Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models,
Artikel i vetenskaplig tidskrift, 2014

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.

Thermal energy storage

Investment planning

Energy optimization

Distributed energy resources



David Steen

Chalmers, Energi och miljö, Elkraftteknik

Michael Stadler

Center for Energy and Innovative Technologies

Lawrence Berkeley National Laboratory

Goçalo Cardoso

Instituto Superior Tecnico

Lawrence Berkeley National Laboratory

Markus Groissböck

Lawrence Berkeley National Laboratory

Center for Energy and Innovative Technologies

Nicholas Deforest

Lawrence Berkeley National Laboratory

Chris Marnay

Lawrence Berkeley National Laboratory

Applied Energy

0306-2619 (ISSN)

Vol. 137 782-792