Find out about some of the previous events held by Durham Energy Institute:
Workshop on smart energy markets and smart grids
As part of the Durham Energy Institute Small Grant project - Distributed Auction Design for Smart Microgrids, the workshop on smart energy markets and smart grids is to be held in School of Engineering and Computing Sciences, Durham University on Friday 17th March 2017.
Experts from both industry and academia are invited to give talks on smart energy markets and smart grids from industrial, engineering, computing sciences, and economics perspectives. Through this workshop, we endeavour to encourage the conversations and discussions on 1) emerging topics in smart grids such as renewables, energy storage and PEVs integrations with their impacts on the power system control/operations and energy market implementation; 2) smart energy market design for smart grids. There will be three technical sessions:
• Energy Storage and Renewables Integration Solutions
• Integrated Whole Energy System Management
• Smart Energy Economics
We welcome Durham researchers with an interest in the above topics to attend. The attendance is FREE but with limited places and the registration is essential for catering purposes. The registration is open at: https://www.eventbrite.co.uk/e/workshop-on-smart-energy-markets-and-smart-grids-tickets-31600531039
If you are a student in Durham and interested in presenting your work in this workshop either orally or via posters, you are very welcomed! Please send a title and a short abstract of your research to Dr Fanlin Meng (lead organiser, firstname.lastname@example.org).
Any queries regarding this workshop, please contact Dr Fanlin Meng (lead organizer) at email@example.com .
Confirmed speakers and presentations
Dr Graham Oakes, Upside Energy Ltd"What happens when the lights go out? Open innovation and the Virtual Energy Store."
Professor Kang Li, Queen's University Belfast "Big data analytics and control technologies in decarbonizing the whole energy chain from top to tail"
Dr Xiao-Jun Zeng, University of Manchester "Integrated Demand and Supply Side Management and Smart Pricing in Smart Grids"
Dr Behzad Kazemtabrizi, Durham University: "Active Network Management (ANM) and Flexibility in Smarter Power Networks"
Dr Yukun Shi, University of Leicester: "Arbitrage opportunities and feedback trading in emissions and energy markets"
Dr Fanlin Meng, Durham University: "Differential pricing in smart grid retail market"
Dr Graham Oakes, Upside Energy Ltd
Title: "What happens when the lights go out? Open innovation and the Virtual Energy Store."
Every time you hit a switch, somewhere a power station needs to work a little harder. But it’s inefficient to have power stations constantly ramping their generation up and down. And in any event, solar and wind generation can’t do this — we can’t get the sun to shine a little more brightly just because we want to boil the kettle. So how do we enable people to keep turning the lights on and off whenever they want to? Energy storage is part of the answer. But to get the energy storage we need, we need to make it economic. That’s what Upside Energy is doing via its Virtual Energy Store. And the algorithms that enable that Virtual Energy Store are being developed via open innovation. This session will describe the the grid’s need for flexibility, the VES and the innovation model which is enabling it.
Dr Graham Oakes is Founder and CEO of Upside Energy Ltd. Upside is building a cloud service that orchestrates energy stored in small systems such as uninterruptible power supplies, home battery systems, domestic hot water tanks, etc, in order to create a Virtual Energy Store that can be used to provide flexibility to the energy system. Upside was formed in response to the Nesta Dynamic Demand Challenge in 2013, has received £1m of grant funding from Innovate UK and DECC and £500k of private investment, and will commence delivering a commercial service in early 2017. Graham is a systems engineer with 30 years’ experience since earning his PhD in Geophysics from Imperial College.
Professor Kang Li, School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast
Title: Big data analytics and control technologies in decarbonizing the whole energy chain from top to tail
No single solution currently exists to achieve the utopian desire of zero fossil fuel consumption in addressing the global challenge of sustainable energy and environment. It is evident that the energy mix will contain a large variation in stochastic, intermittent and distributed sources of renewable energy such as wind and solar power. The increasing prominence of renewable and cleaner energy resources in the pursuit of legally binding UK and European energy targets spurs all stakeholders and sectors on to plan for the unique challenges such promising sources present. This presentation will discuss various challenges towards the de-carbonization of the whole energy chain from top to tail, including emission reductions from traditional thermal plants, integration of significant renewable power, electrification of transportation, and energy efficiency in building and manufacturing industry. The significant role of big data analytics and intelligent control techniques in transforming the energy system to a future energy network is highlighted and demonstrated.
Professor Kang Li received Ph.D. degree in Control Theory and Applications from Shanghai Jiaotong University in 1995, and a DSc degree from Queen’s University Belfast in 2015. Between 1995 and 2002, he worked at Shanghai Jiaotong University, Delft University of Technology and Queen’s University Belfast as a research fellow. Since 2002, he was a Lecturer, a Senior Lecturer (2007), and a Reader (2009) with the School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, U.K, and he is a Professor of Intelligent Systems and Control since 2011. His research interests include nonlinear system modelling, identification, and control, and bio-inspired computational intelligence, with applications to the development of advanced control technologies for decarbonizing the whole energy systems, from integration of renewable energies, smart grid, to electric vehicles, and energy reduction in manufacturing. He is developing a new generation of minimal-invasive energy monitoring system and big data analytics platform which are currently trialled in several energy intensive industries, and has received several prestigious awards for the system developed.
Dr Xiao-Jun Zeng, School of Computer Science, University of Manchester
Title: Integrated Demand and Supply Side Management and Smart Pricing in Smart Grids
The introduce and development of the smart grids and smart meters have created a great opportunity to better manage the energy demand to integrate renewable energy sources and reduce greenhouse gases, minimise the customers’ bill by smarter consumptions, reduce the cost and maximise the profit for energy generators and retailers, and balance the peak and off-pear usages to relieve the pressure and urgency of power network replacement and upgrade.
To achieve these objectives and benefits from smart grids and smart meters, the smart market mechanisms for the demand and supply side management needs to be developed and designed, in which the day-ahead and real-time smart pricing is one of the most promising approaches.
In this talk, a systematic approach of smart pricing for the demand and supply side management is presented:
Firstly, at the energy customer’s level, the home energy management system integrated on the smart meters is developed, which enables the customers to best serve their energy need and minimise their bill. For customers who prefer flexible usage of energy without being controlled by the home energy management system, a monitoring system is introduced for the usage behaviour analysis to help these customers to improve the usage efficiency and reduce bill.
Secondly, at the energy retail market level, the demand management and pricing optimisation approach is developed, which enables an energy retailer to maximise its profits and at the same time meets all market, supply, and peak off-peak ratio. In particular, the realistic step-wise cost function is used in such a pricing optimization to link with the pricing scheme and cost pattern in supply side, rather than the simplified constant cost or the commonly used (theoretic) quadratic cost function. It is shown by using such a smart pricing approach, an energy retailer can reduce the cost of energy consumptions and peak off-peak ratio, and improve its profit without increasing the bills of customers. Therefore achieve the win-win case for both retailers and customers.
Thirdly, at the energy wholesale market level, a new wholesale pricing mechanism based on the mechanism design approach is developed and designed, in order to cope with the difficult and unpredictable demand pattern led by the new demand management approaches and reduce the market clear prices by introducing multi-part market clear pricing mechanism and its corresponding demand-dependent step-wise market clearing price function, in order to secure cheaper energy supply and ensure the balance and effective integration between demand and supply.
Finally a list of future research challenges for the demand management and smart pricing are presented and discussed.
Dr Xiao-Jun Zeng received his Ph.D. degree in Computation from the University of Manchester, where he is currently a Senior Lecturer in Machine Learning and Optimisation at the School of Computer Science. Before joining the University of Manchester in 2002, he was with Knowledge Support Systems, Ltd., Manchester, between 1996- 2002, where he was the Head of Research, developing intelligent pricing decision support systems which won the European Information Society Technologies Award in 1999 and Microsoft European Retail Application Developer (RAD) Awards in 2001 and 2003. His research in intelligent pricing decision support systems was selected by UKCRC, CPHC, and BCS Academy as one of 20 impact cases to highlight the impact made by UK academic Computer Science Research within the UK and worldwide over the period 2008 – 2013.
Dr Zeng’s main research interests include computational intelligence, machine learning, big data and data mining, decision support systems, computational finance, energy demand management, and game theory. He has published about 120 journal and conference papers in these areas and his research has been funded UK EPSRC, Innovate UK TSB, EU 6th and 7th Framework Programmes, and EU H2020 Programme. His currently funded projects include "hierarchical and real-time big data analytics for performance analysis and prediction of mobile networks" by EPSRC Impact Acceleration Account with Wadaro Ltd and "Big Data in Finance" by EU Horizon 2020 Framework Programme.
Dr Zeng has served to scholarly and professional communities in various roles including an Associate Editor of the IEEE Transactions on Fuzzy Systems, Special Session Chair of 2008 IEEE World Congress on Computational Intelligence, Program Chair of 2009- 2016 IEEE Symposium on Computational Intelligence in Control and Automation, Program Co-Chair of the 9th Int Conference on Fuzzy Systems and Knowledge Discovery, and the General Chair of 11th annual UK Workshop on Computational Intelligence, as well as an elected member of the UK EPSRC College. He is also a member of Fuzzy Systems Technical Committee and Intelligent Systems Applications Technical Committee of IEEE Computational Intelligence Society.
Dr Behzad Kazemtabrizi, School of Engineering and Computing Sciences, Durham University
Title: Active Network Management (ANM) and Flexibility in Smarter Power Networks
The idea of "smart grids" has been undoubtedly one of the most hotly debated topics in energy systems research right now. The reality is that the traditional view of vertically integrated bulk power generation-transmission-consumption has been changing gradually and at a steady pace especially with the growth of incentives for more renewable energy resource integration. Sources of energy can now be easily integrated at all levels from transmission to distribution. Individual households no longer perform the role of the passive consumer but have now been gradually changing their role into "prosumers" by integrating their own domestic renewable energy generators directly to the grid. The distribution networks no longer are passive networks for delivering the upstream energy to consumers, rather they are active networks with multiple resources distributed and with flexible loads such as electric vehicles and batteries. All of these changes require a rethinking of how network operators (both at transmission and distribution) plan their network operation in a resilient and secure manner. In this talk I am going to give a general overview of "Active Network Management" which are essentially methods by which the network operator (and by extension other stakeholders including the individual prosumers) can manage the energy in the network more flexibly and reliably. ANM will eventually pave the way toward a more flexible, versatile, and smarter way we use electricity. I will also cover some of the aspects of the work we at Durham are doing in this area from advanced mathematical modelling to developing control methods for ANM applications.
Dr Behzad Kazemtabrizi received his Ph.D. in Electronics and Electrical Engineering with an emphasis on Power Systems from Glasgow University in 2011. He was a Post-Doctoral Research Associate (PDRA) in the School of Engineering and Computing Sciences at Durham University from February 2012 till September 2013. Since September 2013, he has joined the School of Engineering and Computing Sciences as the Lecturer (Assistant Professor) in Electrical Engineering. His principal research interests include advanced electrical power systems modelling, analysis, simulation and optimisation for improving performance and reliability with a focus on renewable energy resources, in particular wind. Within the context of larger offshore wind farms (for example UK Round 3 Offshore), his research is particularly focused on efficient and reliable grid integration, in order to ensure a continuous operation of the wind farm and adherence by the network and wind farm operator to the performance characteristics as outlined by the grid code. The research covers a wide variety of subjects, from developing tools for evaluating effects of large scale wind power in system adequacy (ability to meet demand), cost-benefit analysis of deploying alternative offshore connections, to integration of energy storage for maintaining performance standards within the whole power network.
Dr Yukun Shi, School of Management, University of Leicester
Title: Arbitrage opportunities and feedback trading in emissions and energy markets
In this talk, I am going to present our study on the presence of feedback trading in emissions and energy markets and the extent to which such behaviour is linked to the level of arbitrage opportunities. Applying our augmented models to the carbon emission and major energy markets in Europe, we find evidence of feedback trading in coal and electricity markets, but not in carbon market where the institutional investors dominate. This finding is consistent with the notion that institutional investors are less susceptible to pursuing feedback-style investment strategies. In further analysis, our results show that the intensity of feedback trading is significantly related to the level of arbitrage opportunities, and that the significance of such relationship depends on the market regimes.
Dr Yukun Shi is Lecturer (Assistant Professor) in Finance and Director of MSc Accounting and Finance in University of Leicester. Previously, he was a lecturer at Middlesex University, lecturer at a CFA training institute and Head of Carbon Market Research at Newport Energy Ltd. He holds a PhD in Finance from Durham University Business School, MA in Finance and Investment with Distinction from University of Nottingham, and BA in Financial Economics (1/52) from East China University of Political Science and Law in Shanghai. Yukun is a CFA charter holder and an active member of CFA UK. Yukun also acts as senior consultant for Moody’s Analytics and several carbon investment funds. He is adjunct research fellow in Central University of Economics and Finance in China.
Dr Fanlin Meng, School of Engineering and Computing Sciences, Durham University
Title: Differential pricing in smart grid retail market
Differential pricing has been widely used in retail sectors such as broadband and mobile phone services to offer ‘right prices’ to ‘right’ customers, which can retailers’ profitability and improve customer satisfaction. In smart grid retail market, dynamic pricing (e.g., time-of-use pricing (ToU), real-time pricing) has attracted enormous attention from both academia and industry in recent years. However, most of the existing research focus on uniform dynamic pricing (i.e., all customers are offered at the same prices). In this paper, I am going to present a differential pricing based ToU by considering customer segments. Simulation results show differential pricing based ToU can increase retailer’s profit while reducing customers’ bills compared with the uniform ToU.
: Dr Fanlin Meng is a Postdoctoral Research Associate in Energy Systems Modelling in School of Engineering and Computing Sciences, Durham University. He was a Postdoctoral Research Associate in Autonomous Vehicles in Department of Aeronautical and Automotive Engineering, Loughborough University. Dr Meng received his PhD in Computer Science from University of Manchester in 2015, where his PhD research was fully funded by Manchester EPS Faculty Studentship. He was the recipient of Best Paper Prize in 2015 School of Computer Science Research Symposium and also the recipient of ‘2015 Chinese Government Award for Outstanding Self-financed Students Abroad’. He has been recently awarded a DEI small grant on smart energy market design.
Contact firstname.lastname@example.org for more information about this event.