IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2023i1p11-d1303228.html
   My bibliography  Save this article

Automatic Generation Control Ancillary Service Cost-Allocation Methods Based on Causer-Pays Principle in Electricity Market

Author

Listed:
  • Sunkyo Kim

    (Center for Future S&T Planning, Korea Institute of S&T Evaluation and Planning, Eumseong-gun 27740, Republic of Korea)

  • Pyeong-Ik Hwang

    (Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea)

  • Jaewan Suh

    (Department of Electrical Engineering, Dongyang Mirae University, Seoul 08221, Republic of Korea)

Abstract

The electric power system is rapidly transforming to address the urgent need for decarbonization and combat climate change. Integration of renewable energy sources into the power grid is accelerating, creating new challenges such as intermittency and uncertainty. To address these challenges, this paper proposes a new design of automatic generation control (AGC) ancillary service cost allocation based on the causer-pays rule. The proposed design treats reserves as inventory and aims to minimize them by allocating costs among consumers based on the causative factors for AGC operation. Two cost-allocation methods based on the causer-pays principle are introduced. The first method distributes costs according to the changes in loads causing ancillary service operation, while the second method considers opportunity costs. The case study on the IEEE 39 Bus System demonstrates that the proposed methods incentivize consumers to minimize volatility, resulting in reduced reserve requirements for system operation. In particular, the opportunity cost-based approach encourages loads and variable renewable energy (VRE) to actively reduce volatility, resulting in more efficient power system operation. In conclusion, the novel AGC ancillary service cost allocation methods offer a promising strategy for minimizing spinning reserves, increasing the power system’s efficiency, and incentivizing consumers to actively participate in frequency regulation for a more sustainable and reliable electricity market.

Suggested Citation

  • Sunkyo Kim & Pyeong-Ik Hwang & Jaewan Suh, 2023. "Automatic Generation Control Ancillary Service Cost-Allocation Methods Based on Causer-Pays Principle in Electricity Market," Energies, MDPI, vol. 17(1), pages 1-17, December.
  • Handle: RePEc:gam:jeners:v:17:y:2023:i:1:p:11-:d:1303228
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/1/11/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/1/11/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Agostini, Claudio A. & Armijo, Franco A. & Silva, Carlos & Nasirov, Shahriyar, 2021. "The role of frequency regulation remuneration schemes in an energy matrix with high penetration of renewable energy," Renewable Energy, Elsevier, vol. 171(C), pages 1097-1114.
    2. Lemaire, Jean, 1984. "An Application of Game Theory: Cost Allocation," ASTIN Bulletin, Cambridge University Press, vol. 14(1), pages 61-81, April.
    3. Bruce L. Miller & A. G. Buckman, 1987. "Cost Allocation and Opportunity Costs," Management Science, INFORMS, vol. 33(5), pages 626-639, May.
    4. Badesa, L. & Teng, F. & Strbac, G., 2020. "Pricing inertia and Frequency Response with diverse dynamics in a Mixed-Integer Second-Order Cone Programming formulation," Applied Energy, Elsevier, vol. 260(C).
    Full references (including those not matched with items on IDEAS)

    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. Balachandran, Kashi R. & Radhakrishnan, Suresh, 1996. "Cost of congestion, operational efficiency and management accounting," European Journal of Operational Research, Elsevier, vol. 89(2), pages 237-245, March.
    2. Zhang, Huaiyuan & Liao, Kai & Yang, Jianwei & Zheng, Shunwei & He, Zhengyou, 2024. "Frequency-constrained expansion planning for wind and photovoltaic power in wind-photovoltaic-hydro-thermal multi-power system," Applied Energy, Elsevier, vol. 356(C).
    3. Mourdoukoutas, Fotios & Boonen, Tim J. & Koo, Bonsoo & Pantelous, Athanasios A., 2021. "Pricing in a competitive stochastic insurance market," Insurance: Mathematics and Economics, Elsevier, vol. 97(C), pages 44-56.
    4. Izquierdo, Josep M. & Rafels, Carles, 2001. "Average Monotonic Cooperative Games," Games and Economic Behavior, Elsevier, vol. 36(2), pages 174-192, August.
    5. Grover, Himanshu & Verma, Ashu & Bhatti, T.S., 2022. "DOBC-based frequency & voltage regulation strategy for PV-diesel hybrid microgrid during islanding conditions," Renewable Energy, Elsevier, vol. 196(C), pages 883-900.
    6. Kheshti, Mostafa & Zhao, Xiaowei & Liang, Ting & Nie, Binjian & Ding, Yulong & Greaves, Deborah, 2022. "Liquid air energy storage for ancillary services in an integrated hybrid renewable system," Renewable Energy, Elsevier, vol. 199(C), pages 298-307.
    7. Moret, Fabio & Pinson, Pierre & Papakonstantinou, Athanasios, 2020. "Heterogeneous risk preferences in community-based electricity markets," European Journal of Operational Research, Elsevier, vol. 287(1), pages 36-48.
    8. Tsanakas, Andreas, 2009. "To split or not to split: Capital allocation with convex risk measures," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 268-277, April.
    9. Lozano, Miguel A. & Serra, Luis M. & Pina, Eduardo A., 2022. "Optimal design of trigeneration systems for buildings considering cooperative game theory for allocating production cost to energy services," Energy, Elsevier, vol. 261(PB).
    10. Frisk, M. & Göthe-Lundgren, M. & Jörnsten, K. & Rönnqvist, M., 2010. "Cost allocation in collaborative forest transportation," European Journal of Operational Research, Elsevier, vol. 205(2), pages 448-458, September.
    11. Rehman, Obaid Ur & Khan, Shahid A. & Javaid, Nadeem, 2021. "Decoupled building-to-transmission-network for frequency support in PV systems dominated grid," Renewable Energy, Elsevier, vol. 178(C), pages 930-945.
    12. Ibrahim Abada, Andreas Ehrenmann, and Xavier Lambin, 2020. "On the Viability of Energy Communities," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    13. Asmussen, Søren & Christensen, Bent Jesper & Thøgersen, Julie, 2019. "Nash equilibrium premium strategies for push–pull competition in a frictional non-life insurance market," Insurance: Mathematics and Economics, Elsevier, vol. 87(C), pages 92-100.
    14. Johansen, Soren Glud, 1996. "Transfer pricing of a service department facing random demand," International Journal of Production Economics, Elsevier, vol. 46(1), pages 351-358, December.
    15. Zifeng Zhao & Peng Shi & Xiaoping Feng, 2021. "Knowledge Learning of Insurance Risks Using Dependence Models," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 1177-1196, July.
    16. Kamali Saraji, Mahyar & Aliasgari, Elahe & Streimikiene, Dalia, 2023. "Assessment of the challenges to renewable energy technologies adoption in rural areas: A Fermatean CRITIC-VIKOR approach," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    17. Fang, Xin & Cui, Hantao & Du, Ershun & Li, Fangxing & Kang, Chongqing, 2021. "Characteristics of locational uncertainty marginal price for correlated uncertainties of variable renewable generation and demands," Applied Energy, Elsevier, vol. 282(PA).
    18. M. Fiestras-Janeiro & Ignacio García-Jurado & Manuel Mosquera, 2011. "Cooperative games and cost allocation problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(1), pages 1-22, July.
    19. Josep Maria Izquierdo & Carlos Rafels, 2017. "The incentive core in co-investment problems," UB School of Economics Working Papers 2017/369, University of Barcelona School of Economics.
    20. Guusje Delsing & Michel Mandjes & Peter Spreij & Erik Winands, 2021. "On Capital Allocation for a Risk Measure Derived from Ruin Theory," Papers 2103.16264, arXiv.org.

    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:gam:jeners:v:17:y:2023:i:1:p:11-:d:1303228. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.