Stochastic Model Predictive Control based on Mixed Random Variables for Economic Energy Management

TL;DR

This paper introduces a stochastic model predictive control approach using mixed random variables to optimize battery scheduling for energy cost reduction in residential settings, accounting for uncertainties.

Contribution

It presents a novel interval-based optimization method leveraging mixed random variables to explicitly handle uncertainties in energy management.

Findings

Achieves lower electricity costs than deterministic and probabilistic benchmarks.

Effectively manages uncertainties in consumption and generation.

Demonstrated on real-world data from 15 residential buildings over five months.

Abstract

Optimal scheduling of batteries has significant potential to reduce electricity costs and to enhance grid resilience. However, effective battery scheduling must account for both physical constraints as well as uncertainties in consumption and generation of renewable energy sources. Instead of optimizing fixed battery power setpoints, we propose an approach that optimizes battery power intervals, allowing the optimization to explicitly account for uncertain consumption and generation as well as how the battery system should respond to them within its physical limits. Our method is based on mixed random variables, represented as mixtures of discrete and continuous probability distributions. Building on this representation, we develop an analytical stochastic formulation for minimizing electricity costs in a residential setting with load, photovoltaics, and battery storage. We demonstrate its effectiveness across real-world data from 15 residential buildings over five consecutive months. Compared with deterministic and probabilistic benchmark controllers, the proposed interval-based optimization achieves the lowest costs. These results show that mixed random variables are a practical and promising tool for decision-making under uncertainty.