This project aims to explore the issues associated with the decentralised control, operation and management of future generation electricity networks. It is targetted at 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. This is an industrially funded project from a Hampshire-based company.
Initially, work in this project will have 3 main foci, corresponding to 3 main application settings:
This setting will consider the intelligent use of energy within a single home. It will develop 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 will address 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.
The neighbourhood setting aims to study the optimization of energy for the homes in a local neighbourhood. Each of these homes in a neighbourhood follows some electricity consumption pattern, based on the preferences of the residents. Also, some of these homes could also have a local (green) electricity generator, such as PV solar panels or a wind turbine. Furthermore, there may be some local storage capability of produced energy, which can be either local (e.g. a plug-in hybrid electric vehicle), or a shared neighbourhood storage facility (e.g. a redox flow battery).
Given this setting, we envisage that intelligent sensing devices could not only optimize the local demand in each home, but also buy the required electricity, or trade the locally produced energy, on a neighbourhood energy market. The grid company is also a player in this market. The main concern on the grid side is the reduction of the so-called ``peak demand" (i.e. demand in periods of time when the network is overloaded).
Therefore, the performance criteria in designing such a market are two- fold: it should reduce as much as possible the costs for each home owner, subject to satisfying his/her constraints. But it should also balance loads within the neighbourhood, so as to reduce peak-time demand on the grid side of the system.
Building on the home and neighbourhood setting, in this part of the project we aim to look at the implications of Decentralised Energy (DE) on the coordination of energy production, transmission and distribution. 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. Against the above background, we aim to study the applicability of various multiagent system tools and techniques.