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

TL;DR

This paper presents a probabilistic, forecast-driven robust optimization strategy for managing renewable energy and storage in capacity firming markets, enhancing risk management and decision-making.

Contribution

It introduces a novel robust optimization framework using deep learning-based quantile forecasts and dynamic risk parameters for renewable energy management in small grids.

Findings

Robust approach outperforms deterministic methods in risk management.

Deep learning models effectively generate quantile forecasts for optimization.

Dynamic risk parameters improve the balance between conservativeness and risk-seeking.

Abstract

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 Li\`ege (ULi\`ege), Belgium.