The ability to influence electricity demand from domestic and small business consumers, so that it can be matched to intermittent renewable generation and distribution network constraints is a key capability of a smart grid. This involves signaling to consumers to indicate when electricity use is desirable or undesirable. However, simply signalling a time-dependent price does not always achieve the required demand response and can result in unstable system behaviour. The authors propose a demand response scheme, in which an aggregator mediates between the consumer and the market and provides a signal to a 'smart home' control unit that manages the consumer's appliances, using a novel method for reconciliation of the consumer's needs and preferences with the incentives supplied by the signal. This method involves random allocation of demand within timeslots acceptable to the consumer with a bias depending on the signal provided. By simulating a population of domestic consumers using heat pumps and electric vehicles with properties consistent with UK national statistics, the authors show the method allows total demand to be predicted and shaped in a way that can simultaneously match renewable generation and satisfy network constraints, leading to benefits from reduced use of peaking plant and avoided network reinforcement. © The Institution of Engineering and Technology 2013.
IET Renewable Power Generation
689 - 699