Projects
![](https://www.sramchurn.com/wp-content/plugins/boxer/framework/timthumb.php?src=https://www.sramchurn.com/wp-content/uploads/2014/04/3.png&w=&h=60&a=c&f=" alt="ICIF" title="ICIF" "ICIF")
The International Centre for Infrastructure Futures (ICIF) Project will create a shared, facilitated learning environment in which social scientists, engineers, industrialists, policy makers and other stakeholders can research and learn together to understand how better to exploit the technical and market opportunities that emerge from the increased interdependence of infrastructure systems. The Centre will focus on the development and implementation of innovative business models and aims to support UK firms wishing to exploit them in international markets. The Centre will undertake a wide range of research activities on infrastructure interdependencies with users, which will allow problems to be discovered and addressed earlier and at lower cost. Because infrastructure innovations alter the social distribution of risks and rewards, the public needs to be involved in decision making to ensure business models and forms of regulation are socially robust. As a consequence, the Centre has a major focus on using its research to catalyse a broader national debate about the future of the UK’s infrastructure, and how it might contribute towards a more sustainable, economically vibrant, and fair society.
As systems based on human-agent collectives grow in scale, complexity and temporal extent, we will increasingly require a principled science that allows us to reason about the computational and human aspects of these systems if we are to avoid developments that are unsafe, unreliable and lack the appropriate safeguards to ensure societal acceptance. Delivering this science is the core research objective of this programme.
Publications
2017
Alper Alan Mike Shann, Sven Seuken; Ramchurn, Sarvapali
Save Money or Feel Cozy? A Field Experiment Evaluation of a Smart Thermostat that Learns Heating Preferences Proceedings Article
In: Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems, 2017.
@inproceedings{seuken:etal:2017,
title = {Save Money or Feel Cozy? A Field Experiment Evaluation of a Smart Thermostat that Learns Heating Preferences},
author = {Mike Shann, Alper Alan, Sven Seuken, Enrico Costanza and Sarvapali Ramchurn},
year = {2017},
date = {2017-05-02},
booktitle = {Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Farinelli, Alessandro; Bicego, Manuele; Bistaffa, Filippo; Ramchurn, Sarvapali D.
A Hierarchical Clustering Approach to Large-scale Near-optimal Coalition Formation with Quality Guarantees Journal Article
In: Engineering Applications of Artificial Intelligence (EAAI), vol. 57, pp. 170-185, 2017.
@article{farinelli:etal:2017,
title = {A Hierarchical Clustering Approach to Large-scale Near-optimal Coalition Formation with Quality Guarantees},
author = {Alessandro Farinelli and Manuele Bicego and Filippo Bistaffa and Sarvapali D. Ramchurn},
url = {http://www.sramchurn.com/wp-content/uploads/2017/02/1-s2.0-S0952197616302536-main.pdf},
doi = {http://dx.doi.org/10.1016/j.engappai.2016.12.018},
year = {2017},
date = {2017-02-01},
journal = {Engineering Applications of Artificial Intelligence (EAAI)},
volume = {57},
pages = {170-185},
abstract = {Coalition formation is a fundamental approach to multi-agent coordination, and a key challenge in this context
is the coalition structure generation problem, where a set of agents must be partitioned into the best set of
coalitions. This problem is NP-hard and typical optimal algorithms do not scale to more than 50 agents: efficient
approximate solutions are therefore needed for hundreds or thousands of agents. In this paper we propose a
novel heuristic, based on ideas and tools used in the data clustering domain. In particular, we present a coalition
formation algorithm inspired by the well known class of hierarchical agglomerative clustering techniques
(Linkage algorithms). We present different variants of the algorithm, which we call Coalition Linkage (C-Link)
and demonstrate how such algorithm can be adapted to graph restricted coalition formation problems (where
an interaction graph defined among the agents restricts the set of feasible coalitions). Moreover, we discuss how
we can provide an upper bound on the value of the optimal coalition structure, and we show that for specific
characteristic functions we can provide such bounds while maintaining polynomial computational costs and
memory requirements. We empirically evaluate the different variants of the C-Link algorithm on two synthetic
benchmark data-sets, as well as in two real world scenarios, involving a collective energy purchasing and a ridesharing application. In these settings C-Link achieves promising results providing high quality solutions and
solving problem involving thousands of agents in few minutes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Coalition formation is a fundamental approach to multi-agent coordination, and a key challenge in this context
is the coalition structure generation problem, where a set of agents must be partitioned into the best set of
coalitions. This problem is NP-hard and typical optimal algorithms do not scale to more than 50 agents: efficient
approximate solutions are therefore needed for hundreds or thousands of agents. In this paper we propose a
novel heuristic, based on ideas and tools used in the data clustering domain. In particular, we present a coalition
formation algorithm inspired by the well known class of hierarchical agglomerative clustering techniques
(Linkage algorithms). We present different variants of the algorithm, which we call Coalition Linkage (C-Link)
and demonstrate how such algorithm can be adapted to graph restricted coalition formation problems (where
an interaction graph defined among the agents restricts the set of feasible coalitions). Moreover, we discuss how
we can provide an upper bound on the value of the optimal coalition structure, and we show that for specific
characteristic functions we can provide such bounds while maintaining polynomial computational costs and
memory requirements. We empirically evaluate the different variants of the C-Link algorithm on two synthetic
benchmark data-sets, as well as in two real world scenarios, involving a collective energy purchasing and a ridesharing application. In these settings C-Link achieves promising results providing high quality solutions and
solving problem involving thousands of agents in few minutes.
is the coalition structure generation problem, where a set of agents must be partitioned into the best set of
coalitions. This problem is NP-hard and typical optimal algorithms do not scale to more than 50 agents: efficient
approximate solutions are therefore needed for hundreds or thousands of agents. In this paper we propose a
novel heuristic, based on ideas and tools used in the data clustering domain. In particular, we present a coalition
formation algorithm inspired by the well known class of hierarchical agglomerative clustering techniques
(Linkage algorithms). We present different variants of the algorithm, which we call Coalition Linkage (C-Link)
and demonstrate how such algorithm can be adapted to graph restricted coalition formation problems (where
an interaction graph defined among the agents restricts the set of feasible coalitions). Moreover, we discuss how
we can provide an upper bound on the value of the optimal coalition structure, and we show that for specific
characteristic functions we can provide such bounds while maintaining polynomial computational costs and
memory requirements. We empirically evaluate the different variants of the C-Link algorithm on two synthetic
benchmark data-sets, as well as in two real world scenarios, involving a collective energy purchasing and a ridesharing application. In these settings C-Link achieves promising results providing high quality solutions and
solving problem involving thousands of agents in few minutes.
2015
Sarvapali D. Ramchurn Dengji Zhao, Enrico H. Gerding; Jennings, Nicholas R.
Balanced Trade Reduction for Dual-Role Exchange Markets Proceedings Article
In: Proceedings of the AAAI Conference, 2015.
@inproceedings{zhao:etal:2015,
title = {Balanced Trade Reduction for Dual-Role Exchange Markets},
author = {Dengji Zhao, Sarvapali D. Ramchurn, Enrico H. Gerding, and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/372050/},
year = {2015},
date = {2015-01-25},
booktitle = {Proceedings of the AAAI Conference},
abstract = {We consider dual-role exchange markets, where traders can offer to both buy and sell the same commodity in the exchange but, if they transact, they can only be either a buyer or a seller, which is determined by the market mechanism. To design desirable mechanisms for such exchanges, we show that existing solutions may not be incentive compatible, and more importantly, cause the market maker to suffer a significant deficit. Hence, to combat this problem, following McAfee’s trade reduc- tion approach, we propose a new trade reduction mech- anism, called balanced trade reduction, that is incen- tive compatible and also provides flexible trade-offs be- tween efficiency and deficit.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider dual-role exchange markets, where traders can offer to both buy and sell the same commodity in the exchange but, if they transact, they can only be either a buyer or a seller, which is determined by the market mechanism. To design desirable mechanisms for such exchanges, we show that existing solutions may not be incentive compatible, and more importantly, cause the market maker to suffer a significant deficit. Hence, to combat this problem, following McAfee’s trade reduc- tion approach, we propose a new trade reduction mech- anism, called balanced trade reduction, that is incen- tive compatible and also provides flexible trade-offs be- tween efficiency and deficit.
Sarvapali D. Ramchurn Emmanouil Rigas, Nick Bassiliades
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 = {Emmanouil Rigas, Sarvapali D. Ramchurn, Nick Bassiliades},
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.
},
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pubstate = {published},
tppubtype = {article}
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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.
Holyhead, James C.; Ramchurn, Sarvapali D.; Rogers, Alex
Consumer Targeting in Residential Demand Response Programmes Proceedings Article
In: Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems, pp. 7–16, ACM, Bangalore, India, 2015, ISBN: 978-1-4503-3609-3.
@inproceedings{Holyhead:2015:CTR:2768510.2768531,
title = {Consumer Targeting in Residential Demand Response Programmes},
author = {Holyhead, James C. and Ramchurn, Sarvapali D. and Rogers, Alex},
url = {http://doi.acm.org/10.1145/2768510.2768531},
isbn = {978-1-4503-3609-3},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems},
pages = {7--16},
publisher = {ACM},
address = {Bangalore, India},
series = {e-Energy '15},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2014
F. Recchia M. Bicego, A. Farinelli
Behavioural biometrics using electricity load profiles Journal Article
In: Proceedings of the International Conference on Pattern Recognition, 2014.
@article{bicego:etal:2014,
title = {Behavioural biometrics using electricity load profiles},
author = {M. Bicego, F. Recchia, A. Farinelli, S. D. Ramchurn, E. Grosso},
url = {http://www.sramchurn.com/wp-content/uploads/2014/10/CR_v1.pdf},
year = {2014},
date = {2014-08-24},
journal = {Proceedings of the International Conference on Pattern Recognition},
abstract = {Modelling behavioural biometric patterns is a key
issue for modern user centric applications, aimed at better monitoring
users’ activities, understanding their habits and detecting
their identity. Following this trend, this paper investigates whether
the electrical energy consumption of a user can be a distinctive
behavioural biometric trait. In particular we analyse daily and
weekly load profiles showing that they are closely related to
the identity of the users. Hence, we believe that this level of
analysis can open interesting application scenarios in the field of
energy management and it provides a good working framework
for the continuous development of smart environments with
demonstrable benefits on real-world implementations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Modelling behavioural biometric patterns is a key
issue for modern user centric applications, aimed at better monitoring
users’ activities, understanding their habits and detecting
their identity. Following this trend, this paper investigates whether
the electrical energy consumption of a user can be a distinctive
behavioural biometric trait. In particular we analyse daily and
weekly load profiles showing that they are closely related to
the identity of the users. Hence, we believe that this level of
analysis can open interesting application scenarios in the field of
energy management and it provides a good working framework
for the continuous development of smart environments with
demonstrable benefits on real-world implementations.
issue for modern user centric applications, aimed at better monitoring
users’ activities, understanding their habits and detecting
their identity. Following this trend, this paper investigates whether
the electrical energy consumption of a user can be a distinctive
behavioural biometric trait. In particular we analyse daily and
weekly load profiles showing that they are closely related to
the identity of the users. Hence, we believe that this level of
analysis can open interesting application scenarios in the field of
energy management and it provides a good working framework
for the continuous development of smart environments with
demonstrable benefits on real-world implementations.
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.
Costanza, Enrico; Fischer, Joel E; Colley, James A; Rodden, Tom; Ramchurn, Sarvapali; Jennings, Nicholas R.
Doing the laundry with agents: a field trial of a future smart energy system in the home Proceedings Article
In: ACM CHI Conference on Human Factors in Computing Systems 2014, pp. 813–822, ACM 2014.
@inproceedings{eps361173,
title = {Doing the laundry with agents: a field trial of a future smart energy system in the home},
author = {Enrico Costanza and Joel E Fischer and James A Colley and Tom Rodden and Sarvapali Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/361173/},
year = {2014},
date = {2014-01-01},
booktitle = {ACM CHI Conference on Human Factors in Computing Systems 2014},
pages = {813--822},
organization = {ACM},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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Jennings, Nicholas R.; Moreau, Luc; Nicholson, D; Ramchurn, Sarvapali D.; Roberts, S; Rodden, T; Rogers, Alex
On human-agent collectives Journal Article
In: Communications of the ACM, vol. 57, no. 12, pp. 33-42, 2014.
@article{eps364593,
title = {On human-agent collectives},
author = {Nicholas R. Jennings and Luc Moreau and D Nicholson and Sarvapali D. Ramchurn and S Roberts and T Rodden and Alex Rogers},
url = {http://eprints.soton.ac.uk/364593/},
year = {2014},
date = {2014-01-01},
journal = {Communications of the ACM},
volume = {57},
number = {12},
pages = {33-42},
abstract = {We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People?s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People?s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented.
2013
Alam, Muddasser; Alan, Alper; Rogers, Alex; Ramchurn, Sarvapali D.
Towards a smart home framework Proceedings Article
In: 5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings (BuildSys2013), 2013.
@inproceedings{eps357187,
title = {Towards a smart home framework},
author = {Muddasser Alam and Alper Alan and Alex Rogers and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/357187/},
year = {2013},
date = {2013-01-01},
booktitle = {5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings (BuildSys2013)},
abstract = {We present our Smart Home Framework (SHF) which simplifies the modelling, prototyping and simulation of smart infrastructure (i.e., smart home and smart communities). It provides the buildings blocks (e.g., home appliances) that can be extended and assembled together to build a smart infrastructure model to which appropriate AI techniques can be applied. This approach enables rapid modelling where new research initiatives can build on existing work.},
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We present our Smart Home Framework (SHF) which simplifies the modelling, prototyping and simulation of smart infrastructure (i.e., smart home and smart communities). It provides the buildings blocks (e.g., home appliances) that can be extended and assembled together to build a smart infrastructure model to which appropriate AI techniques can be applied. This approach enables rapid modelling where new research initiatives can build on existing work.
Truong, Ngoc Cuong; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, D. Sarvapali
Activity prediction for agent-based home energy management Proceedings Article
In: Agent Technologies for Energy Systems (ATES 2013), 2013.
@inproceedings{eps351238,
title = {Activity prediction for agent-based home energy management},
author = {Ngoc Cuong Truong and Long Tran-Thanh and Enrico Costanza and D. Sarvapali Ramchurn},
url = {http://eprints.soton.ac.uk/351238/},
year = {2013},
date = {2013-01-01},
booktitle = {Agent Technologies for Energy Systems (ATES 2013)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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}
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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.
Farinelli, Alessandro; Bicego, Manuele; Ramchurn, Sarvapali; Zuchelli, Marco
C-Link: a hierarchical clustering approach to large-scale near-optimal coalition formation Proceedings Article
In: 23rd International Joint Conference on Artificial Intelligence, AAAI Press / International Joint Conferences on Artificial Intelligence, 2013.
@inproceedings{eps351521,
title = {C-Link: a hierarchical clustering approach to large-scale near-optimal coalition formation},
author = {Alessandro Farinelli and Manuele Bicego and Sarvapali Ramchurn and Marco Zuchelli},
url = {http://eprints.soton.ac.uk/351521/},
year = {2013},
date = {2013-01-01},
booktitle = {23rd International Joint Conference on Artificial Intelligence},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
abstract = {Coalition formation is a fundamental approach to multi-agent coordination. In this paper we address the specific problem of coalition structure generation, and focus on providing good-enough solutions using a novel heuristic approach that is based on data clustering methods. In particular, we propose a hierarchical agglomerative clustering approach (C-Link), which uses a similarity criterion between coalitions based on the gain that the system achieves if two coalitions merge. We empirically evaluate C-Link on a synthetic benchmark data-set as well as in collective energy purchasing settings. Our results show that the C-link approach performs very well against an optimal benchmark based on Mixed-Integer Programming, achieving solutions which are in the worst case about 80% of the optimal (in the synthetic data-set), and 98% of the optimal (in the energy data-set). Thus we show that C-Link can return solutions for problems involving thousands of agents within minutes.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Coalition formation is a fundamental approach to multi-agent coordination. In this paper we address the specific problem of coalition structure generation, and focus on providing good-enough solutions using a novel heuristic approach that is based on data clustering methods. In particular, we propose a hierarchical agglomerative clustering approach (C-Link), which uses a similarity criterion between coalitions based on the gain that the system achieves if two coalitions merge. We empirically evaluate C-Link on a synthetic benchmark data-set as well as in collective energy purchasing settings. Our results show that the C-link approach performs very well against an optimal benchmark based on Mixed-Integer Programming, achieving solutions which are in the worst case about 80% of the optimal (in the synthetic data-set), and 98% of the optimal (in the energy data-set). Thus we show that C-Link can return solutions for problems involving thousands of agents within minutes.
Rigas, Emmanouil; Ramchurn, Sarvapali; Bassiliades, Nick; Koutitas, Georgios
Congestion management for urban EV charging systems Proceedings Article
In: 4th IEEE International Conference on Smart Grid Communications (SmartGridComm), IEEE, 2013.
@inproceedings{eps356081,
title = {Congestion management for urban EV charging systems},
author = {Emmanouil Rigas and Sarvapali Ramchurn and Nick Bassiliades and Georgios Koutitas},
url = {http://eprints.soton.ac.uk/356081/},
year = {2013},
date = {2013-01-01},
booktitle = {4th IEEE International Conference on Smart Grid Communications (SmartGridComm)},
volume = {4},
publisher = {IEEE},
abstract = {We consider the problem of managing Electric Vehicle (EV) charging at charging points in a city to ensure that the load on the charging points remains within the desired limits while minimizing the inconvenience to EV owners. We develop solutions that treat charging points and EV users as self-interested agents that aim to maximize their profit and minimize the impact on their schedule. In particular, we propose variants of a decentralised and dynamic approach as well as an optimal centralised static approach. We evaluated these solutions in a real setting based on the road network and the location of parking garages of a UK city and show that the optimal centralised (non-dynamic) solution manages the congestion the best but does not scale well, while the decentralised solutions scale to thousands of agents.},
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We consider the problem of managing Electric Vehicle (EV) charging at charging points in a city to ensure that the load on the charging points remains within the desired limits while minimizing the inconvenience to EV owners. We develop solutions that treat charging points and EV users as self-interested agents that aim to maximize their profit and minimize the impact on their schedule. In particular, we propose variants of a decentralised and dynamic approach as well as an optimal centralised static approach. We evaluated these solutions in a real setting based on the road network and the location of parking garages of a UK city and show that the optimal centralised (non-dynamic) solution manages the congestion the best but does not scale well, while the decentralised solutions scale to thousands of agents.
Truong, Ngoc Cuong; McInerney, James; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Forecasting multi-appliance usage for smart home energy management Proceedings Article
In: 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), 2013.
@inproceedings{eps351242,
title = {Forecasting multi-appliance usage for smart home energy management},
author = {Ngoc Cuong Truong and James McInerney and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/351242/},
year = {2013},
date = {2013-01-01},
booktitle = {23rd International Joint Conference on Artificial Intelligence (IJCAI 2013)},
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Alam, Muddasser; Rogers, Alex; Ramchurn, Sarvapali
Interdependent multi-issue negotiation for energy exchange in remote communities Proceedings Article
In: Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13), 2013.
@inproceedings{eps350941,
title = {Interdependent multi-issue negotiation for energy exchange in remote communities},
author = {Muddasser Alam and Alex Rogers and Sarvapali Ramchurn},
url = {http://eprints.soton.ac.uk/350941/},
year = {2013},
date = {2013-01-01},
booktitle = {Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13)},
abstract = {We present a novel negotiation protocol to facilitate energy exchange between off-grid homes that are equipped with renewable energy generation and electricity storage. Our protocol imposes restrictions over negotiation such that it reduces the complex interdependent multi-issue negotiation to one where agents have a strategy profile in subgame perfect Nash equilibrium. We show that our negotiation protocol is tractable, concurrent, scalable and leads to Pareto-optimal outcomes in a decentralised manner. We empirically evaluate our protocol and show that, in this instance, a society of agents can (i) improve the overall utilities by 14% and (ii) reduce their overall use of the batteries by 37%},
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We present a novel negotiation protocol to facilitate energy exchange between off-grid homes that are equipped with renewable energy generation and electricity storage. Our protocol imposes restrictions over negotiation such that it reduces the complex interdependent multi-issue negotiation to one where agents have a strategy profile in subgame perfect Nash equilibrium. We show that our negotiation protocol is tractable, concurrent, scalable and leads to Pareto-optimal outcomes in a decentralised manner. We empirically evaluate our protocol and show that, in this instance, a society of agents can (i) improve the overall utilities by 14% and (ii) reduce their overall use of the batteries by 37%
Alam, Muddasser; Rogers, Alex; Ramchurn, Sarvapali D.
Interdependent multi-issue negotiation for energy exchange in remote communities Proceedings Article
In: International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE), 2013.
@inproceedings{eps357186,
title = {Interdependent multi-issue negotiation for energy exchange in remote communities},
author = {Muddasser Alam and Alex Rogers and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/357186/},
year = {2013},
date = {2013-01-01},
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Fischer, Joel E.; Ramchurn, Sarvapali D.; Osborne, Michael A.; Parson, Oliver; Huynh, Trung Dong; Alam, Muddasser; Pantidi, Nadia; Moran, Stuart; Bachour, Khaled; Reece, Steven; Costanza, Enrico; Rodden, Tom; Jennings, Nicholas R.
Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling Proceedings Article
In: International Conference on Intelligent User Interfaces, pp. 383–394, 2013.
@inproceedings{eps346991,
title = {Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling},
author = {Joel E. Fischer and Sarvapali D. Ramchurn and Michael A. Osborne and Oliver Parson and Trung Dong Huynh and Muddasser Alam and Nadia Pantidi and Stuart Moran and Khaled Bachour and Steven Reece and Enrico Costanza and Tom Rodden and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/346991/},
year = {2013},
date = {2013-01-01},
booktitle = {International Conference on Intelligent User Interfaces},
pages = {383--394},
abstract = {We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.},
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We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.
2012
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.},
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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.
Richardson, Darren P.; Costanza, Enrico; Ramchurn, Sarvapali D.
Evaluating semi-automatic annotation of domestic energy consumption as a memory aid Proceedings Article
In: UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 613–614, 2012.
@inproceedings{eps349083,
title = {Evaluating semi-automatic annotation of domestic energy consumption as a memory aid},
author = {Darren P. Richardson and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/349083/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {613--614},
abstract = {Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.},
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Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.
Miller, Sam; Ramchurn, Sarvapali D; Rogers, Alex
Optimal Decentralised Dispatch of Embedded Generation in the Smart Grid Journal Article
In: In Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2012.
@article{eps273142,
title = {Optimal Decentralised Dispatch of Embedded Generation in the Smart Grid},
author = {Sam Miller and Sarvapali D Ramchurn and Alex Rogers},
url = {http://eprints.soton.ac.uk/273142/},
year = {2012},
date = {2012-01-01},
booktitle = {Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
journal = {In Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
abstract = {Distribution network operators face a number of challenges; capacity constrained networks, and balancing electricity demand with generation from intermittent renewable resources. Thus, there is an increasing need for scalable approaches to facilitate optimal dispatch in the distribution network. To this end, we cast the optimal dispatch problem as a decentralised agent-based coordination problem and formalise it as a DCOP. We show how this can be decomposed as a factor graph and solved in a decentralised manner using algorithms based on the generalised distributive law; in particular, the max-sum algorithm. We go on to show that max-sum applied na?vely in this setting performs a large number of redundant computations. To address this issue, we present a novel decentralised message passing algorithm using dynamic programming that outperforms max-sum by pruning the search space. We empirically evaluate our algorithm using real distribution network data, showing that it outperforms (in terms of computational time and total size of messages sent) both a centralised approach, which uses IBM?s ILOG CPLEX 12.2, and max-sum, for large networks.},
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Distribution network operators face a number of challenges; capacity constrained networks, and balancing electricity demand with generation from intermittent renewable resources. Thus, there is an increasing need for scalable approaches to facilitate optimal dispatch in the distribution network. To this end, we cast the optimal dispatch problem as a decentralised agent-based coordination problem and formalise it as a DCOP. We show how this can be decomposed as a factor graph and solved in a decentralised manner using algorithms based on the generalised distributive law; in particular, the max-sum algorithm. We go on to show that max-sum applied na?vely in this setting performs a large number of redundant computations. To address this issue, we present a novel decentralised message passing algorithm using dynamic programming that outperforms max-sum by pruning the search space. We empirically evaluate our algorithm using real distribution network data, showing that it outperforms (in terms of computational time and total size of messages sent) both a centralised approach, which uses IBM?s ILOG CPLEX 12.2, and max-sum, for large networks.
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.},
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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.
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.},
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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.
Truong, Ngoc Cuong; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Predicting energy consumption activities for home energy management Proceedings Article
In: Agent Technologies for Energy Systems (ATES 2012), 2012.
@inproceedings{eps339215,
title = {Predicting energy consumption activities for home energy management},
author = {Ngoc Cuong Truong and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/339215/},
year = {2012},
date = {2012-01-01},
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2011
Ramchurn, Sarvapali; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nick
Agent-based control for decentralised demand side management in the smart grid Proceedings Article
In: The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), pp. 5–12, 2011.
@inproceedings{eps271985,
title = {Agent-based control for decentralised demand side management in the smart grid},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/271985/},
year = {2011},
date = {2011-01-01},
booktitle = {The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011)},
pages = {5--12},
abstract = {Central to the vision of the smart grid is the deployment of smart meters that will allow autonomous software agents, representing the consumers, to optimise their use of devices and heating in the smart home while interacting with the grid. However, without some form of coordination, the population of agents may end up with overly-homogeneous optimised consumption patterns that may generate significant peaks in demand in the grid. These peaks, in turn, reduce the efficiency of the overall system, increase carbon emissions, and may even, in the worst case, cause blackouts. Hence, in this paper, we introduce a novel model of a Decentralised Demand Side Management (DDSM) mechanism that allows agents, by adapting the deferment of their loads based on grid prices, to coordinate in a decentralised manner. Specifically, using average UK consumption profiles for 26M homes, we demonstrate that, through an emergent coordination of the agents, the peak demand of domestic consumers in the grid can be reduced by up to 17% and carbon emissions by up to 6%. We also show that our DDSM mechanism is robust to the increasing electrification of heating in UK homes (i.e. it exhibits a similar efficiency).},
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Central to the vision of the smart grid is the deployment of smart meters that will allow autonomous software agents, representing the consumers, to optimise their use of devices and heating in the smart home while interacting with the grid. However, without some form of coordination, the population of agents may end up with overly-homogeneous optimised consumption patterns that may generate significant peaks in demand in the grid. These peaks, in turn, reduce the efficiency of the overall system, increase carbon emissions, and may even, in the worst case, cause blackouts. Hence, in this paper, we introduce a novel model of a Decentralised Demand Side Management (DDSM) mechanism that allows agents, by adapting the deferment of their loads based on grid prices, to coordinate in a decentralised manner. Specifically, using average UK consumption profiles for 26M homes, we demonstrate that, through an emergent coordination of the agents, the peak demand of domestic consumers in the grid can be reduced by up to 17% and carbon emissions by up to 6%. We also show that our DDSM mechanism is robust to the increasing electrification of heating in UK homes (i.e. it exhibits a similar efficiency).
Vytelingum, Perukrishnen; Voice, Thomas; Ramchurn, Sarvapali; Rogers, Alex; Jennings, Nick
Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid Journal Article
In: Journal of Artificial Intelligence Research, vol. 42, pp. 765–813, 2011, (AAMAS 2010 iRobot Best Paper Award).
@article{eps272961,
title = {Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid},
author = {Perukrishnen Vytelingum and Thomas Voice and Sarvapali Ramchurn and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/272961/},
year = {2011},
date = {2011-01-01},
journal = {Journal of Artificial Intelligence Research},
volume = {42},
pages = {765--813},
abstract = {In this paper, we present a novel decentralised management technique that allows electricity micro-storage devices, deployed within individual homes as part of a smart electricity grid, to converge to profitable and efficient behaviours. Specifically, we propose the use of software agents, residing on the users' smart meters, to automate and optimise the charging cycle of micro-storage devices in the home to minimise its costs, and we present a study of both the theoretical underpinnings and the implications of a practical solution, of using software agents for such micro-storage management. First, by formalising the strategic choice each agent makes in deciding when to charge its battery, we develop a game-theoretic framework within which we can analyse the competitive equilibria of an electricity grid populated by such agents and hence predict the best consumption profile for that population given their battery properties and individual load profiles. Our framework also allows us to compute theoretical bounds on the amount of storage that will be adopted by the population. Second, to analyse the practical implications of micro-storage deployments in the grid, we present a novel algorithm that each agent can use to optimise its battery storage profile in order to minimise its owner's costs. This algorithm uses a learning strategy that allows it to adapt as the price of electricity changes in real-time, and we show that the adoption of these strategies results in the system converging to the theoretical equilibria. Finally, we empirically evaluate the adoption of our micro-storage management technique within a complex setting, based on the UK electricity market, where agents may have widely varying load profiles, battery types, and learning rates. In this case, our approach yields savings of up to 14% in energy cost for an average consumer using a storage device with a capacity of less than 4.5 kWh and up to a 7% reduction in carbon emissions resulting from electricity generation (with only domestic consumers adopting micro-storage and, commercial and industrial consumers not changing their demand). Moreover, corroborating our theoretical bound, an equilibrium is shown to exist where no more than 48% of households would wish to own storage devices and where social welfare would also be improved (yielding overall annual savings of nearly pounds1.5B).},
note = {AAMAS 2010 iRobot Best Paper Award},
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In this paper, we present a novel decentralised management technique that allows electricity micro-storage devices, deployed within individual homes as part of a smart electricity grid, to converge to profitable and efficient behaviours. Specifically, we propose the use of software agents, residing on the users' smart meters, to automate and optimise the charging cycle of micro-storage devices in the home to minimise its costs, and we present a study of both the theoretical underpinnings and the implications of a practical solution, of using software agents for such micro-storage management. First, by formalising the strategic choice each agent makes in deciding when to charge its battery, we develop a game-theoretic framework within which we can analyse the competitive equilibria of an electricity grid populated by such agents and hence predict the best consumption profile for that population given their battery properties and individual load profiles. Our framework also allows us to compute theoretical bounds on the amount of storage that will be adopted by the population. Second, to analyse the practical implications of micro-storage deployments in the grid, we present a novel algorithm that each agent can use to optimise its battery storage profile in order to minimise its owner's costs. This algorithm uses a learning strategy that allows it to adapt as the price of electricity changes in real-time, and we show that the adoption of these strategies results in the system converging to the theoretical equilibria. Finally, we empirically evaluate the adoption of our micro-storage management technique within a complex setting, based on the UK electricity market, where agents may have widely varying load profiles, battery types, and learning rates. In this case, our approach yields savings of up to 14% in energy cost for an average consumer using a storage device with a capacity of less than 4.5 kWh and up to a 7% reduction in carbon emissions resulting from electricity generation (with only domestic consumers adopting micro-storage and, commercial and industrial consumers not changing their demand). Moreover, corroborating our theoretical bound, an equilibrium is shown to exist where no more than 48% of households would wish to own storage devices and where social welfare would also be improved (yielding overall annual savings of nearly pounds1.5B).
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.},
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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.
Ramchurn, Sarvapali; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nick
Agent-based homeostatic control for green energy in the smart grid Journal Article
In: ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 4, pp. 35:1–35:28, 2011.
@article{eps272015,
title = {Agent-based homeostatic control for green energy in the smart grid},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nick Jennings},
url = {http://eprints.soton.ac.uk/272015/},
year = {2011},
date = {2011-01-01},
journal = {ACM Transactions on Intelligent Systems and Technology},
volume = {2},
number = {4},
pages = {35:1--35:28},
abstract = {With dwindling non-renewable energy reserves and the adverse effects of climate change, the development of the smart electricity grid is seen as key to solving global energy security issues and to reducing carbon emissions. In this respect, there is a growing need to integrate renewable (or green) energy sources in the grid. However, the intermittency of these energy sources requires that demand must also be made more responsive to changes in supply, and a number of smart grid technologies are being developed, such as high-capacity batteries and smart meters for the home, to enable consumers to be more responsive to conditions on the grid in real-time. Traditional solutions based on these technologies, however, tend to ignore the fact that individual consumers will behave in such a way that best satisfies their own preferences to use or store energy (as opposed to that of the supplier or the grid operator). Hence, in practice, it is unclear how these solutions will cope with large numbers of consumers using their devices in this way. Against this background, in this paper, we develop novel control mechanisms based on the use of autonomous agents to better incorporate consumer preferences in managing demand. These agents, residing on consumers' smart meters, can both communicate with the grid and optimise their owner's energy consumption to satisfy their preferences. More specifically, we provide a novel control mechanism that models and controls a system comprising of a green energy supplier operating within the grid and a number of individual homes (each possibly owning a storage device). This control mechanism is based on the concept of homeostasis whereby control signals are sent to individual components of a system, based on their continuous feedback, in order to change their state so that the system may reach a stable equilibrium. Thus, we define a new carbon-based pricing mechanism for this green energy supplier that takes advantage of carbon-intensity signals available on the internet in order to provide real-time pricing. The pricing scheme is designed in such a way that it can be readily implemented using existing communication technologies and is easily understandable by consumers. Building upon this, we develop new control signals that the supplier can use to incentivise agents to shift demand (using their storage device) to times when green energy is available. Moreover, we show how these signals can be adapted according to changes in supply and to various degrees of penetration of storage in the system. We empirically evaluate our system and show that, when all homes are equipped with storage devices, the supplier can significantly reduce its reliance on other carbon-emitting power sources to cater for its own shortfalls. By so doing, the supplier reduces the carbon emission of the system by up to 25% while the consumer reduces its costs by up to 14.5%. Finally, we demonstrate that our homeostatic control mechanism is not sensitive to small prediction errors and the supplier is incentivised to accurately predict its green production to minimise costs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
With dwindling non-renewable energy reserves and the adverse effects of climate change, the development of the smart electricity grid is seen as key to solving global energy security issues and to reducing carbon emissions. In this respect, there is a growing need to integrate renewable (or green) energy sources in the grid. However, the intermittency of these energy sources requires that demand must also be made more responsive to changes in supply, and a number of smart grid technologies are being developed, such as high-capacity batteries and smart meters for the home, to enable consumers to be more responsive to conditions on the grid in real-time. Traditional solutions based on these technologies, however, tend to ignore the fact that individual consumers will behave in such a way that best satisfies their own preferences to use or store energy (as opposed to that of the supplier or the grid operator). Hence, in practice, it is unclear how these solutions will cope with large numbers of consumers using their devices in this way. Against this background, in this paper, we develop novel control mechanisms based on the use of autonomous agents to better incorporate consumer preferences in managing demand. These agents, residing on consumers' smart meters, can both communicate with the grid and optimise their owner's energy consumption to satisfy their preferences. More specifically, we provide a novel control mechanism that models and controls a system comprising of a green energy supplier operating within the grid and a number of individual homes (each possibly owning a storage device). This control mechanism is based on the concept of homeostasis whereby control signals are sent to individual components of a system, based on their continuous feedback, in order to change their state so that the system may reach a stable equilibrium. Thus, we define a new carbon-based pricing mechanism for this green energy supplier that takes advantage of carbon-intensity signals available on the internet in order to provide real-time pricing. The pricing scheme is designed in such a way that it can be readily implemented using existing communication technologies and is easily understandable by consumers. Building upon this, we develop new control signals that the supplier can use to incentivise agents to shift demand (using their storage device) to times when green energy is available. Moreover, we show how these signals can be adapted according to changes in supply and to various degrees of penetration of storage in the system. We empirically evaluate our system and show that, when all homes are equipped with storage devices, the supplier can significantly reduce its reliance on other carbon-emitting power sources to cater for its own shortfalls. By so doing, the supplier reduces the carbon emission of the system by up to 25% while the consumer reduces its costs by up to 14.5%. Finally, we demonstrate that our homeostatic control mechanism is not sensitive to small prediction errors and the supplier is incentivised to accurately predict its green production to minimise costs.
2010
Vytelingum, Perukrishnen; Ramchurn, Sarvapali D.; Voice, Thomas D.; Rogers, Alex; Jennings, Nicholas R.
Trading agents for the smart electricity grid Proceedings Article
In: The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), pp. 897–904, 2010, (Event Dates: May 10-14, 2010).
@inproceedings{eps268361,
title = {Trading agents for the smart electricity grid},
author = {Perukrishnen Vytelingum and Sarvapali D. Ramchurn and Thomas D. Voice and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/268361/},
year = {2010},
date = {2010-01-01},
booktitle = {The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010)},
pages = {897--904},
abstract = {The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforeseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).},
note = {Event Dates: May 10-14, 2010},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The vision of the Smart Grid includes the creation of intelligent electricity supply networks to allow efficient use of energy resources, reduce carbon emissions and are robust to failures. One of the key assumptions underlying this vision is that it will be possible to manage the trading of electricity between homes and micro-grids while coping with the inherent real-time dynamism in electricity demand and supply. The management of these trades needs to take into account the fact that most, if not all, of the actors in the system are self-interested and transmission line capacities are constrained. Against this background, we develop and evaluate a novel market-based mechanism and novel trading strategies for the Smart Grid. Our mechanism is based on the Continuous Double Auction (CDA) and automatically manages the congestion within the system by pricing the flow of electricity. We also introduce mechanisms to ensure the system can cope with unforeseen demand or increased supply capacity in real time. Finally, we develop new strategies that we show achieve high market efficiency (typically over 90%).
Vytelingum, Perukrishnen; Voice, Thomas D.; Ramchurn, Sarvapali D.; Rogers, Alex; Jennings, Nicholas R.
Agent-Based Micro-Storage Management for the Smart Grid Proceedings Article
In: The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010) - Won the Best Paper Award, pp. 39–46, 2010, (Winner of the Best Paper Award Event Dates: May 10-14, 2010).
@inproceedings{eps268360,
title = {Agent-Based Micro-Storage Management for the Smart Grid},
author = {Perukrishnen Vytelingum and Thomas D. Voice and Sarvapali D. Ramchurn and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/268360/},
year = {2010},
date = {2010-01-01},
booktitle = {The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010) - Won the Best Paper Award},
pages = {39--46},
abstract = {The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, more generation capacity, more carbon emissions, and, in the worst case, breaking down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).},
note = {Winner of the Best Paper Award Event Dates: May 10-14, 2010},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, more generation capacity, more carbon emissions, and, in the worst case, breaking down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).
