An optimal home energy management system for modulating heat pumps and photovoltaic systems

TL;DR

This paper develops an optimal control model for residential energy systems combining heat pumps, PV, batteries, and thermal storage, highlighting the importance of grid feed-in and seasonal effects for efficiency.

Contribution

It introduces a novel mixed-integer linear model for home energy management that considers seasonal dynamics and provides open-source code for reproducibility.

Findings

Batteries are crucial for self-sufficiency.

Grid feed-in enhances PV surplus utilization.

Maximizing self-sufficiency outperforms self-consumption in economic viability.

Abstract

Efficient residential sector coupling plays a key role in supporting the energy transition. In this study, we analyze the structural properties associated with the optimal control of a home energy management system and the effects of common technological configurations and objectives. We conduct this study by modeling a representative building with a modulating air-sourced heat pump, a photovoltaic (PV) system, a battery, and thermal storage systems for floor heating and hot-water supply. In addition, we allow grid feed-in by assuming fixed feed-in tariffs and consider user comfort. In our numerical analysis, we find that the battery, naturally, is the essential building block for improving self-sufficiency. However, in order to use the PV surplus efficiently grid feed-in is necessary. The commonly considered objective of maximizing self-consumption is not economically viable under the given tariff structure; however, close-to-optimal performance and significant reduction in solution times can be achieved by maximizing self-sufficiency. Based on optimal control and considering seasonal effects, the dominant order of PV distribution and the target states of charge of the storage systems can be derived. Using a rolling horizon approach, the solution time can be reduced to less than 1 min (achieving a time resolution of 1 h per year). By evaluating the value of information, we find that the common value of 24 h for the prediction and control horizons results in unintended but avoidable end-of-horizon effects. Our input data and mixed-integer linear model developed using the Julia JuMP programming language are available in an open-source manner.