Stochastic hybrid automata model battery power demands with uncertainty
Stochastic Hybrid Automata for Power Profile Modeling in Energy Systems
Formal Languages and Automata Theory
Summary
Power systems often have uncertain demand and supply, making it hard to know if a battery can meet energy needs. The authors use a special kind of model called stochastic hybrid automata to describe both the continuous changes in power and random events affecting usage. They create formal rules to build these models for different battery power profiles and storage types, helping to calculate the likelihood that a battery can meet those demands. They also explore how different model choices affect complexity and show how their approach works using specific software tools.
What this means in practice
- •For energy system designers: Model and verify battery ability to meet uncertain power demands using stochastic hybrid automata.
- •For energy management software developers: Incorporate formal stochastic models into tools for assessing battery storage feasibility under variable demand.
Authors
Lisa Willemsen, Anne Remke, Johann L. Hurink
Abstract
Power profiles are widely used to describe power demand and production in energy systems. Yet, real-world usage often involves uncertainty, making it challenging to determine, e.g., whether a battery can reliably meet a given profile. Formal modeling enables the computation of probabilities that a battery will satisfy such demands. Given the combination of discrete and continuous dynamics, along with uncertain usage patterns, stochastic hybrid automata (SHA) offer a suitable framework for such modeling and analysis. In this paper, we formalize two orthogonal approaches to specify the (uncertain) power profiles that a specific battery can serve as well as a storage model. For both types of power profiles, we provide formal construction rules for SHA reflecting the behavior of the storage model given a specific power profile. We analyze how design choices, such as the power profile, storage model, and representation of uncertainty, affect the complexity of the resulting SHA. Finally, we demonstrate how these models quantify feasibility using the tools Modest and realyst, and assess how the analysis scales with profile size and the number of stochastic components.