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2011
Ramchurn, Sarvapali; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nick
Agent-based control for decentralised demand side management in the smart grid Proceedings Article
In: The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), pp. 5–12, 2011.
Abstract | Links | BibTeX | Tags: agent-based control, agents, demand-side management electricity, Energy, multi-agent systems
@inproceedings{eps271985,
title = {Agent-based control for decentralised demand side management in the smart grid},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/271985/},
year = {2011},
date = {2011-01-01},
booktitle = {The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011)},
pages = {5–12},
abstract = {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).},
keywords = {agent-based control, agents, demand-side management electricity, Energy, multi-agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
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).
Vytelingum, Perukrishnen; Voice, Thomas; Ramchurn, Sarvapali; Rogers, Alex; Jennings, Nick
Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid Journal Article
In: Journal of Artificial Intelligence Research, vol. 42, pp. 765–813, 2011, (AAMAS 2010 iRobot Best Paper Award).
Abstract | Links | BibTeX | Tags: agents, Energy, mas, multi-agent systems, provenance
@article{eps272961,
title = {Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid},
author = {Perukrishnen Vytelingum and Thomas Voice and Sarvapali Ramchurn and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/272961/},
year = {2011},
date = {2011-01-01},
journal = {Journal of Artificial Intelligence Research},
volume = {42},
pages = {765–813},
abstract = {In this paper, we present a novel decentralised management technique that allows electricity micro-storage devices, deployed within individual homes as part of a smart electricity grid, to converge to profitable and efficient behaviours. Specifically, we propose the use of software agents, residing on the users' smart meters, to automate and optimise the charging cycle of micro-storage devices in the home to minimise its costs, and we present a study of both the theoretical underpinnings and the implications of a practical solution, of using software agents for such micro-storage management. First, by formalising the strategic choice each agent makes in deciding when to charge its battery, we develop a game-theoretic framework within which we can analyse the competitive equilibria of an electricity grid populated by such agents and hence predict the best consumption profile for that population given their battery properties and individual load profiles. Our framework also allows us to compute theoretical bounds on the amount of storage that will be adopted by the population. Second, to analyse the practical implications of micro-storage deployments in the grid, we present a novel algorithm that each agent can use to optimise its battery storage profile in order to minimise its owner's costs. This algorithm uses a learning strategy that allows it to adapt as the price of electricity changes in real-time, and we show that the adoption of these strategies results in the system converging to the theoretical equilibria. Finally, we empirically evaluate the adoption of our micro-storage management technique within a complex setting, based on the UK electricity market, where agents may have widely varying load profiles, battery types, and learning rates. In this case, our approach yields savings of up to 14% in energy cost for an average consumer using a storage device with a capacity of less than 4.5 kWh and up to a 7% reduction in carbon emissions resulting from electricity generation (with only domestic consumers adopting micro-storage and, commercial and industrial consumers not changing their demand). Moreover, corroborating our theoretical bound, an equilibrium is shown to exist where no more than 48% of households would wish to own storage devices and where social welfare would also be improved (yielding overall annual savings of nearly pounds1.5B).},
note = {AAMAS 2010 iRobot Best Paper Award},
keywords = {agents, Energy, mas, multi-agent systems, provenance},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present a novel decentralised management technique that allows electricity micro-storage devices, deployed within individual homes as part of a smart electricity grid, to converge to profitable and efficient behaviours. Specifically, we propose the use of software agents, residing on the users' smart meters, to automate and optimise the charging cycle of micro-storage devices in the home to minimise its costs, and we present a study of both the theoretical underpinnings and the implications of a practical solution, of using software agents for such micro-storage management. First, by formalising the strategic choice each agent makes in deciding when to charge its battery, we develop a game-theoretic framework within which we can analyse the competitive equilibria of an electricity grid populated by such agents and hence predict the best consumption profile for that population given their battery properties and individual load profiles. Our framework also allows us to compute theoretical bounds on the amount of storage that will be adopted by the population. Second, to analyse the practical implications of micro-storage deployments in the grid, we present a novel algorithm that each agent can use to optimise its battery storage profile in order to minimise its owner's costs. This algorithm uses a learning strategy that allows it to adapt as the price of electricity changes in real-time, and we show that the adoption of these strategies results in the system converging to the theoretical equilibria. Finally, we empirically evaluate the adoption of our micro-storage management technique within a complex setting, based on the UK electricity market, where agents may have widely varying load profiles, battery types, and learning rates. In this case, our approach yields savings of up to 14% in energy cost for an average consumer using a storage device with a capacity of less than 4.5 kWh and up to a 7% reduction in carbon emissions resulting from electricity generation (with only domestic consumers adopting micro-storage and, commercial and industrial consumers not changing their demand). Moreover, corroborating our theoretical bound, an equilibrium is shown to exist where no more than 48% of households would wish to own storage devices and where social welfare would also be improved (yielding overall annual savings of nearly pounds1.5B).
2010
Macarthur, Kathryn; Farinelli, Alessandro; Ramchurn, Sarvapali; Jennings, Nick
Efficient, Superstabilizing Decentralised Optimisation for Dynamic Task Allocation Environments Proceedings Article
In: Third International Workshop on: Optimisation in Multi-Agent Systems (OptMas) at the Ninth Joint Conference on Autonomous and Multi-Agent Systems, pp. 25–32, 2010, (Event Dates: 10 May 2010).
Abstract | Links | BibTeX | Tags: agents, Disaster Management, mas, Multi-agent scheduling, multi-agent systems
@inproceedings{eps268588,
title = {Efficient, Superstabilizing Decentralised Optimisation for Dynamic Task Allocation Environments},
author = {Kathryn Macarthur and Alessandro Farinelli and Sarvapali Ramchurn and Nick Jennings},
url = {http://eprints.soton.ac.uk/268588/},
year = {2010},
date = {2010-01-01},
booktitle = {Third International Workshop on: Optimisation in Multi-Agent Systems (OptMas) at the Ninth Joint Conference on Autonomous and Multi-Agent Systems},
pages = {25–32},
abstract = {Decentralised optimisation is a key issue for multi-agent systems, and while many solution techniques have been developed, few provide support for dynamic environments, which change over time, such as disaster management. Given this, in this paper, we present Bounded Fast Max Sum (BFMS): a novel, dynamic, superstabilizing algorithm which provides a bounded approximate solution to certain classes of distributed constraint optimisation problems. We achieve this by eliminating dependencies in the constraint functions, according to how much impact they have on the overall solution value. In more detail, we propose iGHS, which computes a maximum spanning tree on subsections of the constraint graph, in order to reduce communication and computation overheads. Given this, we empirically evaluate BFMS, which shows that BFMS reduces communication and computation done by Bounded Max Sum by up to 99%, while obtaining 60-88% of the optimal utility.},
note = {Event Dates: 10 May 2010},
keywords = {agents, Disaster Management, mas, Multi-agent scheduling, multi-agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
Decentralised optimisation is a key issue for multi-agent systems, and while many solution techniques have been developed, few provide support for dynamic environments, which change over time, such as disaster management. Given this, in this paper, we present Bounded Fast Max Sum (BFMS): a novel, dynamic, superstabilizing algorithm which provides a bounded approximate solution to certain classes of distributed constraint optimisation problems. We achieve this by eliminating dependencies in the constraint functions, according to how much impact they have on the overall solution value. In more detail, we propose iGHS, which computes a maximum spanning tree on subsections of the constraint graph, in order to reduce communication and computation overheads. Given this, we empirically evaluate BFMS, which shows that BFMS reduces communication and computation done by Bounded Max Sum by up to 99%, while obtaining 60-88% of the optimal utility.
Ramchurn, S. D.; Polukarov, Mariya; Farinelli, Alessandro; Jennings, Nick; Trong, Cuong
Coalition Formation with Spatial and Temporal Constraints Proceedings Article
In: International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2010), pp. 1181–1188, 2010, (Event Dates: May 2010).
Abstract | Links | BibTeX | Tags: agents, Coalition Formation, Disaster Management, Multi-agent scheduling, RoboCup Rescue
@inproceedings{eps268497,
title = {Coalition Formation with Spatial and Temporal Constraints},
author = {S. D. Ramchurn and Mariya Polukarov and Alessandro Farinelli and Nick Jennings and Cuong Trong},
url = {http://eprints.soton.ac.uk/268497/},
year = {2010},
date = {2010-01-01},
booktitle = {International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2010)},
pages = {1181–1188},
abstract = {The coordination of emergency responders and robots to undertake a number of tasks in disaster scenarios is a grand challenge for multi-agent systems. Central to this endeavour is the problem of forming the best teams (coalitions) of responders to perform the various tasks in the area where the disaster has struck. Moreover, these teams may have to form, disband, and reform in different areas of the disaster region. This is because in most cases there will be more tasks than agents. Hence, agents need to schedule themselves to attempt each task in turn. Second, the tasks themselves can be very complex: requiring the agents to work on them for different lengths of time and having deadlines by when they need to be completed. The problem is complicated still further when different coalitions perform tasks with different levels of efficiency. Given all these facets, we define this as The Coalition Formation with Spatial and Temporal constraints problem (CFSTP).We show that this problem is NP-hard–in particular, it contains the wellknown complex combinatorial problem of Team Orienteering as a special case. Based on this, we design a Mixed Integer Program to optimally solve small-scale instances of the CFSTP and develop new anytime heuristics that can, on average, complete 97% of the tasks for large problems (20 agents and 300 tasks). In so doing, our solutions represent the first results for CFSTP.},
note = {Event Dates: May 2010},
keywords = {agents, Coalition Formation, Disaster Management, Multi-agent scheduling, RoboCup Rescue},
pubstate = {published},
tppubtype = {inproceedings}
}
The coordination of emergency responders and robots to undertake a number of tasks in disaster scenarios is a grand challenge for multi-agent systems. Central to this endeavour is the problem of forming the best teams (coalitions) of responders to perform the various tasks in the area where the disaster has struck. Moreover, these teams may have to form, disband, and reform in different areas of the disaster region. This is because in most cases there will be more tasks than agents. Hence, agents need to schedule themselves to attempt each task in turn. Second, the tasks themselves can be very complex: requiring the agents to work on them for different lengths of time and having deadlines by when they need to be completed. The problem is complicated still further when different coalitions perform tasks with different levels of efficiency. Given all these facets, we define this as The Coalition Formation with Spatial and Temporal constraints problem (CFSTP).We show that this problem is NP-hard–in particular, it contains the wellknown complex combinatorial problem of Team Orienteering as a special case. Based on this, we design a Mixed Integer Program to optimally solve small-scale instances of the CFSTP and develop new anytime heuristics that can, on average, complete 97% of the tasks for large problems (20 agents and 300 tasks). In so doing, our solutions represent the first results for CFSTP.
Vytelingum, Perukrishnen; Ramchurn, Sarvapali D.; Voice, Thomas D.; Rogers, Alex; Jennings, Nicholas R.
Trading agents for the smart electricity grid Proceedings Article
In: The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), pp. 897–904, 2010, (Event Dates: May 10-14, 2010).
Abstract | Links | BibTeX | Tags: agents, balancing mechanism, continuous double auction, Energy, markets, smart grid
@inproceedings{eps268361,
title = {Trading agents for the smart electricity grid},
author = {Perukrishnen Vytelingum and Sarvapali D. Ramchurn and Thomas D. Voice and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/268361/},
year = {2010},
date = {2010-01-01},
booktitle = {The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010)},
pages = {897–904},
abstract = {The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforeseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).},
note = {Event Dates: May 10-14, 2010},
keywords = {agents, balancing mechanism, continuous double auction, Energy, markets, smart grid},
pubstate = {published},
tppubtype = {inproceedings}
}
The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforeseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).
Vytelingum, Perukrishnen; Voice, Thomas D.; Ramchurn, Sarvapali D.; Rogers, Alex; Jennings, Nicholas R.
Agent-Based Micro-Storage Management for the Smart Grid Proceedings Article
In: The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010) - Won the Best Paper Award, pp. 39–46, 2010, (Winner of the Best Paper Award Event Dates: May 10-14, 2010).
Abstract | Links | BibTeX | Tags: agent-based modelling, agents, Energy, game-theory, smart grid
@inproceedings{eps268360,
title = {Agent-Based Micro-Storage Management for the Smart Grid},
author = {Perukrishnen Vytelingum and Thomas D. Voice and Sarvapali D. Ramchurn and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/268360/},
year = {2010},
date = {2010-01-01},
booktitle = {The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010) - Won the Best Paper Award},
pages = {39–46},
abstract = {The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, more generation capacity, more carbon emissions, and, in the worst case, breaking down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).},
note = {Winner of the Best Paper Award Event Dates: May 10-14, 2010},
keywords = {agent-based modelling, agents, Energy, game-theory, smart grid},
pubstate = {published},
tppubtype = {inproceedings}
}
The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, more generation capacity, more carbon emissions, and, in the worst case, breaking down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).
1.
Ramchurn, Sarvapali; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nick
Agent-based control for decentralised demand side management in the smart grid Proceedings Article
In: The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), pp. 5–12, 2011.
@inproceedings{eps271985,
title = {Agent-based control for decentralised demand side management in the smart grid},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/271985/},
year = {2011},
date = {2011-01-01},
booktitle = {The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011)},
pages = {5–12},
abstract = {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).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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).
2.
Vytelingum, Perukrishnen; Voice, Thomas; Ramchurn, Sarvapali; Rogers, Alex; Jennings, Nick
Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid Journal Article
In: Journal of Artificial Intelligence Research, vol. 42, pp. 765–813, 2011, (AAMAS 2010 iRobot Best Paper Award).
@article{eps272961,
title = {Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid},
author = {Perukrishnen Vytelingum and Thomas Voice and Sarvapali Ramchurn and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/272961/},
year = {2011},
date = {2011-01-01},
journal = {Journal of Artificial Intelligence Research},
volume = {42},
pages = {765–813},
abstract = {In this paper, we present a novel decentralised management technique that allows electricity micro-storage devices, deployed within individual homes as part of a smart electricity grid, to converge to profitable and efficient behaviours. Specifically, we propose the use of software agents, residing on the users' smart meters, to automate and optimise the charging cycle of micro-storage devices in the home to minimise its costs, and we present a study of both the theoretical underpinnings and the implications of a practical solution, of using software agents for such micro-storage management. First, by formalising the strategic choice each agent makes in deciding when to charge its battery, we develop a game-theoretic framework within which we can analyse the competitive equilibria of an electricity grid populated by such agents and hence predict the best consumption profile for that population given their battery properties and individual load profiles. Our framework also allows us to compute theoretical bounds on the amount of storage that will be adopted by the population. Second, to analyse the practical implications of micro-storage deployments in the grid, we present a novel algorithm that each agent can use to optimise its battery storage profile in order to minimise its owner's costs. This algorithm uses a learning strategy that allows it to adapt as the price of electricity changes in real-time, and we show that the adoption of these strategies results in the system converging to the theoretical equilibria. Finally, we empirically evaluate the adoption of our micro-storage management technique within a complex setting, based on the UK electricity market, where agents may have widely varying load profiles, battery types, and learning rates. In this case, our approach yields savings of up to 14% in energy cost for an average consumer using a storage device with a capacity of less than 4.5 kWh and up to a 7% reduction in carbon emissions resulting from electricity generation (with only domestic consumers adopting micro-storage and, commercial and industrial consumers not changing their demand). Moreover, corroborating our theoretical bound, an equilibrium is shown to exist where no more than 48% of households would wish to own storage devices and where social welfare would also be improved (yielding overall annual savings of nearly pounds1.5B).},
note = {AAMAS 2010 iRobot Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present a novel decentralised management technique that allows electricity micro-storage devices, deployed within individual homes as part of a smart electricity grid, to converge to profitable and efficient behaviours. Specifically, we propose the use of software agents, residing on the users' smart meters, to automate and optimise the charging cycle of micro-storage devices in the home to minimise its costs, and we present a study of both the theoretical underpinnings and the implications of a practical solution, of using software agents for such micro-storage management. First, by formalising the strategic choice each agent makes in deciding when to charge its battery, we develop a game-theoretic framework within which we can analyse the competitive equilibria of an electricity grid populated by such agents and hence predict the best consumption profile for that population given their battery properties and individual load profiles. Our framework also allows us to compute theoretical bounds on the amount of storage that will be adopted by the population. Second, to analyse the practical implications of micro-storage deployments in the grid, we present a novel algorithm that each agent can use to optimise its battery storage profile in order to minimise its owner's costs. This algorithm uses a learning strategy that allows it to adapt as the price of electricity changes in real-time, and we show that the adoption of these strategies results in the system converging to the theoretical equilibria. Finally, we empirically evaluate the adoption of our micro-storage management technique within a complex setting, based on the UK electricity market, where agents may have widely varying load profiles, battery types, and learning rates. In this case, our approach yields savings of up to 14% in energy cost for an average consumer using a storage device with a capacity of less than 4.5 kWh and up to a 7% reduction in carbon emissions resulting from electricity generation (with only domestic consumers adopting micro-storage and, commercial and industrial consumers not changing their demand). Moreover, corroborating our theoretical bound, an equilibrium is shown to exist where no more than 48% of households would wish to own storage devices and where social welfare would also be improved (yielding overall annual savings of nearly pounds1.5B).
3.
Macarthur, Kathryn; Farinelli, Alessandro; Ramchurn, Sarvapali; Jennings, Nick
Efficient, Superstabilizing Decentralised Optimisation for Dynamic Task Allocation Environments Proceedings Article
In: Third International Workshop on: Optimisation in Multi-Agent Systems (OptMas) at the Ninth Joint Conference on Autonomous and Multi-Agent Systems, pp. 25–32, 2010, (Event Dates: 10 May 2010).
@inproceedings{eps268588,
title = {Efficient, Superstabilizing Decentralised Optimisation for Dynamic Task Allocation Environments},
author = {Kathryn Macarthur and Alessandro Farinelli and Sarvapali Ramchurn and Nick Jennings},
url = {http://eprints.soton.ac.uk/268588/},
year = {2010},
date = {2010-01-01},
booktitle = {Third International Workshop on: Optimisation in Multi-Agent Systems (OptMas) at the Ninth Joint Conference on Autonomous and Multi-Agent Systems},
pages = {25–32},
abstract = {Decentralised optimisation is a key issue for multi-agent systems, and while many solution techniques have been developed, few provide support for dynamic environments, which change over time, such as disaster management. Given this, in this paper, we present Bounded Fast Max Sum (BFMS): a novel, dynamic, superstabilizing algorithm which provides a bounded approximate solution to certain classes of distributed constraint optimisation problems. We achieve this by eliminating dependencies in the constraint functions, according to how much impact they have on the overall solution value. In more detail, we propose iGHS, which computes a maximum spanning tree on subsections of the constraint graph, in order to reduce communication and computation overheads. Given this, we empirically evaluate BFMS, which shows that BFMS reduces communication and computation done by Bounded Max Sum by up to 99%, while obtaining 60-88% of the optimal utility.},
note = {Event Dates: 10 May 2010},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Decentralised optimisation is a key issue for multi-agent systems, and while many solution techniques have been developed, few provide support for dynamic environments, which change over time, such as disaster management. Given this, in this paper, we present Bounded Fast Max Sum (BFMS): a novel, dynamic, superstabilizing algorithm which provides a bounded approximate solution to certain classes of distributed constraint optimisation problems. We achieve this by eliminating dependencies in the constraint functions, according to how much impact they have on the overall solution value. In more detail, we propose iGHS, which computes a maximum spanning tree on subsections of the constraint graph, in order to reduce communication and computation overheads. Given this, we empirically evaluate BFMS, which shows that BFMS reduces communication and computation done by Bounded Max Sum by up to 99%, while obtaining 60-88% of the optimal utility.
4.
Ramchurn, S. D.; Polukarov, Mariya; Farinelli, Alessandro; Jennings, Nick; Trong, Cuong
Coalition Formation with Spatial and Temporal Constraints Proceedings Article
In: International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2010), pp. 1181–1188, 2010, (Event Dates: May 2010).
@inproceedings{eps268497,
title = {Coalition Formation with Spatial and Temporal Constraints},
author = {S. D. Ramchurn and Mariya Polukarov and Alessandro Farinelli and Nick Jennings and Cuong Trong},
url = {http://eprints.soton.ac.uk/268497/},
year = {2010},
date = {2010-01-01},
booktitle = {International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2010)},
pages = {1181–1188},
abstract = {The coordination of emergency responders and robots to undertake a number of tasks in disaster scenarios is a grand challenge for multi-agent systems. Central to this endeavour is the problem of forming the best teams (coalitions) of responders to perform the various tasks in the area where the disaster has struck. Moreover, these teams may have to form, disband, and reform in different areas of the disaster region. This is because in most cases there will be more tasks than agents. Hence, agents need to schedule themselves to attempt each task in turn. Second, the tasks themselves can be very complex: requiring the agents to work on them for different lengths of time and having deadlines by when they need to be completed. The problem is complicated still further when different coalitions perform tasks with different levels of efficiency. Given all these facets, we define this as The Coalition Formation with Spatial and Temporal constraints problem (CFSTP).We show that this problem is NP-hard–in particular, it contains the wellknown complex combinatorial problem of Team Orienteering as a special case. Based on this, we design a Mixed Integer Program to optimally solve small-scale instances of the CFSTP and develop new anytime heuristics that can, on average, complete 97% of the tasks for large problems (20 agents and 300 tasks). In so doing, our solutions represent the first results for CFSTP.},
note = {Event Dates: May 2010},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The coordination of emergency responders and robots to undertake a number of tasks in disaster scenarios is a grand challenge for multi-agent systems. Central to this endeavour is the problem of forming the best teams (coalitions) of responders to perform the various tasks in the area where the disaster has struck. Moreover, these teams may have to form, disband, and reform in different areas of the disaster region. This is because in most cases there will be more tasks than agents. Hence, agents need to schedule themselves to attempt each task in turn. Second, the tasks themselves can be very complex: requiring the agents to work on them for different lengths of time and having deadlines by when they need to be completed. The problem is complicated still further when different coalitions perform tasks with different levels of efficiency. Given all these facets, we define this as The Coalition Formation with Spatial and Temporal constraints problem (CFSTP).We show that this problem is NP-hard–in particular, it contains the wellknown complex combinatorial problem of Team Orienteering as a special case. Based on this, we design a Mixed Integer Program to optimally solve small-scale instances of the CFSTP and develop new anytime heuristics that can, on average, complete 97% of the tasks for large problems (20 agents and 300 tasks). In so doing, our solutions represent the first results for CFSTP.
5.
Vytelingum, Perukrishnen; Ramchurn, Sarvapali D.; Voice, Thomas D.; Rogers, Alex; Jennings, Nicholas R.
Trading agents for the smart electricity grid Proceedings Article
In: The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), pp. 897–904, 2010, (Event Dates: May 10-14, 2010).
@inproceedings{eps268361,
title = {Trading agents for the smart electricity grid},
author = {Perukrishnen Vytelingum and Sarvapali D. Ramchurn and Thomas D. Voice and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/268361/},
year = {2010},
date = {2010-01-01},
booktitle = {The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010)},
pages = {897–904},
abstract = {The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforeseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).},
note = {Event Dates: May 10-14, 2010},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforeseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).
6.
Vytelingum, Perukrishnen; Voice, Thomas D.; Ramchurn, Sarvapali D.; Rogers, Alex; Jennings, Nicholas R.
Agent-Based Micro-Storage Management for the Smart Grid Proceedings Article
In: The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010) - Won the Best Paper Award, pp. 39–46, 2010, (Winner of the Best Paper Award Event Dates: May 10-14, 2010).
@inproceedings{eps268360,
title = {Agent-Based Micro-Storage Management for the Smart Grid},
author = {Perukrishnen Vytelingum and Thomas D. Voice and Sarvapali D. Ramchurn and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/268360/},
year = {2010},
date = {2010-01-01},
booktitle = {The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010) - Won the Best Paper Award},
pages = {39–46},
abstract = {The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, more generation capacity, more carbon emissions, and, in the worst case, breaking down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).},
note = {Winner of the Best Paper Award Event Dates: May 10-14, 2010},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, more generation capacity, more carbon emissions, and, in the worst case, breaking down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).
2011
Ramchurn, Sarvapali; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nick
Agent-based control for decentralised demand side management in the smart grid Proceedings Article
In: The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), pp. 5–12, 2011.
Abstract | Links | BibTeX | Tags: agent-based control, agents, demand-side management electricity, Energy, multi-agent systems
@inproceedings{eps271985,
title = {Agent-based control for decentralised demand side management in the smart grid},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/271985/},
year = {2011},
date = {2011-01-01},
booktitle = {The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011)},
pages = {5–12},
abstract = {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).},
keywords = {agent-based control, agents, demand-side management electricity, Energy, multi-agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
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).
Vytelingum, Perukrishnen; Voice, Thomas; Ramchurn, Sarvapali; Rogers, Alex; Jennings, Nick
Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid Journal Article
In: Journal of Artificial Intelligence Research, vol. 42, pp. 765–813, 2011, (AAMAS 2010 iRobot Best Paper Award).
Abstract | Links | BibTeX | Tags: agents, Energy, mas, multi-agent systems, provenance
@article{eps272961,
title = {Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid},
author = {Perukrishnen Vytelingum and Thomas Voice and Sarvapali Ramchurn and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/272961/},
year = {2011},
date = {2011-01-01},
journal = {Journal of Artificial Intelligence Research},
volume = {42},
pages = {765–813},
abstract = {In this paper, we present a novel decentralised management technique that allows electricity micro-storage devices, deployed within individual homes as part of a smart electricity grid, to converge to profitable and efficient behaviours. Specifically, we propose the use of software agents, residing on the users' smart meters, to automate and optimise the charging cycle of micro-storage devices in the home to minimise its costs, and we present a study of both the theoretical underpinnings and the implications of a practical solution, of using software agents for such micro-storage management. First, by formalising the strategic choice each agent makes in deciding when to charge its battery, we develop a game-theoretic framework within which we can analyse the competitive equilibria of an electricity grid populated by such agents and hence predict the best consumption profile for that population given their battery properties and individual load profiles. Our framework also allows us to compute theoretical bounds on the amount of storage that will be adopted by the population. Second, to analyse the practical implications of micro-storage deployments in the grid, we present a novel algorithm that each agent can use to optimise its battery storage profile in order to minimise its owner's costs. This algorithm uses a learning strategy that allows it to adapt as the price of electricity changes in real-time, and we show that the adoption of these strategies results in the system converging to the theoretical equilibria. Finally, we empirically evaluate the adoption of our micro-storage management technique within a complex setting, based on the UK electricity market, where agents may have widely varying load profiles, battery types, and learning rates. In this case, our approach yields savings of up to 14% in energy cost for an average consumer using a storage device with a capacity of less than 4.5 kWh and up to a 7% reduction in carbon emissions resulting from electricity generation (with only domestic consumers adopting micro-storage and, commercial and industrial consumers not changing their demand). Moreover, corroborating our theoretical bound, an equilibrium is shown to exist where no more than 48% of households would wish to own storage devices and where social welfare would also be improved (yielding overall annual savings of nearly pounds1.5B).},
note = {AAMAS 2010 iRobot Best Paper Award},
keywords = {agents, Energy, mas, multi-agent systems, provenance},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present a novel decentralised management technique that allows electricity micro-storage devices, deployed within individual homes as part of a smart electricity grid, to converge to profitable and efficient behaviours. Specifically, we propose the use of software agents, residing on the users' smart meters, to automate and optimise the charging cycle of micro-storage devices in the home to minimise its costs, and we present a study of both the theoretical underpinnings and the implications of a practical solution, of using software agents for such micro-storage management. First, by formalising the strategic choice each agent makes in deciding when to charge its battery, we develop a game-theoretic framework within which we can analyse the competitive equilibria of an electricity grid populated by such agents and hence predict the best consumption profile for that population given their battery properties and individual load profiles. Our framework also allows us to compute theoretical bounds on the amount of storage that will be adopted by the population. Second, to analyse the practical implications of micro-storage deployments in the grid, we present a novel algorithm that each agent can use to optimise its battery storage profile in order to minimise its owner's costs. This algorithm uses a learning strategy that allows it to adapt as the price of electricity changes in real-time, and we show that the adoption of these strategies results in the system converging to the theoretical equilibria. Finally, we empirically evaluate the adoption of our micro-storage management technique within a complex setting, based on the UK electricity market, where agents may have widely varying load profiles, battery types, and learning rates. In this case, our approach yields savings of up to 14% in energy cost for an average consumer using a storage device with a capacity of less than 4.5 kWh and up to a 7% reduction in carbon emissions resulting from electricity generation (with only domestic consumers adopting micro-storage and, commercial and industrial consumers not changing their demand). Moreover, corroborating our theoretical bound, an equilibrium is shown to exist where no more than 48% of households would wish to own storage devices and where social welfare would also be improved (yielding overall annual savings of nearly pounds1.5B).
2010
Macarthur, Kathryn; Farinelli, Alessandro; Ramchurn, Sarvapali; Jennings, Nick
Efficient, Superstabilizing Decentralised Optimisation for Dynamic Task Allocation Environments Proceedings Article
In: Third International Workshop on: Optimisation in Multi-Agent Systems (OptMas) at the Ninth Joint Conference on Autonomous and Multi-Agent Systems, pp. 25–32, 2010, (Event Dates: 10 May 2010).
Abstract | Links | BibTeX | Tags: agents, Disaster Management, mas, Multi-agent scheduling, multi-agent systems
@inproceedings{eps268588,
title = {Efficient, Superstabilizing Decentralised Optimisation for Dynamic Task Allocation Environments},
author = {Kathryn Macarthur and Alessandro Farinelli and Sarvapali Ramchurn and Nick Jennings},
url = {http://eprints.soton.ac.uk/268588/},
year = {2010},
date = {2010-01-01},
booktitle = {Third International Workshop on: Optimisation in Multi-Agent Systems (OptMas) at the Ninth Joint Conference on Autonomous and Multi-Agent Systems},
pages = {25–32},
abstract = {Decentralised optimisation is a key issue for multi-agent systems, and while many solution techniques have been developed, few provide support for dynamic environments, which change over time, such as disaster management. Given this, in this paper, we present Bounded Fast Max Sum (BFMS): a novel, dynamic, superstabilizing algorithm which provides a bounded approximate solution to certain classes of distributed constraint optimisation problems. We achieve this by eliminating dependencies in the constraint functions, according to how much impact they have on the overall solution value. In more detail, we propose iGHS, which computes a maximum spanning tree on subsections of the constraint graph, in order to reduce communication and computation overheads. Given this, we empirically evaluate BFMS, which shows that BFMS reduces communication and computation done by Bounded Max Sum by up to 99%, while obtaining 60-88% of the optimal utility.},
note = {Event Dates: 10 May 2010},
keywords = {agents, Disaster Management, mas, Multi-agent scheduling, multi-agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
Decentralised optimisation is a key issue for multi-agent systems, and while many solution techniques have been developed, few provide support for dynamic environments, which change over time, such as disaster management. Given this, in this paper, we present Bounded Fast Max Sum (BFMS): a novel, dynamic, superstabilizing algorithm which provides a bounded approximate solution to certain classes of distributed constraint optimisation problems. We achieve this by eliminating dependencies in the constraint functions, according to how much impact they have on the overall solution value. In more detail, we propose iGHS, which computes a maximum spanning tree on subsections of the constraint graph, in order to reduce communication and computation overheads. Given this, we empirically evaluate BFMS, which shows that BFMS reduces communication and computation done by Bounded Max Sum by up to 99%, while obtaining 60-88% of the optimal utility.
Ramchurn, S. D.; Polukarov, Mariya; Farinelli, Alessandro; Jennings, Nick; Trong, Cuong
Coalition Formation with Spatial and Temporal Constraints Proceedings Article
In: International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2010), pp. 1181–1188, 2010, (Event Dates: May 2010).
Abstract | Links | BibTeX | Tags: agents, Coalition Formation, Disaster Management, Multi-agent scheduling, RoboCup Rescue
@inproceedings{eps268497,
title = {Coalition Formation with Spatial and Temporal Constraints},
author = {S. D. Ramchurn and Mariya Polukarov and Alessandro Farinelli and Nick Jennings and Cuong Trong},
url = {http://eprints.soton.ac.uk/268497/},
year = {2010},
date = {2010-01-01},
booktitle = {International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2010)},
pages = {1181–1188},
abstract = {The coordination of emergency responders and robots to undertake a number of tasks in disaster scenarios is a grand challenge for multi-agent systems. Central to this endeavour is the problem of forming the best teams (coalitions) of responders to perform the various tasks in the area where the disaster has struck. Moreover, these teams may have to form, disband, and reform in different areas of the disaster region. This is because in most cases there will be more tasks than agents. Hence, agents need to schedule themselves to attempt each task in turn. Second, the tasks themselves can be very complex: requiring the agents to work on them for different lengths of time and having deadlines by when they need to be completed. The problem is complicated still further when different coalitions perform tasks with different levels of efficiency. Given all these facets, we define this as The Coalition Formation with Spatial and Temporal constraints problem (CFSTP).We show that this problem is NP-hard–in particular, it contains the wellknown complex combinatorial problem of Team Orienteering as a special case. Based on this, we design a Mixed Integer Program to optimally solve small-scale instances of the CFSTP and develop new anytime heuristics that can, on average, complete 97% of the tasks for large problems (20 agents and 300 tasks). In so doing, our solutions represent the first results for CFSTP.},
note = {Event Dates: May 2010},
keywords = {agents, Coalition Formation, Disaster Management, Multi-agent scheduling, RoboCup Rescue},
pubstate = {published},
tppubtype = {inproceedings}
}
The coordination of emergency responders and robots to undertake a number of tasks in disaster scenarios is a grand challenge for multi-agent systems. Central to this endeavour is the problem of forming the best teams (coalitions) of responders to perform the various tasks in the area where the disaster has struck. Moreover, these teams may have to form, disband, and reform in different areas of the disaster region. This is because in most cases there will be more tasks than agents. Hence, agents need to schedule themselves to attempt each task in turn. Second, the tasks themselves can be very complex: requiring the agents to work on them for different lengths of time and having deadlines by when they need to be completed. The problem is complicated still further when different coalitions perform tasks with different levels of efficiency. Given all these facets, we define this as The Coalition Formation with Spatial and Temporal constraints problem (CFSTP).We show that this problem is NP-hard–in particular, it contains the wellknown complex combinatorial problem of Team Orienteering as a special case. Based on this, we design a Mixed Integer Program to optimally solve small-scale instances of the CFSTP and develop new anytime heuristics that can, on average, complete 97% of the tasks for large problems (20 agents and 300 tasks). In so doing, our solutions represent the first results for CFSTP.
Vytelingum, Perukrishnen; Ramchurn, Sarvapali D.; Voice, Thomas D.; Rogers, Alex; Jennings, Nicholas R.
Trading agents for the smart electricity grid Proceedings Article
In: The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), pp. 897–904, 2010, (Event Dates: May 10-14, 2010).
Abstract | Links | BibTeX | Tags: agents, balancing mechanism, continuous double auction, Energy, markets, smart grid
@inproceedings{eps268361,
title = {Trading agents for the smart electricity grid},
author = {Perukrishnen Vytelingum and Sarvapali D. Ramchurn and Thomas D. Voice and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/268361/},
year = {2010},
date = {2010-01-01},
booktitle = {The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010)},
pages = {897–904},
abstract = {The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforeseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).},
note = {Event Dates: May 10-14, 2010},
keywords = {agents, balancing mechanism, continuous double auction, Energy, markets, smart grid},
pubstate = {published},
tppubtype = {inproceedings}
}
The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforeseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).
Vytelingum, Perukrishnen; Voice, Thomas D.; Ramchurn, Sarvapali D.; Rogers, Alex; Jennings, Nicholas R.
Agent-Based Micro-Storage Management for the Smart Grid Proceedings Article
In: The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010) - Won the Best Paper Award, pp. 39–46, 2010, (Winner of the Best Paper Award Event Dates: May 10-14, 2010).
Abstract | Links | BibTeX | Tags: agent-based modelling, agents, Energy, game-theory, smart grid
@inproceedings{eps268360,
title = {Agent-Based Micro-Storage Management for the Smart Grid},
author = {Perukrishnen Vytelingum and Thomas D. Voice and Sarvapali D. Ramchurn and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/268360/},
year = {2010},
date = {2010-01-01},
booktitle = {The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010) - Won the Best Paper Award},
pages = {39–46},
abstract = {The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, more generation capacity, more carbon emissions, and, in the worst case, breaking down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).},
note = {Winner of the Best Paper Award Event Dates: May 10-14, 2010},
keywords = {agent-based modelling, agents, Energy, game-theory, smart grid},
pubstate = {published},
tppubtype = {inproceedings}
}
The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, more generation capacity, more carbon emissions, and, in the worst case, breaking down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).
1.
Ramchurn, Sarvapali; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nick
Agent-based control for decentralised demand side management in the smart grid Proceedings Article
In: The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), pp. 5–12, 2011.
@inproceedings{eps271985,
title = {Agent-based control for decentralised demand side management in the smart grid},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/271985/},
year = {2011},
date = {2011-01-01},
booktitle = {The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011)},
pages = {5–12},
abstract = {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).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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).
2.
Vytelingum, Perukrishnen; Voice, Thomas; Ramchurn, Sarvapali; Rogers, Alex; Jennings, Nick
Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid Journal Article
In: Journal of Artificial Intelligence Research, vol. 42, pp. 765–813, 2011, (AAMAS 2010 iRobot Best Paper Award).
@article{eps272961,
title = {Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid},
author = {Perukrishnen Vytelingum and Thomas Voice and Sarvapali Ramchurn and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/272961/},
year = {2011},
date = {2011-01-01},
journal = {Journal of Artificial Intelligence Research},
volume = {42},
pages = {765–813},
abstract = {In this paper, we present a novel decentralised management technique that allows electricity micro-storage devices, deployed within individual homes as part of a smart electricity grid, to converge to profitable and efficient behaviours. Specifically, we propose the use of software agents, residing on the users' smart meters, to automate and optimise the charging cycle of micro-storage devices in the home to minimise its costs, and we present a study of both the theoretical underpinnings and the implications of a practical solution, of using software agents for such micro-storage management. First, by formalising the strategic choice each agent makes in deciding when to charge its battery, we develop a game-theoretic framework within which we can analyse the competitive equilibria of an electricity grid populated by such agents and hence predict the best consumption profile for that population given their battery properties and individual load profiles. Our framework also allows us to compute theoretical bounds on the amount of storage that will be adopted by the population. Second, to analyse the practical implications of micro-storage deployments in the grid, we present a novel algorithm that each agent can use to optimise its battery storage profile in order to minimise its owner's costs. This algorithm uses a learning strategy that allows it to adapt as the price of electricity changes in real-time, and we show that the adoption of these strategies results in the system converging to the theoretical equilibria. Finally, we empirically evaluate the adoption of our micro-storage management technique within a complex setting, based on the UK electricity market, where agents may have widely varying load profiles, battery types, and learning rates. In this case, our approach yields savings of up to 14% in energy cost for an average consumer using a storage device with a capacity of less than 4.5 kWh and up to a 7% reduction in carbon emissions resulting from electricity generation (with only domestic consumers adopting micro-storage and, commercial and industrial consumers not changing their demand). Moreover, corroborating our theoretical bound, an equilibrium is shown to exist where no more than 48% of households would wish to own storage devices and where social welfare would also be improved (yielding overall annual savings of nearly pounds1.5B).},
note = {AAMAS 2010 iRobot Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present a novel decentralised management technique that allows electricity micro-storage devices, deployed within individual homes as part of a smart electricity grid, to converge to profitable and efficient behaviours. Specifically, we propose the use of software agents, residing on the users' smart meters, to automate and optimise the charging cycle of micro-storage devices in the home to minimise its costs, and we present a study of both the theoretical underpinnings and the implications of a practical solution, of using software agents for such micro-storage management. First, by formalising the strategic choice each agent makes in deciding when to charge its battery, we develop a game-theoretic framework within which we can analyse the competitive equilibria of an electricity grid populated by such agents and hence predict the best consumption profile for that population given their battery properties and individual load profiles. Our framework also allows us to compute theoretical bounds on the amount of storage that will be adopted by the population. Second, to analyse the practical implications of micro-storage deployments in the grid, we present a novel algorithm that each agent can use to optimise its battery storage profile in order to minimise its owner's costs. This algorithm uses a learning strategy that allows it to adapt as the price of electricity changes in real-time, and we show that the adoption of these strategies results in the system converging to the theoretical equilibria. Finally, we empirically evaluate the adoption of our micro-storage management technique within a complex setting, based on the UK electricity market, where agents may have widely varying load profiles, battery types, and learning rates. In this case, our approach yields savings of up to 14% in energy cost for an average consumer using a storage device with a capacity of less than 4.5 kWh and up to a 7% reduction in carbon emissions resulting from electricity generation (with only domestic consumers adopting micro-storage and, commercial and industrial consumers not changing their demand). Moreover, corroborating our theoretical bound, an equilibrium is shown to exist where no more than 48% of households would wish to own storage devices and where social welfare would also be improved (yielding overall annual savings of nearly pounds1.5B).
3.
Macarthur, Kathryn; Farinelli, Alessandro; Ramchurn, Sarvapali; Jennings, Nick
Efficient, Superstabilizing Decentralised Optimisation for Dynamic Task Allocation Environments Proceedings Article
In: Third International Workshop on: Optimisation in Multi-Agent Systems (OptMas) at the Ninth Joint Conference on Autonomous and Multi-Agent Systems, pp. 25–32, 2010, (Event Dates: 10 May 2010).
@inproceedings{eps268588,
title = {Efficient, Superstabilizing Decentralised Optimisation for Dynamic Task Allocation Environments},
author = {Kathryn Macarthur and Alessandro Farinelli and Sarvapali Ramchurn and Nick Jennings},
url = {http://eprints.soton.ac.uk/268588/},
year = {2010},
date = {2010-01-01},
booktitle = {Third International Workshop on: Optimisation in Multi-Agent Systems (OptMas) at the Ninth Joint Conference on Autonomous and Multi-Agent Systems},
pages = {25–32},
abstract = {Decentralised optimisation is a key issue for multi-agent systems, and while many solution techniques have been developed, few provide support for dynamic environments, which change over time, such as disaster management. Given this, in this paper, we present Bounded Fast Max Sum (BFMS): a novel, dynamic, superstabilizing algorithm which provides a bounded approximate solution to certain classes of distributed constraint optimisation problems. We achieve this by eliminating dependencies in the constraint functions, according to how much impact they have on the overall solution value. In more detail, we propose iGHS, which computes a maximum spanning tree on subsections of the constraint graph, in order to reduce communication and computation overheads. Given this, we empirically evaluate BFMS, which shows that BFMS reduces communication and computation done by Bounded Max Sum by up to 99%, while obtaining 60-88% of the optimal utility.},
note = {Event Dates: 10 May 2010},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Decentralised optimisation is a key issue for multi-agent systems, and while many solution techniques have been developed, few provide support for dynamic environments, which change over time, such as disaster management. Given this, in this paper, we present Bounded Fast Max Sum (BFMS): a novel, dynamic, superstabilizing algorithm which provides a bounded approximate solution to certain classes of distributed constraint optimisation problems. We achieve this by eliminating dependencies in the constraint functions, according to how much impact they have on the overall solution value. In more detail, we propose iGHS, which computes a maximum spanning tree on subsections of the constraint graph, in order to reduce communication and computation overheads. Given this, we empirically evaluate BFMS, which shows that BFMS reduces communication and computation done by Bounded Max Sum by up to 99%, while obtaining 60-88% of the optimal utility.
4.
Ramchurn, S. D.; Polukarov, Mariya; Farinelli, Alessandro; Jennings, Nick; Trong, Cuong
Coalition Formation with Spatial and Temporal Constraints Proceedings Article
In: International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2010), pp. 1181–1188, 2010, (Event Dates: May 2010).
@inproceedings{eps268497,
title = {Coalition Formation with Spatial and Temporal Constraints},
author = {S. D. Ramchurn and Mariya Polukarov and Alessandro Farinelli and Nick Jennings and Cuong Trong},
url = {http://eprints.soton.ac.uk/268497/},
year = {2010},
date = {2010-01-01},
booktitle = {International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2010)},
pages = {1181–1188},
abstract = {The coordination of emergency responders and robots to undertake a number of tasks in disaster scenarios is a grand challenge for multi-agent systems. Central to this endeavour is the problem of forming the best teams (coalitions) of responders to perform the various tasks in the area where the disaster has struck. Moreover, these teams may have to form, disband, and reform in different areas of the disaster region. This is because in most cases there will be more tasks than agents. Hence, agents need to schedule themselves to attempt each task in turn. Second, the tasks themselves can be very complex: requiring the agents to work on them for different lengths of time and having deadlines by when they need to be completed. The problem is complicated still further when different coalitions perform tasks with different levels of efficiency. Given all these facets, we define this as The Coalition Formation with Spatial and Temporal constraints problem (CFSTP).We show that this problem is NP-hard–in particular, it contains the wellknown complex combinatorial problem of Team Orienteering as a special case. Based on this, we design a Mixed Integer Program to optimally solve small-scale instances of the CFSTP and develop new anytime heuristics that can, on average, complete 97% of the tasks for large problems (20 agents and 300 tasks). In so doing, our solutions represent the first results for CFSTP.},
note = {Event Dates: May 2010},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The coordination of emergency responders and robots to undertake a number of tasks in disaster scenarios is a grand challenge for multi-agent systems. Central to this endeavour is the problem of forming the best teams (coalitions) of responders to perform the various tasks in the area where the disaster has struck. Moreover, these teams may have to form, disband, and reform in different areas of the disaster region. This is because in most cases there will be more tasks than agents. Hence, agents need to schedule themselves to attempt each task in turn. Second, the tasks themselves can be very complex: requiring the agents to work on them for different lengths of time and having deadlines by when they need to be completed. The problem is complicated still further when different coalitions perform tasks with different levels of efficiency. Given all these facets, we define this as The Coalition Formation with Spatial and Temporal constraints problem (CFSTP).We show that this problem is NP-hard–in particular, it contains the wellknown complex combinatorial problem of Team Orienteering as a special case. Based on this, we design a Mixed Integer Program to optimally solve small-scale instances of the CFSTP and develop new anytime heuristics that can, on average, complete 97% of the tasks for large problems (20 agents and 300 tasks). In so doing, our solutions represent the first results for CFSTP.
5.
Vytelingum, Perukrishnen; Ramchurn, Sarvapali D.; Voice, Thomas D.; Rogers, Alex; Jennings, Nicholas R.
Trading agents for the smart electricity grid Proceedings Article
In: The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), pp. 897–904, 2010, (Event Dates: May 10-14, 2010).
@inproceedings{eps268361,
title = {Trading agents for the smart electricity grid},
author = {Perukrishnen Vytelingum and Sarvapali D. Ramchurn and Thomas D. Voice and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/268361/},
year = {2010},
date = {2010-01-01},
booktitle = {The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010)},
pages = {897–904},
abstract = {The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforeseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).},
note = {Event Dates: May 10-14, 2010},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforeseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).
6.
Vytelingum, Perukrishnen; Voice, Thomas D.; Ramchurn, Sarvapali D.; Rogers, Alex; Jennings, Nicholas R.
Agent-Based Micro-Storage Management for the Smart Grid Proceedings Article
In: The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010) - Won the Best Paper Award, pp. 39–46, 2010, (Winner of the Best Paper Award Event Dates: May 10-14, 2010).
@inproceedings{eps268360,
title = {Agent-Based Micro-Storage Management for the Smart Grid},
author = {Perukrishnen Vytelingum and Thomas D. Voice and Sarvapali D. Ramchurn and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/268360/},
year = {2010},
date = {2010-01-01},
booktitle = {The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010) - Won the Best Paper Award},
pages = {39–46},
abstract = {The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, more generation capacity, more carbon emissions, and, in the worst case, breaking down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).},
note = {Winner of the Best Paper Award Event Dates: May 10-14, 2010},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, more generation capacity, more carbon emissions, and, in the worst case, breaking down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).
Selected Publications
Multi-agent signal-less intersection management with dynamic platoon formation
No data available.
https://eprints.soton.ac.uk/478647/
@inproceedings{soton478647,
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542--1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542--1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
AI Foundation Models: initial review, CMA Consultation, TAS Hub Response
No data available.
https://eprints.soton.ac.uk/477553/
@misc{soton477553,
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The effect of data visualisation quality and task density on human-swarm interaction
No data available.
https://eprints.soton.ac.uk/479970/
@inproceedings{soton479970,
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Demonstrating performance benefits of human-swarm teaming
Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.
https://eprints.soton.ac.uk/479903/
@inproceedings{soton479903,
title = {Demonstrating performance benefits of human-swarm teaming},
author = {William Hunt and Jack Ryan and Ayodeji O Abioye and Sarvapali D Ramchurn and Mohammad D Soorati},
url = {https://eprints.soton.ac.uk/479903/},
year = {2023},
date = {2023-05-01},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {3062--3064},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {Demonstrating performance benefits of human-swarm teaming},
author = {William Hunt and Jack Ryan and Ayodeji O Abioye and Sarvapali D Ramchurn and Mohammad D Soorati},
url = {https://eprints.soton.ac.uk/479903/},
year = {2023},
date = {2023-05-01},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {3062--3064},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2011
Ramchurn, Sarvapali; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nick
Agent-based control for decentralised demand side management in the smart grid Proceedings Article
In: The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), pp. 5–12, 2011.
@inproceedings{eps271985,
title = {Agent-based control for decentralised demand side management in the smart grid},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/271985/},
year = {2011},
date = {2011-01-01},
booktitle = {The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011)},
pages = {5–12},
abstract = {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).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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).
Vytelingum, Perukrishnen; Voice, Thomas; Ramchurn, Sarvapali; Rogers, Alex; Jennings, Nick
Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid Journal Article
In: Journal of Artificial Intelligence Research, vol. 42, pp. 765–813, 2011, (AAMAS 2010 iRobot Best Paper Award).
@article{eps272961,
title = {Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid},
author = {Perukrishnen Vytelingum and Thomas Voice and Sarvapali Ramchurn and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/272961/},
year = {2011},
date = {2011-01-01},
journal = {Journal of Artificial Intelligence Research},
volume = {42},
pages = {765–813},
abstract = {In this paper, we present a novel decentralised management technique that allows electricity micro-storage devices, deployed within individual homes as part of a smart electricity grid, to converge to profitable and efficient behaviours. Specifically, we propose the use of software agents, residing on the users' smart meters, to automate and optimise the charging cycle of micro-storage devices in the home to minimise its costs, and we present a study of both the theoretical underpinnings and the implications of a practical solution, of using software agents for such micro-storage management. First, by formalising the strategic choice each agent makes in deciding when to charge its battery, we develop a game-theoretic framework within which we can analyse the competitive equilibria of an electricity grid populated by such agents and hence predict the best consumption profile for that population given their battery properties and individual load profiles. Our framework also allows us to compute theoretical bounds on the amount of storage that will be adopted by the population. Second, to analyse the practical implications of micro-storage deployments in the grid, we present a novel algorithm that each agent can use to optimise its battery storage profile in order to minimise its owner's costs. This algorithm uses a learning strategy that allows it to adapt as the price of electricity changes in real-time, and we show that the adoption of these strategies results in the system converging to the theoretical equilibria. Finally, we empirically evaluate the adoption of our micro-storage management technique within a complex setting, based on the UK electricity market, where agents may have widely varying load profiles, battery types, and learning rates. In this case, our approach yields savings of up to 14% in energy cost for an average consumer using a storage device with a capacity of less than 4.5 kWh and up to a 7% reduction in carbon emissions resulting from electricity generation (with only domestic consumers adopting micro-storage and, commercial and industrial consumers not changing their demand). Moreover, corroborating our theoretical bound, an equilibrium is shown to exist where no more than 48% of households would wish to own storage devices and where social welfare would also be improved (yielding overall annual savings of nearly pounds1.5B).},
note = {AAMAS 2010 iRobot Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present a novel decentralised management technique that allows electricity micro-storage devices, deployed within individual homes as part of a smart electricity grid, to converge to profitable and efficient behaviours. Specifically, we propose the use of software agents, residing on the users' smart meters, to automate and optimise the charging cycle of micro-storage devices in the home to minimise its costs, and we present a study of both the theoretical underpinnings and the implications of a practical solution, of using software agents for such micro-storage management. First, by formalising the strategic choice each agent makes in deciding when to charge its battery, we develop a game-theoretic framework within which we can analyse the competitive equilibria of an electricity grid populated by such agents and hence predict the best consumption profile for that population given their battery properties and individual load profiles. Our framework also allows us to compute theoretical bounds on the amount of storage that will be adopted by the population. Second, to analyse the practical implications of micro-storage deployments in the grid, we present a novel algorithm that each agent can use to optimise its battery storage profile in order to minimise its owner's costs. This algorithm uses a learning strategy that allows it to adapt as the price of electricity changes in real-time, and we show that the adoption of these strategies results in the system converging to the theoretical equilibria. Finally, we empirically evaluate the adoption of our micro-storage management technique within a complex setting, based on the UK electricity market, where agents may have widely varying load profiles, battery types, and learning rates. In this case, our approach yields savings of up to 14% in energy cost for an average consumer using a storage device with a capacity of less than 4.5 kWh and up to a 7% reduction in carbon emissions resulting from electricity generation (with only domestic consumers adopting micro-storage and, commercial and industrial consumers not changing their demand). Moreover, corroborating our theoretical bound, an equilibrium is shown to exist where no more than 48% of households would wish to own storage devices and where social welfare would also be improved (yielding overall annual savings of nearly pounds1.5B).
2010
Macarthur, Kathryn; Farinelli, Alessandro; Ramchurn, Sarvapali; Jennings, Nick
Efficient, Superstabilizing Decentralised Optimisation for Dynamic Task Allocation Environments Proceedings Article
In: Third International Workshop on: Optimisation in Multi-Agent Systems (OptMas) at the Ninth Joint Conference on Autonomous and Multi-Agent Systems, pp. 25–32, 2010, (Event Dates: 10 May 2010).
@inproceedings{eps268588,
title = {Efficient, Superstabilizing Decentralised Optimisation for Dynamic Task Allocation Environments},
author = {Kathryn Macarthur and Alessandro Farinelli and Sarvapali Ramchurn and Nick Jennings},
url = {http://eprints.soton.ac.uk/268588/},
year = {2010},
date = {2010-01-01},
booktitle = {Third International Workshop on: Optimisation in Multi-Agent Systems (OptMas) at the Ninth Joint Conference on Autonomous and Multi-Agent Systems},
pages = {25–32},
abstract = {Decentralised optimisation is a key issue for multi-agent systems, and while many solution techniques have been developed, few provide support for dynamic environments, which change over time, such as disaster management. Given this, in this paper, we present Bounded Fast Max Sum (BFMS): a novel, dynamic, superstabilizing algorithm which provides a bounded approximate solution to certain classes of distributed constraint optimisation problems. We achieve this by eliminating dependencies in the constraint functions, according to how much impact they have on the overall solution value. In more detail, we propose iGHS, which computes a maximum spanning tree on subsections of the constraint graph, in order to reduce communication and computation overheads. Given this, we empirically evaluate BFMS, which shows that BFMS reduces communication and computation done by Bounded Max Sum by up to 99%, while obtaining 60-88% of the optimal utility.},
note = {Event Dates: 10 May 2010},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Decentralised optimisation is a key issue for multi-agent systems, and while many solution techniques have been developed, few provide support for dynamic environments, which change over time, such as disaster management. Given this, in this paper, we present Bounded Fast Max Sum (BFMS): a novel, dynamic, superstabilizing algorithm which provides a bounded approximate solution to certain classes of distributed constraint optimisation problems. We achieve this by eliminating dependencies in the constraint functions, according to how much impact they have on the overall solution value. In more detail, we propose iGHS, which computes a maximum spanning tree on subsections of the constraint graph, in order to reduce communication and computation overheads. Given this, we empirically evaluate BFMS, which shows that BFMS reduces communication and computation done by Bounded Max Sum by up to 99%, while obtaining 60-88% of the optimal utility.
Ramchurn, S. D.; Polukarov, Mariya; Farinelli, Alessandro; Jennings, Nick; Trong, Cuong
Coalition Formation with Spatial and Temporal Constraints Proceedings Article
In: International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2010), pp. 1181–1188, 2010, (Event Dates: May 2010).
@inproceedings{eps268497,
title = {Coalition Formation with Spatial and Temporal Constraints},
author = {S. D. Ramchurn and Mariya Polukarov and Alessandro Farinelli and Nick Jennings and Cuong Trong},
url = {http://eprints.soton.ac.uk/268497/},
year = {2010},
date = {2010-01-01},
booktitle = {International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2010)},
pages = {1181–1188},
abstract = {The coordination of emergency responders and robots to undertake a number of tasks in disaster scenarios is a grand challenge for multi-agent systems. Central to this endeavour is the problem of forming the best teams (coalitions) of responders to perform the various tasks in the area where the disaster has struck. Moreover, these teams may have to form, disband, and reform in different areas of the disaster region. This is because in most cases there will be more tasks than agents. Hence, agents need to schedule themselves to attempt each task in turn. Second, the tasks themselves can be very complex: requiring the agents to work on them for different lengths of time and having deadlines by when they need to be completed. The problem is complicated still further when different coalitions perform tasks with different levels of efficiency. Given all these facets, we define this as The Coalition Formation with Spatial and Temporal constraints problem (CFSTP).We show that this problem is NP-hard–in particular, it contains the wellknown complex combinatorial problem of Team Orienteering as a special case. Based on this, we design a Mixed Integer Program to optimally solve small-scale instances of the CFSTP and develop new anytime heuristics that can, on average, complete 97% of the tasks for large problems (20 agents and 300 tasks). In so doing, our solutions represent the first results for CFSTP.},
note = {Event Dates: May 2010},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The coordination of emergency responders and robots to undertake a number of tasks in disaster scenarios is a grand challenge for multi-agent systems. Central to this endeavour is the problem of forming the best teams (coalitions) of responders to perform the various tasks in the area where the disaster has struck. Moreover, these teams may have to form, disband, and reform in different areas of the disaster region. This is because in most cases there will be more tasks than agents. Hence, agents need to schedule themselves to attempt each task in turn. Second, the tasks themselves can be very complex: requiring the agents to work on them for different lengths of time and having deadlines by when they need to be completed. The problem is complicated still further when different coalitions perform tasks with different levels of efficiency. Given all these facets, we define this as The Coalition Formation with Spatial and Temporal constraints problem (CFSTP).We show that this problem is NP-hard–in particular, it contains the wellknown complex combinatorial problem of Team Orienteering as a special case. Based on this, we design a Mixed Integer Program to optimally solve small-scale instances of the CFSTP and develop new anytime heuristics that can, on average, complete 97% of the tasks for large problems (20 agents and 300 tasks). In so doing, our solutions represent the first results for CFSTP.
Vytelingum, Perukrishnen; Ramchurn, Sarvapali D.; Voice, Thomas D.; Rogers, Alex; Jennings, Nicholas R.
Trading agents for the smart electricity grid Proceedings Article
In: The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), pp. 897–904, 2010, (Event Dates: May 10-14, 2010).
@inproceedings{eps268361,
title = {Trading agents for the smart electricity grid},
author = {Perukrishnen Vytelingum and Sarvapali D. Ramchurn and Thomas D. Voice and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/268361/},
year = {2010},
date = {2010-01-01},
booktitle = {The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010)},
pages = {897–904},
abstract = {The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforeseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).},
note = {Event Dates: May 10-14, 2010},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforeseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).
Vytelingum, Perukrishnen; Voice, Thomas D.; Ramchurn, Sarvapali D.; Rogers, Alex; Jennings, Nicholas R.
Agent-Based Micro-Storage Management for the Smart Grid Proceedings Article
In: The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010) - Won the Best Paper Award, pp. 39–46, 2010, (Winner of the Best Paper Award Event Dates: May 10-14, 2010).
@inproceedings{eps268360,
title = {Agent-Based Micro-Storage Management for the Smart Grid},
author = {Perukrishnen Vytelingum and Thomas D. Voice and Sarvapali D. Ramchurn and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/268360/},
year = {2010},
date = {2010-01-01},
booktitle = {The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010) - Won the Best Paper Award},
pages = {39–46},
abstract = {The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, more generation capacity, more carbon emissions, and, in the worst case, breaking down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).},
note = {Winner of the Best Paper Award Event Dates: May 10-14, 2010},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, more generation capacity, more carbon emissions, and, in the worst case, breaking down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).