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Cited by 126Smart Grid Energy ManagementMicrogrid Control and OptimizationIoT-based Smart Home Systems

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

Open access
Ramchurn, Sarvapali D., Vytelingum, Perukrishnen, Rogers, Alex, Jennings, Nicholas R.·2011·ACM Transactions on Intelligent Systems and Technology
DOI 10.1145/1989734.1989739
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Abstract

With dwindling nonrenewable 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 article, 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 optimize 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 incentivize 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 incentivized to accurately predict its green production to minimize costs.

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  • Techniques such as deep RL, agent-based models, online optimization, and dynamic programming can help maximize profits for decentralized storage, demand, and generation, given real-time prices.

    “Techniques such as (deep) RL [291,292,810,881], agent-based models [173,662,663,863], online optimization [99], and dynamic programming [704] can then help maximize profits for decentralized storage, demand, and generation, given real-time prices.”

    From Tackling climate change with machine learning (2023) · cites this paper as [663]

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Research works printed but not linked: 2

  1. KOK, K. AND VENEKAMP, G. 2010. Market-based control in decentralized power systems. In Proceedings of the First International Workshop on Agent Technology for Energy Systems.
  2. MARIEB, E. AND HOEHN, K. 2007. Human anatomy & physiology. Pearson Education.

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Authors
Ramchurn, S. D., Vytelingum, P., Rogers, A., & Jennings, N. R.
Affiliations
University of Southampton (GB)
ORCID
Venue
ACM Transactions on Intelligent Systems and Technology
Publisher
Association for Computing Machinery (ACM)
Published
2011-07
Volume
2
Issue
4
Pages
1-28
ISSN
  • 2157-6904 (ISSN-L)
  • 2157-6912
DOI
10.1145/1989734.1989739
OpenAlex
W1977146286
Cited by
126
References
37
Citations by year
2026: 1 · 2025: 15 · 2024: 7 · 2023: 5 · 2022: 5 · 2021: 3 · 2020: 3 · 2019: 4 · 2018: 4 · 2017: 9 · 2016: 13 · 2015: 8 · 2014: 19 · 2013: 20 · 2012: 7
FWCI
10.98
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Transactions on Intelligent Systems and Technology (publishedVersion)
  • ePrints Soton (University of Southampton) (submittedVersion)
  • Spiral (Imperial College London) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
  • Physical Sciences › Engineering › Control and Systems Engineering › Microgrid Control and Optimization
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
Keywords
smart grid, clean energy, renewable energy integration, homeostatic regulation, autonomous agents, demand response, energy storage, smart meters, carbon pricing, real-time pricing, carbon intensity, carbon emission reduction, consumer preferences, load shifting, energy cost reduction, multi-agent systems, energy security, renewable energy intermittency

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