Research Output per year
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.
Novel advances have been made in the evaluation of self* network operation and control, which is designed to provide autonomous behaviour within electricity networks. The scientific advances have drawn techniques from the computer science, artificial intelligence and self-organising system communities into power systems. For example, AI Planning, Distributed Constraint Optimisation and Type-2 Fuzzy Systems have all been applied to power system control with the view of achieving re-confugurable, flexible, self-aware systems in the future. This has been fully complemented by new market models that support such control, the inpact on risk and resilience and the most effective methods of including consumers actively in the control and operation of electricity networks. In terms of markets, a framework of fully decentralised trading is required which also respects the limited available network capacity. Research has produced mechanisms yielding market solutions of proven global optimality without assuming centralised knowledge of any participants' characteristics Research on consumer engagement is being targeted at a range of advances from social studies into how vulnerable customers can see their role as a prosumer, through the evaluation of utilization of collective awareness and collective actions to resolve a common good problem, to the combination of economic and technical solutions for integrating demand side flexibility
|Effective start/end date||1/10/11 → 31/10/16|
- EPSRC (Engineering and Physical Sciences Research Council): £14,675.00
- EPSRC (Engineering and Physical Sciences Research Council): £3,414,426.00
Probabilistic framework for online identification of dynamic behavior of power systems with renewable generationPapadopoulos, P. N., Guo, T. & Milanović, J. V., 31 Jan 2018, In : IEEE Transactions on Power Systems. 33, 1, p. 45-54 10 p.
Research output: Contribution to journal › Article
Probabilistic framework for transient stability assessment of power systems with high penetration of renewable generationPapadopoulos, P. N. & Milanović, J. V., 1 Jul 2017, In : IEEE Transactions on Power Systems. 32, 4, p. 3078-3088 11 p.
Research output: Contribution to journal › Article
Participation of customers in active demand side participation programs under different pricing schemesXu, H. & Kockar, I., 17 Feb 2016, (Accepted/In press). 5 p.
Research output: Contribution to conference › Paper
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Projects per year