Thermal energy storage to minimize cost and improve efficiency of a polygeneration district energy system in a real-time electricity market

Powell, Kody M.; Kim, Jong Suk; Cole, Wesley J.; Kapoor, Kriti; Mojica, Jose L.; Hedengren, John D.; Edgar, Thomas F.

doi:10.1016/j.energy.2016.07.009

Title: Thermal energy storage to minimize cost and improve efficiency of a polygeneration district energy system in a real-time electricity market

Journal Article · Sat Jul 16 00:00:00 EDT 2016 · Energy (Oxford)

DOI:https://doi.org/10.1016/j.energy.2016.07.009· OSTI ID:1469807

^[1]; Kim, Jong Suk ^[2]; Cole, Wesley J. ^[3]; Kapoor, Kriti ^[4]; Mojica, Jose L. ^[5]; Hedengren, John D. ^[6]; Edgar, Thomas F. ^[7]

Univ. of Utah, Salt Lake City, UT (United States). Dept. of Chemical Engineering
Idaho National Lab. (INL), Idaho Falls, ID (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States)
NXP Semiconductors, Austin, TX (United States)
ExxonMobil, Houston, TX (United States)
Brigham Young Univ., Provo, UT (United States). Dept. of Chemical Engineering
Univ. of Texas, Austin, TX (United States)

District energy systems can produce low-cost utilities for large energy networks, but can also be a resource for the electric grid by their ability to ramp production or to store thermal energy by responding to real-time market signals. In this work, dynamic optimization exploits the flexibility of thermal energy storage by determining optimal times to store and extract excess energy. This concept is applied to a polygeneration distributed energy system with combined heat and power, district heating, district cooling, and chilled water thermal energy storage. The system is a university campus responsible for meeting the energy needs of tens of thousands of people. The objective for the dynamic optimization problem is to minimize cost over a 24-h period while meeting multiple loads in real time. The paper presents a novel algorithm to solve this dynamic optimization problem with energy storage by decomposing the problem into multiple static mixed-integer nonlinear programming (MINLP) problems. Another innovative feature of this work is the study of a large, complex energy network which includes the interrelations of a wide variety of energy technologies. Results indicate that a cost savings of 16.5% is realized when the system can participate in the wholesale electricity market.

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Research Organization:: Idaho National Lab. (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE; Univ. of Texas Office of Sustainability

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 1469807

Report Number(s):: INL/JOU-16-39854-Rev000

Journal Information:: Energy (Oxford), Vol. 113, Issue C; ISSN 0360-5442

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 60 works

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