IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i12p12390-13709d59814.html
   My bibliography  Save this article

Unit Commitment Model Considering Flexible Scheduling of Demand Response for High Wind Integration

Author

Listed:
  • Beibei Wang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Xiaocong Liu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    These authors contributed equally to this work.)

  • Feng Zhu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    These authors contributed equally to this work.)

  • Xiaoqing Hu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    These authors contributed equally to this work.)

  • Wenlu Ji

    (Nanjing Power Supply Company, No. 1 Aoti Street, Nanjing 210019, China
    These authors contributed equally to this work.)

  • Shengchun Yang

    (China Electric Power Research Institute, Nanjing 210096, China
    These authors contributed equally to this work.)

  • Ke Wang

    (China Electric Power Research Institute, Nanjing 210096, China
    These authors contributed equally to this work.)

  • Shuhai Feng

    (China Electric Power Research Institute, Nanjing 210096, China
    These authors contributed equally to this work.)

Abstract

In this paper, a two-stage stochastic unit commitment (UC) model considering flexible scheduling of demand response (DR) is proposed. In the proposed UC model, the DR resources can be scheduled: (1) in the first stage, as resources on a day-ahead basis to integrate the predicted wind fluctuation with lower uncertainty; (2) in the second stage, as resources on an intra-day basis to compensate for the deviation among multiple wind power scenarios considering the coupling relationship of DR on available time and capacity. Simulation results on the Pennsylvania-New Jersey-Maryland (PJM) 5-bus system and IEEE 118-bus system indicate that the proposed model can maximize the DR value with lower cost. Moreover, different types of DR resources may vary in the contract costs (capacity costs), the responsive costs (energy costs), the time of advance notice, and the minimum on-site hours. The responsive cost is considered as the most important factor affecting DR scheduling. In addition, the first-stage DR is dispatched more frequently when transmission constraints congestion occurs.

Suggested Citation

  • Beibei Wang & Xiaocong Liu & Feng Zhu & Xiaoqing Hu & Wenlu Ji & Shengchun Yang & Ke Wang & Shuhai Feng, 2015. "Unit Commitment Model Considering Flexible Scheduling of Demand Response for High Wind Integration," Energies, MDPI, vol. 8(12), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:12:p:12390-13709:d:59814
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/12/12390/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/8/12/12390/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wei Gu & Haojun Yu & Wei Liu & Junpeng Zhu & Xiaohui Xu, 2013. "Demand Response and Economic Dispatch of Power Systems Considering Large-Scale Plug-in Hybrid Electric Vehicles/Electric Vehicles (PHEVs/EVs): A Review," Energies, MDPI, vol. 6(9), pages 1-24, August.
    2. Torres, David & Crichigno, Jorge & Padilla, Gregg & Rivera, Ruben, 2014. "Scheduling coupled photovoltaic, battery and conventional energy sources to maximize profit using linear programming," Renewable Energy, Elsevier, vol. 72(C), pages 284-290.
    3. Chinhao Chong & Weidou Ni & Linwei Ma & Pei Liu & Zheng Li, 2015. "The Use of Energy in Malaysia: Tracing Energy Flows from Primary Source to End Use," Energies, MDPI, vol. 8(4), pages 1-39, April.
    4. Falsafi, Hananeh & Zakariazadeh, Alireza & Jadid, Shahram, 2014. "The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming," Energy, Elsevier, vol. 64(C), pages 853-867.
    5. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    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. Motta, Vinicius N. & Anjos, Miguel F. & Gendreau, Michel, 2024. "Survey of optimization models for power system operation and expansion planning with demand response," European Journal of Operational Research, Elsevier, vol. 312(2), pages 401-412.
    2. Songli Fan & Qian Ai & Longjian Piao, 2018. "Hierarchical Energy Management of Microgrids including Storage and Demand Response," Energies, MDPI, vol. 11(5), pages 1-23, May.
    3. Manish Mohanpurkar & Yusheng Luo & Danny Terlip & Fernando Dias & Kevin Harrison & Joshua Eichman & Rob Hovsapian & Jennifer Kurtz, 2017. "Electrolyzers Enhancing Flexibility in Electric Grids," Energies, MDPI, vol. 10(11), pages 1-17, November.
    4. Ilias G. Marneris & Pandelis N. Biskas & Anastasios G. Bakirtzis, 2017. "Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration," Energies, MDPI, vol. 10(1), pages 1-25, January.
    5. Jiafu Yin & Dongmei Zhao, 2018. "Fuzzy Stochastic Unit Commitment Model with Wind Power and Demand Response under Conditional Value-At-Risk Assessment," Energies, MDPI, vol. 11(2), pages 1-18, February.

    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. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(C).
    2. Kocaman, Ayse Selin & Ozyoruk, Emin & Taneja, Shantanu & Modi, Vijay, 2020. "A stochastic framework to evaluate the impact of agricultural load flexibility on the sizing of renewable energy systems," Renewable Energy, Elsevier, vol. 152(C), pages 1067-1078.
    3. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    4. Liu, Xuewei & Yuan, Zengwei & Xu, Yuan & Jiang, Songyan, 2017. "Greening cement in China: A cost-effective roadmap," Applied Energy, Elsevier, vol. 189(C), pages 233-244.
    5. Cheng, Meng & Sami, Saif Sabah & Wu, Jianzhong, 2017. "Benefits of using virtual energy storage system for power system frequency response," Applied Energy, Elsevier, vol. 194(C), pages 376-385.
    6. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2014. "Diffusion and adoption of dynamic electricity tariffs: An agent-based modeling approach," HSC Research Reports HSC/14/01, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    7. Kowalska-Pyzalska, Anna & Maciejowska, Katarzyna & Suszczyński, Karol & Sznajd-Weron, Katarzyna & Weron, Rafał, 2014. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," Energy Policy, Elsevier, vol. 72(C), pages 164-174.
    8. Daví-Arderius, Daniel & Sanin, María-Eugenia & Trujillo-Baute, Elisa, 2017. "CO2 content of electricity losses," Energy Policy, Elsevier, vol. 104(C), pages 439-445.
    9. Claire M. Weiller & Michael G. Pollitt, 2013. "Platform markets and energy services," Working Papers EPRG 1334, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    10. Costa-Campi, Maria Teresa & Daví-Arderius, Daniel & Trujillo-Baute, Elisa, 2018. "The economic impact of electricity losses," Energy Economics, Elsevier, vol. 75(C), pages 309-322.
    11. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    12. Ovidiu Ivanov & Samiran Chattopadhyay & Soumya Banerjee & Bogdan-Constantin Neagu & Gheorghe Grigoras & Mihai Gavrilas, 2020. "A Novel Algorithm with Multiple Consumer Demand Response Priorities in Residential Unbalanced LV Electricity Distribution Networks," Mathematics, MDPI, vol. 8(8), pages 1-24, July.
    13. Liu, Yingqi, 2017. "Demand response and energy efficiency in the capacity resource procurement: Case studies of forward capacity markets in ISO New England, PJM and Great Britain," Energy Policy, Elsevier, vol. 100(C), pages 271-282.
    14. Schachter, Jonathan A. & Mancarella, Pierluigi & Moriarty, John & Shaw, Rita, 2016. "Flexible investment under uncertainty in smart distribution networks with demand side response: Assessment framework and practical implementation," Energy Policy, Elsevier, vol. 97(C), pages 439-449.
    15. Mazur, Christoph & Hoegerle, Yannick & Brucoli, Maria & van Dam, Koen & Guo, Miao & Markides, Christos N. & Shah, Nilay, 2019. "A holistic resilience framework development for rural power systems in emerging economies," Applied Energy, Elsevier, vol. 235(C), pages 219-232.
    16. Maleki, Akbar & Ameri, Mehran & Keynia, Farshid, 2015. "Scrutiny of multifarious particle swarm optimization for finding the optimal size of a PV/wind/battery hybrid system," Renewable Energy, Elsevier, vol. 80(C), pages 552-563.
    17. Nouha Dkhili & David Salas & Julien Eynard & Stéphane Thil & Stéphane Grieu, 2021. "Innovative Application of Model-Based Predictive Control for Low-Voltage Power Distribution Grids with Significant Distributed Generation," Energies, MDPI, vol. 14(6), pages 1-28, March.
    18. Antonio Paola & David Angeli & Goran Strbac, 2018. "On Distributed Scheduling of Flexible Demand and Nash Equilibria in the Electricity Market," Dynamic Games and Applications, Springer, vol. 8(4), pages 761-798, December.
    19. Olga Bogdanova & Karīna Viskuba & Laila Zemīte, 2023. "A Review of Barriers and Enables in Demand Response Performance Chain," Energies, MDPI, vol. 16(18), pages 1-33, September.
    20. Wadim Strielkowski & Dalia Streimikiene & Alena Fomina & Elena Semenova, 2019. "Internet of Energy (IoE) and High-Renewables Electricity System Market Design," Energies, MDPI, vol. 12(24), pages 1-17, December.

    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:8:y:2015:i:12:p:12390-13709:d:59814. 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.