A Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market

被引:8
作者
Dumas, Jonathan [1 ,2 ,3 ]
Cointe, Colin [1 ,2 ,3 ]
Wehenkel, Antoine [1 ,2 ,3 ]
Sutera, Antonio [1 ,2 ,3 ]
Fettweis, Xavier [1 ,2 ,3 ]
Cornelusse, Bertrand [1 ,2 ,3 ]
机构
[1] Univ Liege, Dept Comp Sci, B-4000 Liege, Belgium
[2] Univ Liege, Dept Elect Engn, B-4000 Liege, Belgium
[3] Univ Liege, Dept Geog, B-4000 Liege, Belgium
关键词
Renewable energy sources; Uncertainty; Optimization; Real-time systems; Probabilistic logic; Predictive models; Heuristic algorithms; Capacity firming; electricity market; robust optimization; Benders decomposition; renewable generation uncertainty; deep learning; normalizing flows; ROBUST OPTIMIZATION; UNIT COMMITMENT;
D O I
10.1109/TSTE.2021.3117594
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper addresses the energy management of a grid-connected renewable generation plant coupled with a battery energy storage device in the capacity firming market, designed to promote renewable power generation facilities in small non-interconnected grids. The core contribution is to propose a probabilistic forecast-driven strategy, modeled as a min-max-min robust optimization problem with recourse. It is solved using a Benders-dual cutting plane algorithm and a column and constraints generation algorithm in a tractable manner. A dynamic risk-averse parameters selection strategy based on the quantile forecasts distribution is proposed to improve the results. A secondary contribution is to use a recently developed deep learning model known as normalizing flows to generate quantile forecasts of renewable generation for the robust optimization problem. This technique provides a general mechanism for defining expressive probability distributions, only requiring the specification of a base distribution and a series of bijective transformations. Overall, the robust approach improves the results over a deterministic approach with nominal point forecasts by finding a trade-off between conservative and risk-seeking policies. The case study uses the photovoltaic generation monitored on-site at the University of Liege (ULiege), Belgium.
引用
收藏
页码:1234 / 1243
页数:10
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