Loading [a11y]/accessibility-menu.js
Making Money in Energy Markets: Probabilistic Forecasting and Stochastic Programming Paradigms | IEEE Conference Publication | IEEE Xplore

Making Money in Energy Markets: Probabilistic Forecasting and Stochastic Programming Paradigms


Abstract:

Undoubtedly, evolving wholesale electricity markets continue to provide new revenue opportunities for diverse generation, energy storage, and flexible demand technologies...Show More

Abstract:

Undoubtedly, evolving wholesale electricity markets continue to provide new revenue opportunities for diverse generation, energy storage, and flexible demand technologies. In this paper, we quantitatively explore how price uncertainty impacts optimal market participation strategies and resulting revenues. Specifically, we benchmark 2-stage stochastic programming formulations for self-schedule and bidding market participation modes in a receding horizon model predictive control framework. To generate probabilistic price forecasts, we propose an autoregressive Gaussian process regression model and compare three sampling strategies. As an illustrative example, we study a price-taker generation company with six unique generation units using historical price data from CAISO (California market). We show that self-schedule is sensitive to the error in the forecast mean, whereas bidding requires price forecasts that cover extreme events (e.g., tails of the distribution). We benchmark realized market revenue against optimal bidding with perfect information and find static bid curve, time-varying bid curve, and self-schedule modes recovery 95.29%, 94.85%, and 84.87% of perfect information revenue, respectively.
Date of Conference: 01-03 July 2020
Date Added to IEEE Xplore: 27 July 2020
ISBN Information:

ISSN Information:

Conference Location: Denver, CO, USA

Contact IEEE to Subscribe

References

References is not available for this document.