Burt, Graeme

Florida-James, Anthony

Giles, Alexander

McArthur, Stephen

University of Strathclyde Institutional Repository

Florida-James, Anthony and Giles, Alexander and Burt, Graeme and McArthur, Stephen (2018) Predictive capabilities of linearised models for converter-interfaced battery storage in LV networks. In: CDT Masterclass 2018, 2018-07-09 - 2018-07-11, University of Sheffield. , This version is available at https://strathprints.strath.ac.uk/66124/Strathprints is  designed  to  allow  users  to  access  the  research  output  of  the  University  of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may  not  engage  in  further  distribution  of  the  material  for  any  profitmaking  activities  or  any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge. Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output.Figure 3. Impact of step changes in id and iq reference on voltage, active, and reactive powerFigure. 2. Block diagram for linearised state-space model for system (Fig. 1) with embedded PLL dynamics Predictive Capabilities of Linearised Models for Converter-Interfaced Battery Storage in LV NetworksAnthony Florida-James1, Alexander Giles1 Graeme Burt1, Stephen McArthur11- Institute for Energy and Environment, University of Strathclyde, Glasgow (anthony.florida-james@strath.ac.uk, alexander.giles@strath.ac.uk, grame.burt@strath.ac.uk, s.mcarthur@strath.ac.uk)1. Background Converter-interfaced resources are becoming increasingly ubiquitous in low voltage (LV) power networks Time-series analysis can be computationally demanding and thus time consuming Linearised models of converters can provide advantages over models used for time-series simulations, demonstrated in this poster2. Advantages of Linearised State-Space Models Linearised models are computationally less demanding than non-linear models, giving faster simulation times. Linearised models can be used for time or frequency domain analyses. Frequency domain analyses with linear models enables each frequency to be evaluated independently, making it easy to embed frequencydependence of system elements Linear models can quickly identify where problems are4. Results 3. Representation of a Converter and Power System5. Conclusions and Future Work Fig. 4 verifies the effectiveness of the linearised state-space approach Æ Frequency scanning(red dots) verify accuracy This work demonstrates the capabilities of state-space modelling for predicting theperformance of a single converter in a typical LV network (R/X ratio  1) Future work will incorporate outer loop and droop control to analyse and improvelocal/decentralised converter control performance on LV networks The approach for one converter will also be µUHF\FOHG¶ to enable modelling of multipleconverters for system analyses (e.g. as shown in Fig. 5) ± key to predicting converter controlperformance and interaction between battery units in LV networks Incorporation of state-space model for Li-Ion battery will provide more accurate dynamics byincluding nonlinearities in batteryFigure.1 - Representation of a converter with an LC-filter, grid impedance, and wider network.  PLL dynamics incorporated into state-space model (C1, C3, C4 andC9)Æ this provides greater accuracyFigure 4. Bode plots for inner current controller, with: (a) Effect of idref on active power (b) Effect of iqref on active power (c) Effect of iqref on voltage/reactive power (d) Effect of idref on voltage/reactive power Fig.1 can be linearised and utilised for state-space analysis, asshown in Fig. 2, The current controller, the network response, etc are highlightedwithin the dotted boxesFigure 5. multiple converter model When controlling id and iq, it is important to identify the affect thateach element has This method enables assessment of internal controller stability andany impact upon the system (e.g. voltage) as seen in Fig.4

Predictive capabilities of linearised models for converter-interfaced battery storage in LV networks

https://strathprints.strath.ac.uk/66124/1/Florida_James_etal_CDT_2018_Predictive_capabilities_of_linearised_models_for_converter_interfaced_battery_storage_in_LV_networks.pdf

Predictive capabilities of linearised models for converter-interfaced battery storage in LV networks

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