a CSP Plant in Electricity Markets

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.

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On Optimization of Copula-Based Extended Tail Value-at-Risk and its Application in Energy Risk

“…TVaR, with VaR, is probably the most frequently used risk measure in managing risk in the energy sector. Some of the authors who developed optimization using TVaR are [8], [9], and [10]. Shen et al [8] considered risk management for TVaR in the objective function when they developed a novel energy management strategy for a microgrid under an electricity market environment.…”

“…Paul et al [9] proposed a robust TVaR optimization approach for a day ahead home energy management system (HEMS) to reduce the effect of the risk of real-time exposure to energy price and solar power generation uncertainties. Zhao et al [10] proposed a hybrid stochastic information gap approach (SIGA) integrating the well-established information gap decision theory with the mixed TVaR to hedge the revenue risk against nonstochastic uncertainties (NSUs) and stochastic uncertainties (SUs) in the offering problem based on the risk preference of the decision maker. In contrast to TVaR, throughout our investigation, no authors have utilized MTVaR and CTVaR to optimize objective functions in the energy sector.…”