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2015
Rigas, Nick Bassiliades Sarvapali D. Ramchurn Emmanouil
Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey Journal Article
In: IEEE Transactions on Intelligent Transportation Systems, 2015.
Abstract | Links | BibTeX | Tags: Electric Vehicles, electricity, Energy, Multi-agent scheduling, Survey
@article{rigas:etal:2015,
title = {Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey},
author = {Nick Bassiliades Sarvapali D. Ramchurn Emmanouil Rigas},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7000557&filter%3DAND%28p_IS_Number%3A7174612%29},
year = {2015},
date = {2015-01-16},
journal = {IEEE Transactions on Intelligent Transportation Systems},
abstract = {Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space.},
keywords = {Electric Vehicles, electricity, Energy, Multi-agent scheduling, Survey},
pubstate = {published},
tppubtype = {article}
}
Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space.
2014
Alan, Alper; Costanza, Enrico; Fischer, J.; Ramchurn, Sarvapali; Rodden, T.; Jennings, N. R.
A field study of human-agent interaction for electricity tariff switching Proceedings Article
In: Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems, 2014.
Abstract | Links | BibTeX | Tags: electricity, Energy, hai, hci, human-agent interaction
@inproceedings{eps360820,
title = {A field study of human-agent interaction for electricity tariff switching},
author = {Alper Alan and Enrico Costanza and J. Fischer and Sarvapali Ramchurn and T. Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/360820/},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems},
abstract = {Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called Tariff Agent, to study notions of flexible autonomy in the context of tariff switching. Tariff Agent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonom.},
keywords = {electricity, Energy, hai, hci, human-agent interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called Tariff Agent, to study notions of flexible autonomy in the context of tariff switching. Tariff Agent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonom.
2013
Ramchurn, Sarvapali; Osborne, Michael; Parson, Oliver; Rahwan, Talal; Maleki, Sasan; Reece, Steve; Huynh, Trung Dong; Alam, Muddasser; Fischer, Joel; Rodden, Tom; Moreau, Luc; Roberts, Sephen
AgentSwitch: towards smart electricity tariff selection Proceedings Article
In: 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), International Foundation for Autonomous Agents and Multiagent Systems, 2013.
Abstract | Links | BibTeX | Tags: electricity, Energy, group buying, optimisation, provenance, recommender systems, smart grid
@inproceedings{eps349815,
title = {AgentSwitch: towards smart electricity tariff selection},
author = {Sarvapali Ramchurn and Michael Osborne and Oliver Parson and Talal Rahwan and Sasan Maleki and Steve Reece and Trung Dong Huynh and Muddasser Alam and Joel Fischer and Tom Rodden and Luc Moreau and Sephen Roberts},
url = {http://eprints.soton.ac.uk/349815/},
year = {2013},
date = {2013-01-01},
booktitle = {12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
abstract = {In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.},
keywords = {electricity, Energy, group buying, optimisation, provenance, recommender systems, smart grid},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.
2012
Costanza, Enrico; Ramchurn, Sarvapali D.; Jennings, Nicholas R.
Understanding domestic energy consumption through interactive visualisation: a field study Proceedings Article
In: UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 216–225, 2012.
Abstract | Links | BibTeX | Tags: electricity, Energy, hai, home energy management, human-agent interaction
@inproceedings{eps338804,
title = {Understanding domestic energy consumption through interactive visualisation: a field study},
author = {Enrico Costanza and Sarvapali D. Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/338804/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {216–225},
abstract = {Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.},
keywords = {electricity, Energy, hai, home energy management, human-agent interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.
Ramchurn, Sarvapali; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nicholas R.
Putting the Smarts into the Smart Grid: A Grand Challenge for Artificial Intelligence Journal Article
In: Communications of the ACM, vol. 55, no. 4, pp. 86–97, 2012.
Abstract | Links | BibTeX | Tags: electricity, Energy, smart grid, smart home
@article{eps272606,
title = {Putting the Smarts into the Smart Grid: A Grand Challenge for Artificial Intelligence},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/272606/},
year = {2012},
date = {2012-01-01},
journal = {Communications of the ACM},
volume = {55},
number = {4},
pages = {86–97},
publisher = {ACM},
abstract = {The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.},
keywords = {electricity, Energy, smart grid, smart home},
pubstate = {published},
tppubtype = {article}
}
The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.
Rogers, Alex; Ramchurn, Sarvapali; Jennings, Nicholas R.
Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research Proceedings Article
In: Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), pp. 2166–2172, 2012.
Abstract | Links | BibTeX | Tags: electricity, Energy, home energy management
@inproceedings{eps337560,
title = {Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research},
author = {Alex Rogers and Sarvapali Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/337560/},
year = {2012},
date = {2012-01-01},
booktitle = {Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
pages = {2166–2172},
abstract = {Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.},
keywords = {electricity, Energy, home energy management},
pubstate = {published},
tppubtype = {inproceedings}
}
Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.
2011
Voice, Thomas; Vytelingum, Perukrishnen; Ramchurn, Sarvapali; Rogers, Alex; Jennings, Nick
Decentralised Control of Micro-Storage in the Smart Grid Proceedings Article
In: AAAI-11: Twenty-Fifth Conference on Artificial Intelligence, pp. 1421–1426, 2011, (Event Dates: August 7?11, 2011).
Abstract | Links | BibTeX | Tags: electricity, Energy, smart grid
@inproceedings{eps272262,
title = {Decentralised Control of Micro-Storage in the Smart Grid},
author = {Thomas Voice and Perukrishnen Vytelingum and Sarvapali Ramchurn and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/272262/},
year = {2011},
date = {2011-01-01},
booktitle = {AAAI-11: Twenty-Fifth Conference on Artificial Intelligence},
pages = {1421–1426},
abstract = {In this paper, we propose a novel decentralised control mechanism to manage micro-storage in the smart grid. Our approach uses an adaptive pricing scheme that energy suppliers apply to home smart agents controlling micro-storage devices. In particular, we prove that the interaction between a supplier using our pricing scheme and the actions of selfish micro-storage agents forms a globally stable feedback loop that converges to an efficient equilibrium. We further propose a market strategy that allows the supplier to reduce wholesale purchasing costs without increasing the uncertainty and variance for its aggregate consumer demand. Moreover, we empirically evaluate our mechanism (based on the UK grid data) and show that it yields savings of up to 16% in energy cost for consumers using storage devices with average capacity 10 kWh. Furthermore, we show that it is robust against extreme system changes.},
note = {Event Dates: August 7?11, 2011},
keywords = {electricity, Energy, smart grid},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a novel decentralised control mechanism to manage micro-storage in the smart grid. Our approach uses an adaptive pricing scheme that energy suppliers apply to home smart agents controlling micro-storage devices. In particular, we prove that the interaction between a supplier using our pricing scheme and the actions of selfish micro-storage agents forms a globally stable feedback loop that converges to an efficient equilibrium. We further propose a market strategy that allows the supplier to reduce wholesale purchasing costs without increasing the uncertainty and variance for its aggregate consumer demand. Moreover, we empirically evaluate our mechanism (based on the UK grid data) and show that it yields savings of up to 16% in energy cost for consumers using storage devices with average capacity 10 kWh. Furthermore, we show that it is robust against extreme system changes.
1.
Rigas, Nick Bassiliades Sarvapali D. Ramchurn Emmanouil
Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey Journal Article
In: IEEE Transactions on Intelligent Transportation Systems, 2015.
@article{rigas:etal:2015,
title = {Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey},
author = {Nick Bassiliades Sarvapali D. Ramchurn Emmanouil Rigas},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7000557&filter%3DAND%28p_IS_Number%3A7174612%29},
year = {2015},
date = {2015-01-16},
journal = {IEEE Transactions on Intelligent Transportation Systems},
abstract = {Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space.
2.
Alan, Alper; Costanza, Enrico; Fischer, J.; Ramchurn, Sarvapali; Rodden, T.; Jennings, N. R.
A field study of human-agent interaction for electricity tariff switching Proceedings Article
In: Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems, 2014.
@inproceedings{eps360820,
title = {A field study of human-agent interaction for electricity tariff switching},
author = {Alper Alan and Enrico Costanza and J. Fischer and Sarvapali Ramchurn and T. Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/360820/},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems},
abstract = {Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called Tariff Agent, to study notions of flexible autonomy in the context of tariff switching. Tariff Agent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonom.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called Tariff Agent, to study notions of flexible autonomy in the context of tariff switching. Tariff Agent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonom.
3.
Ramchurn, Sarvapali; Osborne, Michael; Parson, Oliver; Rahwan, Talal; Maleki, Sasan; Reece, Steve; Huynh, Trung Dong; Alam, Muddasser; Fischer, Joel; Rodden, Tom; Moreau, Luc; Roberts, Sephen
AgentSwitch: towards smart electricity tariff selection Proceedings Article
In: 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), International Foundation for Autonomous Agents and Multiagent Systems, 2013.
@inproceedings{eps349815,
title = {AgentSwitch: towards smart electricity tariff selection},
author = {Sarvapali Ramchurn and Michael Osborne and Oliver Parson and Talal Rahwan and Sasan Maleki and Steve Reece and Trung Dong Huynh and Muddasser Alam and Joel Fischer and Tom Rodden and Luc Moreau and Sephen Roberts},
url = {http://eprints.soton.ac.uk/349815/},
year = {2013},
date = {2013-01-01},
booktitle = {12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
abstract = {In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.
4.
Costanza, Enrico; Ramchurn, Sarvapali D.; Jennings, Nicholas R.
Understanding domestic energy consumption through interactive visualisation: a field study Proceedings Article
In: UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 216–225, 2012.
@inproceedings{eps338804,
title = {Understanding domestic energy consumption through interactive visualisation: a field study},
author = {Enrico Costanza and Sarvapali D. Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/338804/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {216–225},
abstract = {Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.
5.
Ramchurn, Sarvapali; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nicholas R.
Putting the Smarts into the Smart Grid: A Grand Challenge for Artificial Intelligence Journal Article
In: Communications of the ACM, vol. 55, no. 4, pp. 86–97, 2012.
@article{eps272606,
title = {Putting the Smarts into the Smart Grid: A Grand Challenge for Artificial Intelligence},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/272606/},
year = {2012},
date = {2012-01-01},
journal = {Communications of the ACM},
volume = {55},
number = {4},
pages = {86–97},
publisher = {ACM},
abstract = {The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.
6.
Rogers, Alex; Ramchurn, Sarvapali; Jennings, Nicholas R.
Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research Proceedings Article
In: Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), pp. 2166–2172, 2012.
@inproceedings{eps337560,
title = {Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research},
author = {Alex Rogers and Sarvapali Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/337560/},
year = {2012},
date = {2012-01-01},
booktitle = {Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
pages = {2166–2172},
abstract = {Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.
7.
Voice, Thomas; Vytelingum, Perukrishnen; Ramchurn, Sarvapali; Rogers, Alex; Jennings, Nick
Decentralised Control of Micro-Storage in the Smart Grid Proceedings Article
In: AAAI-11: Twenty-Fifth Conference on Artificial Intelligence, pp. 1421–1426, 2011, (Event Dates: August 7?11, 2011).
@inproceedings{eps272262,
title = {Decentralised Control of Micro-Storage in the Smart Grid},
author = {Thomas Voice and Perukrishnen Vytelingum and Sarvapali Ramchurn and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/272262/},
year = {2011},
date = {2011-01-01},
booktitle = {AAAI-11: Twenty-Fifth Conference on Artificial Intelligence},
pages = {1421–1426},
abstract = {In this paper, we propose a novel decentralised control mechanism to manage micro-storage in the smart grid. Our approach uses an adaptive pricing scheme that energy suppliers apply to home smart agents controlling micro-storage devices. In particular, we prove that the interaction between a supplier using our pricing scheme and the actions of selfish micro-storage agents forms a globally stable feedback loop that converges to an efficient equilibrium. We further propose a market strategy that allows the supplier to reduce wholesale purchasing costs without increasing the uncertainty and variance for its aggregate consumer demand. Moreover, we empirically evaluate our mechanism (based on the UK grid data) and show that it yields savings of up to 16% in energy cost for consumers using storage devices with average capacity 10 kWh. Furthermore, we show that it is robust against extreme system changes.},
note = {Event Dates: August 7?11, 2011},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a novel decentralised control mechanism to manage micro-storage in the smart grid. Our approach uses an adaptive pricing scheme that energy suppliers apply to home smart agents controlling micro-storage devices. In particular, we prove that the interaction between a supplier using our pricing scheme and the actions of selfish micro-storage agents forms a globally stable feedback loop that converges to an efficient equilibrium. We further propose a market strategy that allows the supplier to reduce wholesale purchasing costs without increasing the uncertainty and variance for its aggregate consumer demand. Moreover, we empirically evaluate our mechanism (based on the UK grid data) and show that it yields savings of up to 16% in energy cost for consumers using storage devices with average capacity 10 kWh. Furthermore, we show that it is robust against extreme system changes.
2015
Rigas, Nick Bassiliades Sarvapali D. Ramchurn Emmanouil
Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey Journal Article
In: IEEE Transactions on Intelligent Transportation Systems, 2015.
Abstract | Links | BibTeX | Tags: Electric Vehicles, electricity, Energy, Multi-agent scheduling, Survey
@article{rigas:etal:2015,
title = {Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey},
author = {Nick Bassiliades Sarvapali D. Ramchurn Emmanouil Rigas},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7000557&filter%3DAND%28p_IS_Number%3A7174612%29},
year = {2015},
date = {2015-01-16},
journal = {IEEE Transactions on Intelligent Transportation Systems},
abstract = {Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space.},
keywords = {Electric Vehicles, electricity, Energy, Multi-agent scheduling, Survey},
pubstate = {published},
tppubtype = {article}
}
Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space.
2014
Alan, Alper; Costanza, Enrico; Fischer, J.; Ramchurn, Sarvapali; Rodden, T.; Jennings, N. R.
A field study of human-agent interaction for electricity tariff switching Proceedings Article
In: Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems, 2014.
Abstract | Links | BibTeX | Tags: electricity, Energy, hai, hci, human-agent interaction
@inproceedings{eps360820,
title = {A field study of human-agent interaction for electricity tariff switching},
author = {Alper Alan and Enrico Costanza and J. Fischer and Sarvapali Ramchurn and T. Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/360820/},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems},
abstract = {Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called Tariff Agent, to study notions of flexible autonomy in the context of tariff switching. Tariff Agent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonom.},
keywords = {electricity, Energy, hai, hci, human-agent interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called Tariff Agent, to study notions of flexible autonomy in the context of tariff switching. Tariff Agent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonom.
2013
Ramchurn, Sarvapali; Osborne, Michael; Parson, Oliver; Rahwan, Talal; Maleki, Sasan; Reece, Steve; Huynh, Trung Dong; Alam, Muddasser; Fischer, Joel; Rodden, Tom; Moreau, Luc; Roberts, Sephen
AgentSwitch: towards smart electricity tariff selection Proceedings Article
In: 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), International Foundation for Autonomous Agents and Multiagent Systems, 2013.
Abstract | Links | BibTeX | Tags: electricity, Energy, group buying, optimisation, provenance, recommender systems, smart grid
@inproceedings{eps349815,
title = {AgentSwitch: towards smart electricity tariff selection},
author = {Sarvapali Ramchurn and Michael Osborne and Oliver Parson and Talal Rahwan and Sasan Maleki and Steve Reece and Trung Dong Huynh and Muddasser Alam and Joel Fischer and Tom Rodden and Luc Moreau and Sephen Roberts},
url = {http://eprints.soton.ac.uk/349815/},
year = {2013},
date = {2013-01-01},
booktitle = {12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
abstract = {In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.},
keywords = {electricity, Energy, group buying, optimisation, provenance, recommender systems, smart grid},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.
2012
Costanza, Enrico; Ramchurn, Sarvapali D.; Jennings, Nicholas R.
Understanding domestic energy consumption through interactive visualisation: a field study Proceedings Article
In: UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 216–225, 2012.
Abstract | Links | BibTeX | Tags: electricity, Energy, hai, home energy management, human-agent interaction
@inproceedings{eps338804,
title = {Understanding domestic energy consumption through interactive visualisation: a field study},
author = {Enrico Costanza and Sarvapali D. Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/338804/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {216–225},
abstract = {Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.},
keywords = {electricity, Energy, hai, home energy management, human-agent interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.
Ramchurn, Sarvapali; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nicholas R.
Putting the Smarts into the Smart Grid: A Grand Challenge for Artificial Intelligence Journal Article
In: Communications of the ACM, vol. 55, no. 4, pp. 86–97, 2012.
Abstract | Links | BibTeX | Tags: electricity, Energy, smart grid, smart home
@article{eps272606,
title = {Putting the Smarts into the Smart Grid: A Grand Challenge for Artificial Intelligence},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/272606/},
year = {2012},
date = {2012-01-01},
journal = {Communications of the ACM},
volume = {55},
number = {4},
pages = {86–97},
publisher = {ACM},
abstract = {The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.},
keywords = {electricity, Energy, smart grid, smart home},
pubstate = {published},
tppubtype = {article}
}
The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.
Rogers, Alex; Ramchurn, Sarvapali; Jennings, Nicholas R.
Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research Proceedings Article
In: Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), pp. 2166–2172, 2012.
Abstract | Links | BibTeX | Tags: electricity, Energy, home energy management
@inproceedings{eps337560,
title = {Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research},
author = {Alex Rogers and Sarvapali Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/337560/},
year = {2012},
date = {2012-01-01},
booktitle = {Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
pages = {2166–2172},
abstract = {Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.},
keywords = {electricity, Energy, home energy management},
pubstate = {published},
tppubtype = {inproceedings}
}
Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.
2011
Voice, Thomas; Vytelingum, Perukrishnen; Ramchurn, Sarvapali; Rogers, Alex; Jennings, Nick
Decentralised Control of Micro-Storage in the Smart Grid Proceedings Article
In: AAAI-11: Twenty-Fifth Conference on Artificial Intelligence, pp. 1421–1426, 2011, (Event Dates: August 7?11, 2011).
Abstract | Links | BibTeX | Tags: electricity, Energy, smart grid
@inproceedings{eps272262,
title = {Decentralised Control of Micro-Storage in the Smart Grid},
author = {Thomas Voice and Perukrishnen Vytelingum and Sarvapali Ramchurn and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/272262/},
year = {2011},
date = {2011-01-01},
booktitle = {AAAI-11: Twenty-Fifth Conference on Artificial Intelligence},
pages = {1421–1426},
abstract = {In this paper, we propose a novel decentralised control mechanism to manage micro-storage in the smart grid. Our approach uses an adaptive pricing scheme that energy suppliers apply to home smart agents controlling micro-storage devices. In particular, we prove that the interaction between a supplier using our pricing scheme and the actions of selfish micro-storage agents forms a globally stable feedback loop that converges to an efficient equilibrium. We further propose a market strategy that allows the supplier to reduce wholesale purchasing costs without increasing the uncertainty and variance for its aggregate consumer demand. Moreover, we empirically evaluate our mechanism (based on the UK grid data) and show that it yields savings of up to 16% in energy cost for consumers using storage devices with average capacity 10 kWh. Furthermore, we show that it is robust against extreme system changes.},
note = {Event Dates: August 7?11, 2011},
keywords = {electricity, Energy, smart grid},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a novel decentralised control mechanism to manage micro-storage in the smart grid. Our approach uses an adaptive pricing scheme that energy suppliers apply to home smart agents controlling micro-storage devices. In particular, we prove that the interaction between a supplier using our pricing scheme and the actions of selfish micro-storage agents forms a globally stable feedback loop that converges to an efficient equilibrium. We further propose a market strategy that allows the supplier to reduce wholesale purchasing costs without increasing the uncertainty and variance for its aggregate consumer demand. Moreover, we empirically evaluate our mechanism (based on the UK grid data) and show that it yields savings of up to 16% in energy cost for consumers using storage devices with average capacity 10 kWh. Furthermore, we show that it is robust against extreme system changes.
1.
Rigas, Nick Bassiliades Sarvapali D. Ramchurn Emmanouil
Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey Journal Article
In: IEEE Transactions on Intelligent Transportation Systems, 2015.
@article{rigas:etal:2015,
title = {Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey},
author = {Nick Bassiliades Sarvapali D. Ramchurn Emmanouil Rigas},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7000557&filter%3DAND%28p_IS_Number%3A7174612%29},
year = {2015},
date = {2015-01-16},
journal = {IEEE Transactions on Intelligent Transportation Systems},
abstract = {Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space.
2.
Alan, Alper; Costanza, Enrico; Fischer, J.; Ramchurn, Sarvapali; Rodden, T.; Jennings, N. R.
A field study of human-agent interaction for electricity tariff switching Proceedings Article
In: Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems, 2014.
@inproceedings{eps360820,
title = {A field study of human-agent interaction for electricity tariff switching},
author = {Alper Alan and Enrico Costanza and J. Fischer and Sarvapali Ramchurn and T. Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/360820/},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems},
abstract = {Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called Tariff Agent, to study notions of flexible autonomy in the context of tariff switching. Tariff Agent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonom.},
keywords = {},
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Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called Tariff Agent, to study notions of flexible autonomy in the context of tariff switching. Tariff Agent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonom.
3.
Ramchurn, Sarvapali; Osborne, Michael; Parson, Oliver; Rahwan, Talal; Maleki, Sasan; Reece, Steve; Huynh, Trung Dong; Alam, Muddasser; Fischer, Joel; Rodden, Tom; Moreau, Luc; Roberts, Sephen
AgentSwitch: towards smart electricity tariff selection Proceedings Article
In: 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), International Foundation for Autonomous Agents and Multiagent Systems, 2013.
@inproceedings{eps349815,
title = {AgentSwitch: towards smart electricity tariff selection},
author = {Sarvapali Ramchurn and Michael Osborne and Oliver Parson and Talal Rahwan and Sasan Maleki and Steve Reece and Trung Dong Huynh and Muddasser Alam and Joel Fischer and Tom Rodden and Luc Moreau and Sephen Roberts},
url = {http://eprints.soton.ac.uk/349815/},
year = {2013},
date = {2013-01-01},
booktitle = {12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
abstract = {In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.},
keywords = {},
pubstate = {published},
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In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.
4.
Costanza, Enrico; Ramchurn, Sarvapali D.; Jennings, Nicholas R.
Understanding domestic energy consumption through interactive visualisation: a field study Proceedings Article
In: UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 216–225, 2012.
@inproceedings{eps338804,
title = {Understanding domestic energy consumption through interactive visualisation: a field study},
author = {Enrico Costanza and Sarvapali D. Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/338804/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {216–225},
abstract = {Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.
5.
Ramchurn, Sarvapali; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nicholas R.
Putting the Smarts into the Smart Grid: A Grand Challenge for Artificial Intelligence Journal Article
In: Communications of the ACM, vol. 55, no. 4, pp. 86–97, 2012.
@article{eps272606,
title = {Putting the Smarts into the Smart Grid: A Grand Challenge for Artificial Intelligence},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/272606/},
year = {2012},
date = {2012-01-01},
journal = {Communications of the ACM},
volume = {55},
number = {4},
pages = {86–97},
publisher = {ACM},
abstract = {The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.},
keywords = {},
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The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.
6.
Rogers, Alex; Ramchurn, Sarvapali; Jennings, Nicholas R.
Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research Proceedings Article
In: Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), pp. 2166–2172, 2012.
@inproceedings{eps337560,
title = {Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research},
author = {Alex Rogers and Sarvapali Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/337560/},
year = {2012},
date = {2012-01-01},
booktitle = {Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
pages = {2166–2172},
abstract = {Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.},
keywords = {},
pubstate = {published},
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Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.
7.
Voice, Thomas; Vytelingum, Perukrishnen; Ramchurn, Sarvapali; Rogers, Alex; Jennings, Nick
Decentralised Control of Micro-Storage in the Smart Grid Proceedings Article
In: AAAI-11: Twenty-Fifth Conference on Artificial Intelligence, pp. 1421–1426, 2011, (Event Dates: August 7?11, 2011).
@inproceedings{eps272262,
title = {Decentralised Control of Micro-Storage in the Smart Grid},
author = {Thomas Voice and Perukrishnen Vytelingum and Sarvapali Ramchurn and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/272262/},
year = {2011},
date = {2011-01-01},
booktitle = {AAAI-11: Twenty-Fifth Conference on Artificial Intelligence},
pages = {1421–1426},
abstract = {In this paper, we propose a novel decentralised control mechanism to manage micro-storage in the smart grid. Our approach uses an adaptive pricing scheme that energy suppliers apply to home smart agents controlling micro-storage devices. In particular, we prove that the interaction between a supplier using our pricing scheme and the actions of selfish micro-storage agents forms a globally stable feedback loop that converges to an efficient equilibrium. We further propose a market strategy that allows the supplier to reduce wholesale purchasing costs without increasing the uncertainty and variance for its aggregate consumer demand. Moreover, we empirically evaluate our mechanism (based on the UK grid data) and show that it yields savings of up to 16% in energy cost for consumers using storage devices with average capacity 10 kWh. Furthermore, we show that it is robust against extreme system changes.},
note = {Event Dates: August 7?11, 2011},
keywords = {},
pubstate = {published},
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In this paper, we propose a novel decentralised control mechanism to manage micro-storage in the smart grid. Our approach uses an adaptive pricing scheme that energy suppliers apply to home smart agents controlling micro-storage devices. In particular, we prove that the interaction between a supplier using our pricing scheme and the actions of selfish micro-storage agents forms a globally stable feedback loop that converges to an efficient equilibrium. We further propose a market strategy that allows the supplier to reduce wholesale purchasing costs without increasing the uncertainty and variance for its aggregate consumer demand. Moreover, we empirically evaluate our mechanism (based on the UK grid data) and show that it yields savings of up to 16% in energy cost for consumers using storage devices with average capacity 10 kWh. Furthermore, we show that it is robust against extreme system changes.
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}
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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 = {},
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tppubtype = {misc}
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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}
}
2015
Rigas, Nick Bassiliades Sarvapali D. Ramchurn Emmanouil
Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey Journal Article
In: IEEE Transactions on Intelligent Transportation Systems, 2015.
@article{rigas:etal:2015,
title = {Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey},
author = {Nick Bassiliades Sarvapali D. Ramchurn Emmanouil Rigas},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7000557&filter%3DAND%28p_IS_Number%3A7174612%29},
year = {2015},
date = {2015-01-16},
journal = {IEEE Transactions on Intelligent Transportation Systems},
abstract = {Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space.
2014
Alan, Alper; Costanza, Enrico; Fischer, J.; Ramchurn, Sarvapali; Rodden, T.; Jennings, N. R.
A field study of human-agent interaction for electricity tariff switching Proceedings Article
In: Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems, 2014.
@inproceedings{eps360820,
title = {A field study of human-agent interaction for electricity tariff switching},
author = {Alper Alan and Enrico Costanza and J. Fischer and Sarvapali Ramchurn and T. Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/360820/},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems},
abstract = {Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called Tariff Agent, to study notions of flexible autonomy in the context of tariff switching. Tariff Agent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonom.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called Tariff Agent, to study notions of flexible autonomy in the context of tariff switching. Tariff Agent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonom.
2013
Ramchurn, Sarvapali; Osborne, Michael; Parson, Oliver; Rahwan, Talal; Maleki, Sasan; Reece, Steve; Huynh, Trung Dong; Alam, Muddasser; Fischer, Joel; Rodden, Tom; Moreau, Luc; Roberts, Sephen
AgentSwitch: towards smart electricity tariff selection Proceedings Article
In: 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), International Foundation for Autonomous Agents and Multiagent Systems, 2013.
@inproceedings{eps349815,
title = {AgentSwitch: towards smart electricity tariff selection},
author = {Sarvapali Ramchurn and Michael Osborne and Oliver Parson and Talal Rahwan and Sasan Maleki and Steve Reece and Trung Dong Huynh and Muddasser Alam and Joel Fischer and Tom Rodden and Luc Moreau and Sephen Roberts},
url = {http://eprints.soton.ac.uk/349815/},
year = {2013},
date = {2013-01-01},
booktitle = {12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
abstract = {In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.},
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pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.
2012
Costanza, Enrico; Ramchurn, Sarvapali D.; Jennings, Nicholas R.
Understanding domestic energy consumption through interactive visualisation: a field study Proceedings Article
In: UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 216–225, 2012.
@inproceedings{eps338804,
title = {Understanding domestic energy consumption through interactive visualisation: a field study},
author = {Enrico Costanza and Sarvapali D. Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/338804/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {216–225},
abstract = {Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.
Ramchurn, Sarvapali; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nicholas R.
Putting the Smarts into the Smart Grid: A Grand Challenge for Artificial Intelligence Journal Article
In: Communications of the ACM, vol. 55, no. 4, pp. 86–97, 2012.
@article{eps272606,
title = {Putting the Smarts into the Smart Grid: A Grand Challenge for Artificial Intelligence},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/272606/},
year = {2012},
date = {2012-01-01},
journal = {Communications of the ACM},
volume = {55},
number = {4},
pages = {86–97},
publisher = {ACM},
abstract = {The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.
Rogers, Alex; Ramchurn, Sarvapali; Jennings, Nicholas R.
Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research Proceedings Article
In: Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), pp. 2166–2172, 2012.
@inproceedings{eps337560,
title = {Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research},
author = {Alex Rogers and Sarvapali Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/337560/},
year = {2012},
date = {2012-01-01},
booktitle = {Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
pages = {2166–2172},
abstract = {Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.
2011
Voice, Thomas; Vytelingum, Perukrishnen; Ramchurn, Sarvapali; Rogers, Alex; Jennings, Nick
Decentralised Control of Micro-Storage in the Smart Grid Proceedings Article
In: AAAI-11: Twenty-Fifth Conference on Artificial Intelligence, pp. 1421–1426, 2011, (Event Dates: August 7?11, 2011).
@inproceedings{eps272262,
title = {Decentralised Control of Micro-Storage in the Smart Grid},
author = {Thomas Voice and Perukrishnen Vytelingum and Sarvapali Ramchurn and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/272262/},
year = {2011},
date = {2011-01-01},
booktitle = {AAAI-11: Twenty-Fifth Conference on Artificial Intelligence},
pages = {1421–1426},
abstract = {In this paper, we propose a novel decentralised control mechanism to manage micro-storage in the smart grid. Our approach uses an adaptive pricing scheme that energy suppliers apply to home smart agents controlling micro-storage devices. In particular, we prove that the interaction between a supplier using our pricing scheme and the actions of selfish micro-storage agents forms a globally stable feedback loop that converges to an efficient equilibrium. We further propose a market strategy that allows the supplier to reduce wholesale purchasing costs without increasing the uncertainty and variance for its aggregate consumer demand. Moreover, we empirically evaluate our mechanism (based on the UK grid data) and show that it yields savings of up to 16% in energy cost for consumers using storage devices with average capacity 10 kWh. Furthermore, we show that it is robust against extreme system changes.},
note = {Event Dates: August 7?11, 2011},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a novel decentralised control mechanism to manage micro-storage in the smart grid. Our approach uses an adaptive pricing scheme that energy suppliers apply to home smart agents controlling micro-storage devices. In particular, we prove that the interaction between a supplier using our pricing scheme and the actions of selfish micro-storage agents forms a globally stable feedback loop that converges to an efficient equilibrium. We further propose a market strategy that allows the supplier to reduce wholesale purchasing costs without increasing the uncertainty and variance for its aggregate consumer demand. Moreover, we empirically evaluate our mechanism (based on the UK grid data) and show that it yields savings of up to 16% in energy cost for consumers using storage devices with average capacity 10 kWh. Furthermore, we show that it is robust against extreme system changes.