[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i11p973-d83432.html
   My bibliography  Save this article

Accelerated Model Predictive Control for Electric Vehicle Integrated Microgrid Energy Management: A Hybrid Robust and Stochastic Approach

Author

Listed:
  • Zhenya Ji

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Nanjing 210096, China)

  • Xueliang Huang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Nanjing 210096, China)

  • Changfu Xu

    (Electric Power Research Institute, State Grid Jiangsu Power Supply Company, Nanjing 211103, China)

  • Houtao Sun

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Nanjing 210096, China)

Abstract
A microgrid with an advanced energy management approach is a feasible solution for accommodating the development of distributed generators (DGs) and electric vehicles (EVs). At the primary stage of development, the total number of EVs in a microgrid is fairly small but increases promptly. Thus, it makes most prediction models for EV charging demand difficult to apply at present. To overcome the inadaptability, a novel robust approach is proposed to handle EV charging demand predictions along with demand-side management (DSM) on the condition of satisfying each EV user’s demand. Variables with stochastic forecast models join the objective function in the form of probability-constrained scenarios. This paper proposes a scenario-based model predictive control (MPC) approach combining both robust and stochastic models to minimize the total operational cost for energy management. To overcome the concern about the convergence time increasing from the combination of scenarios, the Benders decomposition (BD) technique is further adopted to improve computational efficiency. Simulation results on a combined heat and power microgrid indicate that the proposed scenario-based MPC approach achieves a better economic performance than a traditional deterministic MPC (DMPC) approach, while ensuring EV charging demands, as well as minimizing the trade-off between optimal solutions and computing times.

Suggested Citation

  • Zhenya Ji & Xueliang Huang & Changfu Xu & Houtao Sun, 2016. "Accelerated Model Predictive Control for Electric Vehicle Integrated Microgrid Energy Management: A Hybrid Robust and Stochastic Approach," Energies, MDPI, vol. 9(11), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:11:p:973-:d:83432
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/11/973/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/9/11/973/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
    2. Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2016. "Robust Optimization-Based Scheduling of Multi-Microgrids Considering Uncertainties," Energies, MDPI, vol. 9(4), pages 1-21, April.
    3. Nah-Oak Song & Ji-Hye Lee & Hak-Man Kim, 2016. "Optimal Electric and Heat Energy Management of Multi-Microgrids with Sequentially-Coordinated Operations," Energies, MDPI, vol. 9(6), pages 1-18, June.
    4. Muhammad Aziz & Takuya Oda & Takashi Mitani & Yoko Watanabe & Takao Kashiwagi, 2015. "Utilization of Electric Vehicles and Their Used Batteries for Peak-Load Shifting," Energies, MDPI, vol. 8(5), pages 1-19, April.
    5. Hao Liang & Weihua Zhuang, 2014. "Stochastic Modeling and Optimization in a Microgrid: A Survey," Energies, MDPI, vol. 7(4), pages 1-24, March.
    6. Shariatzadeh, Farshid & Mandal, Paras & Srivastava, Anurag K., 2015. "Demand response for sustainable energy systems: A review, application and implementation strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 343-350.
    7. Hu, Junjie & Morais, Hugo & Sousa, Tiago & Lind, Morten, 2016. "Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1207-1226.
    8. Jip Kim & Seung Wan Kim & Young Gyu Jin & Jong-Keun Park & Yong Tae Yoon, 2016. "Optimal Coordinated Management of a Plug-In Electric Vehicle Charging Station under a Flexible Penalty Contract for Voltage Security," Energies, MDPI, vol. 9(7), pages 1-15, July.
    9. Whei-Min Lin & Chia-Sheng Tu & Ming-Tang Tsai, 2015. "Energy Management Strategy for Microgrids by Using Enhanced Bee Colony Optimization," Energies, MDPI, vol. 9(1), pages 1-16, December.
    10. Bello, Antonio & Reneses, Javier & Muñoz, Antonio & Delgadillo, Andrés, 2016. "Probabilistic forecasting of hourly electricity prices in the medium-term using spatial interpolation techniques," International Journal of Forecasting, Elsevier, vol. 32(3), pages 966-980.
    11. Zhongwen Li & Chuanzhi Zang & Peng Zeng & Haibin Yu, 2016. "Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty," Energies, MDPI, vol. 9(7), pages 1-16, June.
    12. Marco E. T. Gerards & Johann L. Hurink, 2016. "Robust Peak-Shaving for a Neighborhood with Electric Vehicles," Energies, MDPI, vol. 9(8), pages 1-16, July.
    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. Kyung-Sang Ryu & Dae-Jin Kim & Heesang Ko & Chang-Jin Boo & Jongrae Kim & Young-Gyu Jin & Ho-Chan Kim, 2021. "MPC Based Energy Management System for Hosting Capacity of PVs and Customer Load with EV in Stand-Alone Microgrids," Energies, MDPI, vol. 14(13), pages 1-20, July.
    2. Dávid Csercsik & Ádám Sleisz & Péter Márk Sőrés, 2019. "The Uncertain Bidder Pays Principle and Its Implementation in a Simple Integrated Portfolio-Bidding Energy-Reserve Market Model," Energies, MDPI, vol. 12(15), pages 1-25, August.
    3. Xiaogang Guo & Zhejing Bao & Zhijie Li & Wenjun Yan, 2018. "Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid," Energies, MDPI, vol. 11(1), pages 1-17, January.
    4. Cesar Diaz-Londono & Luigi Colangelo & Fredy Ruiz & Diego Patino & Carlo Novara & Gianfranco Chicco, 2019. "Optimal Strategy to Exploit the Flexibility of an Electric Vehicle Charging Station," Energies, MDPI, vol. 12(20), pages 1-29, October.
    5. Tan, Kang Miao & Padmanaban, Sanjeevikumar & Yong, Jia Ying & Ramachandaramurthy, Vigna K., 2019. "A multi-control vehicle-to-grid charger with bi-directional active and reactive power capabilities for power grid support," Energy, Elsevier, vol. 171(C), pages 1150-1163.
    6. Ana Cabrera-Tobar & Alessandro Massi Pavan & Giovanni Petrone & Giovanni Spagnuolo, 2022. "A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids," Energies, MDPI, vol. 15(23), pages 1-38, December.
    7. Edorta Carrascal-Lekunberri & Izaskun Garrido & Bram Van der Heijde & Aitor J. Garrido & José María Sala & Lieve Helsen, 2017. "Energy Conservation in an Office Building Using an Enhanced Blind System Control," Energies, MDPI, vol. 10(2), pages 1-23, February.
    8. Feras Alasali & Stephen Haben & Husam Foudeh & William Holderbaum, 2020. "A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads," Energies, MDPI, vol. 13(10), pages 1-19, May.
    9. Maciej Ławryńczuk & Piotr M. Marusak & Patryk Chaber & Dawid Seredyński, 2022. "Initialisation of Optimisation Solvers for Nonlinear Model Predictive Control: Classical vs. Hybrid Methods," Energies, MDPI, vol. 15(7), pages 1-21, March.
    10. Jean-Michel Clairand & Javier Rodríguez-García & Carlos Álvarez-Bel, 2018. "Electric Vehicle Charging Strategy for Isolated Systems with High Penetration of Renewable Generation," Energies, MDPI, vol. 11(11), pages 1-21, November.
    11. Stefano Rinaldi & Marco Pasetti & Emiliano Sisinni & Federico Bonafini & Paolo Ferrari & Mattia Rizzi & Alessandra Flammini, 2018. "On the Mobile Communication Requirements for the Demand-Side Management of Electric Vehicles," Energies, MDPI, vol. 11(5), pages 1-27, May.
    12. Luis Gabriel Marín & Mark Sumner & Diego Muñoz-Carpintero & Daniel Köbrich & Seksak Pholboon & Doris Sáez & Alfredo Núñez, 2019. "Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control," Energies, MDPI, vol. 12(23), pages 1-19, November.
    13. Hafiz Abdul Muqeet & Hafiz Mudassir Munir & Haseeb Javed & Muhammad Shahzad & Mohsin Jamil & Josep M. Guerrero, 2021. "An Energy Management System of Campus Microgrids: State-of-the-Art and Future Challenges," Energies, MDPI, vol. 14(20), pages 1-34, October.
    14. Feras Alasali & Stephen Haben & Victor Becerra & William Holderbaum, 2017. "Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems," Energies, MDPI, vol. 10(10), pages 1-18, October.

    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. 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.
    2. Vahid Amir & Shahram Jadid & Mehdi Ehsan, 2017. "Probabilistic Optimal Power Dispatch in Multi-Carrier Networked Microgrids under Uncertainties," Energies, MDPI, vol. 10(11), pages 1-21, November.
    3. Thomas Sachs & Anna Gründler & Milos Rusic & Gilbert Fridgen, 2019. "Framing Microgrid Design from a Business and Information Systems Engineering Perspective," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(6), pages 729-744, December.
    4. Hyeong-Jun Yoo & Thai-Thanh Nguyen & Hak-Man Kim, 2017. "Multi-Frequency Control in a Stand-Alone Multi-Microgrid System Using a Back-To-Back Converter," Energies, MDPI, vol. 10(6), pages 1-18, June.
    5. Boram Kim & Sunghwan Bae & Hongseok Kim, 2017. "Optimal Energy Scheduling and Transaction Mechanism for Multiple Microgrids," Energies, MDPI, vol. 10(4), pages 1-17, April.
    6. Umeozor, Evar Chinedu & Trifkovic, Milana, 2016. "Operational scheduling of microgrids via parametric programming," Applied Energy, Elsevier, vol. 180(C), pages 672-681.
    7. Lenhart, Stephanie & Araújo, Kathleen, 2021. "Microgrid decision-making by public power utilities in the United States: A critical assessment of adoption and technological profiles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    8. 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.
    9. Michael H. Spiegel & Eric M. S. P. Veith & Thomas I. Strasser, 2020. "The Spectrum of Proactive, Resilient Multi-Microgrid Scheduling: A Systematic Literature Review," Energies, MDPI, vol. 13(17), pages 1-37, September.
    10. Manur, Ashray & Venkataramanan, Giri & Sehloff, David, 2018. "Simple electric utility platform: A hardware/software solution for operating emergent microgrids," Applied Energy, Elsevier, vol. 210(C), pages 748-763.
    11. Akhtar Hussain & Van-Hai Bui & Hak-Man Kim & Yong-Hoon Im & Jae-Yong Lee, 2017. "Optimal Energy Management of Combined Cooling, Heat and Power in Different Demand Type Buildings Considering Seasonal Demand Variations," Energies, MDPI, vol. 10(6), pages 1-21, June.
    12. Craparo, Emily & Karatas, Mumtaz & Singham, Dashi I., 2017. "A robust optimization approach to hybrid microgrid operation using ensemble weather forecasts," Applied Energy, Elsevier, vol. 201(C), pages 135-147.
    13. Fausto Calderon-Obaldia & Jordi Badosa & Anne Migan-Dubois & Vincent Bourdin, 2020. "A Two-Step Energy Management Method Guided by Day-Ahead Quantile Solar Forecasts: Cross-Impacts on Four Services for Smart-Buildings," Energies, MDPI, vol. 13(22), pages 1-29, November.
    14. Abdellatif Elmouatamid & Radouane Ouladsine & Mohamed Bakhouya & Najib El Kamoun & Mohammed Khaidar & Khalid Zine-Dine, 2020. "Review of Control and Energy Management Approaches in Micro-Grid Systems," Energies, MDPI, vol. 14(1), pages 1-30, December.
    15. Yu, Min Gyung & Pavlak, Gregory S., 2023. "Risk-aware sizing and transactive control of building portfolios with thermal energy storage," Applied Energy, Elsevier, vol. 332(C).
    16. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    17. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(C).
    18. Wang, Dongxiao & Qiu, Jing & Reedman, Luke & Meng, Ke & Lai, Loi Lei, 2018. "Two-stage energy management for networked microgrids with high renewable penetration," Applied Energy, Elsevier, vol. 226(C), pages 39-48.
    19. Avilés A., Camilo & Oliva H., Sebastian & Watts, David, 2019. "Single-dwelling and community renewable microgrids: Optimal sizing and energy management for new business models," Applied Energy, Elsevier, vol. 254(C).
    20. Aziz, Muhammad & Oda, Takuya & Ito, Masakazu, 2016. "Battery-assisted charging system for simultaneous charging of electric vehicles," Energy, Elsevier, vol. 100(C), pages 82-90.

    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:9:y:2016:i:11:p:973-:d:83432. 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.