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Location Awareness in Multi-Agent Control of Distributed Energy Resources

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Location Awareness in Multi-Agent Control of Distributed Energy Resources Durham E-Theses Location Awareness in Multi-Agent Control of Distributed Energy Resources HUTCHINSON, HARRIET How to cite: HUTCHINSON, HARRIET (2017) Location Awareness in Multi-Agent Control of Distributed Energy Resources, Durham theses, Durham University. Available at Durham E-Theses Online: http://etheses.dur.ac.uk/12291/ Use policy The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that: • a full bibliographic reference is made to the original source • a link is made to the metadata record in Durham E-Theses • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders. Please consult the full Durham E-Theses policy for further details. Academic Support Oce, Durham University, University Oce, Old Elvet, Durham DH1 3HP e-mail: [email protected] Tel: +44 0191 334 6107 http://etheses.dur.ac.uk 2 Location Awareness in Multi-Agent Control of Distributed Energy Resources Harriet Hutchinson Thesis submitted towards the degree of Doctor of Philosophy School of Engineering and Computing Sciences Durham University United Kingdom April 2017 Location Awareness in Multi-Agent Control of Distributed Energy Resources Harriet Hutchinson Abstract The integration of Distributed Energy Resource (DER) technologies such as heat pumps, electric vehicles and small-scale generation into the electricity grid at the household level is limited by technical constraints. This work argues that location is an important aspect for the control and integration of DER and that network topology can inferred without the use of a centralised network model. It addresses DER integration challenges by presenting a novel approach that uses a decentralised multi-agent system where equipment controllers learn and use their location within the low-voltage section of the power system. Models of electrical networks exhibiting technical constraints were developed. Through theo- retical analysis and real network data collection, various sources of location data were iden- tified and new geographical and electrical techniques were developed for deriving network topology using Global Positioning System (GPS) and 24-hour voltage logs. The multi-agent system paradigm and societal structures were examined as an approach to a multi-stakeholder domain and congregations were used as an aid to decentralisation in a non-hierarchical, non- market-based approach. Through formal description of the agent attitude INTEND2, the novel technique of Intention Transfer was applied to an agent congregation to provide an opt-in, collaborative system. Test facilities for multi-agent systems were developed and culminated in a new embedded controller test platform that integrated a real-time dynamic electrical network simulator to provide a full-feedback system integrated with control hardware. Finally, a multi-agent control system was developed and implemented that used location data in providing demand-side response to a voltage excursion, with the goals of improving power quality, reducing generator disconnections, and deferring network reinforcement. The resulting communicating and self-organising energy agent community, as demonstrated on a unique hardware-in-the-loop platform, provides an application model and test facility to inspire agent-based, location-aware smart grid applications across the power systems domain. { i { Declaration The work in this thesis is based on research carried out in the New and Renewable Energy Group, School of Engineering and Computing Sciences, Durham University. No part of this report has been submitted elsewhere for any other degree or qualification and it is all my own work unless referenced to the contrary in the text. Parts of this work have been published in the following: G Hutchinson, P C Taylor, G Coates, R Hair, \Multi-Agent System for Decentralised Control of Low Voltage Distribution Networks", Universities Power Engineering Conference, 2010. G Hutchinson, R Hair, P C Taylor, \Leveraging Location Awareness for Distributed Energy Resources: Self-Organising Energy Communities", International Conference and Exhibition on Electricity Distribu- tion (CIRED), 2013. Copyright © 2017 Harriet Hutchinson. The copyright of this thesis rests with the author. No quotations from it should be published without the author's prior written consent and information derived from it should be acknowledged. { ii { Acknowledgements This work was funded by EPSRC and E.ON. It would have not been possible without the support of supervisiors, technicians, colleagues and friends. To my parents, to Phil, and to Margaret, without whom I would never have made it here. To all my loved ones, with thanks for their enduring patience, support and confidence. Harriet Hutchinson Durham, April 2017 { iii { { iv { Contents Abstract i Declaration ii Acknowledgements iii Contents v List of Figures viii List of Tables x Glossary xi Nomenclature xiv 1 Introduction 1 1.1 Structure of this Chapter . .1 1.2 Research Background . .2 1.3 Regulatory Framework and Non-Technical Context . .4 1.3.1 UK Regulations . .4 1.3.2 The Broken Value Chain . .4 1.3.3 Market Drivers . .5 1.4 Low Voltage Distribution Networks . .7 1.4.1 Characteristics of LVDNs . .7 1.4.2 Distributed Electricity Generation . .8 1.4.2.1 Combined Heat and Power . .9 1.4.2.2 Photovoltaics . .9 1.4.3 Electric Vehicles . 10 1.4.4 Heat Pumps . 11 1.4.5 Demand-Side Management . 11 1.4.5.1 Benefits of DSM . 11 1.5 Conclusion . 13 1.6 Solution Overview . 13 1.7 Structure of this Document . 14 2 Literature Review 17 2.1 Low-Voltage Distribution Networks . 17 2.1.1 Legacy LVDNs . 17 2.1.2 Properties of LVDNs . 18 2.1.3 Current Distributed Energy Resource (DER) Integration Paradigm . 20 2.2 Distributed Energy Resources . 20 2.2.1 Impacts of DERs . 20 2.2.2 Relevance of Location . 24 2.2.3 Active Approaches to DER Integration . 24 2.2.4 SSEZ Approaches . 26 { v { 2.2.5 Microgrid Approaches . 27 2.2.5.1 UK Research . 28 2.2.5.2 EU Research . 29 2.2.5.3 US Research . 33 2.2.5.4 Rest of World . 34 2.2.6 Using Location in Integration . 35 2.3 Multi-Agent Systems in Power Systems . 36 2.3.1 Applications Overview . 36 2.3.2 Decentralisation . 37 2.3.2.1 Problems of Centralisation . 37 2.3.2.2 Scalability . 37 2.3.2.3 Existing Approaches to Decentralisation . 38 2.4 Research Problem Statement . 41 2.5 Problem Solution Requirements . 41 2.6 Summary . 42 3 Electrical Network Modelling 45 3.1 Electrical Networks . 45 3.1.1 Basic Network Theory . 45 3.1.1.1 Voltage Drop . 45 3.1.1.2 Analysis by Symmetric Components . 48 3.1.1.3 Unbalance . 51 3.1.1.4 Three-Phase Voltage Drop and Rise . 51 3.2 Network Modelling . 52 3.2.1 Model Properties . 54 3.3 Model Selection . 54 3.3.1 Customers and Loading . 55 3.4 New Tool Development . 56 3.4.1 PSCAD Model . 56 3.4.1.1 Modelling Results . 60 3.4.2 IPSA+ Model . 61 3.4.3 RSCAD Model . 64 3.5 Conclusion . 66 4 Location in Low-Voltage Distribution Networks 67 4.1 Location . 67 4.1.1 Location in control . 67 4.1.2 Describing location in LVDNs . 68 4.1.3 Local and global information . 68 4.1.4 Electrical network and modelling . 69 4.1.5 Types of Location . 73 4.1.5.1 Electrical Topology Analogues . 73 4.1.5.2 Multi-Factor Location . 74 4.2 Location from Geographical Information . 75 4.2.1 Geographical tree creation . 76 4.2.2 2D Geometry . 77 4.2.2.1 Tree walk . 79 4.2.3 Proof of concept . 79 4.3 Location from electrical signals . 81 4.3.1 Overview . 81 4.3.2 Signal processing techniques . 84 4.3.2.1 Cross-correlation . ..
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