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**Harvard**

Steen, D., Stadler, M., Cardoso, G., Groissböck, M., Deforest, N. och Marnay, C. (2014) *Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models,*.

** BibTeX **

@article{

Steen2014,

author={Steen, David and Stadler, Michael and Cardoso, Goçalo and Groissböck, Markus and Deforest, Nicholas and Marnay, Chris},

title={Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models,},

journal={Applied Energy},

issn={0306-2619},

volume={137},

pages={782-792},

abstract={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.},

year={2014},

keywords={Distributed energy resources, Investment planning, Renewables, Energy optimization, Thermal energy storage},

}

** RefWorks **

RT Journal Article

SR Electronic

ID 206293

A1 Steen, David

A1 Stadler, Michael

A1 Cardoso, Goçalo

A1 Groissböck, Markus

A1 Deforest, Nicholas

A1 Marnay, Chris

T1 Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models,

YR 2014

JF Applied Energy

SN 0306-2619

VO 137

SP 782

OP 792

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.

LA eng

DO 10.1016/j.apenergy.2014.07.036

LK http://dx.doi.org/10.1016/j.apenergy.2014.07.036

OL 30