Accommodating renewable generation through an aggregator‐focused method for inducing demand side response from electricity consumers
| Autor: | Peter John Boait, J.R. Snape, Babak M. Ardestani |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Demand management
education.field_of_study smart home Renewable Energy Sustainability and the Environment Computer science Demand patterns Population Demand side management Environmental economics computer.software_genre News aggregator Demand response Smart grid demand response Demand curve aggregator Derived demand education computer |
| Zdroj: | IET Renewable Power Generation. 7:689-699 |
| ISSN: | 1752-1424 1752-1416 |
| DOI: | 10.1049/iet-rpg.2012.0229 |
| Popis: | Consortium project with Cranfield University The file attached is a postprint of a paper submitted to and accepted for publication in [journal] and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library" 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 signalling 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. We 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, we 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. Engineering and Physical Sciences Research Council (EPSRC) under the CASCADE project (EP/GO59969/1). |
| Databáze: | OpenAIRE |
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