Context-Aware Stochastic Modeling of Consumer Energy Resource Aggregators in Electricity Markets

Abstract

Aggregators of consumer energy resources (CERs) like rooftop solar and battery energy storage (BES) face challenges due to their inherent uncertainties. A sensible approach is to use stochastic optimization to handle such uncertainties, which can lead to infeasible problems or loss in revenues if not chosen appropriately. This paper presents three efficient two-stage stochastic optimization methods: risk-neutral, robust, and chance-constrained, to address the impact of CER uncertainties for aggregators who participate in energy and regulation services markets in the Australian National Electricity Market. Furthermore, these methods utilize the flexibility of BES, considering precise state-of-charge dynamics and complementarity constraints, aiming for scalable performance while managing uncertainty. The problems are formed as two-stage stochastic mixed-integer linear programs, with relaxations adopted for large scenario sets. The solution approach employs scenario-based methodologies and affine recourse policies to obtain tractable reformulations. These methods are evaluated across use cases reflecting diverse operational and market settings, uncertainty characteristics, and decision-making preferences, demonstrating their ability to mitigate uncertainty, enhance profitability, and provide context-aware guidance for aggregators in choosing the most appropriate stochastic optimization method.