Degradation-aware data-enabled predictive control of energy hubs

TL;DR

This paper presents a data-enabled predictive control method for energy hubs that improves comfort and reduces battery degradation in buildings, outperforming traditional rule-based controllers without increasing energy consumption.

Contribution

The paper introduces a novel application of DeePC to energy hubs, demonstrating its effectiveness in enhancing comfort and significantly reducing battery degradation.

Findings

DeePC outperforms rule-based controllers in comfort satisfaction.

DeePC halves battery degradation over one year.

Energy consumption remains unchanged with DeePC.

Abstract

Mitigating the energy use in buildings, together with satisfaction of comfort requirements are the main objectives of efficient building control systems. Augmenting building energy systems with batteries can improve the energy use of a building, while posing the challenge of considering battery degradation during control operation. We demonstrate the performance of a data-enabled predictive control (DeePC) approach applied to a single multi-zone building and an energy hub comprising an electric heat pump and a battery. In a comparison with a standard rule-based controller, results demonstrate that the performance of DeePC is superior in terms of satisfaction of comfort constraints without increasing grid power consumption. Moreover, DeePC achieved two-fold decrease in battery degradation over one year, as compared to a rule-based controller.