IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v247y2022ics0360544222003851.html
   My bibliography  Save this article

Optimal intra-day operations of behind-the-meter battery storage for primary frequency regulation provision: A hybrid lookahead method

Author

Listed:
  • Wen, Kerui
  • Li, Weidong
  • Yu, Samson Shenglong
  • Li, Ping
  • Shi, Peng

Abstract

Battery energy storage systems (BESSs) are being widely installed behind-the-meter to reduce electricity bill. By providing grid ancillary services, behind-the-meter BESSs can increase potential revenue streams. This study targets the simultaneous electricity bill reduction and primary frequency regulation (PFR) provision. With the expansion of the application spectrum, the intra-day operations become more and more complicated. In this paper, a hybrid lookahead method with value function approximation strategy is proposed for intra-day operations, wherein the concept of “offline calculation—online application” is devised and implemented. The approximate value function is trained offline to represent the expected long-term benefit. A two-stage robust approximate dynamic programming (ADP) model is formulated for one day operation which is optimized to adjust the power baseline with a forward rolling horizon. Furthermore, multi-dimensional indicators are introduced to evaluate the proposed strategy. Simulations and benchmarking comparisons are performed for a 0.5 MW/1.0 MWh BESS to verify the superior performance of the proposed strategy. The results show that the approximate value function can be obtained offline with 99.07% convergence precision. Moreover, the proposed strategy can ensure the economic benefit and PFR provision within a short online computing time. The resulting intra-day economic benefit can reach 95.55% of the theoretical optimum, and the online optimization consumes only 4.65s for a prediction horizon of 5 min, which ensures the feasibility of real-time predictive optimization.

Suggested Citation

  • Wen, Kerui & Li, Weidong & Yu, Samson Shenglong & Li, Ping & Shi, Peng, 2022. "Optimal intra-day operations of behind-the-meter battery storage for primary frequency regulation provision: A hybrid lookahead method," Energy, Elsevier, vol. 247(C).
  • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003851
    DOI: 10.1016/j.energy.2022.123482
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222003851
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2022.123482?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. Walawalkar, Rahul & Apt, Jay & Mancini, Rick, 2007. "Economics of electric energy storage for energy arbitrage and regulation in New York," Energy Policy, Elsevier, vol. 35(4), pages 2558-2568, April.
    2. Xu, Bo & Wang, Jiexin & Guo, Mengyuan & Lu, Jiayu & Li, Gehui & Han, Liang, 2021. "A hybrid demand response mechanism based on real-time incentive and real-time pricing," Energy, Elsevier, vol. 231(C).
    3. Angenendt, Georg & Zurmühlen, Sebastian & Figgener, Jan & Kairies, Kai-Philipp & Sauer, Dirk Uwe, 2020. "Providing frequency control reserve with photovoltaic battery energy storage systems and power-to-heat coupling," Energy, Elsevier, vol. 194(C).
    4. 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).
    5. Xie, Min & Ji, Xiang & Hu, Xintong & Cheng, Peijun & Du, Yuxin & Liu, Mingbo, 2018. "Autonomous optimized economic dispatch of active distribution system with multi-microgrids," Energy, Elsevier, vol. 153(C), pages 479-489.
    6. Daniel F. Salas & Warren B. Powell, 2018. "Benchmarking a Scalable Approximate Dynamic Programming Algorithm for Stochastic Control of Grid-Level Energy Storage," INFORMS Journal on Computing, INFORMS, vol. 30(1), pages 106-123, February.
    7. Zhou, Hou Sheng & Passey, Rob & Bruce, Anna & Sproul, Alistair B., 2021. "Aggregated impact of coordinated commercial-scale battery energy storage systems on network peak demand, and financial outcomes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    8. El-Bidairi, Kutaiba S. & Nguyen, Hung Duc & Mahmoud, Thair S. & Jayasinghe, S.D.G. & Guerrero, Josep M., 2020. "Optimal sizing of Battery Energy Storage Systems for dynamic frequency control in an islanded microgrid: A case study of Flinders Island, Australia," Energy, Elsevier, vol. 195(C).
    9. Vitale, F. & Rispoli, N. & Sorrentino, M. & Rosen, M.A. & Pianese, C., 2021. "On the use of dynamic programming for optimal energy management of grid-connected reversible solid oxide cell-based renewable microgrids," Energy, Elsevier, vol. 225(C).
    10. Mulleriyawage, U.G.K. & Shen, W.X., 2021. "Impact of demand side management on optimal sizing of residential battery energy storage system," Renewable Energy, Elsevier, vol. 172(C), pages 1250-1266.
    11. A. Stephan & B. Battke & M. D. Beuse & J. H. Clausdeinken & T. S. Schmidt, 2016. "Limiting the public cost of stationary battery deployment by combining applications," Nature Energy, Nature, vol. 1(7), pages 1-9, July.
    12. van der Meer, Dennis & Wang, Guang Chao & Munkhammar, Joakim, 2021. "An alternative optimal strategy for stochastic model predictive control of a residential battery energy management system with solar photovoltaic," Applied Energy, Elsevier, vol. 283(C).
    13. Sepúlveda-Mora, Sergio B. & Hegedus, Steven, 2021. "Making the case for time-of-use electric rates to boost the value of battery storage in commercial buildings with grid connected PV systems," Energy, Elsevier, vol. 218(C).
    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. Zhang, Mingze & Li, Weidong & Yu, Samson Shenglong & Wang, Haixia & Ba, Yu, 2024. "Optimal day-ahead large-scale battery dispatch model for multi-regulation participation considering full timescale uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Amine, Hartani Mohamed & Aissa, Benhammou & Rezk, Hegazy & Messaoud, Hamouda & Othmane, Adbdelkhalek & Saad, Mekhilef & Abdelkareem, Mohammad Ali, 2023. "Enhancing hybrid energy storage systems with advanced low-pass filtration and frequency decoupling for optimal power allocation and reliability of cluster of DC-microgrids," Energy, Elsevier, vol. 282(C).
    3. Soheil Mohseni & Jay Rutovitz & Heather Smith & Scott Dwyer & Farzan Tahir, 2023. "Economic Viability Assessment of Neighbourhood versus Residential Batteries: Insights from an Australian Case Study," Sustainability, MDPI, vol. 15(23), pages 1-27, November.
    4. Zhang, Mingze & Li, Weidong & Yu, Samson Shenglong & Wen, Kerui & Muyeen, S.M., 2023. "Day-ahead optimization dispatch strategy for large-scale battery energy storage considering multiple regulation and prediction failures," Energy, Elsevier, vol. 270(C).
    5. Wei Chen & Na Sun & Zhicheng Ma & Wenfei Liu & Haiying Dong, 2023. "A Two-Layer Optimization Strategy for Battery Energy Storage Systems to Achieve Primary Frequency Regulation of Power Grid," Energies, MDPI, vol. 16(6), pages 1-18, March.

    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. Yin, Linfei & Luo, Shikui & Ma, Chenxiao, 2021. "Expandable depth and width adaptive dynamic programming for economic smart generation control of smart grids," Energy, Elsevier, vol. 232(C).
    2. Zakeri, Behnam & Gissey, Giorgio Castagneto & Dodds, Paul E. & Subkhankulova, Dina, 2021. "Centralized vs. distributed energy storage – Benefits for residential users," Energy, Elsevier, vol. 236(C).
    3. Lin, Jin & Dong, Jun & Dou, Xihao & Liu, Yao & Yang, Peiwen & Ma, Tongtao, 2022. "Psychological insights for incentive-based demand response incorporating battery energy storage systems: A two-loop Stackelberg game approach," Energy, Elsevier, vol. 239(PC).
    4. Ahmadiahangar, Roya & Karami, Hossein & Husev, Oleksandr & Blinov, Andrei & Rosin, Argo & Jonaitis, Audrius & Sanjari, Mohammad Javad, 2022. "Analytical approach for maximizing self-consumption of nearly zero energy buildings- case study: Baltic region," Energy, Elsevier, vol. 238(PB).
    5. Jungsub Sim & Minsoo Kim & Dongjoo Kim & Hongseok Kim, 2021. "Cloud Energy Storage System Operation with Capacity P2P Transaction," Energies, MDPI, vol. 14(2), pages 1-13, January.
    6. Megan Culler & Hannah Burroughs, 2021. "Cybersecurity Considerations for Grid-Connected Batteries with Hardware Demonstrations," Energies, MDPI, vol. 14(11), pages 1-20, May.
    7. Daniel Fett & Dogan Keles & Thomas Kaschub & Wolf Fichtner, 2019. "Impacts of self-generation and self-consumption on German household electricity prices," Journal of Business Economics, Springer, vol. 89(7), pages 867-891, September.
    8. McConnell, Dylan & Forcey, Tim & Sandiford, Mike, 2015. "Estimating the value of electricity storage in an energy-only wholesale market," Applied Energy, Elsevier, vol. 159(C), pages 422-432.
    9. Drago, Carlo & Gatto, Andrea, 2022. "Policy, regulation effectiveness, and sustainability in the energy sector: A worldwide interval-based composite indicator," Energy Policy, Elsevier, vol. 167(C).
    10. Sandro Sitompul & Goro Fujita, 2021. "Impact of Advanced Load-Frequency Control on Optimal Size of Battery Energy Storage in Islanded Microgrid System," Energies, MDPI, vol. 14(8), pages 1-18, April.
    11. Li, Qiang & Gao, Mengkai & Lin, Houfei & Chen, Ziyu & Chen, Minyou, 2019. "MAS-based distributed control method for multi-microgrids with high-penetration renewable energy," Energy, Elsevier, vol. 171(C), pages 284-295.
    12. Cowan, Kelly & Daim, Tugrul & Anderson, Tim, 2010. "Exploring the impact of technology development and adoption for sustainable hydroelectric power and storage technologies in the Pacific Northwest United States," Energy, Elsevier, vol. 35(12), pages 4771-4779.
    13. Wen, Shuli & Lan, Hai & Hong, Ying-Yi & Yu, David C. & Zhang, Lijun & Cheng, Peng, 2016. "Allocation of ESS by interval optimization method considering impact of ship swinging on hybrid PV/diesel ship power system," Applied Energy, Elsevier, vol. 175(C), pages 158-167.
    14. Beuse, Martin & Dirksmeier, Mathias & Steffen, Bjarne & Schmidt, Tobias S., 2020. "Profitability of commercial and industrial photovoltaics and battery projects in South-East-Asia," Applied Energy, Elsevier, vol. 271(C).
    15. Sioshansi, Ramteen & Denholm, Paul & Jenkin, Thomas, 2011. "A comparative analysis of the value of pure and hybrid electricity storage," Energy Economics, Elsevier, vol. 33(1), pages 56-66, January.
    16. Mulleriyawage, U.G.K. & Shen, W.X., 2021. "Impact of demand side management on optimal sizing of residential battery energy storage system," Renewable Energy, Elsevier, vol. 172(C), pages 1250-1266.
    17. Xiong, Kang & Hu, Weihao & Cao, Di & Li, Sichen & Zhang, Guozhou & Liu, Wen & Huang, Qi & Chen, Zhe, 2023. "Coordinated energy management strategy for multi-energy hub with thermo-electrochemical effect based power-to-ammonia: A multi-agent deep reinforcement learning enabled approach," Renewable Energy, Elsevier, vol. 214(C), pages 216-232.
    18. Farihan Mohamad & Jiashen Teh & Ching-Ming Lai & Liang-Rui Chen, 2018. "Development of Energy Storage Systems for Power Network Reliability: A Review," Energies, MDPI, vol. 11(9), pages 1-19, August.
    19. Fathy, Ahmed & Ferahtia, Seydali & Rezk, Hegazy & Yousri, Dalia & Abdelkareem, Mohammad Ali & Olabi, A.G., 2022. "Optimal adaptive fuzzy management strategy for fuel cell-based DC microgrid," Energy, Elsevier, vol. 247(C).
    20. Hossein Heirani & Naser Bagheri Moghaddam & Sina Labbafi & Seyedali Sina, 2022. "A Business Model for Developing Distributed Photovoltaic Systems in Iran," Sustainability, MDPI, vol. 14(18), pages 1-21, September.

    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:247:y:2022:i:c:s0360544222003851. 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.