IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i4p1303-d746911.html
   My bibliography  Save this article

Research on Capacity Allocation Optimization of Commercial Virtual Power Plant (CVPP)

Author

Listed:
  • Songkai Wang

    (School of Water Resources and Hydropower, Xi’an University of Technology, Xi’an 710048, China
    Key Laboratory of Smart Energy in Xi’an, Xi’an University of Technology, Xi’an 710048, China)

  • Rong Jia

    (Key Laboratory of Smart Energy in Xi’an, Xi’an University of Technology, Xi’an 710048, China
    School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Xiaoyu Shi

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Chang Luo

    (Hanjiang-to-Weihe River Valley Water Diversion Project Construction Co., Ltd., Xi’an 710048, China)

  • Yuan An

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Qiang Huang

    (School of Water Resources and Hydropower, Xi’an University of Technology, Xi’an 710048, China)

  • Pengcheng Guo

    (School of Water Resources and Hydropower, Xi’an University of Technology, Xi’an 710048, China)

  • Xueyan Wang

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Xuewen Lei

    (School of Water Resources and Hydropower, Xi’an University of Technology, Xi’an 710048, China)

Abstract

Commercial virtual power plants (CVPP) connect the form of renewable energy resource portfolio to the power market and reduce the risk of the unstable operation of a single renewable energy. Combining different kinds of large-scale renewable energy in CVPP to provide capacity services like base load, peak shaving, and valley-filling, etc., for the system loads is an urgent problem to be solved. Therefore, it is valuable to analyze the capacity allocation ratio of the CVPP to maximize the utilization of all kinds of energy, especially for the large-scale multi-energy base. This paper proposed a multi-energy coordinated operation framework by considering various load demands, including base load and peak shaving for the capacity allocation of CVPP based on the world’s largest renewable energy resource base on the upstream area of the Yellow River. The main procedures of this framework are as follows: (1) A paratactic model satisfying base load and peak shaving is proposed to determine the ability of the CVPP operation model’s capacity services to meet the different demands of the power system load. (2) A hybrid dimension reduction algorithm with a better convergence rate and optimization effect solves the proposed paratactic model based on the ReliefF and the Adaptive Particle Swarm Optimization (APSO). The results show that the large-scale CVPP with different compositions can achieve both of the goals of a stable base load output and stable residual load under different weather conditions. Compared with the operation on sunny days, the base load fluctuation and residual load fluctuation of CVPP on rainy days are reduced by 14.5% and 21.9%, respectively, proving that CVPP can alleviate renewable energy’s dependence on weather and improve energy utilization.

Suggested Citation

  • Songkai Wang & Rong Jia & Xiaoyu Shi & Chang Luo & Yuan An & Qiang Huang & Pengcheng Guo & Xueyan Wang & Xuewen Lei, 2022. "Research on Capacity Allocation Optimization of Commercial Virtual Power Plant (CVPP)," Energies, MDPI, vol. 15(4), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1303-:d:746911
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/4/1303/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/4/1303/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Zaman, Forhad & Guerrero, Josep M., 2019. "Energy scheduling of community microgrid with battery cost using particle swarm optimisation," Applied Energy, Elsevier, vol. 254(C).
    2. Sharafi, Masoud & ELMekkawy, Tarek Y., 2014. "Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach," Renewable Energy, Elsevier, vol. 68(C), pages 67-79.
    3. Heide, Dominik & von Bremen, Lueder & Greiner, Martin & Hoffmann, Clemens & Speckmann, Markus & Bofinger, Stefan, 2010. "Seasonal optimal mix of wind and solar power in a future, highly renewable Europe," Renewable Energy, Elsevier, vol. 35(11), pages 2483-2489.
    4. Olamaei, Javad & Nazari, Mohammad Esmaeil & Bahravar, Sepideh, 2018. "Economic environmental unit commitment for integrated CCHP-thermal-heat only system with considerations for valve-point effect based on a heuristic optimization algorithm," Energy, Elsevier, vol. 159(C), pages 737-750.
    5. Maraver, Daniel & Sin, Ana & Sebastián, Fernando & Royo, Javier, 2013. "Environmental assessment of CCHP (combined cooling heating and power) systems based on biomass combustion in comparison to conventional generation," Energy, Elsevier, vol. 57(C), pages 17-23.
    6. Dufo-López, Rodolfo & Bernal-Agustín, José L. & Yusta-Loyo, José M. & Domínguez-Navarro, José A. & Ramírez-Rosado, Ignacio J. & Lujano, Juan & Aso, Ismael, 2011. "Multi-objective optimization minimizing cost and life cycle emissions of stand-alone PV–wind–diesel systems with batteries storage," Applied Energy, Elsevier, vol. 88(11), pages 4033-4041.
    7. Hany Elgamal, Ahmed & Kocher-Oberlehner, Gudrun & Robu, Valentin & Andoni, Merlinda, 2019. "Optimization of a multiple-scale renewable energy-based virtual power plant in the UK," Applied Energy, Elsevier, vol. 256(C).
    8. Anand, Hithu & Ramasubbu, Rengaraj, 2018. "A real time pricing strategy for remote micro-grid with economic emission dispatch and stochastic renewable energy sources," Renewable Energy, Elsevier, vol. 127(C), pages 779-789.
    9. Papaefthymiou, Stefanos V. & Papathanassiou, Stavros A., 2014. "Optimum sizing of wind-pumped-storage hybrid power stations in island systems," Renewable Energy, Elsevier, vol. 64(C), pages 187-196.
    10. Shafiekhani, Morteza & Ahmadi, Abdollah & Homaee, Omid & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal bidding strategy of a renewable-based virtual power plant including wind and solar units and dispatchable loads," Energy, Elsevier, vol. 239(PD).
    11. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2017. "A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 341-363.
    12. Das, Pronob & Das, Barun K. & Rahman, Mushfiqur & Hassan, Rakibul, 2022. "Evaluating the prospect of utilizing excess energy and creating employments from a hybrid energy system meeting electricity and freshwater demands using multi-objective evolutionary algorithms," Energy, Elsevier, vol. 238(PB).
    13. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    14. Xianxun Wang & Lihua Chen & Qijuan Chen & Yadong Mei & Hao Wang, 2018. "Model and Analysis of Integrating Wind and PV Power in Remote and Core Areas with Small Hydropower and Pumped Hydropower Storage," Energies, MDPI, vol. 11(12), pages 1-24, December.
    15. Ming, Bo & Liu, Pan & Guo, Shenglian & Zhang, Xiaoqi & Feng, Maoyuan & Wang, Xianxun, 2017. "Optimizing utility-scale photovoltaic power generation for integration into a hydropower reservoir by incorporating long- and short-term operational decisions," Applied Energy, Elsevier, vol. 204(C), pages 432-445.
    16. Pandžić, Hrvoje & Kuzle, Igor & Capuder, Tomislav, 2013. "Virtual power plant mid-term dispatch optimization," Applied Energy, Elsevier, vol. 101(C), pages 134-141.
    17. A. Muthu Manokar & M. Vimala & Ravishankar Sathyamurthy & A. E. Kabeel & D. Prince Winston & Ali J. Chamkha, 2020. "Enhancement of potable water production from an inclined photovoltaic panel absorber solar still by integrating with flat-plate collector," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4145-4167, June.
    Full references (including those not matched with items on IDEAS)

    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. Hadayeghparast, Shahrzad & SoltaniNejad Farsangi, Alireza & Shayanfar, Heidarali, 2019. "Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant," Energy, Elsevier, vol. 172(C), pages 630-646.
    2. Bhuiyan, Erphan A. & Hossain, Md. Zahid & Muyeen, S.M. & Fahim, Shahriar Rahman & Sarker, Subrata K. & Das, Sajal K., 2021. "Towards next generation virtual power plant: Technology review and frameworks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    3. Songkai Wang & Rong Jia & Chang Luo & Yuan An & Pengcheng Guo, 2022. "Spatiotemporal Complementary Characteristics of Large-Scale Wind Power, Photovoltaic Power, and Hydropower," Sustainability, MDPI, vol. 14(15), pages 1-17, July.
    4. Mohammad Mohammadi Roozbehani & Ehsan Heydarian-Forushani & Saeed Hasanzadeh & Seifeddine Ben Elghali, 2022. "Virtual Power Plant Operational Strategies: Models, Markets, Optimization, Challenges, and Opportunities," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    5. Xie, Haonan & Ahmad, Tanveer & Zhang, Dongdong & Goh, Hui Hwang & Wu, Thomas, 2024. "Community-based virtual power plants’ technology and circular economy models in the energy sector: A Techno-economy study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    6. 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.
    7. 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.
    8. Sachs, Julia & Sawodny, Oliver, 2016. "Multi-objective three stage design optimization for island microgrids," Applied Energy, Elsevier, vol. 165(C), pages 789-800.
    9. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2016. "Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy," Applied Energy, Elsevier, vol. 164(C), pages 590-606.
    10. Rui Gao & Hongxia Guo & Ruihong Zhang & Tian Mao & Qianyao Xu & Baorong Zhou & Ping Yang, 2019. "A Two-Stage Dispatch Mechanism for Virtual Power Plant Utilizing the CVaR Theory in the Electricity Spot Market," Energies, MDPI, vol. 12(17), pages 1-18, September.
    11. Furquan Nadeem & Mohd Asim Aftab & S.M. Suhail Hussain & Ikbal Ali & Prashant Kumar Tiwari & Arup Kumar Goswami & Taha Selim Ustun, 2019. "Virtual Power Plant Management in Smart Grids with XMPP Based IEC 61850 Communication," Energies, MDPI, vol. 12(12), pages 1-20, June.
    12. Gong, Yu & Liu, Pan & Ming, Bo & Xu, Weifeng & Huang, Kangdi & Li, Xiao, 2021. "Deriving pack rules for hydro–photovoltaic hybrid power systems considering diminishing marginal benefit of energy," Applied Energy, Elsevier, vol. 304(C).
    13. Dong, Lianxin & Fan, Shuai & Wang, Zhihua & Xiao, Jucheng & Zhou, Huan & Li, Zuyi & He, Guangyu, 2021. "An adaptive decentralized economic dispatch method for virtual power plant," Applied Energy, Elsevier, vol. 300(C).
    14. Haji Bashi, Mazaher & De Tommasi, Luciano & Le Cam, Andreea & Relaño, Lorena Sánchez & Lyons, Padraig & Mundó, Joana & Pandelieva-Dimova, Ivanka & Schapp, Henrik & Loth-Babut, Karolina & Egger, Christ, 2023. "A review and mapping exercise of energy community regulatory challenges in European member states based on a survey of collective energy actors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    15. Kim, Seokwoo & Choi, Dong Gu, 2024. "A sample robust optimal bidding model for a virtual power plant," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1101-1113.
    16. Luo, Zhe & Hong, SeungHo & Ding, YueMin, 2019. "A data mining-driven incentive-based demand response scheme for a virtual power plant," Applied Energy, Elsevier, vol. 239(C), pages 549-559.
    17. Guo, Hongye & Chen, Qixin & Shahidehpour, Mohammad & Xia, Qing & Kang, Chongqing, 2022. "Bidding behaviors of GENCOs under bounded rationality with renewable energy," Energy, Elsevier, vol. 250(C).
    18. Azaza, Maher & Wallin, Fredrik, 2017. "Multi objective particle swarm optimization of hybrid micro-grid system: A case study in Sweden," Energy, Elsevier, vol. 123(C), pages 108-118.
    19. Park, Sung-Won & Son, Sung-Yong, 2020. "Interaction-based virtual power plant operation methodology for distribution system operator’s voltage management," Applied Energy, Elsevier, vol. 271(C).
    20. Schyska, Bruno U. & Kies, Alexander, 2020. "How regional differences in cost of capital influence the optimal design of power systems," Applied Energy, Elsevier, vol. 262(C).

    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:4:p:1303-:d:746911. 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.