IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i11p2900-d367987.html
   My bibliography  Save this article

Water-Energy Management for Demand Charges and Energy Cost Optimization of a Pumping Stations System under a Renewable Virtual Power Plant Model

Author

Listed:
  • Natalia Naval

    (Department of Electrical Engineering, University of Zaragoza, C/Maria de Luna 3, 50018 Zaragoza, Spain)

  • Jose M. Yusta

    (Department of Electrical Engineering, University of Zaragoza, C/Maria de Luna 3, 50018 Zaragoza, Spain)

Abstract

The effects of climate change seriously affect agriculture at different latitudes of the planet because periods of drought are intensifying and the availability of water for agricultural irrigation is reducing. In addition, the energy cost associated with pumping water has increased notably in recent years due to, among other reasons, the maximum demand charges that are applied annually according to the contracted demand in each facility. Therefore, very efficient management of both water resources and energy resources is required. This article proposes the integration of water-energy management in a virtual power plant (VPP) model for the optimization of energy costs and maximum demand charges. For the development of the model, a problem related to the optimal operation of electricity generation and demand resources arises, which is formulated as a nonlinear mixed-integer programming model (MINLP). The objective is to maximize the annual operating profit of the VPP. It is worth mentioning that the model is applied to a large irrigation system using real data on consumption and power generation, exclusively renewable. In addition, different scenarios are analyzed to evaluate the variability of the operating profit of the VPP with and without intraday demand management as well as the influence of the wholesale electricity market price on the model. In view of the results obtained, the model that integrates the management of the water-energy binomial increases the self-consumption of renewable energy and saves electricity supply costs.

Suggested Citation

  • Natalia Naval & Jose M. Yusta, 2020. "Water-Energy Management for Demand Charges and Energy Cost Optimization of a Pumping Stations System under a Renewable Virtual Power Plant Model," Energies, MDPI, vol. 13(11), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2900-:d:367987
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/11/2900/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/11/2900/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zamani, Ali Ghahgharaee & Zakariazadeh, Alireza & Jadid, Shahram, 2016. "Day-ahead resource scheduling of a renewable energy based virtual power plant," Applied Energy, Elsevier, vol. 169(C), pages 324-340.
    2. Shabanzadeh, Morteza & Sheikh-El-Eslami, Mohammad-Kazem & Haghifam, Mahmoud-Reza, 2016. "A medium-term coalition-forming model of heterogeneous DERs for a commercial virtual power plant," Applied Energy, Elsevier, vol. 169(C), pages 663-681.
    3. Tajeddini, Mohammad Amin & Rahimi-Kian, Ashkan & Soroudi, Alireza, 2014. "Risk averse optimal operation of a virtual power plant using two stage stochastic programming," Energy, Elsevier, vol. 73(C), pages 958-967.
    4. 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.
    5. Loßner, Martin & Böttger, Diana & Bruckner, Thomas, 2017. "Economic assessment of virtual power plants in the German energy market — A scenario-based and model-supported analysis," Energy Economics, Elsevier, vol. 62(C), pages 125-138.
    6. Kakran, Sandeep & Chanana, Saurabh, 2018. "Smart operations of smart grids integrated with distributed generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 524-535.
    7. Tan, Zhongfu & Wang, Guan & Ju, Liwei & Tan, Qingkun & Yang, Wenhai, 2017. "Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage system with uncertainties and demand r," Energy, Elsevier, vol. 124(C), pages 198-213.
    8. Wozabal, David & Rameseder, Gunther, 2020. "Optimal bidding of a virtual power plant on the Spanish day-ahead and intraday market for electricity," European Journal of Operational Research, Elsevier, vol. 280(2), pages 639-655.
    9. Bourbon, R. & Ngueveu, S.U. & Roboam, X. & Sareni, B. & Turpin, C. & Hernandez-Torres, D., 2019. "Energy management optimization of a smart wind power plant comparing heuristic and linear programming methods," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 158(C), pages 418-431.
    10. Amam Hossain Bagdadee & Li Zhang, 2019. "A Review of the Smart Grid Concept for Electrical Power System," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 8(4), pages 105-126, October.
    11. 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.
    12. 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.
    13. Paraschiv, Florentina & Erni, David & Pietsch, Ralf, 2014. "The impact of renewable energies on EEX day-ahead electricity prices," Energy Policy, Elsevier, vol. 73(C), pages 196-210.
    14. Zuoyu Liu & Weimin Zheng & Feng Qi & Lei Wang & Bo Zou & Fushuan Wen & You Xue, 2018. "Optimal Dispatch of a Virtual Power Plant Considering Demand Response and Carbon Trading," Energies, MDPI, vol. 11(6), pages 1-19, June.
    15. Shabanzadeh, Morteza & Sheikh-El-Eslami, Mohammad-Kazem & Haghifam, Mahmoud-Reza, 2017. "An interactive cooperation model for neighboring virtual power plants," Applied Energy, Elsevier, vol. 200(C), pages 273-289.
    16. Dodiek Ika Candra & Kilian Hartmann & Michael Nelles, 2018. "Economic Optimal Implementation of Virtual Power Plants in the German Power Market," Energies, MDPI, vol. 11(9), pages 1-24, September.
    17. Naval, Natalia & Sánchez, Raul & Yusta, Jose M., 2020. "A virtual power plant optimal dispatch model with large and small-scale distributed renewable generation," Renewable Energy, Elsevier, vol. 151(C), pages 57-69.
    18. Pandžić, Hrvoje & Kuzle, Igor & Capuder, Tomislav, 2013. "Virtual power plant mid-term dispatch optimization," Applied Energy, Elsevier, vol. 101(C), pages 134-141.
    19. Moreno, Blanca & Díaz, Guzmán, 2019. "The impact of virtual power plant technology composition on wholesale electricity prices: A comparative study of some European Union electricity markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 99(C), pages 100-108.
    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. Gencho Popov & Stanislaw Legutko & Kliment Klimentov & Boris Kostov, 2021. "Applying Criteria Equations in Studying the Energy Efficiency of Pump Systems," Energies, MDPI, vol. 14(17), pages 1-13, August.
    2. Yitong Yin & Gang Lin & Dong Jiang & Jingying Fu & Donglin Dong, 2021. "Multi-Scenario Simulation of a Water–Energy Coupling System Based on System Dynamics: A Case Study of Ningbo City," Energies, MDPI, vol. 14(18), pages 1-22, September.
    3. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    4. Dominika Kaczorowska & Jacek Rezmer & Michal Jasinski & Tomasz Sikorski & Vishnu Suresh & Zbigniew Leonowicz & Pawel Kostyla & Jaroslaw Szymanda & Przemyslaw Janik, 2020. "A Case Study on Battery Energy Storage System in a Virtual Power Plant: Defining Charging and Discharging Characteristics," Energies, MDPI, vol. 13(24), pages 1-22, December.

    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. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    2. 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.
    3. 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.
    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. Amit Kumer Podder & Sayemul Islam & Nallapaneni Manoj Kumar & Aneesh A. Chand & Pulivarthi Nageswara Rao & Kushal A. Prasad & T. Logeswaran & Kabir A. Mamun, 2020. "Systematic Categorization of Optimization Strategies for Virtual Power Plants," Energies, MDPI, vol. 13(23), pages 1-46, November.
    6. Naval, Natalia & Sánchez, Raul & Yusta, Jose M., 2020. "A virtual power plant optimal dispatch model with large and small-scale distributed renewable generation," Renewable Energy, Elsevier, vol. 151(C), pages 57-69.
    7. Mahmud, Khizir & Khan, Behram & Ravishankar, Jayashri & Ahmadi, Abdollah & Siano, Pierluigi, 2020. "An internet of energy framework with distributed energy resources, prosumers and small-scale virtual power plants: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    8. 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.
    9. Moreno, Blanca & Díaz, Guzmán, 2019. "The impact of virtual power plant technology composition on wholesale electricity prices: A comparative study of some European Union electricity markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 99(C), pages 100-108.
    10. 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.
    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. 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).
    13. Zhou, Kaile & Peng, Ning & Yin, Hui & Hu, Rong, 2023. "Urban virtual power plant operation optimization with incentive-based demand response," Energy, Elsevier, vol. 282(C).
    14. Khalil Gholami & Behnaz Behi & Ali Arefi & Philip Jennings, 2022. "Grid-Forming Virtual Power Plants: Concepts, Technologies and Advantages," Energies, MDPI, vol. 15(23), pages 1-26, November.
    15. Oluwaseun Oladimeji & Álvaro Ortega & Lukas Sigrist & Luis Rouco & Pedro Sánchez-Martín & Enrique Lobato, 2022. "Optimal Participation of Heterogeneous, RES-Based Virtual Power Plants in Energy Markets," Energies, MDPI, vol. 15(9), pages 1-19, April.
    16. Li, Qiang & Wei, Fanchao & Zhou, Yongcheng & Li, Jiajia & Zhou, Guowen & Wang, Zhonghao & Liu, Jinfu & Yan, Peigang & Yu, Daren, 2023. "A scheduling framework for VPP considering multiple uncertainties and flexible resources," Energy, Elsevier, vol. 282(C).
    17. Bianca Goia & Tudor Cioara & Ionut Anghel, 2022. "Virtual Power Plant Optimization in Smart Grids: A Narrative Review," Future Internet, MDPI, vol. 14(5), pages 1-22, April.
    18. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    19. 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).
    20. Guoqiang Sun & Weihang Qian & Wenjin Huang & Zheng Xu & Zhongxing Fu & Zhinong Wei & Sheng Chen, 2019. "Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach," Energies, MDPI, vol. 12(10), pages 1-23, May.

    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:13:y:2020:i:11:p:2900-:d:367987. 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.