OptTopo: Automated set-point optimization for coupled systems using topology information

Thiele, Gregor; Johanni, Theresa; Sommer, David; Eigel, Martin; Krüger, Jörg

doi:10.1109/codit55151.2022.9803985

Cited by 2 publications

(4 citation statements)

References 15 publications

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“…The mathematical connection of the models according to their topology is realized by the formulation of constraints over the respective dimensions: If the benefit ν A of subcomponent A acts as effort α B for subcomponent B, then their values have to be (approximately) equal. These dimensions as parts of a system of EnPI and the hereon based modeling of energy-converting industrial systems is also elaborated in [1], [2] and [3].…”

Section: General Workflow To Optimize a System Based On Topologymentioning

confidence: 99%

“…Due to the format of this paper, it can not be discussed in detail. For further insight, the papers [2] and [3] are suggested. The idea of solving a complex problem by means of a decomposition in smaller subproblems comes from the algorithmic paradigms Divide and Conquer and Dynamic Programming.…”

Section: Optimization Algorithm Based On Topologymentioning

confidence: 99%

“…For the sake of brevity, we will not present details of the implementation of the testbed developed to evaluate OptTopo and compare it to other optimizers. The interested reader can obtain this information from former publications on the presented approach [2] and [3].…”

Section: Implementation and Experimentsmentioning

confidence: 99%

“…A draft of the approach was first presented in 2020 [2]. Further details about performance measures are given in [3]. secondary and a tertiary circuit are coupled, the first transition being realized by an air-air heat exchanger, the second by an air-water heat exchanger.…”

Section: Introductionmentioning

confidence: 99%

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Decomposition of Industrial Systems for Energy Efficiency Optimization with OptTopo

Thiele¹,

Johanni²,

Sommer³

et al. 2022

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The operation of industrial facilities is a broad field for optimization. Industrial plants are often a) composed of several components, b) linked using network technology, c) physically interconnected and d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization but also to a high complexity of the emerging optimization problems. The decomposition of complex systems allows the modeling of individual models which can be structured according to the physical topology. A method for energy performance indicators (EnPI) helps to formulate an optimization problem. The optimization algorithm OptTopo achieves efficient set-points by traversing a graph representation of the overall system. Keywords optimization • energy efficiency • decomposition • system of systems • OptTopo 2 Related Work Cascade control for coupled HVAC systems Komareji et al. consider a system which consists of several heat exchangers whose set temperatures are regulated by presetting the volume flows of air and water [4]. A primary, a

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Section: General Workflow To Optimize a System Based On Topologymentioning

confidence: 99%

Section: Optimization Algorithm Based On Topologymentioning

confidence: 99%

Section: Implementation and Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Decomposition of Industrial Systems for Energy Efficiency Optimization with OptTopo

Thiele¹,

Johanni²,

Sommer³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Decomposition of a Cooling Plant for Energy Efficiency Optimization Using OptTopo

et al. 2022

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The operation of industrial supply technology is a broad field for optimization. Industrial cooling plants are often (a) composed of several components, (b) linked using network technology, (c) physically interconnected, and (d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization. An example containing a cooling tower, water circulations, and chillers entails a non-linear optimization problem with five dimensions. The decomposition of such a system allows the modeling of separate subsystems which can be structured according to the physical topology. An established method for energy performance indicators (EnPI) helps to formulate an optimization problem in a coherent way. The novel optimization algorithm OptTopo strives for efficient set-points by traversing a graph representation of the overall system. The advantages are (a) the ability to combine models of several types (e.g., neural networks and polynomials) and (b) an constant runtime independent from the number of operation points requested because new optimization needs just to be performed in case of plant model changes. An experimental implementation of the algorithm is validated using a simscape simulation. For a batch of five requests, OptTopo needs 61 min while the solvers Cobyla, SDPEN, and COUENNE need 0.3 min, 1.4 min, and 3.1 min, respectively. OptTopo achieves an efficiency improvement similar to that of established solvers. This paper demonstrates the general feasibility of the concept and fortifies further improvements to reduce computing time.

show abstract

OptTopo: Automated set-point optimization for coupled systems using topology information

Cited by 2 publications

References 15 publications

Decomposition of Industrial Systems for Energy Efficiency Optimization with OptTopo

Decomposition of Industrial Systems for Energy Efficiency Optimization with OptTopo

Decomposition of a Cooling Plant for Energy Efficiency Optimization Using OptTopo

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