Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models

Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models

TitleModeling of Thermal Storage Systems in MILP Distributed Energy Resource Models
Publication TypeJournal Article
Year of Publication2014
AuthorsDavid Steen, Michael Stadler, Gonçalo Cardoso, Markus Groissböck, Nicholas DeForest, Chris Marnay
JournalApplied Energy
Volume137
Pagination782-792
Date Published01/2015
Type of Article
Keywordsdistributed energy resources (der), Energy optimization, Investment planning, Renewables, Thermal energy storage
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.

URLhttp://www.sciencedirect.com/science/article/pii/S0306261914007181
DOI10.1016/j.apenergy.2014.07.036
LBNL Report Number

LBNL-6757E