Impact of variable electricity price on heat pump operated buildings

Open Res Eur. 2022 Dec 7:2:135. doi: 10.12688/openreseurope.15268.1. eCollection 2022.

Abstract

Background: Residential buildings with heat pumps show promising possibilities for demand-side management. The operation optimization of such heating systems can lead to cost reduction and, at the same time, change electricity consumption patterns, which is especially prevalent in the case of a variable price signal. In this work, we deal with the following question: How does the volatility of a variable retail electricity price change the energy consumption of buildings with a smart energy management system? Methods: In this context, we take Austria as an example and aggregate the findings of individual households to the national stock of single-family houses. This is done by simulating and optimzing heating operation in single representative buildings. The aggregation is done based on national building information statistics. Results: This part of the Austrian building stock could shift 19.7 GWh of electricity per year through thermal inertia using a real-time electricity price from 2021. We show the future potential under the assumption of three electricity price trends for 2030, representing different decarbonisation ambition levels. The trends show that higher decarbonisation levels which lead to higher electricity prices increase the incentive to shift electric loads. Conclusions: Real time pricing turns out to be an effective incentive for buildings to shift electric loads by pre-heating the building mass. However, cost savings for individuals are relatively low which is why additional monetary incentives are needed to tap into that potential. Increased daily peak-to-peak demand from these buildings has to put into perspective to the overall grid load.

Keywords: building stock; demand-side management; heat pump; load shifting; optimization.

Grants and funding

This research was financially supported by the European Union’s Horizon 2020 research and innovation programme under the grant agreement No [893311](NEW TRENDS IN ENERGY DEMAND MODELING [NEWTRENDS]). Additionally, this paper was carried out within the framework of the IEA research cooperation on behalf of the BMK (Bundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und Technologie) under grant agreement NO [883016](IEA HPT Annex 57).