LAPSE:2023.23464
Published Article

LAPSE:2023.23464
Control of Heat Pumps with CO2 Emission Intensity Forecasts
March 27, 2023
Abstract
An optimized heat pump control for building heating was developed for minimizing CO 2 emissions from related electrical power generation. The control is using weather and CO 2 emission forecasts as inputs to a Model Predictive Control (MPC)—a multivariate control algorithm using a dynamic process model, constraints and a cost function to be minimized. In a simulation study, the control was applied using weather and power grid conditions during a full-year period in 2017−2018 for the power bidding zone DK2 (East, Denmark). Two scenarios were studied; one with a family house and one with an office building. The buildings were dimensioned based on standards and building codes/regulations. The main results are measured as the CO 2 emission savings relative to a classical thermostatic control. Note that this only measures the gain achieved using the MPC control, that is, the energy flexibility, not the absolute savings. The results show that around 16% of savings could have been achieved during the period in well-insulated new buildings with floor heating. Further, a sensitivity analysis was carried out to evaluate the effect of various building properties, for example, level of insulation and thermal capacity. Danish building codes from 1977 and forward were used as benchmarks for insulation levels. It was shown that both insulation and thermal mass influence the achievable flexibility savings, especially for floor heating. Buildings that comply with building codes later than 1979 could provide flexibility emission savings of around 10%, while buildings that comply with earlier codes provided savings in the range of 0−5% depending on the heating system and thermal mass.
An optimized heat pump control for building heating was developed for minimizing CO 2 emissions from related electrical power generation. The control is using weather and CO 2 emission forecasts as inputs to a Model Predictive Control (MPC)—a multivariate control algorithm using a dynamic process model, constraints and a cost function to be minimized. In a simulation study, the control was applied using weather and power grid conditions during a full-year period in 2017−2018 for the power bidding zone DK2 (East, Denmark). Two scenarios were studied; one with a family house and one with an office building. The buildings were dimensioned based on standards and building codes/regulations. The main results are measured as the CO 2 emission savings relative to a classical thermostatic control. Note that this only measures the gain achieved using the MPC control, that is, the energy flexibility, not the absolute savings. The results show that around 16% of savings could have been achieved during the period in well-insulated new buildings with floor heating. Further, a sensitivity analysis was carried out to evaluate the effect of various building properties, for example, level of insulation and thermal capacity. Danish building codes from 1977 and forward were used as benchmarks for insulation levels. It was shown that both insulation and thermal mass influence the achievable flexibility savings, especially for floor heating. Buildings that comply with building codes later than 1979 could provide flexibility emission savings of around 10%, while buildings that comply with earlier codes provided savings in the range of 0−5% depending on the heating system and thermal mass.
Record ID
Keywords
buildings, CO2-emissions, dynamic systems, electrical grid power, heat pumps, model predictive control (MPC)
Subject
Suggested Citation
Leerbeck K, Bacher P, Junker RG, Tveit A, Corradi O, Madsen H, Ebrahimy R. Control of Heat Pumps with CO2 Emission Intensity Forecasts. (2023). LAPSE:2023.23464
Author Affiliations
Leerbeck K: Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark [ORCID]
Bacher P: Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark [ORCID]
Junker RG: Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark [ORCID]
Tveit A: Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark
Corradi O: Tomorrow (Tmrow IVS), Njalsgade, 2300 Copenhagen, Denmark
Madsen H: Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark; Faculty of Architecture and Design, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway [ORCID]
Ebrahimy R: Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark
Bacher P: Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark [ORCID]
Junker RG: Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark [ORCID]
Tveit A: Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark
Corradi O: Tomorrow (Tmrow IVS), Njalsgade, 2300 Copenhagen, Denmark
Madsen H: Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark; Faculty of Architecture and Design, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway [ORCID]
Ebrahimy R: Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark
Journal Name
Energies
Volume
13
Issue
11
Article Number
E2851
Year
2020
Publication Date
2020-06-03
ISSN
1996-1073
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Original Submission
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PII: en13112851, Publication Type: Journal Article
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LAPSE:2023.23464
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https://doi.org/10.3390/en13112851
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Mar 27, 2023
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