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Optimization and Engineering

, Volume 15, Issue 1, pp 167–197 | Cite as

A mixed-integer nonlinear program for the optimal design and dispatch of distributed generation systems

  • Kristopher A. Pruitt
  • Sven Leyffer
  • Alexandra M. Newman
  • Robert J. Braun
Article

Abstract

Maturing distributed generation (DG) technologies have promoted interest in alternative sources of energy for commercial building applications due to their potential to supply on-site heat and power at a lower cost and emissions rate compared to centralized generation. Accordingly, we present an optimization model that determines the mix, capacity, and operational schedule of DG technologies that minimize economic and environmental costs subject to the heat and power demands of a building and to the performance characteristics of the technologies. The technologies available to design the system include lead-acid batteries, photovoltaic cells, solid oxide fuel cells, heat exchangers, and a hot water storage tank. Modeling the acquisition and operation of discrete technologies requires integer restrictions, and modeling the variable electric efficiency of the fuel cells and the variable temperature of the tank water introduces nonlinear equality constraints. Thus, our optimization model is a nonconvex, mixed-integer nonlinear programming (MINLP) problem. Given the difficulties associated with solving large, nonconvex MINLPs to global optimality, we present convex underestimation and linearization techniques to bound and solve the problem. The solutions provided by our techniques are close to those provided by existing MINLP solvers for small problem instances. However, our methodology offers the possibility to solve large problem instances that exceed the capacity of existing solvers and that are critical to the real-world application of the model.

Keywords

Global optimization Mixed-integer nonlinear programming Distributed generation Combined heat and power 

Notes

Acknowledgements

The authors would like to thank the National Science Foundation for partial support of this research effort under award #CNS-0931748. We are also grateful to Andrew Schmidt, Department of Mechanical Engineering, Colorado School of Mines, for providing the building load data from EnergyPlus.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Kristopher A. Pruitt
    • 1
  • Sven Leyffer
    • 2
  • Alexandra M. Newman
    • 1
  • Robert J. Braun
    • 3
  1. 1.Division of Economics and BusinessColorado School of MinesGoldenUSA
  2. 2.Mathematics and Computer Science DivisionArgonne National LaboratoryArgonneUSA
  3. 3.Department of Mechanical EngineeringColorado School of MinesGoldenUSA

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