Aggregative charging control of electric vehicle populations

Lead Research Organisation: University of Oxford
Department Name: Engineering Science

Abstract

The ambitious targets in the United Kingdom for increasing the share of renewable energy sources integrated to the network, and the need for providing affordable, resilient and clean energy, call for a paradigm shift in energy systems operations. Electric vehicles offer the means to address these challenges and achieve uninterrupted operation by deferring their demand in time and acting as dynamic storage devices. As a result, their number is expected to increase rapidly over the next years, leading to a "green car revolution". This constitutes an opportunity for modernizing energy systems operation, but will unavoidably give rise to coordination and scheduling issues at a population level so that cost savings are achieved and reliability is ensured. The latter is of significant importance to prevent from undesirable disruptions of service.

This project will address this problem using tools at the intersection of control theory, optimization and machine learning, allowing for a decentralized computation of the electric vehicle charging strategies, while preventing vehicles from sharing information about their local utility functions and consumption patterns that is considered to be private. We will develop algorithms capable of dealing both with cooperative and non-cooperative vehicle behaviours in large fleets of vehicles, and immunize the resulting strategies against uncertainty due to unpredictability in the vehicles' driving behaviour and due to the presence of renewable energy sources. The presence of an algorithmic tool with these features will allow for scalable charging solutions amenable to problems of practical relevance, will provide insight on the mechanism driving the response of large populations of electric vehicles, and embed robustness in the resulting charging schedules. As such, the proposed project will offer the means for reliable system operation and facilitate the integration of higher shares of renewable energy sources.

Planned Impact

The project will develop an algorithmic toolkit for analysis and synthesis of charging control strategies in large populations of electric vehicles, addressing two key issues that are faced by the industry: dealing with shared resource constraints, as it is most often the case in situations of practical relevance; and handling uncertainty in an efficient, albeit rigorous, manner. These contributions will facilitate future application and experimental validation of the developed electric vehicle charging control strategies by the energy and transportation industrial sector, and accommodate vehicle populations of growing size.

The following impact criteria are addressed:
1. Economic: Performance improvement in electric vehicle charging control methods, leading to charging cost savings, intelligent resource allocation and facilitating the integration of higher shares of renewable energy sources. These features will make the developed algorithms appealing to electric vehicle charging network operators, and to leading industrial partners in energy efficient management technologies and smart-grid solutions.
2. Societal: Robustness in charging schedules enhancing reliability. This will increase the confidence of electric vehicle users and provide cost efficient solutions, while respecting their privacy concerns regarding their consumption patterns.
3. Knowledge & Skills: Developing novel solutions for control of multi-agent systems, merging tools from different disciplines, namely, control theory, optimization and machine learning. This will set the ground for breakthrough solutions to the problem of control and performance optimization in uncertain environments with multiple agents.
4. Users: Direct engagement with industrial representatives from the energy and transportation sector, exploiting support by Siemens and Honeywell. Development of an open access algorithmic repository, allowing for benchmarking and result reproducibility; support by MathWorks will be exploited.
 
Description The following key findings were achieved, providing the means to overcome existing challenges and go beyond state-of-the-art methodologies.

1) Developed intelligent scheduling algorithms for electric vehicle charging (Work Packages 1, 2.1 and 2.2 of the EPSRC award proposal)

To avoid vehicles charging at time instances of high electricity price (as a result of an uncoordinated scheme), intelligent automated solutions are needed. To achieve a reliable and at the same time sustainable performance, novel algorithmic schemes based on solid mathematical foundations have been developed. Within the EPSRC award we developed and analyzed algorithms for two settings of contemporary interest: i) cooperative setting: electric vehicles belong to the same company and/or are managed by the same aggregating authority, thus cooperate in view of coordinating their charging schedule so that they collectively minimize the energy cost for the entire fleet. ii) non-cooperative setting: electric vehicles belong to different companies/aggregators, competing for shared resources (energy). We treated this case from a game theoretic perspective, providing an algorithmic solution which is guaranteed to return a Nash equilibrium solution, or in other words a solution of the least regret for all vehicles.

For both cases, we adopted tools from decentralized optimization, allowing us to trade different, often conflicting objectives for each vehicle, and respect constraints pertaining the individual vehicles. Moreover, the developed skills are by construction scalable, thus amenable to parallel computation. At the same time, an important feature of the proposed algorithms is that they only based on local information, thus preventing vehicles from sharing information considered as private.

2) Quantified the price of (non-)cooperation (Work Package 2.3 of the EPSRC award proposal)

Another key finding of the project is to provide a conclusive answer to a theoretical question with significant practical implications. In particular, we showed that as the number of vehicles in a fleet increases, even if vehicles act in a non-cooperative manner, the resulting strategy ends up being the same with its cooperative counterpart.

This finding is based on transferring tools from the computer science literature based on the celebrated "price of anarchy" to an optimization context tailored to the electric vehicle management problem. Such a result opens the road for the development of appropriate pricing mechanisms to achieve a social welfare maximizing behaviour from self-interested users/vehicles.

3) Embedded robustness against uncertainty (Work Package 3 of the EPSRC award proposal)

To the best of our knowledge, we constructed the first (probabilistically) robust charging schedule with respect to price volatility, a major challenge, as charging strategies computed based on a nominal price signals may fail to remain at an equilibrium when a different price is realized.
We determined an set of equilibrium charging strategies that are robust with respect to a specific collection of price signals available through historical data, and accompanied the computed tuple of strategies with an a priori probabilistic certificate that determines the confidence with which such a solution remains an equilibrium when it comes to new price/uncertainty realizations other than those included in the data.

4) Trading robustness with performance

Following the award's completion, our team has further expanded its research towards analyzing the robustness properties of equilibria in multi-agent games. The algorithms that emanated out of this award, have more general feature that facilitate their deployment to a more general class of games. In a series of publications/submissions we shifted focus towards a posteriori robustness certificates (extending key finding 3 above); the latter offers the means to trade robustness with performance, this offering cost-efficient, less conservative solutions. Moreover, our activities attempted to overcome the need for a central authority/coordinator, focusing on the development of algorithms that require communication only with neighbouring agents/vehicles. This feature embeds resilience to communication failures and accounts for time varying connectivity architectures.
Exploitation Route Academic value:

The developed algorithms set the foundations for further research in the field of multi-agent games arising in electric vehicle charging control problems. In particular the work related to the development of probabilistically robust charging schedules sets the first theoretical framework to account for price volatility, hence immunising the computed charging strategies with respect to uncertainty and partial information.
The proposed approach is of immediate interest to researchers in game theory, machine learning and optimization, as the developed theoretical approach and proof line captures a wide class of multi-agent games over networks, not necessarily limited to electric vehicle problems.

Non-academic value:

Industrial representative from the energy and transportation sector like electric vehicle aggregators constitute the direct non-academic beneficiaries of the project outcomes. The developed algorithms offer a toolkit for coordinated vehicle charging, accommodating the so called peak shaving and valley filling concerns. The scalability properties of the proposed solutions and the fact that they are only based on local information, thus preventing users from sharing information considered as private, makes them amenable to parallelized computation. As such the could be integrated into high level controllers for providing consumption set points in electric vehicle fleets of realistic size.

Educational value:

Results originating in this project set the ground for the development of advanced control and optimization courses on distributed and robust optimization. This will have a direct educational impact when training the next generation of control engineers on topics related to control of large scale multi-agent systems.
Sectors Education,Energy,Transport
URL https://ev-charge.github.io/
 
Description 1) Economic: The performance improvement, as this is quantified by the developed electric vehicle charging control methods, has the potential to lead to substantial charging cost savings, and facilitate the integration of higher shares of renewable energy sources. 2)Societal: Robustness in charging schedules (a key finding of the project), has the potential to increase the confidence of electric vehicle users while respecting their privacy concerns. 3) Education: Our developed algorithms are currently documented and will appear in an open access algorithmic repository (including codes and documentation), allowing for benchmarking, result reproducibility, and contributing to interoperability activities. It should be noted, that our algorithms have direct implication on the current operational paradigm shift witnessed in the energy and transportation sector. The strong interest in the methods and tools developed by our team is reflected by the several invitation to present our findings either in dedicated seminars, or as part of courses with an international audience. Beyond the academic impact associated with these activities, participation in such events contributes to the education of the next generation of researchers, enhancing not only their technical skills but also their awareness on the challenges associated with energy and transportation problems contemporary interest, related to the penetration of electric vehicles. Besides the application of the developed methods to charging control of electric vehicles, algorithms that emanated from the research carried out within this project motivated subsequent theoretical and algorithmic developments. In particular, current research activities o our team in the area of distributed optimization and data driven algorithms for control under uncertainty have their roots to the project findings. This is also witnessed by the volume of publication submitted upon completion of the project, albeit in directly relevant topics: 5 journal submissions, 9 conference manuscripts (accepted, scheduled to appear at conference eproceedings). Further generalizing these algorithms and analyzing their relative merits is topic of current investigation. Since January 2018, our team has been invited to contributed to five courses at international Universities; one highly attended and selective summer school (SIDRA); one multi-disciplinary educational event as part of an elite program between the University of Oxford and China; two graduate courses organized at Politecnico di Milano, Italy, and one at the University of Seville, Spain. Moreover, our team has contributed to three invited sessions at international conferences. One tutorial and one invited session were organized as part of an international conference just before the project started, introducing the attendees to the challenges and the problems to be tackled within the award.
Sector Education,Energy,Transport
Impact Types Societal,Economic
 
Description Distributed Control of Aerial Vehicles
Amount £91,323 (GBP)
Organisation MathWorks 
Sector Private
Country United States
Start 10/2022 
End 12/2024
 
Description Communication, Dissemination and Technology Transfer: Invited conference sessions; Invited lectures 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact The algorithms developed within the EPSRC award have direct implication to the current operational paradigm shift witnessed in the energy and transportation sector. The strong interest in the methods and tools developed by our team is reflected by the several invitation to present our findings either in dedicated seminars, or as part of courses with an international audience. Beyond the academic impact associated with these activities, participation in such events contributes to the education of the next generation of researchers, enhancing not only their technical skills but also their awareness on the challenges associated with energy and transportation problems contemporary interest, related to the penetration of electric vehicles.

Since January 2018, our team has been invited to contributed to two courses at international Universities; one highly attended and selective summer school (SIDRA); one multi-disciplinary educational event as part of an elite program between the University of Oxford and China; and one contribution in invited sessions at international conferences (in all cases attendees were between 50-100). See for detailed description in the awards section.
Moreover, the PI co-organized one tutorial and one invited session as part of an international conference just before the project started, introducing the attendees to the problems to be tackled within the award and the associated challenges .

There are two invitations to special thematic sessions organized in upcoming international conferences, which we have accepted and manuscripts of our team are currently under review. Upon acceptance, we will present the key findings of our project to these venues. At this stage that our results have reached a mature level, we plan to organize an invited session/workshop at the upcoming European Control Conference, as an opportunity to bring together outstanding researchers worldwide with complementary research interests to further communicate our results and maximize the academic impact of our findings.

Such activities are inline with Task 4 of the award proposal.
Year(s) Of Engagement Activity 2017,2018,2019
URL http://www.ifac2017.org/workshops-and-tutorials.html
 
Description Intelligent sustainable networked City (i-City) competition 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Postgraduate students
Results and Impact i-City (Intelligent sustainable networked City) is an exciting postgraduate student innovation competition running January-February 2023, sponsored by MathWorks and jointly run by MPLS division, University of Oxford and Reuben College. It involves training of post-graduate students from diverse disciplines to work in teams and find new ways to deploy AI and Machine Learning tools to solve challenges in the following three broad pillars of focus:

-- Connected Mobility: How can we use AI tools to regulate traffic? Can we use aerial vehicles to monitor ground ones and guide them in real time?
-- Smart Electric Vehicle Charging: Can we achieve vehicle-to-grid and/or vehicle-to-home charging? How to optimally use electric vehicles as dynamic storage devices and direct the stored power to cover grid or home needs?
-- Energy Efficiency and Sustainability: How can we enhance integration of renewable energy sources? How to control buildings efficiently? How can we avoid wasting energy?
Year(s) Of Engagement Activity 2023
URL https://www.mpls.ox.ac.uk/training/mpls-training/our-courses/mpls-enterprise-courses/i-city
 
Description Invited Session Organization 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact Two invited sessions are currently being organized for the international peer-reviewed Conference on Decision and Control on "Learning with Guarantees in Control
and Decision-Making". The sessions are in collaboration with Filippo Fabiani, Filiberto Fele (researcher under the current award) and Paul Goulart.
Year(s) Of Engagement Activity 2021