Upside Energy and Heriot-Watt University have been awarded a Knowledge Transfer Partnership (KTP) grant by Innovate UK to maximise the opportunities presented by the emerging energy demand response market.
The project will use machine learning, and distributed artificial intelligence methods to manage a portfolio of storage assets to provide real-time energy reserves to the grid.
Upside Energy's Virtual Energy Store aims to relieve stress on the grid by managing a number of distributed storage resources, thereby reducing the UK's reliance on the spinning reserve capacity provided by traditional power stations.
Upside has developed an Advanced Algorithmic Platform (AAP) which allows a substantial ensemble of algorithms that manage demand response of different devices to be run in parallel. Upside will work with Heriot-Watt University to optimise their existing selection of control algorithms and how they are utilised in different scenarios using the University's specialist skills in machine learning, artificial intelligence and stochastic optimisation.
The team from Heriot-Watt University will work closely with Upside to facilitate the transfer of these skills and to develop a novel ensemble learning and algorithmic selection approach that will be required to support algorithm evolution within Upside's unique open innovation architecture.
Dr Graham Oakes, Founder and CEO of Upside Energy, said:
"This is a really exciting project. Both because machine learning is going to be fundamental to how Upside evolves its algorithms and hence delivers growing value to the energy system and wider society, and because it builds from the longstanding relationship that we have been developing with Heriot-Watt University. Our strategy is to work with academic partners to develop the intellectual property that will be at the heart of an intelligent energy system, one where resources are used carefully and thoughtfully and hence at low cost and with minimal impact on the environment. This partnership with Heriot-Watt is a great example of that strategy coming to fruition."
Dr Valentin Robu from Heriot-Watt University said:
"Demand response is emerging as a key technology to assure the stability of next-generation power grids, and there is an increasing need for smart control strategies that enable distributed energy storage assets to perform demand response. Techniques developed in the machine learning (ML) and distributed artificial intelligence (AI) communities will have an increasing role to play in enabling these efforts. ML and AI techniques can help not only in the design of control algorithms for individual assets, but also in the selection process of which of these algorithms perform the best under specific scenarios and conditions on the grid. Moreover, there is an increasing interest in advanced fusion prognostics techniques that enable real-time monitoring and accurate forecasting of the state of health of energy assets.
Upside Energy are one of the most innovative start-ups in the UK in this area, and their Advanced Algorithmic Platform (AAP) to evaluate the performance of different demand response strategies is a truly cutting edge development. The Heriot-Watt team is excited to work with Upside Energy to help them to achieve this vision."
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Notes to editors:
About Upside Energy
Upside Energy is an innovative energy start-up based in the UK. Their aim is to reduce greenhouse gases and to generate an income for people by enabling them to make smart choices about when to use energy. Upside are driven by a belief that they can address some of the fundamental quandaries the UK's traditional, asset heavy energy infrastructure faces, as it grapples with the trilemma of affordability, sustainability and security of supply.
Upside smooths the flow of energy across the grid. They have developed a flexible and scalable cloud service that enables tens of thousands of households and business sites to participate in schemes for 'demand response', where grid operators and energy suppliers pay people to shift their electricity usage from peak to off-peak times.
By orchestrating the energy stored in devices people already own e.g. uninterruptible power supplies UPS), batteries attached to solar PV systems, electric vehicles and their charge points, heating systems, etc. Upside creates a Virtual Energy Store™ (VES). The VES is then used to sell services to balance supply and demand on the grid. Upside then redistributes this service revenue back to the original device owners. http://www.upsideenergy.co.uk
Heriot-Watt is a specialist university, globally minded and calibrated to the needs of society. A leading technological and business university, renowned for innovation in business, engineering, design and the physical, social and life sciences, Heriot-Watt defines its presence on the international stage in areas of world importance and value.
Our communities of scholars come from across the world and are leaders in ideas and solutions; delivering innovation and educational excellence, the University is ranked in the top 10 for Research Impact in the UK. Heriot-Watt's leadership in energy research is supported by the HW Energy Academy, which promotes cooperation across schools in the university, and with a wide range of industrial and societal partners.
Dr. Valentin Robu is an Assistant Professor in the Smart Systems Group at Heriot-Watt University. He is an expert in artificial intelligence, machine learning and intelligent control and his research includes the application of artificial intelligence and machine learning techniques to address challenges in smart power grids such as those being addressed by Upside. He will collaborate closely with a team of Heriot-Watt academics, including Dr. David Flynn and Dr. Wolf-Gerrit Früh.
Other information:
Dr. Valentin Robu and Dr. David Flynn are also associate directors of the £20m EPSRC National Centre for Energy Systems Integration (CESI), funded by the Engineering and Physical Sciences Research Council (EPSRC). The Centre has been designed to bring together academic and industrial experts to help understanding of energy networks and model future supply and demand. The Centre aims to bridge a major information gap in the drive towards a fully integrated, smart energy network, by looking for the first time at the energy system as a whole; gas, power, renewables, heating and cooling http://www.ncl.ac.uk/press/news/2016/05/smartgrid
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