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

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, such as Overseas France islands. A recently developed new deep learning model named normalizing flows is used to generate quantile forecasts of renewable generation. Normalizing flows are an increasingly active and promising area of machine learning research. They provide a general mechanism for defining expressive probability distributions, only requiring the specification of a base distribution and a series of bijective transformations. Then, a probabilistic forecast-driven strategy is designed, modeled as a min-max-min robust optimization problem with recourse, and solved using a Benders decomposition. The convergence is improved by building an initial set of cuts derived from domain knowledge. Robust optimization models the generation randomness using an uncertainty set which includes the worst-case generation scenario, and protects this scenario under the minimal increment of costs. This approach improves the results over a deterministic approach with nominal point forecasts by finding a trade-off between conservative and risk-seeking policies. Finally, a dynamic risk-averse parameters selection strategy based on the quantile forecasts distribution provides an additional gain. The case study uses the photovoltaic generation monitored on site at the University of Liège (ULiège), Belgium.