Agent-based control for decentralised demand side management in the smart grid

By Sarvapali D. Ramchurn, Perukrishnen Vytelingum, Alex Rogers, Nicholas R. Jennings. Adaptive Agents and Multi-Agent Systems, 2011

410 citations recorded by the source corpus.

Central to the vision of the smart grid is the deployment of smart meters that will allow autonomous software agents, representing the consumers, to optimise their use of devices and heating in the smart home while interacting with the grid. However, without some form of coordination, the population of agents may end up with overly-homogeneous optimised consumption patterns that may generate significant peaks in demand in the grid. These peaks, in turn, reduce the efficiency of the overall system, increase carbon emissions, and may even, in the worst case, cause blackouts. Hence, in this paper, we introduce a novel model of a Decentralised Demand Side Management (DDSM) mechanism that allows agents, by adapting the deferment of their loads based on grid prices, to coordinate in a decentralised manner. Specifically, using average UK consumption profiles for 26M homes, we demonstrate that, through an emergent coordination of the agents, the peak demand of domestic consumers in the grid can be reduced by up to 17% and carbon emissions by up to 6%. We also show that our DDSM mechanism is robust to the increasing electrification of heating in UK homes (i.e., it exhibits a similar efficiency).

View Paper: https://www.semanticscholar.org/paper/6d70afc9c1802be6767d32b9caa9560af813fff1

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Citations in the corpus

Coordinating occupant behavior for building energy and comfort management using multi-agent systems (2012, methodology): “MAS have been used to coordinate the use of building electrical devices and heating to optimize demand on a smart grid [38].”
Carbon-Aware Computing for Datacenters (2021, unknown): “Example of theoretical frameworks for flexible load include control methods [25] using linear time-invariant control systems [26], a Markov decision process with deferrable loads [27], decentralized control [28], and distributed dynamic online convex optimization [29].”
The role of communication systems in smart grids: Architectures, technical solutions and research challenges (2013, background): “This is also demonstrated by the large number of multi-agent systems that have been designed for a variety of smart grid applications, including power system restoration [256,257], fault diagnosis [258–261], management of distributed energy resources [262–264], demand-side management [265], manageme”
Tackling Climate Change with Machine Learning (2019, background): “[80] Sarvapali D Ramchurn, Perukrishnen Vytelingum, Alex Rogers, and Nick Jennings.”
Prediction-Based Multi-Agent Reinforcement Learning in Inherently Non-Stationary Environments (2017, background): “Examples include: agent-based control algorithms based on grid prices and emergent coordination of agents [Ramchurn et al. 2011]; imbalance cost reductions through a multi-agent solution for coordinated plug-in hybrid EVs [Vandael et al. 2011]; EV fleets as virtual power plants for energy trading…”
Putting the 'smarts' into the smart grid (2012, background): “Against this background, recent work has begun to research the use of autonomous agents, representing individual consumers , that interact through markets [40] [10], and individually learn to optimise their use of electrical loads or storage devices in a number of simplified settings [30] [28].”