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The challenge of integrating offshore wind power in the U.S. electric grid. Part II: Simulation of electricity market operations

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  • Simão, H.P.
  • Powell, W.B.
  • Archer, C.L.
  • Kempton, W.

Abstract

The purpose of this two-part study is to analyze large penetrations of offshore wind power into a large electric grid, using the case of the grid operated by PJM Interconnection in the northeastern U.S. Part I of the study introduces the wind forecast error model and Part II, this paper, describes Smart-ISO, a simulator of PJM's planning process for generator scheduling, including day-ahead and intermediate-term commitments to energy generators and real-time economic dispatch. Results show that, except in summer, an unconstrained transmission grid can meet the load at five build-out levels spanning 7–70 GW of capacity, with the addition of at most 1–8 GW of reserves.

Suggested Citation

  • Simão, H.P. & Powell, W.B. & Archer, C.L. & Kempton, W., 2017. "The challenge of integrating offshore wind power in the U.S. electric grid. Part II: Simulation of electricity market operations," Renewable Energy, Elsevier, vol. 103(C), pages 418-431.
    • Handle: RePEc:eee:renene:v:103:y:2017:i:c:p:418-431
    DOI: 10.1016/j.renene.2016.11.049
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    References listed on IDEAS

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    1. Andresen, Gorm B. & Rodriguez, Rolando A. & Becker, Sarah & Greiner, Martin, 2014. "The potential for arbitrage of wind and solar surplus power in Denmark," Energy, Elsevier, vol. 76(C), pages 49-58.
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    Cited by:

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    2. Joseph Oyekale & Mario Petrollese & Vittorio Tola & Giorgio Cau, 2020. "Impacts of Renewable Energy Resources on Effectiveness of Grid-Integrated Systems: Succinct Review of Current Challenges and Potential Solution Strategies," Energies, MDPI, vol. 13(18), pages 1-48, September.
    3. Dokur, Emrah & Erdogan, Nuh & Salari, Mahdi Ebrahimi & Karakuzu, Cihan & Murphy, Jimmy, 2022. "Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine," Energy, Elsevier, vol. 248(C).
    4. Shahriari, M. & Cervone, G. & Clemente-Harding, L. & Delle Monache, L., 2020. "Using the analog ensemble method as a proxy measurement for wind power predictability," Renewable Energy, Elsevier, vol. 146(C), pages 789-801.
    5. Wang, Xuefei & Zeng, Xiangwu & Li, Xinyao & Li, Jiale, 2019. "Investigation on offshore wind turbine with an innovative hybrid monopile foundation: An experimental based study," Renewable Energy, Elsevier, vol. 132(C), pages 129-141.
    6. Banshwar, Anuj & Sharma, Naveen Kumar & Sood, Yog Raj & Shrivastava, Rajnish, 2018. "An international experience of technical and economic aspects of ancillary services in deregulated power industry: Lessons for emerging BRIC electricity markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 774-801.
    7. Carlos Otero-Casal & Platon Patlakas & Miguel A. Prósper & George Galanis & Gonzalo Miguez-Macho, 2019. "Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter," Energies, MDPI, vol. 12(16), pages 1-19, August.
    8. Jordehi, A. Rezaee, 2018. "How to deal with uncertainties in electric power systems? A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 145-155.
    9. Pierro, Marco & Perez, Richard & Perez, Marc & Prina, Matteo Giacomo & Moser, David & Cornaro, Cristina, 2021. "Italian protocol for massive solar integration: From solar imbalance regulation to firm 24/365 solar generation," Renewable Energy, Elsevier, vol. 169(C), pages 425-436.
    10. Noel, Lance & Brodie, Joseph F. & Kempton, Willett & Archer, Cristina L. & Budischak, Cory, 2017. "Cost minimization of generation, storage, and new loads, comparing costs with and without externalities," Applied Energy, Elsevier, vol. 189(C), pages 110-121.

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