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• W4285132596 endingPage "1486" @default.
• W4285132596 startingPage "1474" @default.
• W4285132596 abstract "Recent market changes in power systems with high renewable energy penetration highlighted the need for complex profit maximization and hedging strategies against price volatility and generation uncertainty. This work proposes a dynamic model to represent sequential decision making in this current scenario. Unlike previously reported works, we consider uncertainties in both strategic (long-term) and operational (short-term) levels, all considered as path-dependent stochastic processes. The problem is modeled as a multistage stochastic programming model in which the correlations between inflow forecasts, renewable generation, spot and contract prices are accounted for by means of interconnected long- and short-term decision trees. Additionally, risk aversion is considered through intuitive time-consistent constraints. A case study of the Brazilian power sector is presented, in which real data was used to define the optimal trading strategy of a wind power generator, conditioned to the future evolution of market prices. The model provides the trader with useful information such as the optimal contractual amount, settlement timing, and term. Furthermore, the value of this solution is demonstrated when compared to state-of-the-art static approaches using a multistage-based certainty equivalent performance measure." @default.
• W4285132596 created "2022-07-14" @default.
• W4285132596 creator A5007865825 @default.
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• W4285132596 date "2023-03-01" @default.
• W4285132596 modified "2023-09-27" @default.
• W4285132596 title "Risk-Constrained Optimal Dynamic Trading Strategies Under Short- and Long-Term Uncertainties" @default.
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• W4285132596 doi "https://doi.org/10.1109/tpwrs.2022.3177954" @default.
• W4285132596 hasPublicationYear "2023" @default.
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