IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i7p2350-d777923.html
   My bibliography  Save this article

Operation of a Power Grid with Embedded Networked Microgrids and Onsite Renewable Technologies

Author

Listed:
  • José Luis Ruiz Duarte

    (Department of Marketing and Business Analytics, San Jose State University, San Jose, CA 95192, USA)

  • Neng Fan

    (Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ 85721, USA)

Abstract

The international community has set ambitious targets to replace the use of fossil fuels for electricity generation with renewable energy sources. The use of large-scale (e.g., solar farms) and small-scale solutions (e.g., onsite green technologies) represents one way to achieve these goals. This paper presents a mathematical optimization framework to coordinate the energy decisions between the distribution network and the networked microgrids embedded within it. Utility-scale renewable and conventional generators are considered in the distribution network, while the microgrids include onsite renewable generation and energy storage. The distribution network operator utilizes demand-side management policies to improve the network’s efficiency, and the microgrids operate under these programs by reducing their energy usage, scheduling the electricity usage under dynamic tariffs, and supplying energy to the grid. The uncertainty of renewable energy sources is addressed by robust optimization. The decisions of the distribution network and the microgrids are made independently, whereas the proposed collaboration scheme allows for the alignment of the systems’ objectives. A case study is analyzed to show the capability of the model to assess multiple configurations, eliminating the necessity of load shedding, and increasing the power supplied by the microgrids (22.3 MW) and the renewable energy share by up to 5.03%.

Suggested Citation

  • José Luis Ruiz Duarte & Neng Fan, 2022. "Operation of a Power Grid with Embedded Networked Microgrids and Onsite Renewable Technologies," Energies, MDPI, vol. 15(7), pages 1-24, March.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2350-:d:777923
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/7/2350/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/7/2350/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ruiz Duarte, José Luis & Fan, Neng & Jin, Tongdan, 2020. "Multi-process production scheduling with variable renewable integration and demand response," European Journal of Operational Research, Elsevier, vol. 281(1), pages 186-200.
    2. Sławomir Zator, 2021. "Power Scheduling Scheme for DSM in Smart Homes with Photovoltaic and Energy Storage," Energies, MDPI, vol. 14(24), pages 1-20, December.
    3. Behrangrad, Mahdi, 2015. "A review of demand side management business models in the electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 270-283.
    4. Fang, Xinli & Ma, Shihao & Yang, Qiang & Zhang, Jintao, 2016. "Cooperative energy dispatch for multiple autonomous microgrids with distributed renewable sources and storages," Energy, Elsevier, vol. 99(C), pages 48-57.
    5. 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.
    6. Fang, Xinli & Yang, Qiang & Wang, Jianhui & Yan, Wenjun, 2016. "Coordinated dispatch in multiple cooperative autonomous islanded microgrids," Applied Energy, Elsevier, vol. 162(C), pages 40-48.
    7. Lv, Tianguang & Ai, Qian, 2016. "Interactive energy management of networked microgrids-based active distribution system considering large-scale integration of renewable energy resources," Applied Energy, Elsevier, vol. 163(C), pages 408-422.
    8. Kou, Peng & Liang, Deliang & Gao, Lin, 2017. "Distributed EMPC of multiple microgrids for coordinated stochastic energy management," Applied Energy, Elsevier, vol. 185(P1), pages 939-952.
    9. Kumar, R. Seshu & Raghav, L. Phani & Raju, D. Koteswara & Singh, Arvind R., 2021. "Intelligent demand side management for optimal energy scheduling of grid connected microgrids," Applied Energy, Elsevier, vol. 285(C).
    10. Haddadian, Hossein & Noroozian, Reza, 2017. "Multi-microgrids approach for design and operation of future distribution networks based on novel technical indices," Applied Energy, Elsevier, vol. 185(P1), pages 650-663.
    11. Anjos, Miguel F. & Brotcorne, Luce & Gomez-Herrera, Juan A., 2021. "Optimal setting of time-and-level-of-use prices for an electricity supplier," Energy, Elsevier, vol. 225(C).
    12. Vasileios M. Laitsos & Dimitrios Bargiotas & Aspassia Daskalopulu & Athanasios Ioannis Arvanitidis & Lefteri H. Tsoukalas, 2021. "An Incentive-Based Implementation of Demand Side Management in Power Systems," Energies, MDPI, vol. 14(23), pages 1-24, November.
    13. Kanakadhurga, Dharmaraj & Prabaharan, Natarajan, 2022. "Demand side management in microgrid: A critical review of key issues and recent trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    14. Seshu Kumar, R. & Phani Raghav, L. & Koteswara Raju, D. & Singh, Arvind R., 2021. "Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids," Applied Energy, Elsevier, vol. 301(C).
    15. Arshad Mohammad & Mohd Zuhaib & Imtiaz Ashraf & Marwan Alsultan & Shafiq Ahmad & Adil Sarwar & Mali Abdollahian, 2021. "Integration of Electric Vehicles and Energy Storage System in Home Energy Management System with Home to Grid Capability," Energies, MDPI, vol. 14(24), pages 1-27, December.
    16. Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.
    17. Giulio Ferro & Riccardo Minciardi & Luca Parodi & Michela Robba & Mansueto Rossi, 2020. "Optimal Control of Multiple Microgrids and Buildings by an Aggregator," Energies, MDPI, vol. 13(5), pages 1-23, February.
    18. Yoldaş, Yeliz & Önen, Ahmet & Muyeen, S.M. & Vasilakos, Athanasios V. & Alan, İrfan, 2017. "Enhancing smart grid with microgrids: Challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 205-214.
    19. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2016. "Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage," Applied Energy, Elsevier, vol. 163(C), pages 93-104.
    20. Gomez-Herrera, Juan A. & Anjos, Miguel F., 2018. "Optimal collaborative demand-response planner for smart residential buildings," Energy, Elsevier, vol. 161(C), pages 370-380.
    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. Vijayaraja Loganathan & Dhanasekar Ravikumar & Rupa Kesavan & Kanakasri Venkatesan & Raadha Saminathan & Raju Kannadasan & Mahalingam Sudhakaran & Mohammed H. Alsharif & Zong Woo Geem & Junhee Hong, 2022. "A Case Study on Renewable Energy Sources, Power Demand, and Policies in the States of South India—Development of a Thermoelectric Model," Sustainability, MDPI, vol. 14(14), pages 1-29, July.
    2. Santanu Kumar Dash & Suprava Chakraborty & Michele Roccotelli & Umesh Kumar Sahu, 2022. "Hydrogen Fuel for Future Mobility: Challenges and Future Aspects," Sustainability, MDPI, vol. 14(14), pages 1-22, 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. Li, Yan & Zhang, Peng & Yue, Meng, 2018. "Networked microgrid stability through distributed formal analysis," Applied Energy, Elsevier, vol. 228(C), pages 279-288.
    2. Ren, Lingyu & Qin, Yanyuan & Li, Yan & Zhang, Peng & Wang, Bing & Luh, Peter B. & Han, Song & Orekan, Taofeek & Gong, Tao, 2018. "Enabling resilient distributed power sharing in networked microgrids through software defined networking," Applied Energy, Elsevier, vol. 210(C), pages 1251-1265.
    3. Nawaz, Arshad & Zhou, Min & Wu, Jing & Long, Chengnian, 2022. "A comprehensive review on energy management, demand response, and coordination schemes utilization in multi-microgrids network," Applied Energy, Elsevier, vol. 323(C).
    4. Wu, Pan & Huang, Wentao & Tai, Nengling & Liang, Shuo, 2018. "A novel design of architecture and control for multiple microgrids with hybrid AC/DC connection," Applied Energy, Elsevier, vol. 210(C), pages 1002-1016.
    5. Li, Qiang & Gao, Mengkai & Lin, Houfei & Chen, Ziyu & Chen, Minyou, 2019. "MAS-based distributed control method for multi-microgrids with high-penetration renewable energy," Energy, Elsevier, vol. 171(C), pages 284-295.
    6. Zhang, Bingying & Li, Qiqiang & Wang, Luhao & Feng, Wei, 2018. "Robust optimization for energy transactions in multi-microgrids under uncertainty," Applied Energy, Elsevier, vol. 217(C), pages 346-360.
    7. Kou, Peng & Liang, Deliang & Gao, Lin, 2017. "Distributed EMPC of multiple microgrids for coordinated stochastic energy management," Applied Energy, Elsevier, vol. 185(P1), pages 939-952.
    8. Xiao, Zhao-xia & Guerrero, Josep M. & Shuang, Jia & Sera, Dezso & Schaltz, Erik & Vásquez, Juan C., 2018. "Flat tie-line power scheduling control of grid-connected hybrid microgrids," Applied Energy, Elsevier, vol. 210(C), pages 786-799.
    9. Nikmehr, Nima & Najafi-Ravadanegh, Sajad & Khodaei, Amin, 2017. "Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty," Applied Energy, Elsevier, vol. 198(C), pages 267-279.
    10. Ali M. Jasim & Basil H. Jasim & Habib Kraiem & Aymen Flah, 2022. "A Multi-Objective Demand/Generation Scheduling Model-Based Microgrid Energy Management System," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    11. Yuansheng Huang & Lei Yang & Shijian Liu & Guangli Wang, 2018. "Cooperation between Two Micro-Grids Considering Power Exchange: An Optimal Sizing Approach Based on Collaborative Operation," Sustainability, MDPI, vol. 10(11), pages 1-21, November.
    12. Makrygiorgou, Despoina I. & Alexandridis, Antonio T., 2018. "Distributed stabilizing modular control for stand-alone microgrids," Applied Energy, Elsevier, vol. 210(C), pages 925-935.
    13. Umeozor, Evar Chinedu & Trifkovic, Milana, 2016. "Operational scheduling of microgrids via parametric programming," Applied Energy, Elsevier, vol. 180(C), pages 672-681.
    14. Mehdizadeh, Ali & Taghizadegan, Navid & Salehi, Javad, 2018. "Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management," Applied Energy, Elsevier, vol. 211(C), pages 617-630.
    15. Lo Piano, S. & Smith, S.T., 2022. "Energy demand and its temporal flexibility: Approaches, criticalities and ways forward," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    16. Khaizaran Abdulhussein Al Sumarmad & Nasri Sulaiman & Noor Izzri Abdul Wahab & Hashim Hizam, 2022. "Microgrid Energy Management System Based on Fuzzy Logic and Monitoring Platform for Data Analysis," Energies, MDPI, vol. 15(11), pages 1-19, June.
    17. 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).
    18. Liu, Yixin & Guo, Li & Wang, Chengshan, 2018. "A robust operation-based scheduling optimization for smart distribution networks with multi-microgrids," Applied Energy, Elsevier, vol. 228(C), pages 130-140.
    19. Phani Raghav, L. & Seshu Kumar, R. & Koteswara Raju, D. & Singh, Arvind R., 2022. "Analytic Hierarchy Process (AHP) – Swarm intelligence based flexible demand response management of grid-connected microgrid," Applied Energy, Elsevier, vol. 306(PB).
    20. Tang, Chong & Liu, Mingbo & Dai, Yue & Wang, Zhijun & Xie, Min, 2019. "Decentralized saddle-point dynamics solution for optimal power flow of distribution systems with multi-microgrids," Applied Energy, Elsevier, vol. 252(C), pages 1-1.

    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:7:p:2350-:d:777923. 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.