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An Adaptive Discretization MINLP Algorithm for Optimal Synthesis of Decentralized Energy Supply Systems

Sebastian Goderbauer(goderbauer***at***math2.rwth-aachen.de)
Björn Bahl(bjoern.bahl***at***ltt.rwth-aachen.de)
Philip Voll(philip.voll***at***gmail.com)
Marco Lübbecke(luebbecke***at***or.rwth-aachen.de)
André Bardow(andre.bardow***at***ltt.rwth-aachen.de)
Arie M.C.A. Koster(koster***at***math2.rwth-aachen.de)

Abstract: Decentralized energy supply systems (DESS) are highly integrated and complex systems designed to meet time-varying energy demands, e.g., heating, cooling, and electricity. The synthesis problem of DESS addresses combining various types of energy conversion units, choosing their sizing and operations to maximize an objective function, e.g., the net present value. In practice, investment costs and part-load performances are nonlinear. Thus, this optimization problem can be modeled as a nonconvex mixed-integer nonlinear programming (MINLP) problem. We present an adaptive discretization algorithm to solve such synthesis problems containing an iterative interaction between mixed-integer linear programs (MIPs) and nonlinear programs (NLPs). The proposed algorithm outperformes state-of-the-art MINLP solvers as well as linearization approaches with regard to solution quality and computation times on a test set obtained from real industrial data, which we made available online.

Keywords: Mixed-Integer Nonlinear Programming, Decentralized Energy Supply System, Synthesis, Structural Optimization, Adaptive Discretization

Category 1: Applications -- Science and Engineering

Category 2: Integer Programming ((Mixed) Integer Nonlinear Programming )


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Entry Submitted: 06/22/2016
Entry Accepted: 06/22/2016
Entry Last Modified: 06/22/2016

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