RESEARCH PAPER
Ability of Black-Box Optimisation to Efficiently Perform Simulation Studies in Power Engineering
1
University of Applied Sciences and Arts Hannover, Faculty I – Electrical Engineering and Information Technology, , Ricklinger Stadtweg 120, 30459 , Hannover, Germany
2
Siemens Energy Gas and Power Combustion Systems, Mellinghofer Str. 55, 45473 Mülheim a.d. Ruhr, Germany
3
Leibniz University Hannover, Institute of Electric Power Systems, Electric Power Engineering Section, Appelstraße 9a, 30167 , Hannover, Germany
Submission date: 2022-11-14
Acceptance date: 2023-02-12
Online publication date: 2023-05-10
Publication date: 2023-06-01
Acta Mechanica et Automatica 2023;17(2):292-302
KEYWORDS
black-box optimisationpower plant engineeringderivative-free optimisationsimulation studies in power engineering
ABSTRACT
In this study, the potential of the so-called black-box optimisation (BBO) to increase the efficiency of simulation studies in power engineering is evaluated. Three algorithms (“Multilevel Coordinate Search” (MCS) and “Stable Noisy Optimization by Branch and Fit” (SNOBFIT) by Huyer and Neumaier and “blackbox: A Procedure for Parallel Optimization of Expensive Black-box Functions” (blackbox) by Knysh and Korkolis) are implemented in MATLAB and compared for solving two use cases: the analysis of the maximum rotational speed of a gas turbine after a load rejection and the identification of transfer function parameters by measurements. The first use case has a high computational cost, whereas the second use case is computationally cheap. For each run of the algorithms, the accuracy of the found solution and the number of simulations or function evaluations needed to determine the optimum and the overall runtime are used to identify the potential of the algorithms in comparison to currently used methods. All methods provide solutions for potential optima that are at least 99.8% accurate compared to the reference methods. The number of evaluations of the objective functions differs significantly but cannot be directly compared as only the SNOBFIT algorithm does stop when the found solution does not improve further, whereas the other algorithms use a predefined number of function evaluations. Therefore, SNOBFIT has the shortest runtime for both examples. For computationally expensive simulations, it is shown that parallelisation of the function evaluations (SNOBFIT and blackbox) and quantisation of the input variables (SNOBFIT) are essential for the algorithmic performance. For the gas turbine overspeed analysis, only SNOBFIT can compete with the reference procedure concerning the runtime. Further studies will have to investigate whether the quantisation of input variables can be applied to other algorithms and whether the BBO algorithms can outperform the reference methods for problems with a higher dimensionality.
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| eISSN: | 2300-5319 |
| ISSN: | 1898-4088 |
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