Optimization-driven uncertainty forecasting: Application to day-ahead commitment with renewable energy resources. (15th November 2022)

• Record Type: Journal Article
• Authors: Karimi, Sajad; Kwon, Soongeol
• Is Part Of: Applied energy. Volume 326(2022)
• Publication Date: 2022-11-15
• Subjects: Forecasting-first optimization-second approach -- Optimization-driven forecasting -- Day-ahead commitment -- Renewable energy resource; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.119929
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: The participation of power producers who generate electricity from renewable energy resources, e.g., wind and solar, in the electricity market has been significantly promoted by the high penetration of renewable electricity generation. In this case, the unit commitment is required to be formulated and solved to determine the optimal commitment in response to uncertainty existing in renewable electricity generation. To address this problem, a forecasting-first optimization-second approach has been practically applied in that the next-day renewable electricity generation is forecasted first using historical data, and then the unit commitment problem is solved using the forecasted renewable energy generation. However, the forecasting-first optimization-second approach has limitation that forecasting and optimization are decoupled, and thus, forecasting cannot be tuned by reflecting the effect of forecasted renewable energy generation on optimizing unit commitment. Given this context, this study proposes an optimization-driven renewable energy generation forecasting that is designed to integrate the unit commitment problem with the course of regression so that the regression can be done to minimize forecasting error while maximizing profit. Numerical experiments are conducted based on general regression models, e.g., auto-regressive and multiple linear regression models, with various parameter settings, and the results demonstrate that the proposed approach provides better renewable energy forecasting in terms of resulting unit commitment, i.e., greater profit and less penalty, without degrading forecasting accuracy significantly. Highlights: Proposed model is designed to improve forecasting-first optimization-second approach. Optimal solution obtained by forecasting is considered for parameter regression. Both forecasting error and optimality gap are enforced to be minimized simultaneously. Proposed model is applied to day-ahead renewable energy commitment problem. Forecasting by the proposed model results in more profit without degraded accuracy.