The University of Southampton
University of Southampton Institutional Repository

Agent-based homeostatic control for green energy in the smart grid

Agent-based homeostatic control for green energy in the smart grid
Agent-based homeostatic control for green energy in the smart grid
With dwindling non-renewable energy reserves and the adverse effects of climate change, the development of the smart electricity grid is seen as key to solving global energy security issues and to reducing carbon emissions. In this respect, there is a growing need to integrate renewable (or green) energy sources in the grid. However, the intermittency of these energy sources requires that demand must also be made more responsive to changes in supply, and a number of smart grid technologies are being developed, such as high-capacity batteries and smart meters for the home, to enable consumers to be more responsive to conditions on the grid in real-time. Traditional solutions based on these technologies, however, tend to ignore the fact that individual consumers will behave in such a way that best satisfies their own preferences to use or store energy (as opposed to that of the supplier or the grid operator). Hence, in practice, it is unclear how these solutions will cope with large numbers of consumers using their devices in this way. Against this background, in this paper, we develop novel control mechanisms based on the use of autonomous agents to better incorporate consumer preferences in managing demand. These agents, residing on consumers' smart meters, can both communicate with the grid and optimise their owner's energy consumption to satisfy their preferences. More specifically, we provide a novel control mechanism that models and controls a system comprising of a green energy supplier operating within the grid and a number of individual homes (each possibly owning a storage device). This control mechanism is based on the concept of homeostasis whereby control signals are sent to individual components of a system, based on their continuous feedback, in order to change their state so that the system may reach a stable equilibrium. Thus, we define a new carbon-based pricing mechanism for this green energy supplier that takes advantage of carbon-intensity signals available on the internet in order to provide real-time pricing. The pricing scheme is designed in such a way that it can be readily implemented using existing communication technologies and is easily understandable by consumers. Building upon this, we develop new control signals that the supplier can use to incentivise agents to shift demand (using their storage device) to times when green energy is available. Moreover, we show how these signals can be adapted according to changes in supply and to various degrees of penetration of storage in the system. We empirically evaluate our system and show that, when all homes are equipped with storage devices, the supplier can significantly reduce its reliance on other carbon-emitting power sources to cater for its own shortfalls. By so doing, the supplier reduces the carbon emission of the system by up to 25% while the consumer reduces its costs by up to 14.5%. Finally, we demonstrate that our homeostatic control mechanism is not sensitive to small prediction errors and the supplier is incentivised to accurately predict its green production to minimise costs.
2157-6904
35:1-35:28
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Vytelingum, Perukrishnen
51f06fc5-024c-450d-bff2-e19c943aa87e
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Vytelingum, Perukrishnen
51f06fc5-024c-450d-bff2-e19c943aa87e
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Ramchurn, Sarvapali, Vytelingum, Perukrishnen, Rogers, Alex and Jennings, Nick (2011) Agent-based homeostatic control for green energy in the smart grid. ACM Transactions on Intelligent Systems and Technology, 2 (4), 35:1-35:28. (doi:10.1145/1989734.1989739).

Record type: Article

Abstract

With dwindling non-renewable energy reserves and the adverse effects of climate change, the development of the smart electricity grid is seen as key to solving global energy security issues and to reducing carbon emissions. In this respect, there is a growing need to integrate renewable (or green) energy sources in the grid. However, the intermittency of these energy sources requires that demand must also be made more responsive to changes in supply, and a number of smart grid technologies are being developed, such as high-capacity batteries and smart meters for the home, to enable consumers to be more responsive to conditions on the grid in real-time. Traditional solutions based on these technologies, however, tend to ignore the fact that individual consumers will behave in such a way that best satisfies their own preferences to use or store energy (as opposed to that of the supplier or the grid operator). Hence, in practice, it is unclear how these solutions will cope with large numbers of consumers using their devices in this way. Against this background, in this paper, we develop novel control mechanisms based on the use of autonomous agents to better incorporate consumer preferences in managing demand. These agents, residing on consumers' smart meters, can both communicate with the grid and optimise their owner's energy consumption to satisfy their preferences. More specifically, we provide a novel control mechanism that models and controls a system comprising of a green energy supplier operating within the grid and a number of individual homes (each possibly owning a storage device). This control mechanism is based on the concept of homeostasis whereby control signals are sent to individual components of a system, based on their continuous feedback, in order to change their state so that the system may reach a stable equilibrium. Thus, we define a new carbon-based pricing mechanism for this green energy supplier that takes advantage of carbon-intensity signals available on the internet in order to provide real-time pricing. The pricing scheme is designed in such a way that it can be readily implemented using existing communication technologies and is easily understandable by consumers. Building upon this, we develop new control signals that the supplier can use to incentivise agents to shift demand (using their storage device) to times when green energy is available. Moreover, we show how these signals can be adapted according to changes in supply and to various degrees of penetration of storage in the system. We empirically evaluate our system and show that, when all homes are equipped with storage devices, the supplier can significantly reduce its reliance on other carbon-emitting power sources to cater for its own shortfalls. By so doing, the supplier reduces the carbon emission of the system by up to 25% while the consumer reduces its costs by up to 14.5%. Finally, we demonstrate that our homeostatic control mechanism is not sensitive to small prediction errors and the supplier is incentivised to accurately predict its green production to minimise costs.

Text
ramchurn_etal.pdf - Accepted Manuscript
Download (252kB)
Text
a35-ramchurn.pdf - Version of Record
Download (868kB)
Text
citation.cfm?id=1329125.1329186&coll=DL&dl=ACM&CFID=33406648&CFTOKEN=23665559 - Version of Record
Download (982kB)
Text
author_page.cfm?id=81384599123&coll=DL&dl=ACM&CFID=33406648&CFTOKEN=23665559 - Version of Record
Download (38kB)
Text
author_page.cfm?id=81365592217&coll=DL&dl=ACM&CFID=33406648&CFTOKEN=23665559 - Version of Record
Download (27kB)
Text
citation.cfm?id=1842993.1843052&coll=DL&dl=ACM&CFID=33406648&CFTOKEN=23665559 - Version of Record
Download (401kB)
Text
results.cfm?query="distributed artificial intelligence"&querydisp="distributed artificial intelligence"&termshow=matchall&CFID=33406648&CFTOKEN=23665559&dl=ACM&dimgroup=6&dim=3210 - Version of Record
Download (96kB)
Text
author_page.cfm?id=81100315927&coll=DL&dl=ACM&CFID=33406648&CFTOKEN=23665559 - Version of Record
Download (61kB)
Text
author_page.cfm?id=81100370805&coll=DL&dl=ACM&CFID=33406648&CFTOKEN=23665559 - Version of Record
Download (64kB)
Text
results.cfm?query=Name:"Alex Rogers"&querydisp=Name:"Alex Rogers"&termshow=matchboolean&coll=DL&CFID=33406648&CFTOKEN=23665559&dl=ACM&dimgroup=6&dim=3213 - Version of Record
Download (112kB)
Text
results.cfm?query="multiagent systems"&querydisp="multiagent systems"&termshow=matchall&cfid=33406648&cftoken=23665559&dl=ACM&dimgroup=3&dim=3208 - Version of Record
Download (113kB)
Text
results.cfm?query="multiagent systems"&querydisp="multiagent systems"&termshow=matchall&CFID=33406648&CFTOKEN=23665559&dl=ACM&dimgroup=4&dim=3212 - Version of Record
Download (113kB)
Text
results.cfm?query="multiagent systems"&querydisp="multiagent systems"&termshow=matchall&CFID=33406648&CFTOKEN=23665559&dl=ACM&dimgroup=6&dim=1 - Version of Record
Download (114kB)
Text
results.cfm?query="economics"&querydisp="economics"&termshow=matchall&cfid=33406648&cftoken=23665559&dl=ACM - Version of Record
Download (99kB)

Show all 14 downloads.

More information

Published date: July 2011
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 272015
URI: http://eprints.soton.ac.uk/id/eprint/272015
ISSN: 2157-6904
PURE UUID: aa62b976-cf08-49b8-82bf-222d8a5f9052
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 12 Feb 2011 15:42
Last modified: 15 Mar 2024 03:22

Export record

Altmetrics

Contributors

Author: Sarvapali Ramchurn ORCID iD
Author: Perukrishnen Vytelingum
Author: Alex Rogers
Author: Nick Jennings

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×