The iDEaS Project aims to explore the issues associated with the decentralised control, operation and management of future generation electricity networks. It is an industrially funded project from a Hampshire-based company.
The project targets scenarios in which micro-generation and storage capabilities are ubiquitous, where intelligent sensing devices allow users to make informed choices about the control of devices in their home, and where producers and consumers are connected via a series of dynamically negotiated supply contracts.
Work in the project has 3 main application settings:
Home Setting
This setting considers the intelligent use of energy within a single home. It is developing algorithms and methodologies that will enable intelligent appliances and energy storage devices (such as plug-in hybrid electric vehicles) to autonomously negotiate and coordinate for optimal energy use. In particular, it addresses the need for algorithms that can continuously adapt the behaviour of the home in response to information such as weather, energy prices, energy carbon content and the lifestyle and preferences of the home owners.
Neighbourhood setting
The neighbourhood setting studies the optimisation of energy for the homes in a local neighbourhood. Each of these homes might have some local generation capacity (e.g. PV solar panels, a micro combined heap and power plant, or a wind turbine) and/or local storage (e.g. a plug-in hybrid electric vehicle, or a shared neighbourhood storage facility). We are invesitgating how intelligent agents can both optimise the local demand in each home, and also trade locally produced energy in a neighbourhood energy market balancing loads within the neighbourhood and reducing peak-time demand.
Grid setting
Building on the home and neighbourhood setting, we are investigating the coordination of energy production, transmission and distribution across the entire grid. In particular, the coordination of switches (when there is a surge in demand or breakage of transmission lines) is important in building robustness into the network. Moreover, the fact that energy production can take various forms (e.g. from batteries, green energy sources, or coal power stations) and the fact that consumers may express preferences on the type of energy source referred means that transmission and distribution needs to be coordinated to ensure effective delivery of electricity. We are studying the application of various multi-agent system tools and techniques to this problem.