ReviewOn making energy demand and network constraints compatible in the last mile of the power grid☆
Section snippets
The classic paradigm
By and large, the electrical power grid was conceived at the end of the 19th century to connect a few generators to a spatially distributed large collection of consumers via a transmission and distribution network, consisting essentially of wires, transformers, capacitors and switches as well as safety equipment. Throughout the 20th century this has been the conventional picture expounded in every electrical power engineering text book; see for example Bergen and Vittal (2000).
In this
Relevant time scales in the electricity grid
Can power demand be adapted to match supply derived from renewable energy sources? Arguably the answer is yes: changing power demand, under normal demand operating conditions, suffices to respond to variations in supply levels as observed when renewable energy sources are in use. In justifying this assertion it is helpful to understand time constants in the grid across the entire range of possible time scales. An overview of typical time scales is displayed in Fig. 3.
The fastest transients in
Existing storage
Energy storage (i.e. buffers) provide a simple mechanism to compensate for inadequate time scale matching between supply variations and demand variations. The question of how much additional buffering is required in terms of electrical energy storage is often considered in the context of renewables (Brekken et al., 2011, Vlot et al., 2013, Yang et al., 2008). More often than not, in these studies one ignores how much energy storage is actually available on the consumer side, which leads to
The role of electric vehicles
To demonstrate how demand response may be practically applied, electric vehicles are chosen as a case study. Global uptake of electric vehicles has been slow but steady thanks in part to government programs and incentives, with early uptake of fully electric vehicles exceeding uptake of hybrids when comparing the same stages in the technology life cycle (US Department of Energy, 2014). In Norway, for example, the electric Tesla Model S is now the highest selling passenger vehicle (Merrill, 2014
Modeling the last mile of the network
As more distributed generation and novel loads such as electric vehicles are integrated in the low voltage distribution network it becomes increasingly important for modern power system planning that the last mile of the network is modelled well.
The simulations presented in this paper use a model that represents a typical low voltage distribution network in Melbourne, Australia. Fig. 5 shows one of the case studies. In Australia, houses are typically connected single phase. The network used in
Energy matching as a centralised receding horizon control problem
When smart metering and the ability to centrally control loads is present, the energy matching problem can be solved in real- or near real-time using the full system state. The state space variables for the model are the voltages at the nodes in the network, as well as the battery state of charge for each grid connected vehicle. The demand is measured via the load current at each node. It is not unreasonable to assume that such information is available and readily accessible at a central node.
Energy matching as a distributed control problem
Metering and communication infrastructure are not always available. In many cases a complete synthesis of the state of the low voltage grid may not be feasible, or may be considered too fragile. This leads to the question what can be achieved with decentralised, distributed decision making based on local measurements only. In the case of electric vehicle charging, two quantities that can be measured locally are the node voltage and the mains frequency. Frequency is a useful indicator of the
Simulation setup
To compare the performance of these different charging regimes, two sets of simulations were run on the model described in Section 5. For the first set, an electric vehicle uptake of 50% was assumed, in other words half of all households own an electric vehicle. For the second set, an electric vehicle uptake of 80% was assumed. Such levels of electric vehicle uptake may not be expected for some years (Paevere et al., 2012), nevertheless it is useful to examine what the impact could be. For each
Conclusion
This paper discusses the feasibility of using demand side energy storage as a means to shape power demand, and hence allow power demand and power supply to match in real time whilst observing the network’s operational envelope as imposed by the distribution code from a control engineering perspective. How realistic such a future grid operation is, will be determined by the regulatory framework, the grid policy setting and incentives for consumers as well as grid operators. Simplistically there
Acknowledgements
The authors would like to express their thanks for the many constructive comments made by the reviewers, that served to make the paper much more accessible.
Iven Mareels is Professor of Electrical and Electronic Engineering, and Dean of the Melbourne School of Engineering at The University of Melbourne since 2008. In 2013, he was named Commander in the Order of the Crown (of Belgium). He is Fellow of the Academy of Technological Sciences and Engineering, Australia, a Fellow of the Institute of Electrical and Electronics Engineers (USA), a Fellow of the Institute of Engineers Australia and a Fellow of the Royal Flemish Academy of Belgium for Science
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Iven Mareels is Professor of Electrical and Electronic Engineering, and Dean of the Melbourne School of Engineering at The University of Melbourne since 2008. In 2013, he was named Commander in the Order of the Crown (of Belgium). He is Fellow of the Academy of Technological Sciences and Engineering, Australia, a Fellow of the Institute of Electrical and Electronics Engineers (USA), a Fellow of the Institute of Engineers Australia and a Fellow of the Royal Flemish Academy of Belgium for Science and the Arts. In Australia, he is registered as a Corporate Professional Engineer and a member of the Engineering Executives chapter of Engineers Australia. He is an expert in the area of systems engineering. He has published 5 monographs, and more than 120 journal papers in this field, and is co-author of a suite of word-wide patents related to modelling, and control of open water channels.
Julian de Hoog has been a Research Fellow in the Department of Mechanical Engineering at the University of Melbourne since January 2012. He received a B.Sc. in Computer Science and Mathematics from McGill University in Montreal, Canada (2004), and both the M.Sc. and Ph.D. in Computer Science from the University of Oxford, UK (2007 and 2011, respectively). His research focusses on technical impacts and control strategies for electric vehicles, demand response, and integration of renewable generation and storage in electrical distribution networks.
Doreen Thomas has a D.Phil. degree in Mathematics from the University of Oxford. She is a Professor and Head of the Department of Mechanical Engineering at The University of Melbourne, and Associate Dean (Research and Research Training) for the Melbourne School of Engineering. In more 150 papers, Professor Thomas has applied her fundamental mathematical research in network optimisation to applications in a range of areas including power networks, sensor networks, VLSI chip design and underground mine access. In November 2012 Professor Thomas was elected as a Fellow of the Australian Academy of Technological Sciences and Engineering. She is also a Fellow of Engineers Australia.
Marcus Brazil is an Associate Professor and Reader in the Department of Electrical and Electronic Engineering at The University of Melbourne. He received a Ph.D. in Mathematics (in the field of Computational and Geometric Group Theory) from La Trobe University in 1995. Currently, his main research interests are in Optimal Network Design with applications to Telecommunications, Wireless Sensor Networks, VLSI Physical Design, Underground Mining, and Infrastructure for Electric Vehicles. He also combines this with more theoretical work, particularly in the area of Steiner Trees.
Tansu Alpcan received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (UIUC). He has been a Senior Lecturer at The University of Melbourne, Australia, since October 2011. His main research interests are distributed decision making, game theory, and control with applications to energy markets, smart grid, and demand response. He is the author and editor, respectively, of the books “Network Security: A Decision and Game Theoretic Approach and Mechanisms” and “Games for Dynamic Spectrum Allocation”, both of which are published by Cambridge University Press.
Derek Jayasuriya completed a Masters of Philosophy in Electrical Engineering at the University of Melbourne in August, 2013. He is a Distributed Energy Engineer for AusNet Services, a distribution network operator in Victoria, Australia, where he runs projects on grid-side storage and distributed generation. His research interests include storage based Embedded Generators and Low Voltage Network Modelling.
Valentin Muenzel is a Ph.D. Candidate in Electrical and Electronic Engineering at The University of Melbourne, and a Research Intern at IBM Research – Australia. He received both his BEng in Electromechanical Engineering and M.Sc. in Sustainable Energy Technologies from the University of Southampton, UK. His research interests include lithium-ion batteries, battery management systems, electric vehicles, stationary energy storage, and smart grid technologies.
Lu Xia received his BE(R&D) with 1st Class Honors in Mechatronics Engineering from the Australian National University. He is currently working towards a Ph.D. degree in Electrical and Electronic Engineering at The University of Melbourne. His research interests include power demand management, electric vehicle charging strategies and distributed generation.
Ramachandra Rao Kolluri received his B.Tech. degree in Electronics and Communications Engineering from GITAM University, India in 2011 and his M.Sc. degree in Energy and Sustainability with Electrical Power Engineering from University of Southampton, United Kingdom in 2012. Since April 2013, he has been a Ph.D. student in Electrical and Electronic Engineering at The University of Melbourne. His research interests include power system dynamics and control, renewable energy integration in microgrids and applications of power electronics.
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This paper is based on a plenary presentation by the first author as part of the IFAC Symposium for Large Scale and Complex Systems, Shanghai, China, 2013. The authors would like to thank Better Place Australia, Senergy Australia, SP AusNet, and United Energy for the opportunity to work together on addressing some of the current issues in managing the electricity grid. Much of this work was sponsored through the Australian Research Council, under a Linkage Project involving both Better Place Australia and Senergy Australia.