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.
翻译:本文论述一个与电网相连的可再生能源发电厂的能源管理以及能力固存市场中的电池能源储存装置,目的是在小型非互连电网中促进可再生能源发电设施;核心贡献是提出一个概率预测驱动战略,以最小最大强度强优化为模型,以追索方式提出一个最强优化问题;通过本德尔双向切割平面算法和一个柱子和制约生成算法,以可移动的方式加以解决;根据量化预测分布,提出一个动态风险规避参数选择战略,以改进结果;第二贡献是利用最近开发的深层次学习模型,称为正常流动,为强大的优化问题生成可再生能源的量化预测;这一技术为确定直观概率分布提供了一般机制,只需要规定基本分布和一系列双向转换。总体而言,稳健的方法通过寻找保守和风险追求政策之间的权衡,通过名义点预测,改进了威慑性方法的结果。案例研究利用比利时利盖埃基大学(ULiege)现场监测的光伏发电。