IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i14p5296-d868274.html
   My bibliography  Save this article

Modeling, Optimization, and Analysis of a Virtual Power Plant Demand Response Mechanism for the Internal Electricity Market Considering the Uncertainty of Renewable Energy Sources

Author

Listed:
  • Zahid Ullah

    (Institute for Globally Distributed Open Research and Education (IGDORE), Cleveland, Middlesbrough TS1 4JE, UK)

  • Arshad

    (School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK)

  • Hany Hassanin

    (School of Engineering, Technology, and Design, Canterbury Christ Church University, Canterbury CT1 1QU, UK)

Abstract

The penetration of renewable energy sources (RESs) in the electrical power system has increased significantly over the past years due to increasing global concern about climate change. However, integrating RESs into the power market is highly problematic. The output of RESs such as wind turbines (WTs) and photovoltaics (PVs) is highly uncertain. Their correlation with load demand is not always guaranteed, which compromises system reliability. Distributed energy resources (DERs), especially demand response (DR) programs and energy storage systems (ESSs), are possible options to overcome these operational challenges under the virtual power plant (VPP) setting. This study investigates the impact of using a DR program and battery energy storage system (BESS) on the VPP’s internal electricity market, and also cost-minimization analysis from a utility viewpoint. Three different constrained optimal power flow (OPF) problems are solved such as base case, DR case, and BESS case to determine total incurred costs, locational marginal prices (LMPs), and generator commitments. A scenario tree approach is used to model the uncertainties associated with WTs, PVs, and load demand. The proposed model is investigated on a 14-bus distribution system. The simulation results obtained demonstrate a favorable impact of DR and a BESS on renewable operational challenges.

Suggested Citation

  • Zahid Ullah & Arshad & Hany Hassanin, 2022. "Modeling, Optimization, and Analysis of a Virtual Power Plant Demand Response Mechanism for the Internal Electricity Market Considering the Uncertainty of Renewable Energy Sources," Energies, MDPI, vol. 15(14), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5296-:d:868274
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/14/5296/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/14/5296/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Salkuti, Surender Reddy, 2019. "Day-ahead thermal and renewable power generation scheduling considering uncertainty," Renewable Energy, Elsevier, vol. 131(C), pages 956-965.
    2. Zubo, Rana H.A. & Mokryani, Geev & Abd-Alhameed, Raed, 2018. "Optimal operation of distribution networks with high penetration of wind and solar power within a joint active and reactive distribution market environment," Applied Energy, Elsevier, vol. 220(C), pages 713-722.
    3. Xiaojuan Lu & Leilei Cheng, 2021. "Day-Ahead Scheduling for Renewable Energy Generation Systems considering Concentrating Solar Power Plants," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-14, August.
    4. Hao Bai & Shihong Miao & Xiaohong Ran & Chang Ye, 2015. "Optimal Dispatch Strategy of a Virtual Power Plant Containing Battery Switch Stations in a Unified Electricity Market," Energies, MDPI, vol. 8(3), pages 1-22, March.
    5. Li, Yanfu & Zio, Enrico, 2012. "Uncertainty analysis of the adequacy assessment model of a distributed generation system," Renewable Energy, Elsevier, vol. 41(C), pages 235-244.
    6. Robert Fourer & David M. Gay & Brian W. Kernighan, 1990. "A Modeling Language for Mathematical Programming," Management Science, INFORMS, vol. 36(5), pages 519-554, May.
    7. Jiakai Hu & Chuanwen Jiang & Yangyang Liu, 2019. "Short-Term Bidding Strategy for a Price-Maker Virtual Power Plant Based on Interval Optimization," Energies, MDPI, vol. 12(19), pages 1-22, September.
    8. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    9. Yu, Songyuan & Fang, Fang & Liu, Yajuan & Liu, Jizhen, 2019. "Uncertainties of virtual power plant: Problems and countermeasures," Applied Energy, Elsevier, vol. 239(C), pages 454-470.
    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. Mohammad Shalby & Mohamed R. Gomaa & Ahmad Salah & Abdullah Marashli & Talal Yusaf & Mohamd Laimon, 2023. "Impact of the Air Filtration in the Nacelle on the Wind Turbine Performance," Energies, MDPI, vol. 16(9), pages 1-12, April.
    2. Mostafa Darvishi & Mehrdad Tahmasebi & Ehsan Shokouhmand & Jagadeesh Pasupuleti & Pitshou Bokoro & Jwan Satei Raafat, 2023. "Optimal Operation of Sustainable Virtual Power Plant Considering the Amount of Emission in the Presence of Renewable Energy Sources and Demand Response," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
    3. Zahid Ullah & Arshad & Hany Hassanin & James Cugley & Mohammed Al Alawi, 2022. "Planning, Operation, and Design of Market-Based Virtual Power Plant Considering Uncertainty," Energies, MDPI, vol. 15(19), pages 1-16, October.
    4. Łukasz Mazur & Sławomir Cieślik & Stanislaw Czapp, 2023. "Trends in Locally Balanced Energy Systems without the Use of Fossil Fuels: A Review," Energies, MDPI, vol. 16(12), pages 1-31, June.
    5. Angel L. Cedeño & Reinier López Ahuar & José Rojas & Gonzalo Carvajal & César Silva & Juan C. Agüero, 2022. "Model Predictive Control for Photovoltaic Plants with Non-Ideal Energy Storage Using Mixed Integer Linear Programming," Energies, MDPI, vol. 15(17), pages 1-21, September.
    6. Ali Ahmadian & Kumaraswamy Ponnambalam & Ali Almansoori & Ali Elkamel, 2023. "Optimal Management of a Virtual Power Plant Consisting of Renewable Energy Resources and Electric Vehicles Using Mixed-Integer Linear Programming and Deep Learning," Energies, MDPI, vol. 16(2), pages 1-17, January.
    7. Zahid Ullah & Arshad & Jawad Ahmad, 2022. "The Development of a Cross-Border Energy Trade Cooperation Model of Interconnected Virtual Power Plants Using Bilateral Contracts," Energies, MDPI, vol. 15(21), pages 1-16, November.
    8. Anatoliy Swishchuk, 2023. "Overview of Some Recent Results of Energy Market Modeling and Clean Energy Vision in Canada," Risks, MDPI, vol. 11(8), pages 1-30, August.
    9. Bartłomiej Mroczek & Paweł Pijarski, 2022. "Machine Learning in Operating of Low Voltage Future Grid," Energies, MDPI, vol. 15(15), pages 1-30, July.

    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. Zahid Ullah & Arshad & Jawad Ahmad, 2022. "The Development of a Cross-Border Energy Trade Cooperation Model of Interconnected Virtual Power Plants Using Bilateral Contracts," Energies, MDPI, vol. 15(21), pages 1-16, November.
    2. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    3. Zahid Ullah & Arshad & Hany Hassanin & James Cugley & Mohammed Al Alawi, 2022. "Planning, Operation, and Design of Market-Based Virtual Power Plant Considering Uncertainty," Energies, MDPI, vol. 15(19), pages 1-16, October.
    4. Reza Nadimi & Masahito Takahashi & Koji Tokimatsu & Mika Goto, 2024. "The Reliability and Profitability of Virtual Power Plant with Short-Term Power Market Trading and Non-Spinning Reserve Diesel Generator," Energies, MDPI, vol. 17(9), pages 1-19, April.
    5. Jiaqi Liu & Hongji Hu & Samson S. Yu & Hieu Trinh, 2023. "Virtual Power Plant with Renewable Energy Sources and Energy Storage Systems for Sustainable Power Grid-Formation, Control Techniques and Demand Response," Energies, MDPI, vol. 16(9), pages 1-28, April.
    6. Martin Bichler & Hans Ulrich Buhl & Johannes Knörr & Felipe Maldonado & Paul Schott & Stefan Waldherr & Martin Weibelzahl, 2022. "Electricity Markets in a Time of Change: A Call to Arms for Business Research," Schmalenbach Journal of Business Research, Springer, vol. 74(1), pages 77-102, March.
    7. Wafa Nafkha-Tayari & Seifeddine Ben Elghali & Ehsan Heydarian-Forushani & Mohamed Benbouzid, 2022. "Virtual Power Plants Optimization Issue: A Comprehensive Review on Methods, Solutions, and Prospects," Energies, MDPI, vol. 15(10), pages 1-20, May.
    8. Wang, Xuejie & zhao, Huiru & Lu, Hao & Zhang, Yuanyuan & Wang, Yuwei & Wang, Jingbo, 2022. "Decentralized coordinated operation model of VPP and P2H systems based on stochastic-bargaining game considering multiple uncertainties and carbon cost," Applied Energy, Elsevier, vol. 312(C).
    9. Catra Indra Cahyadi & Suwarno Suwarno & Aminah Asmara Dewi & Musri Kona & Muhammad Arif & Muhammad Caesar Akbar, 2023. "Solar Prediction Strategy for Managing Virtual Power Stations," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 503-512, July.
    10. Li, Qiang & Zhou, Yongcheng & Wei, Fanchao & Li, Shuangxiu & Wang, Zhonghao & Li, Jiajia & Zhou, Guowen & Liu, Jinfu & Yan, Peigang & Yu, Daren, 2024. "Multi-time scale scheduling for virtual power plants: Integrating the flexibility of power generation and multi-user loads while considering the capacity degradation of energy storage systems," Applied Energy, Elsevier, vol. 362(C).
    11. Pichler, Anton & Poledna, Sebastian & Thurner, Stefan, 2021. "Systemic risk-efficient asset allocations: Minimization of systemic risk as a network optimization problem," Journal of Financial Stability, Elsevier, vol. 52(C).
    12. Christos N. Dimitriadis & Evangelos G. Tsimopoulos & Michael C. Georgiadis, 2021. "A Review on the Complementarity Modelling in Competitive Electricity Markets," Energies, MDPI, vol. 14(21), pages 1-27, November.
    13. Chen, Houhe & Wang, Di & Zhang, Rufeng & Jiang, Tao & Li, Xue, 2022. "Optimal participation of ADN in energy and reserve markets considering TSO-DSO interface and DERs uncertainties," Applied Energy, Elsevier, vol. 308(C).
    14. Sinha, Ankur & Rämö, Janne & Malo, Pekka & Kallio, Markku & Tahvonen, Olli, 2017. "Optimal management of naturally regenerating uneven-aged forests," European Journal of Operational Research, Elsevier, vol. 256(3), pages 886-900.
    15. Popović, Željko N. & KovaÄ ki, Neven V. & Popović, Dragan S., 2020. "Resilient distribution network planning under the severe windstorms using a risk-based approach," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    16. Fabio Massaro & Maria Luisa Di Silvestre & Marco Ferraro & Francesco Montana & Eleonora Riva Sanseverino & Salvatore Ruffino, 2024. "Energy Hub Model for the Massive Adoption of Hydrogen in Power Systems," Energies, MDPI, vol. 17(17), pages 1-31, September.
    17. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & David Celeita & George J. Anders, 2024. "A Stochastic Decision-Making Tool Suite for Distributed Energy Resources Integration in Energy Markets," Energies, MDPI, vol. 17(10), pages 1-28, May.
    18. Xu, Jiuping & Wang, Fengjuan & Lv, Chengwei & Huang, Qian & Xie, Heping, 2018. "Economic-environmental equilibrium based optimal scheduling strategy towards wind-solar-thermal power generation system under limited resources," Applied Energy, Elsevier, vol. 231(C), pages 355-371.
    19. Duck Bong Kim, 2019. "An approach for composing predictive models from disparate knowledge sources in smart manufacturing environments," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1999-2012, April.
    20. Raji Atia & Noboru Yamada, 2016. "Distributed Renewable Generation and Storage System Sizing Based on Smart Dispatch of Microgrids," Energies, MDPI, vol. 9(3), pages 1-16, March.

    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:15:y:2022:i:14:p:5296-:d:868274. 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.