Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models

DOI 10.1016/j.apenergy.2014.07.036

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Thermal energy storage (TES) and distributed generation technologies, such as combined heat and power (CHP) or photovoltaics (PV), can be used to reduce energy costs and decrease CO2 emissions from buildings by shifting energy consumption to times with less emissions and/or lower energy prices. To determine the feasibility of investing in TES in combination with other distributed energy resources (DER), mixed integer linear programming (MILP) can be used. Such a MILP model is the well-established Distributed Energy Resources Customer Adoption Model (DER-CAM); however, it currently uses only a simplified TES model to guarantee linearity and short run-times. Loss calculations are based only on the energy contained in the storage. This paper presents a new DER-CAM TES model that allows improved tracking of losses based on ambient and storage temperatures, and compares results with the previous version. A multi-layer TES model is introduced that retains linearity and avoids creating an endogenous optimization problem. The improved model increases the accuracy of the estimated storage losses and enables use of heat pumps for low temperature storage charging. Results indicate that the previous model overestimates the attractiveness of TES investments for cases without possibility to invest in heat pumps and underestimates it for some locations when heat pumps are allowed. Despite a variation in optimal technology selection between the two models, the objective function value stays quite stable, illustrating the complexity of optimal DER sizing problems in buildings and microgrids.

Citation Formats

TY  - DATA
AB  - Thermal energy storage (TES) and distributed generation technologies, such as combined heat and power (CHP) or photovoltaics (PV), can be used to reduce energy costs and decrease CO2 emissions from buildings by shifting energy consumption to times with less emissions and/or lower energy prices. To determine the feasibility of investing in TES in combination with other distributed energy resources (DER), mixed integer linear programming (MILP) can be used. Such a MILP model is the well-established Distributed Energy Resources Customer Adoption Model (DER-CAM); however, it currently uses only a simplified TES model to guarantee linearity and short run-times. Loss calculations are based only on the energy contained in the storage. This paper presents a new DER-CAM TES model that allows improved tracking of losses based on ambient and storage temperatures, and compares results with the previous version. A multi-layer TES model is introduced that retains linearity and avoids creating an endogenous optimization problem. The improved model increases the accuracy of the estimated storage losses and enables use of heat pumps for low temperature storage charging. Results indicate that the previous model overestimates the attractiveness of TES investments for cases without possibility to invest in heat pumps and underestimates it for some locations when heat pumps are allowed. Despite a variation in optimal technology selection between the two models, the objective function value stays quite stable, illustrating the complexity of optimal DER sizing problems in buildings and microgrids.
AU  - Steen, David
A2  - Stadler, Michael
A3  - Cardoso, Gonçalo
A4  - Groissböck, Markus
A5  - DeForest, Nicholas
A6  - Marnay, Chris
DB  - C-MIX - Community Microgrid Information Exchange
DP  - Open EI | National Laboratory of the Rockies
DO  - 10.1016/j.apenergy.2014.07.036
KW  - Combined heat and power (CHP)
KW  - Thermal energy systems
KW  - TENs
KW  - District energy
KW  - Fuel cells
KW  - Financing
KW  - Business models
KW  - Case studies
KW  - Performance
KW  - Planning and design
KW  - Planning
KW  - Design
LA  - English
DA  - 2015/01/01
PY  - 2015
PB  - Lawrence Berkeley National Laboratory
T1  - Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models
UR  - https://doi.org/10.1016/j.apenergy.2014.07.036
ER  -

Export Citation to RIS

Steen, David, et al. Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models. Lawrence Berkeley National Laboratory, 1 January, 2015, C-MIX - Community Microgrid Information Exchange. https://doi.org/10.1016/j.apenergy.2014.07.036.

Steen, D., Stadler, M., Cardoso, G., Groissböck, M., DeForest, N., & Marnay, C. (2015). Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models. [Data set]. C-MIX - Community Microgrid Information Exchange. Lawrence Berkeley National Laboratory. https://doi.org/10.1016/j.apenergy.2014.07.036

Steen, David, Michael Stadler, Gonçalo Cardoso, Markus Groissböck, Nicholas DeForest, and Chris Marnay. Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models. Lawrence Berkeley National Laboratory, January, 1, 2015.  Distributed by C-MIX - Community Microgrid Information Exchange. https://doi.org/10.1016/j.apenergy.2014.07.036

@misc{CMIX_Dataset_232,
title = {Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models},
author = {Steen, David and Stadler, Michael and Cardoso, Gonçalo and Groissb\"{o}ck, Markus and DeForest, Nicholas and Marnay, Chris},
abstractNote = {Thermal energy storage (TES) and distributed generation technologies, such as combined heat and power (CHP) or photovoltaics (PV), can be used to reduce energy costs and decrease CO2 emissions from buildings by shifting energy consumption to times with less emissions and/or lower energy prices. To determine the feasibility of investing in TES in combination with other distributed energy resources (DER), mixed integer linear programming (MILP) can be used. Such a MILP model is the well-established Distributed Energy Resources Customer Adoption Model (DER-CAM); however, it currently uses only a simplified TES model to guarantee linearity and short run-times. Loss calculations are based only on the energy contained in the storage. This paper presents a new DER-CAM TES model that allows improved tracking of losses based on ambient and storage temperatures, and compares results with the previous version. A multi-layer TES model is introduced that retains linearity and avoids creating an endogenous optimization problem. The improved model increases the accuracy of the estimated storage losses and enables use of heat pumps for low temperature storage charging. Results indicate that the previous model overestimates the attractiveness of TES investments for cases without possibility to invest in heat pumps and underestimates it for some locations when heat pumps are allowed. Despite a variation in optimal technology selection between the two models, the objective function value stays quite stable, illustrating the complexity of optimal DER sizing problems in buildings and microgrids.},
url = {https://cmix.openei.org/submissions/232},
year = {2015},
howpublished = {C-MIX - Community Microgrid Information Exchange, Lawrence Berkeley National Laboratory, https://doi.org/10.1016/j.apenergy.2014.07.036},
note = {Accessed: 2026-10-11},
doi = {10.1016/j.apenergy.2014.07.036}
}

https://dx.doi.org/10.1016/j.apenergy.2014.07.036

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Data from Jan 1, 2015

Last updated Mar 30, 2026

Submitted Jun 2, 2026