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Projects: Projects for Investigator
Reference Number EP/I031650/1
Title The Autonomic Power System
Status Completed
Energy Categories Other Power and Storage Technologies(Electricity transmission and distribution) 100%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields SOCIAL SCIENCES (Business and Management Studies) 20%;
PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 40%;
ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 40%;
UKERC Cross Cutting Characterisation Systems Analysis related to energy R&D (Other Systems Analysis) 75%;
Sociological economical and environmental impact of energy (Environmental dimensions) 5%;
Sociological economical and environmental impact of energy (Policy and regulation) 20%;
Principal Investigator Dr S McArthur
No email address given
Electronic and Electrical Engineering
University of Strathclyde
Award Type Standard
Funding Source EPSRC
Start Date 01 October 2011
End Date 31 October 2016
Duration 61 months
Total Grant Value £3,429,099
Industrial Sectors Energy
Region Scotland
Programme Energy Multidisciplinary Applications, Energy Research Capacity
Investigators Principal Investigator Dr S McArthur , Electronic and Electrical Engineering, University of Strathclyde (99.982%)
  Other Investigator Prof GA (Graham ) Ault , Electronic and Electrical Engineering, University of Strathclyde (0.001%)
Dr I Kockar , Electronic and Electrical Engineering, University of Strathclyde (0.001%)
Professor PC (Phil ) Taylor , Engineering, Durham University (0.001%)
Professor JW Bialek , Engineering, Durham University (0.001%)
Dr C Dent , Engineering, Durham University (0.001%)
Professor J Milanovic , Electrical & Electronic Engineering, University of Manchester (0.001%)
Dr J Mutale , Electrical & Electronic Engineering, University of Manchester (0.001%)
Dr MG (Michael ) Pollitt , Judge Business School, University of Cambridge (0.001%)
Dr PV Johnson , Mathematics, University of Manchester (0.001%)
Professor G (Goran ) Strbac , Department of Electrical and Electronic Engineering, Imperial College London (0.001%)
Dr B Chaudhuri , Department of Electrical and Electronic Engineering, Imperial College London (0.001%)
Dr J Pitt , Department of Electrical and Electronic Engineering, Imperial College London (0.001%)
Professor M Fox , Informatics, King's College London (0.001%)
Professor D Long , Informatics, King's College London (0.001%)
Professor M Goldstein , Mathematical Sciences, Durham University (0.001%)
Prof J (John ) Moriarty , Mathematical Sciences, Queen Mary, University of London (0.001%)
Professor H (Hajo ) Broersma , Applied Mathematics, University of Twente, The Netherlands (0.001%)
Prof J (Jim ) Watson , Bartlett Sch of Env, Energy & Resources, University College London (0.001%)
  Industrial Collaborator Project Contact , KEMA (0.000%)
Project Contact , National Grid plc (0.000%)
Project Contact , Agilent Technologies UK Ltd (0.000%)
Project Contact , PB Power (0.000%)
Project Contact , Mott Macdonald UK Ltd (0.000%)
Project Contact , IBM United Kingdom Ltd (0.000%)
Project Contact , Scottish and Southern Energy plc (0.000%)
Project Contact , Accenture (0.000%)
Project Contact , E.ON E&P UK Ltd (0.000%)
Web Site
Abstract This proposal focuses on the electricity network of 2050. In the move to a decarbonised energy network the heat and transport sectors will be fully integrated into the electricity system. Therefore, the grand challenge in energy networks is to deliver the fundamental changes in the electrical power system that will support this transition, without being constrained by the current infrastructure, operational rules, market structure, regulations, and design guidelines. The drivers that will shape the 2050 electricity network 2050 are numerous: increasing energy prices; increased variability in the availability of generation; reduced system inertia; increased utilisation due to growth of loads such as electric vehicles and heat pumps; electric vehicles as randomly roving loads and energy storage; increased levels of distributed generation; more diverse range of energy sources contributing to electricity generation; and increased customer participation.These changes mean that the energy networks of the future will be far more difficult to manage and design than those of today, for technical, social and commercial reasons. In order to cater for this complexity, future energy networks must be organised to provide increased flexibility and controllability through the provision of appropriate real time decision-making techniques. These techniques must coordinate the simultaneous operation of a large number of diverse components and functions, including storage devices, demand side actions, network topology, data management, electricity markets, electric vehicle charging regimes, dynamic ratings systems, distributed generation, network power flow management, fault level management, supply restoration and fuel choice. Additionally, future flexible grids will present many more options for energy trading philosophies and investment decisions. The risks and implications associated with these decisions and the real-time control of the networks will be harder to identify and quantify due to the increased uncertainty and complexity.We propose the design of an autonomic power system for 2050 as the grand challenge to be investigated. This draws upon the computer science community's vision of autonomic computing and extends it into the electricity network. The concept is based on biological autonomic systems that set high-level goals but delegate the decision making on how to achieve them to the lower level intelligence. No centralised control is evident, and behaviour often emerges from low-level interactions. This allows highly complex systems to achieve real-time and just-in-time optimisation of operations. We believe that this approach will be required to manage the complex trans-national power system of 2050 with many millions of active devices. The autonomic power system will be self-configuring, self-healing, self-optimising and self-protecting.This proposal is not focused on the application of established autonomic computing techniques to power systems (as they don't exist) but the design of an autonomic power system, which relies on distributed intelligence and localised goal setting. This is a significant step forward from the current Smart Grid vision and roadmaps. The autonomic power system is a completely integrated and distributed control system which self-manages and optimises all network operational decisions in real time. To deliver this, fundamental research is required to determine the level of distributed control achievable (or the balance between distributed, centralised, and hierarchical controls) and its impact on investment decisions, resilience, risk and control of a transnational interconnected electricity network.The research within the programme is ambitious and challenges many current philosophies and design approaches. It is also multi-disciplinary, and will foster cross-fertilisation between power systems, complexity science, computer science, mathematics, economics and social sciences
Publications (none)
Final Report (none)
Added to Database 06/12/11