IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v167y2019icp1266-1277.html
   My bibliography  Save this article

A two-layer model for dynamic pricing of electricity and optimal charging of electric vehicles under price spikes

Author

Listed:
  • Subramanian, Vignesh
  • Das, Tapas K.

Abstract

Pilot projects in power networks conducted across continents have established the benefits of dynamic pricing by inducing increased demand response. However, a key hurdle in the growth of demand response is the lack of widespread availability of advanced metering infrastructure, which has stymied the adoption of dynamic pricing. We believe that this hurdle will be partially addressed by the growth of electric vehicles (EVs), as smart and connected EV parking lots will be a provider of demand response. We develop a two-layer optimization model that simultaneously determines dynamic pricing policy for the system operator and demand response strategies for the EV parking lots. The model minimizes the cost to consumers, while ensuring the system operator's revenue neutral status and addressing real-time price uncertainties. A variant of the 5-bus PJM network is used to demonstrate model implementation. Numerical results show that for a low to moderate price spike scenario, dynamic pricing with demand response from EVs alone can lower the daily average consumer cost of 1.42% compared to the cost of flat pricing. A cost reduction of 6.5% is achieved when price spikes are relatively high. Computational challenges of implementing our model for real networks are discussed in the concluding remarks.

Suggested Citation

  • Subramanian, Vignesh & Das, Tapas K., 2019. "A two-layer model for dynamic pricing of electricity and optimal charging of electric vehicles under price spikes," Energy, Elsevier, vol. 167(C), pages 1266-1277.
    • Handle: RePEc:eee:energy:v:167:y:2019:i:c:p:1266-1277
    DOI: 10.1016/j.energy.2018.10.171
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218321686
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.10.171?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mohseni, Amin & Mortazavi, Seyed Saeidollah & Ghasemi, Ahmad & Nahavandi, Ali & Talaei abdi, Masoud, 2017. "The application of household appliances' flexibility by set of sequential uninterruptible energy phases model in the day-ahead planning of a residential microgrid," Energy, Elsevier, vol. 139(C), pages 315-328.
    2. Alipour, Manijeh & Mohammadi-Ivatloo, Behnam & Moradi-Dalvand, Mohammad & Zare, Kazem, 2017. "Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets," Energy, Elsevier, vol. 118(C), pages 1168-1179.
    3. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
    4. Shafie-khah, M. & Heydarian-Forushani, E. & Golshan, M.E.H. & Siano, P. & Moghaddam, M.P. & Sheikh-El-Eslami, M.K. & Catalão, J.P.S., 2016. "Optimal trading of plug-in electric vehicle aggregation agents in a market environment for sustainability," Applied Energy, Elsevier, vol. 162(C), pages 601-612.
    5. Faruqui, Ahmad & Hledik, Ryan & Newell, Sam & Pfeifenberger, Hannes, 2007. "The Power of 5 Percent," The Electricity Journal, Elsevier, vol. 20(8), pages 68-77, October.
    6. Nezamoddini, Nasim & Wang, Yong, 2016. "Risk management and participation planning of electric vehicles in smart grids for demand response," Energy, Elsevier, vol. 116(P1), pages 836-850.
    7. Aghajani, Saemeh & Kalantar, Mohsen, 2017. "A cooperative game theoretic analysis of electric vehicles parking lot in smart grid," Energy, Elsevier, vol. 137(C), pages 129-139.
    8. Khan, Ahsan Raza & Mahmood, Anzar & Safdar, Awais & Khan, Zafar A. & Khan, Naveed Ahmed, 2016. "Load forecasting, dynamic pricing and DSM in smart grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1311-1322.
    9. Ottesen, Stig Ødegaard & Tomasgard, Asgeir & Fleten, Stein-Erik, 2016. "Prosumer bidding and scheduling in electricity markets," Energy, Elsevier, vol. 94(C), pages 828-843.
    10. Faruqui, A. & Hajos, A. & Hledik, R.M. & Newell, S.A., 2010. "Fostering economic demand response in the Midwest ISO," Energy, Elsevier, vol. 35(4), pages 1544-1552.
    11. Iria, José & Soares, Filipe & Matos, Manuel, 2018. "Optimal supply and demand bidding strategy for an aggregator of small prosumers," Applied Energy, Elsevier, vol. 213(C), pages 658-669.
    12. S. Borenstein, 2013. "Effective and Equitable Adoption of Opt-In Residential Dynamic Electricity Pricing," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 42(2), pages 127-160, March.
    13. Ahmad Faruqui, Sanem Sergici, and Lamine Akaba, 2014. "The Impact of Dynamic Pricing on Residential and Small Commercial and Industrial Usage: New Experimental Evidence from Connecticut," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    14. San Román, Tomás Gómez & Momber, Ilan & Abbad, Michel Rivier & Sánchez Miralles, Álvaro, 2011. "Regulatory framework and business models for charging plug-in electric vehicles: Infrastructure, agents, and commercial relationships," Energy Policy, Elsevier, vol. 39(10), pages 6360-6375, October.
    15. Aghajani, Saemeh & Kalantar, Mohsen, 2017. "Operational scheduling of electric vehicles parking lot integrated with renewable generation based on bilevel programming approach," Energy, Elsevier, vol. 139(C), pages 422-432.
    16. Hu, Zheng & Kim, Jin-ho & Wang, Jianhui & Byrne, John, 2015. "Review of dynamic pricing programs in the U.S. and Europe: Status quo and policy recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 743-751.
    17. Ottesen, Stig Ødegaard & Tomasgard, Asgeir & Fleten, Stein-Erik, 2018. "Multi market bidding strategies for demand side flexibility aggregators in electricity markets," Energy, Elsevier, vol. 149(C), pages 120-134.
    18. Gomez-Herrera, Juan A. & Anjos, Miguel F., 2018. "Optimal collaborative demand-response planner for smart residential buildings," Energy, Elsevier, vol. 161(C), pages 370-380.
    19. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Paudel, Diwas & Das, Tapas K., 2023. "A deep reinforcement learning approach for power management of battery-assisted fast-charging EV hubs participating in day-ahead and real-time electricity markets," Energy, Elsevier, vol. 283(C).
    2. Walied Alharbi & Abdullah S. Bin Humayd & Praveen R. P. & Ahmed Bilal Awan & Anees V. P., 2022. "Optimal Scheduling of Battery-Swapping Station Loads for Capacity Enhancement of a Distribution System," Energies, MDPI, vol. 16(1), pages 1-12, December.
    3. Anjos, Miguel F. & Brotcorne, Luce & Gomez-Herrera, Juan A., 2021. "Optimal setting of time-and-level-of-use prices for an electricity supplier," Energy, Elsevier, vol. 225(C).
    4. Guo, Peng & Chen, Zhihua & Yang, Yang & Miao, Rui, 2024. "A multistage simulation-optimization-integrated methodology framework for user-oriented electric vehicle carsharing reallocation under dynamic price subsidy," Energy, Elsevier, vol. 290(C).
    5. Melendez, Kevin A. & Subramanian, Vignesh & Das, Tapas K. & Kwon, Changhyun, 2019. "Empowering end-use consumers of electricity to aggregate for demand-side participation," Applied Energy, Elsevier, vol. 248(C), pages 372-382.
    6. Menghwar, Mohan & Yan, Jie & Chi, Yongning & Asim Amin, M. & Liu, Yongqian, 2024. "A market-based real-time algorithm for congestion alleviation incorporating EV demand response in active distribution networks," Applied Energy, Elsevier, vol. 356(C).
    7. Melendez, Kevin A. & Das, Tapas K. & Kwon, Changhyun, 2020. "Optimal operation of a system of charging hubs and a fleet of shared autonomous electric vehicles," Applied Energy, Elsevier, vol. 279(C).
    8. Zhou, Kaile & Cheng, Lexin & Lu, Xinhui & Wen, Lulu, 2020. "Scheduling model of electric vehicles charging considering inconvenience and dynamic electricity prices," Applied Energy, Elsevier, vol. 276(C).
    9. Subramanian, Vignesh & Feijoo, Felipe & Sankaranarayanan, Sriram & Melendez, Kevin & Das, Tapas K., 2022. "A bilevel conic optimization model for routing and charging of EV fleets serving long distance delivery networks," Energy, Elsevier, vol. 251(C).
    10. Song, Yanqiu & Shangguan, Lingzhi & Li, Guijun, 2021. "Simulation analysis of flexible concession period contracts in electric vehicle charging infrastructure public-private-partnership (EVCI-PPP) projects based on time-of-use (TOU) charging price strateg," Energy, Elsevier, vol. 228(C).
    11. Ibrahim Alotaibi & Mohammed A. Abido & Muhammad Khalid & Andrey V. Savkin, 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources," Energies, MDPI, vol. 13(23), pages 1-41, November.
    12. Steffen Limmer, 2019. "Dynamic Pricing for Electric Vehicle Charging—A Literature Review," Energies, MDPI, vol. 12(18), pages 1-24, September.
    13. Yang, Jie & Ma, Tieding & Ma, Kai & Yang, Bo & Guerrero, Josep M. & Liu, Zhixin, 2021. "Trading mechanism and pricing strategy of integrated energy systems based on credit rating and Bayesian game," Energy, Elsevier, vol. 232(C).
    14. Visaria, Anant Atul & Jensen, Anders Fjendbo & Thorhauge, Mikkel & Mabit, Stefan Eriksen, 2022. "User preferences for EV charging, pricing schemes, and charging infrastructure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 120-143.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aghajani, Saemeh & Kalantar, Mohsen, 2017. "Operational scheduling of electric vehicles parking lot integrated with renewable generation based on bilevel programming approach," Energy, Elsevier, vol. 139(C), pages 422-432.
    2. Aghajani, Saemeh & Kalantar, Mohsen, 2017. "Optimal scheduling of distributed energy resources in smart grids: A complementarity approach," Energy, Elsevier, vol. 141(C), pages 2135-2144.
    3. Homa Rashidizadeh-Kermani & Hamid Reza Najafi & Amjad Anvari-Moghaddam & Josep M. Guerrero, 2018. "Optimal Decision-Making Strategy of an Electric Vehicle Aggregator in Short-Term Electricity Markets," Energies, MDPI, vol. 11(9), pages 1-20, September.
    4. Iria, José & Scott, Paul & Attarha, Ahmad, 2020. "Network-constrained bidding optimization strategy for aggregators of prosumers," Energy, Elsevier, vol. 207(C).
    5. Bohlayer, Markus & Fleschutz, Markus & Braun, Marco & Zöttl, Gregor, 2020. "Energy-intense production-inventory planning with participation in sequential energy markets," Applied Energy, Elsevier, vol. 258(C).
    6. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    7. Ahmadian, Ali & Sedghi, Mahdi & Fgaier, Hedia & Mohammadi-ivatloo, Behnam & Golkar, Masoud Aliakbar & Elkamel, Ali, 2019. "PEVs data mining based on factor analysis method for energy storage and DG planning in active distribution network: Introducing S2S effect," Energy, Elsevier, vol. 175(C), pages 265-277.
    8. Okur, Özge & Heijnen, Petra & Lukszo, Zofia, 2021. "Aggregator’s business models in residential and service sectors: A review of operational and financial aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    9. Xiao, Xiangsheng & Wang, Jianxiao & Lin, Rui & Hill, David J. & Kang, Chongqing, 2020. "Large-scale aggregation of prosumers toward strategic bidding in joint energy and regulation markets," Applied Energy, Elsevier, vol. 271(C).
    10. Krishnendranath Mitra & Goutam Dutta, 2021. "A novel method of market segmentation and market study for dynamic pricing of retail electricity in India: an experimental approach in a university setup," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(2), pages 162-184, April.
    11. Parag, Yael, 2021. "Which factors influence large households’ decision to join a time-of-use program? The interplay between demand flexibility, personal benefits and national benefits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    12. Correia-da-Silva, João & Soares, Isabel & Fernández, Raquel, 2020. "Impact of dynamic pricing on investment in renewables," Energy, Elsevier, vol. 202(C).
    13. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
    14. Charilaos Latinopoulos & Aruna Sivakumar & John W. Polak, 2021. "Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models," Energies, MDPI, vol. 14(4), pages 1-27, February.
    15. Backe, Stian & Kara, Güray & Tomasgard, Asgeir, 2020. "Comparing individual and coordinated demand response with dynamic and static power grid tariffs," Energy, Elsevier, vol. 201(C).
    16. Faia, Ricardo & Lezama, Fernando & Soares, João & Pinto, Tiago & Vale, Zita, 2024. "Local electricity markets: A review on benefits, barriers, current trends and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    17. Cédric Clastres & Haikel Khalfallah, 2020. "Retailers' strategies facing demand response and markets interactions," Working Papers hal-03167543, HAL.
    18. Hussain, I. & Ali, S.M. & Khan, B. & Ullah, Z. & Mehmood, C.A. & Jawad, M. & Farid, U. & Haider, A., 2019. "Stochastic Wind Energy Management Model within smart grid framework: A joint Bi-directional Service Level Agreement (SLA) between smart grid and Wind Energy District Prosumers," Renewable Energy, Elsevier, vol. 134(C), pages 1017-1033.
    19. Wang, Wenjie & Ida, Takanori & Shimada, Hideki, 2020. "Default effect versus active decision: Evidence from a field experiment in Los Alamos," European Economic Review, Elsevier, vol. 128(C).
    20. Liu, Xin & Li, Yang & Lin, Xueshan & Guo, Jiqun & Shi, Yunpeng & Shen, Yunwei, 2022. "Dynamic bidding strategy for a demand response aggregator in the frequency regulation market," Applied Energy, Elsevier, vol. 314(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:167:y:2019:i:c:p:1266-1277. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.