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Optimization and Engineering

, Volume 20, Issue 2, pp 575–603 | Cite as

Dynamic optimization of a district energy system with storage using a novel mixed-integer quadratic programming algorithm

  • Landen Blackburn
  • Aaron Young
  • Pratt Rogers
  • John Hedengren
  • Kody PowellEmail author
Research Article

Abstract

As more renewable energy is integrated into the power grid, it is increasingly important to exploit variable electricity pricing structures to minimize commercial utility costs and enable more intermittent renewables on the grid through proactive management of energy storage. Using data from a large campus district energy system, equipped with centralized chilled water plants and a thermal energy storage tank, a novel technique is proposed to optimize this system in real-time, formulated as a mixed-integer quadratic programming problem. This method, titled Quadratic Programming Hybrid with Augmented Constraints, is sufficiently fast to be computed in real-time for a district chiller system. This method is compared to both Branch and Bound and a simple logical decision algorithm in both speed and optimality. The proposed method for solving this mixed-integer quadratic programming problem proves very successful at achieving a near-optimal solution when compared to a standard Branch and Bound (BnB) algorithm. Although suboptimal, the proposed algorithm takes 99.96–99.99% less computational time than the standard BnB and computes an answer to within 29.9% of the BnB objective function. When compared to a simple logical decision algorithm, which represents an operator manually controlling the plant, the proposed method is estimated to yield 8.10–33.7% in savings on chiller energy costs. The Quadratic Programming Hybrid with Augmented Constraints algorithm shows potential for use in a real-time optimization application to exploit variable electricity pricing and significantly reduce the costs of running a chiller plant with thermal energy storage.

Keywords

Dynamic optimization Chiller Mixed-integer quadratic programming Thermal energy storage 

Notes

Acknowledgements

The authors wish to thank the USTAR Energy Research Triangle program for funding this project and the University of Utah for providing generous access to facilities and historic data.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Landen Blackburn
    • 1
  • Aaron Young
    • 2
  • Pratt Rogers
    • 2
  • John Hedengren
    • 3
  • Kody Powell
    • 1
    Email author
  1. 1.Department of Chemical EngineeringUniversity of UtahSalt Lake CityUSA
  2. 2.Department of Mining EngineeringUniversity of UtahSalt Lake CityUSA
  3. 3.Department of Chemical EngineeringBrigham Young UniversityProvoUSA

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