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Validating the Real-Time Performance of Distributed Energy Resources Participating on Primary Frequency Reserves

Author

Listed:
  • Niko Karhula

    (Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland)

  • Seppo Sierla

    (Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland)

  • Valeriy Vyatkin

    (Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland
    Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden
    International Research Laboratory of Computer Technologies, ITMO University, 197101 St. Petersburg, Russia)

Abstract

A significant body of research has emerged for adapting diverse intelligent distributed energy resources to provide primary frequency reserves (PFR). However, such works are usually vague about the technical specifications for PFR. Industrial practitioners designing systems for PFR markets must pre-qualify their PFR resources against the specifications of the market operator, which is usually a transmission system operator (TSO) or independent system operator (ISO). TSO and ISO requirements for PFR have been underspecified with respect to real-time performance, but as fossil-fuel based PFR is being replaced by various distributed energy resources, these requirements are being tightened. The TSOs of Denmark, Finland, Norway, and Sweden have recently released a joint pilot phase specification with novel requirements on the dynamic performance of PFR resources. This paper presents an automated procedure for performing the pre-qualification procedure against this specification. The procedure is generic and has been demonstrated with a testbed of light emitting diode (LED) lights. The implications of low bandwidth Internet of Things communications, as well as the need to avoid abrupt control actions that irritate human users, have been investigated in the automated procedure.

Suggested Citation

  • Niko Karhula & Seppo Sierla & Valeriy Vyatkin, 2021. "Validating the Real-Time Performance of Distributed Energy Resources Participating on Primary Frequency Reserves," Energies, MDPI, vol. 14(21), pages 1-19, October.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:6914-:d:661443
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    References listed on IDEAS

    as
    1. El Geneidy, Rami & Howard, Bianca, 2020. "Contracted energy flexibility characteristics of communities: Analysis of a control strategy for demand response," Applied Energy, Elsevier, vol. 263(C).
    2. Alaperä, Ilari & Honkapuro, Samuli & Paananen, Janne, 2018. "Data centers as a source of dynamic flexibility in smart girds," Applied Energy, Elsevier, vol. 229(C), pages 69-79.
    3. Pusceddu, Elian & Zakeri, Behnam & Castagneto Gissey, Giorgio, 2021. "Synergies between energy arbitrage and fast frequency response for battery energy storage systems," Applied Energy, Elsevier, vol. 283(C).
    4. Díaz-González, Francisco & Hau, Melanie & Sumper, Andreas & Gomis-Bellmunt, Oriol, 2014. "Participation of wind power plants in system frequency control: Review of grid code requirements and control methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 551-564.
    5. Mashlakov, Aleksei & Pournaras, Evangelos & Nardelli, Pedro H.J. & Honkapuro, Samuli, 2021. "Decentralized cooperative scheduling of prosumer flexibility under forecast uncertainties," Applied Energy, Elsevier, vol. 290(C).
    6. Herre, Lars & Tomasini, Federica & Paridari, Kaveh & Söder, Lennart & Nordström, Lars, 2020. "Simplified model of integrated paper mill for optimal bidding in energy and reserve markets," Applied Energy, Elsevier, vol. 279(C).
    7. Braeuer, Fritz & Rominger, Julian & McKenna, Russell & Fichtner, Wolf, 2019. "Battery storage systems: An economic model-based analysis of parallel revenue streams and general implications for industry," Applied Energy, Elsevier, vol. 239(C), pages 1424-1440.
    8. Stathopoulos, P. & Sleem, T. & Paschereit, C.O., 2017. "Steam generation with stoichiometric combustion of H2/O2 as a way to simultaneously provide primary control reserve and energy storage," Applied Energy, Elsevier, vol. 205(C), pages 692-702.
    9. Waseem, Muhammad & Lin, Zhenzhi & Liu, Shengyuan & Zhang, Zhi & Aziz, Tarique & Khan, Danish, 2021. "Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources," Applied Energy, Elsevier, vol. 290(C).
    10. Diaz-Londono, Cesar & Enescu, Diana & Ruiz, Fredy & Mazza, Andrea, 2020. "Experimental modeling and aggregation strategy for thermoelectric refrigeration units as flexible loads," Applied Energy, Elsevier, vol. 272(C).
    11. Lu, Renzhi & Bai, Ruichang & Huang, Yuan & Li, Yuting & Jiang, Junhui & Ding, Yuemin, 2021. "Data-driven real-time price-based demand response for industrial facilities energy management," Applied Energy, Elsevier, vol. 283(C).
    12. Evgeny Nefedov & Seppo Sierla & Valeriy Vyatkin, 2018. "Internet of Energy Approach for Sustainable Use of Electric Vehicles as Energy Storage of Prosumer Buildings," Energies, MDPI, vol. 11(8), pages 1-18, August.
    13. Lee, Rachel & Homan, Samuel & Mac Dowell, Niall & Brown, Solomon, 2019. "A closed-loop analysis of grid scale battery systems providing frequency response and reserve services in a variable inertia grid," Applied Energy, Elsevier, vol. 236(C), pages 961-972.
    14. Engels, Jonas & Claessens, Bert & Deconinck, Geert, 2019. "Techno-economic analysis and optimal control of battery storage for frequency control services, applied to the German market," Applied Energy, Elsevier, vol. 242(C), pages 1036-1049.
    15. Hosseini, Seyyed Ahmad & Toubeau, Jean-François & De Grève, Zacharie & Vallée, François, 2020. "An advanced day-ahead bidding strategy for wind power producers considering confidence level on the real-time reserve provision," Applied Energy, Elsevier, vol. 280(C).
    16. Christian Giovanelli & Seppo Sierla & Ryutaro Ichise & Valeriy Vyatkin, 2018. "Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices," Energies, MDPI, vol. 11(7), pages 1-22, July.
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