research-article

Making a Virtual Power Plant out of Privately Owned Electric Vehicles: From Contract Design to Scheduling

Authors:

Javier Sales-Ortiz,

Omid ArdakanianAuthors Info & Claims

e-Energy '23: Proceedings of the 14th ACM International Conference on Future Energy Systems

Pages 459 - 472

https://doi.org/10.1145/3575813.3597353

Published: 16 June 2023 Publication History

Abstract

With the rollout of bidirectional chargers, electric vehicle (EV) battery packs can be used in lieu of utility-scale energy storage systems to support the grid. These batteries, if aggregated and coordinated at scale, will act as a virtual power plant (VPP) that could offer flexibility and other services to the grid. To realize this vision, EV owners must be incentivized to let their battery be discharged before it is charged to the desired level. In this paper, we use contract theory to design incentive-compatible, fixed-term contracts between the VPP and EV owners. Each contract defines the maximum amount of energy that can be discharged from an EV battery and exported to the grid over a certain period of time, and the compensation paid to the EV owner upon successful execution of the contract, for reducing the cycle life of their battery. We then propose an algorithm for the optimal operation of this VPP that participates in day-ahead and balancing markets. This algorithm maximizes the expected VPP profit by taking advantage of the accepted contracts that are still valid, while honoring day-ahead commitments and fulfilling the charging demand of each EV by its deadline. We show through simulation that by offering a menu of fixed-term contracts to EVs that arrive at the charging station, trading energy and scheduling EV charging according to the proposed algorithm, the VPP profitability increases by up to 12.2%, while allowing EVs to partially offset the cost of charging their battery.

References

[1]

[n. d.]. ElaadNL Open Data. https://platform.elaad.io/download-data/

[2]

[n. d.]. ENTSO-E Transparency Platform. https://transparency.entsoe.eu

[3]

[n. d.]. Swell Energy’s Virtual Power Plants. https://www.swellenergy.com/

[4]

[n. d.]. Tennet Export data. https://www.tennet.org/english/operational_management/export_data.aspx

[5]

[n. d.]. Tesla Virtual Power Plant. https://www.tesla.com/support/energy/tesla-virtual-power-plant-pge-2022

[6]

[n. d.]. Toyota To Study V2G In Texas In 2023. https://cleantechnica.com/2023/01/01/toyota-to-study-v2g-in-texas-in-2023/

[7]

[n. d.]. United States distributed energy resources outlook. https://www.woodmac.com/news/editorial/der-growth-united-states/

[8]

[n. d.]. V2X Suisse project launches with V2G-ready Honda e cars. https://www.electrive.com/2022/09/07/v2x-suisse-project-launches-with-v2g-ready-honda-e-cars/

[9]

Khalid Abdulla 2016. Optimal operation of energy storage systems considering forecasts and battery degradation. IEEE Transactions on Smart Grid 9, 3 (2016), 2086–2096.

[10]

Kankam Adu-Kankam 2018. Towards collaborative Virtual Power Plants: Trends and convergence. Sustainable Energy, Grids and Networks 16 (2018), 217–230.

[11]

MOSEK ApS. 2022. Mosek optimizer API for Python. Version 9, 17 (2022), 6–4.

[12]

Arijit Bagchi, Lalit Goel, and Peng Wang. 2018. Adequacy assessment of generating systems incorporating storage integrated virtual power plants. IEEE Transactions on Smart Grid 10, 3 (2018), 3440–3451.

[13]

Patrick Bolton and Mathias Dewatripont. 2004. Contract theory. MIT press.

[14]

Phuthipong Bovornkeeratiroj, John Wamburu, David Irwin, and Prashant Shenoy. 2021. VPeak: Exploiting Volunteer Energy Resources for Flexible Peak Shaving. In Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (Coimbra, Portugal) (BuildSys ’21). ACM, 121–130.

Digital Library

[15]

Kyle Bradbury 2014. Economic viability of energy storage systems based on price arbitrage potential in real-time US electricity markets. Applied Energy 114 (2014), 512–519.

[16]

Partha Dasgupta, Peter Hammond, and Eric Maskin. 1979. The implementation of social choice rules: Some general results on incentive compatibility. The Review of Economic Studies 46, 2 (1979), 185–216.

[17]

EU. 2020. Decisions of the Agency for the Cooperation of Energy Cooperation of Energy Regulators No 18-2020. https://nordicbalancingmodel.net/roadmap-and-projects/single-price-model/

[18]

Shuai Fan 2020. Optimal coordination of virtual power plant with photovoltaics and electric vehicles: A temporally coupled distributed online algorithm. Applied Energy 277 (2020), 115583.

[19]

Yang Gao 2013. A contract-based approach for ancillary services in V2G networks: Optimality and learning. In 2013 Proceedings IEEE INFOCOM. IEEE, 1151–1159.

[20]

Logan Goldie-Scot. 2019. A behind the scenes take on lithium-ion battery prices. https://about.bnef.com/blog/behind-scenes-take-lithium-ion-battery-prices/

[21]

Bing Huang, Aart Gerard Meijssen, Jan Anne Annema, and Zofia Lukszo. 2021. Are electric vehicle drivers willing to participate in vehicle-to-grid contracts? A context-dependent stated choice experiment. Energy Policy 156 (2021), 112410.

[22]

José Iria, Filipe Soares, and Manuel Matos. 2019. Optimal bidding strategy for an aggregator of prosumers in energy and secondary reserve markets. Applied Energy 238 (2019), 1361–1372.

[23]

Bernhard Jansen, Carl Binding, Olle Sundström, and Dieter Gantenbein. 2010. Architecture and Communication of an Electric Vehicle Virtual Power Plant. In 2010 First IEEE International Conference on Smart Grid Communications. 149–154.

[24]

Adugna Gebrie Jember 2020. Game and contract theory-based energy transaction management for Internet of electric vehicle. IEEE Access 8 (2020), 203478–203487.

[25]

Evangelos L Karfopoulos 2015. Distributed coordination of electric vehicles providing V2G regulation services. IEEE Transactions on Power Systems 31, 4 (2015), 2834–2846.

[26]

Mostafa Kazemi 2017. Operation scheduling of battery storage systems in joint energy and ancillary services markets. IEEE Transactions on Sustainable Energy 8, 4 (2017), 1726–1735.

[27]

Jean Kumagai. 2012. Virtual power plants, real power. IEEE Spectrum 49, 3 (2012), 13–14.

[28]

Tor Lattimore and Csaba Szepesvári. 2020. Bandit algorithms. Cambridge University Press.

[29]

Fabio Lilliu, Torben Bach Pedersen, and Laurynas Šikšnys. 2021. Capturing Battery Flexibility in a General and Scalable Way Using the FlexOffer Model. In 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). IEEE, 64–70.

[30]

Bijay Neupane, Laurynas Šikšnys, and Torben Bach Pedersen. 2017. Generation and evaluation of flex-offers from flexible electrical devices. In Proceedings of the Eighth International Conference on Future Energy Systems. 143–156.

Digital Library

[31]

N. Nisan, T. Roughgarden, E. Tardos, and V. Vazirani. 2007. Algorithmic Game Theory. Cambridge University Press. https://doi.org/10.1017/CBO9780511800481

[32]

John W. Pratt. 1964. Risk Aversion in the Small and in the Large. Econometrica 32, 1/2 (1964), 122–136. http://www.jstor.org/stable/1913738

[33]

Saidur Rahman 2022. On Efficient Operation of a V2G-Enabled Virtual Power Plant: When Solar Power Meets Bidirectional Electric Vehicle Charging. In Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation(BuildSys ’22). ACM, 119–128.

Digital Library

[34]

Bernard Salanié. 2005. The economics of contracts: a primer. MIT press.

[35]

Emmanouil Valsomatzis, Torben Bach Pedersen, and Alberto Abelló. 2018. Day-Ahead Trading of Aggregated Energy Flexibility. In Proceedings of the Ninth International Conference on Future Energy Systems (Karlsruhe, Germany) (e-Energy ’18). Association for Computing Machinery, New York, NY, USA, 134–138. https://doi.org/10.1145/3208903.3208936

Digital Library

[36]

Matteo Vasirani 2013. An agent-based approach to virtual power plants of wind power generators and electric vehicles. IEEE Transactions on Smart Grid 4, 3 (2013), 1314–1322.

[37]

Laurynas Šikšnys, Torben Bach Pedersen, Muhammad Aftab, and Bijay Neupane. 2019. Flexibility Modeling, Management, and Trading in Bottom-up Cellular Energy Systems. In Proceedings of the Tenth ACM International Conference on Future Energy Systems(e-Energy ’19). ACM, 170–180.

Digital Library

[38]

Jianing Wang, Chunlin Guo, Changshu Yu, and Yanchang Liang. 2022. Virtual power plant containing electric vehicles scheduling strategies based on deep reinforcement learning. Electric Power Systems Research 205 (2022), 107714.

[39]

Zhiwei Wei 2022. Contract-Based Charging Protocol for Electric Vehicles with Vehicular Fog Computing: An Integrated Charging and Computing Perspective. IEEE Internet of Things Journal (2022).

[40]

Roger J-B. Wets. 2002. Stochastic Programming Models: Wait-and-See Versus Here-and-Now. In Decision Making Under Uncertainty, Claude Greengard and Andrzej Ruszczynski (Eds.). Springer New York, 1–15.

[41]

Chenye Wu 2011. Vehicle-to-aggregator interaction game. IEEE Transactions on Smart Grid 3, 1 (2011), 434–442.

[42]

Ming Zeng 2015. Group bidding for guaranteed quality of energy in v2g smart grid networks. In 2015 IEEE International Conference on Communications (ICC). IEEE, 5266–5271.

[43]

Ming Zeng 2015. An incentivized auction-based group-selling approach for demand response management in V2G systems. IEEE Transactions on Industrial Informatics 11, 6 (2015), 1554–1563.

[44]

Ke Zhang 2018. Optimal Charging Schemes for Electric Vehicles in Smart Grid: A Contract Theoretic Approach. IEEE Transactions on Intelligent Transportation Systems 19, 9 (2018), 3046–3058.

[45]

Ke Zhang, Yuming Mao, Supeng Leng, Ming Zeng, Liang Xu, Li Jiang, and Alexey Vinel. 2016. Optimal energy exchange schemes in smart grid networks: A contract theoretic approach. In 2016 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 1–6.

[46]

Weifeng Zhong 2017. Efficient auction mechanisms for two-layer vehicle-to-grid energy trading in smart grid. In 2017 IEEE International Conference on Communications (ICC). IEEE, 1–6.

Cited By

Sales-Ortiz JArdakanian O(2024)Efficient Trading of Aggregate Bidirectional EV Charging Flexibility with Reinforcement LearningProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3661949(134-146)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3632775.3661949
Chadokar LKirar MYadav GSalaria USajjad M(2024)Aggregation and Bidding Strategy of Virtual Power PlantJournal of Electrical Engineering & Technology10.1007/s42835-024-02027-yOnline publication date: 13-Sep-2024
https://doi.org/10.1007/s42835-024-02027-y
Susanto J(2024)Virtual Power Plant Participation in Australian Wholesale Electricity MarketsMicrogrids and Virtual Power Plants10.1007/978-981-97-6623-9_13(371-390)Online publication date: 2-Nov-2024
https://doi.org/10.1007/978-981-97-6623-9_13

Index Terms

Making a Virtual Power Plant out of Privately Owned Electric Vehicles: From Contract Design to Scheduling
1. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
  2. Theory and algorithms for application domains
    1. Algorithmic game theory and mechanism design

Recommendations

Electric Vehicles: An Agent-Based Approach to Sustainability
MATES 2015: Revised Selected Papers of the 13th German Conference on Multiagent System Technologies - Volume 9433

Renewable energy sources such as wind and solar are difficult to balance for the grid because they are weather dependent. We study how the storage of electric vehicles can balance a grid with an increasing intermittent renewable energy content in the ...
Agent-coordinated virtual power plants of electric vehicles
AAMAS '14: Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems

Challenges with energy provision due to intermittent renewable energy sources can be addressed with information systems in smart energy markets. One specific solution is virtual power plants (VPP) of electric vehicles (EV). The operation of the VPP is ...
Decentralised online charging scheduling for large populations of electric vehicles: a cyber-physical system approach

As the number of electric vehicles EVs grows, their electricity demands may have significant detrimental impacts on electric power grid when not scheduled properly. In this paper, we model an EV charging system as a cyber-physical system, and design a ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

e-Energy '23: Proceedings of the 14th ACM International Conference on Future Energy Systems

June 2023

545 pages

ISBN:9798400700323

DOI:10.1145/3575813

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGEnergy: ACM Special Interest Group on Energy Systems and Informatics

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 June 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Natural Sciences and Engineering Research Council of Canada

Conference

e-Energy '23

Sponsor:

SIGEnergy

e-Energy '23: The 14th ACM International Conference on Future Energy Systems

June 20 - 23, 2023

FL, Orlando, USA

Acceptance Rates

Overall Acceptance Rate 160 of 446 submissions, 36%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
113
Total Downloads

Downloads (Last 12 months)59
Downloads (Last 6 weeks)6

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Sales-Ortiz JArdakanian O(2024)Efficient Trading of Aggregate Bidirectional EV Charging Flexibility with Reinforcement LearningProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3661949(134-146)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3632775.3661949
Chadokar LKirar MYadav GSalaria USajjad M(2024)Aggregation and Bidding Strategy of Virtual Power PlantJournal of Electrical Engineering & Technology10.1007/s42835-024-02027-yOnline publication date: 13-Sep-2024
https://doi.org/10.1007/s42835-024-02027-y
Susanto J(2024)Virtual Power Plant Participation in Australian Wholesale Electricity MarketsMicrogrids and Virtual Power Plants10.1007/978-981-97-6623-9_13(371-390)Online publication date: 2-Nov-2024
https://doi.org/10.1007/978-981-97-6623-9_13

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents