(393c) Uncertainty Quantification and Stochastic Programming Strategies for Energy Market Participation
A standard technique for economic assessment is to formulate market participation as an optimization problem and calculate the maximum possible revenue in hindsight, i.e., with perfect information . Here physical limitations and market rules are modeled as constraints. Our recent analysis of California market data revealed over 60% of revenue opportunities come from the fastest market layers (i.e., real-time market, ancillary services) for batteries, concentrated solar thermal power, and industrial systems [3-5]. The finding that energy arbitrage in the day-ahead market is the least profitable participation strategy is consistent with other studies [2,6,7].
An important limitation of these studies is the assumption of perfect information, while in reality, market participants must bid under price and weather uncertainties [8,9]. We develop an integrated approach for uncertainty quantification and stochastic optimization of energy systems biding into complex energy markets. First, Gaussian Process (GP) statistical models  are trained using historical data and used to generate probabilistic forecasts for market prices. These forecasts are input parameters for a two-stage stochastic model predictive control framework to compute optimal market participation policies. Preliminary benchmarks indicate the proposed approach, implemented in a receding horizon framework, is capable of capturing over 90% of theoretical market revenues, compared to only 75% with simple backcasting. We also explore different strategies to integrate probabilistic forecasts including sampling and Smolyak quadrature rules (sparse grids) [11-13]. Finally, we discuss how the proposed framework enables comparison of different energy storage technologies based on the ability to cope with exogenous (market, weather, etc.) uncertainty.
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