Bretschneider, Peter

Flemming, Sebastian

Naumann, Steffi

Winkelkotte, Martin

English

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TU Ilmenau | Universitätsbibliothek | ilmedia, 2023 https://www.tu-ilmenau.de/ilmedia Winkelkotte, Martin; Naumann, Steffi; Flemming, Sebastian; Bretschneider, Peter: Machine learning based model fitting concept for energy system components in energy management Original published in: Proceedings 33. Workshop Computational Intelligence : Berlin, 23.-24. November 2023 / Schulte, Horst; Hoffmann, Frank; Mikut, Ralf (Hrsg.). - Karlsruhe: KIT Scientific Publishing, 2023, S. 53 – 59. ISBN: 978-3-7315-1324-7 DOI: 10.5445/KSP/1000162754 Conference: 33. Workshop Computational Intelligence (Berlin, 23.-24.11.2023)Original published: 2023-10-30 [Visited: 2023-12-05 © by the Author(s) DOI: 10.22032/dbt.59184 URN: urn:nbn:de:gbv:ilm1-2023200278 Machine learning based model fitting conceptfor energy system components in energymanagementMartin Winkelkotte1, Steffi Naumann2, Sebastian Flemming2,Peter Bretschneider11Technische Universität IlmenauGustav-Kirchhoff-Str. 5, 98693 IlmenauE-Mail: martin.winkelkotte@tu-ilmenau.deE-Mail: peter.bretschneider@tu-ilmenau.de2Fraunhofer IOSB-ASTAm Vogelherd 90, 98693 IlmenauE-Mail: steffi.naumann@iosb-ast.fraunhofer.deE-Mail: sebastian.flemming@iosb-ast.fraunhofer.de1 IntroductionThe heterogenous nature of energy systems is a big challenge for the large-scale implementation of energy management systems (EMS). The differentenergy systems can be small energy communities, factories, or large-scalesupply areas. A consistent challenge throughout the various energy systemforms is the accurate modelling of the generation and storage units as well asthe prediction of consumer behaviour, all what is needed to optimize whichenergy source to use. While significant work has been put into the automatedprediction of consumption timeseries, the modelling of generation and storageunits is still mostly done “by hand” and is reliant on external informationsources like datasheets. Models created using this data basis are extremelyvulnerable to discrepancies between the external information they are basedon and the actual unit properties. Such inaccuracies often go unnoticed in theinitial model generation, until they cause large errors in the optimization whichProc. 33. Workshop Computational Intelligence, Berlin, 23.-24.11.2023 53then require time consuming analysis and finetuning to correct. In this paperan initial approach to automate the modelling of generation and storage units ispresented. After that the approach is tested on its sensitivity to different trainingdata sets since a dependency on high quality training data would severely limitits transferability.2 ConceptThe idea of the approach is to create a library of simplified generic modelsfor the most common types of generation and storage technologies. Thesemodels can then be fitted to match the properties of the target units using keyparameters whose values are identified by an artificial neural network (ANN).For this purpose, the ANN is trained on timeseries data that has been createdusing numerus simulated units with different properties of one unit type. Thedefining properties of the simulated units have been distributed to reflect theentire feasible parameter range. The trained ANN is then used on measureddata of the target unit to identify the relevant parameter values to modify thegeneric model. The resulting fitted model should more accurately reflect theproperties and performance limits of the target than a model based on genericdatasheet information. Additionally, it would be easy to implement a periodicrefitting of the model on the most recently measured data. This would allow themodel to reflect changes due to aging and could even be used as an indicatorfor maintenance scheduling.3 Generation of training dataThe performance of ANN is dependent on the relation between the data usedfor its training and the target. Since the goal is the recognition of the physicalparameters describing the behaviour of the target unit, the input timeseries mustbe related to / influenced by, said parameters. Unfortunately, high resolutiontimeseries measurements of the target unit are rarely available which forcesengineers to install temporary measurement equipment which then can onlyprovide data for a short time range.54 Proc. 33. Workshop Computational Intelligence, Berlin, 23.-24.11.2023For this paper a vanadium redox flow battery storage, build as part of theresearch project: "Smart Region Pellworm" [1], was used as target unit. Firsta model for this unit type was chosen from the available literature [2]. Thismodel was further simplified until its behaviour was defined by 6 variableparameters. In the next step plausible parameter ranges were set and discretizedinto steps, resulting in 125 000 possible parameter combinations. These com-binations provide the labels for the training data. As input variables, timeseriesfor the voltage (U), current (I) and state of charge (SoC) were chosen.In the next step an initial SoC was selected for each model configuration afterwhich they were run through specific load profiles to generate the requiredtimeseries for I, U and SoC. Here 3 different sets of load profiles were used totest the ANN’s dependency on data that is highly correlated to the target unit.In set 1, each model configuration was given one of 4 historic measured loadprofiles and related initial SoC’s of the target unit to create the training data. Inset 2, 10 historic measured load profiles and SoC’s were used and in the last,set 3, each model configuration was given a randomly generated load profileand starting SoC. This resulted in 3 separate training data sets.4 Parameter recognitionThe ANN used in this work is a basic MLP [3] with 3 hidden layers. All layersused the tanh activation function except the output layer for which a linear ac-tivation function was used. The width of the layers was arranged in descendingsize with the intention of compressing the information contained in the inputtimeseries, with 3 times 288 timesteps, down to the 6 target parameters. Thetimeseries and labels were normalized between -1 to 1 and 0 to 1 respectively,before being used in the training. During training overfitting was limited by anearly stopping function with a patience of 10 steps.The resulting trained ANN were first tested solely on the historic data belong-ing to the load profiles used in the creation of their respective training data. Itbecame apparent that the parameter values identified by the two ANN trainedon historic load profiles of the target united, differed significantly form the val-ues of the ANN trained on randomized load profiles. Especially the parameterstotal vanadium concentration and maximum flow speed of the electrolyte areProc. 33. Workshop Computational Intelligence, Berlin, 23.-24.11.2023 55200 150 100 50 0 50 100 150 200power [kW]020406080100state of charge [%]010203040506070(a) Fitted by ANN trained on data based on4 historic load profiles200 150 100 50 0 50 100 150 200power [kW]020406080100state of charge [%]010203040506070(b) Fitted by ANN trained on data based on10 historic load profiles200 150 100 50 0 50 100 150 200power [kW]020406080100state of charge [%]020406080(c) Fitted by ANN trained on data based onrandom load profiles200 150 100 50 0 50 100 150 200power [kW]020406080100state of charge [%]020406080(d) Generated based on detailed measurementsof the target BatteryFigure 1: Heatmaps depicting the efficiency of the battery system models in realation to the SoCand charging (positiv) or discharging (negative) powerlower than expected in the historic data trained ANN. These parameters, incombination with the SoC, are responsible for determining how much usablevanadium ions can be pumped into the cell, which in turn defines the maximumcharge and discharge power of the battery.To allow for a better comparison between the parameter sets a second test wasperformed during which the ANN where each given historic data of 50 daysand the resulting parameters where averaged and put into individual sets. Toevaluate the accuracy of the identified parameters sets they were then used tofit 3 generic models to the target battery. The fitted models were then used tosimulate a test scenario and from the resulting data the efficiency across thefull range of possible operation situations was calculated.The results are presented in the heatmaps of Figure 1 which show the efficiencyacross the full range of possible SoC’s and all charge and discharge speeds.56 Proc. 33. Workshop Computational Intelligence, Berlin, 23.-24.11.2023The heatmap of Figure 1d was generated during experiments with the targetunit and represent the real battery properties.The heatmap in Figure 1a was generated by the model fitted with dataset 1 andshows a very early discharge power restriction, these restrictions can be seenin the heatmaps as 0 % efficiency zones. According to this model it would beimpossible to discharge the battery further than roughly 50 %. Additionally, itshows an artifact near a SoC of 10 to 0 % which would indicate that a furtherdischarge would suddenly become possible again. The charging side howeveris much better represented, while the charging power restriction near a full SoCis significantly overestimated, the remaining field is much closer to the realitythan the discharge field.The heatmap in Figure 1b, generated using data set 2, looks very similar toFigure 1a even though its training data was based on a larger set of load profiles.The most notable improvements are the removal of the artifact near the bottomof the discharge field and a slight widening of the possible discharge zone onthe top. The charging side however shows no improvement and instead exhibitsa slight expansion of the charging power restriction in the top right corner.Figure 1c shows the results of the model fitted with data set 3, generatedwith the ANN using randomized training data. It produces by far the mostaccurate representation of the target properties. The largest improvement canbe seen on the discharge side, that now, while still overestimating the dischargepower restriction, shows a much more plausible range of usage. A similarimprovement is visible on the charging side where the restricted zone has beensignificantly reduced and now closely resembles the restriction shown by thetarget unit. Besides the estimated power restrictions another modelling error isvisualised in the heatmaps.All 3 models exhibit a clear overestimation of the battery efficiency. Thisoverestimation is more severe during discharge processes. This error wouldcause the model SoC to drift away from the actual SoC over time and thusrequire frequent refreshing with a measured value.Proc. 33. Workshop Computational Intelligence, Berlin, 23.-24.11.2023 575 ConclusionThe paper proposed a data driven approach to fit generic models to the targetgeneration or storage units. The approach was tested on the example of avanadium redox flow battery system and further evaluated on its sensitivityregarding the required training data. The results show the best performance canbe achieved using randomized load profiles and initial SoC’s for the trainingdata generation instead of historic load profiles of the target unit. This has somepositive implications for the transferability of the approach on different targetunits of the same type. While the generation of training data using historicload profiles would have required a new set of training data and retrainingof the ANN for each target unit, the randomized approach could allow theusage of one pretrained ANN for all target units of the same type, no retrainingnecessary. This would also justify putting more resources into the training ofthe ANN since it would be possible to not only create a library with genericmodels for each unit type but also create a library of pretrained, ready to use,ANN to fit them to the desired target. Thus significantly reducing the expertiserequired from users. Further research is required to determine if a single ANNcan really fit any unit of a single type regardless of its scale and to test if otherANN architectures could offer higher accuracy. There is also the possibilitythat completely random load profiles might not be the best approach to trainingdata generation since some of the load profiles might end up being nonsensicaland their resulting data devoid of useful information for training.References[1] Forschungsprojekt: “Smart Region Pellworm”. Gefördert durch BMWi,Förderkennzeichen: 0325498A-F[2] C. Blanc. “Modeling of a Vanadium Redoxflow Battery ElectricityStorage system”. École Polytechniqe Fédérale de Lausanne, Suisse2009.58 Proc. 33. Workshop Computational Intelligence, Berlin, 23.-24.11.2023[3] I. Goodfellow, Y. Bengio, A. Cousville “Deep Learning - das um-fassende Handbuch”. mitp-Verlag, Frechen 2018.Proc. 33. Workshop Computational Intelligence, Berlin, 23.-24.11.2023 59

Machine learning based model fitting concept for energy system components in energy management

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Machine learning based model fitting concept for energy system components in energy management

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