IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i7p499-d73088.html
   My bibliography  Save this article

Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty

Author

Listed:
  • Zhongwen Li

    (Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Chuanzhi Zang

    (Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China)

  • Peng Zeng

    (Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China)

  • Haibin Yu

    (Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China)

Abstract

Microgrids (MGs) are presented as a cornerstone of smart grids. With the potential to integrate intermittent renewable energy sources (RES) in a flexible and environmental way, the MG concept has gained even more attention. Due to the randomness of RES, load, and electricity price in MG, the forecast errors of MGs will affect the performance of the power scheduling and the operating cost of an MG. In this paper, a combined stochastic programming and receding horizon control (SPRHC) strategy is proposed for microgrid energy management under uncertainty, which combines the advantages of two-stage stochastic programming (SP) and receding horizon control (RHC) strategy. With an SP strategy, a scheduling plan can be derived that minimizes the risk of uncertainty by involving the uncertainty of MG in the optimization model. With an RHC strategy, the uncertainty within the MG can be further compensated through a feedback mechanism with the lately updated forecast information. In our approach, a proper strategy is also proposed to maintain the SP model as a mixed integer linear constrained quadratic programming (MILCQP) problem, which is solvable without resorting to any heuristics algorithms. The results of numerical experiments explicitly demonstrate the superiority of the proposed strategy for both island and grid-connected operating modes of an MG.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:7:p:499-:d:73088
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/7/499/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/9/7/499/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation," Applied Energy, Elsevier, vol. 99(C), pages 455-470.
    2. Holjevac, Ninoslav & Capuder, Tomislav & Kuzle, Igor, 2015. "Adaptive control for evaluation of flexibility benefits in microgrid systems," Energy, Elsevier, vol. 92(P3), pages 487-504.
    3. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
    4. Zhou, Zhe & Zhang, Jianyun & Liu, Pei & Li, Zheng & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "A two-stage stochastic programming model for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 103(C), pages 135-144.
    5. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    6. Kriett, Phillip Oliver & Salani, Matteo, 2012. "Optimal control of a residential microgrid," Energy, Elsevier, vol. 42(1), pages 321-330.
    7. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico, 2014. "An integrated framework of agent-based modelling and robust optimization for microgrid energy management," Applied Energy, Elsevier, vol. 129(C), pages 70-88.
    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. Agüera-Pérez, Agustín & Palomares-Salas, José Carlos & González de la Rosa, Juan José & Florencias-Oliveros, Olivia, 2018. "Weather forecasts for microgrid energy management: Review, discussion and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 265-278.
    2. Bai, Hanyu & Lei, Shunbo & Geng, Sijia & Hu, Xiaosong & Li, Zhaojian & Song, Ziyou, 2024. "Techno-economic assessment of isolated micro-grids with second-life batteries: A reliability-oriented iterative design framework," Applied Energy, Elsevier, vol. 364(C).
    3. Yang, Jiaojiao & Sun, Zeyi & Hu, Wenqing & Steinmeister, Louis, 2022. "Joint control of manufacturing and onsite microgrid system via novel neural-network integrated reinforcement learning algorithms," Applied Energy, Elsevier, vol. 315(C).
    4. Wakui, Tetsuya & Sawada, Kento & Yokoyama, Ryohei & Aki, Hirohisa, 2018. "Predictive management of cogeneration-based energy supply networks using two-stage multi-objective optimization," Energy, Elsevier, vol. 162(C), pages 1269-1286.
    5. Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
    6. 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.
    7. 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.
    8. Pouria Sheikhahmadi & Ramyar Mafakheri & Salah Bahramara & Maziar Yazdani Damavandi & João P. S. Catalão, 2018. "Risk-Based Two-Stage Stochastic Optimization Problem of Micro-Grid Operation with Renewables and Incentive-Based Demand Response Programs," Energies, MDPI, vol. 11(3), pages 1-17, March.
    9. Wakui, Tetsuya & Sawada, Kento & Yokoyama, Ryohei & Aki, Hirohisa, 2019. "Predictive management for energy supply networks using photovoltaics, heat pumps, and battery by two-stage stochastic programming and rule-based control," Energy, Elsevier, vol. 179(C), pages 1302-1319.
    10. Ng, Rong Wang & Begam, K.M. & Rajkumar, Rajprasad Kumar & Wong, Yee Wan & Chong, Lee Wai, 2022. "A novel dynamic two-stage controller of battery energy storage system for maximum demand reductions," Energy, Elsevier, vol. 248(C).
    11. 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.
    12. Ying Ji & Jianhui Wang & Jiacan Xu & Donglin Li, 2021. "Data-Driven Online Energy Scheduling of a Microgrid Based on Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-19, April.
    13. Ying Ji & Jianhui Wang & Jiacan Xu & Xiaoke Fang & Huaguang Zhang, 2019. "Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning," Energies, MDPI, vol. 12(12), pages 1-21, June.
    14. 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.
    15. Mihai Sanduleac & Irina Ciornei & Mihaela Albu & Lucian Toma & Marta Sturzeanu & João F. Martins, 2017. "Resilient Prosumer Scenario in a Changing Regulatory Environment—The UniRCon Solution," Energies, MDPI, vol. 10(12), pages 1-22, November.

    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. 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. Shin, Joohyun & Lee, Jay H. & Realff, Matthew J., 2017. "Operational planning and optimal sizing of microgrid considering multi-scale wind uncertainty," Applied Energy, Elsevier, vol. 195(C), pages 616-633.
    3. Fontenot, Hannah & Dong, Bing, 2019. "Modeling and control of building-integrated microgrids for optimal energy management – A review," Applied Energy, Elsevier, vol. 254(C).
    4. Ferruzzi, Gabriella & Cervone, Guido & Delle Monache, Luca & Graditi, Giorgio & Jacobone, Francesca, 2016. "Optimal bidding in a Day-Ahead energy market for Micro Grid under uncertainty in renewable energy production," Energy, Elsevier, vol. 106(C), pages 194-202.
    5. Setlhaolo, Ditiro & Sichilalu, Sam & Zhang, Jiangfeng, 2017. "Residential load management in an energy hub with heat pump water heater," Applied Energy, Elsevier, vol. 208(C), pages 551-560.
    6. Velik, Rosemarie & Nicolay, Pascal, 2014. "Grid-price-dependent energy management in microgrids using a modified simulated annealing triple-optimizer," Applied Energy, Elsevier, vol. 130(C), pages 384-395.
    7. Parisio, Alessandra & Rikos, Evangelos & Tzamalis, George & Glielmo, Luigi, 2014. "Use of model predictive control for experimental microgrid optimization," Applied Energy, Elsevier, vol. 115(C), pages 37-46.
    8. 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.
    9. Liu, Da & Wang, Jilong & Wang, Hui, 2015. "Short-term wind speed forecasting based on spectral clustering and optimised echo state networks," Renewable Energy, Elsevier, vol. 78(C), pages 599-608.
    10. Umar, Abdullah & Kumar, Deepak & Ghose, Tirthadip, 2022. "Blockchain-based decentralized energy intra-trading with battery storage flexibility in a community microgrid system," Applied Energy, Elsevier, vol. 322(C).
    11. 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.
    12. Pascual, Julio & Barricarte, Javier & Sanchis, Pablo & Marroyo, Luis, 2015. "Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting," Applied Energy, Elsevier, vol. 158(C), pages 12-25.
    13. Noor, Sana & Yang, Wentao & Guo, Miao & van Dam, Koen H. & Wang, Xiaonan, 2018. "Energy Demand Side Management within micro-grid networks enhanced by blockchain," Applied Energy, Elsevier, vol. 228(C), pages 1385-1398.
    14. Kyu-Hyung Jo & Mun-Kyeom Kim, 2018. "Improved Genetic Algorithm-Based Unit Commitment Considering Uncertainty Integration Method," Energies, MDPI, vol. 11(6), pages 1-18, May.
    15. Purkayastha, Sagar N. & Chen, Yujun & Gates, Ian D. & Trifkovic, Milana, 2020. "A kelly criterion based optimal scheduling of a microgrid on a steam-assisted gravity drainage (SAGD) facility," Energy, Elsevier, vol. 204(C).
    16. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    17. Quan, Hao & Srinivasan, Dipti & Khambadkone, Ashwin M. & Khosravi, Abbas, 2015. "A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources," Applied Energy, Elsevier, vol. 152(C), pages 71-82.
    18. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach," Applied Energy, Elsevier, vol. 222(C), pages 932-950.
    19. Danny Espín-Sarzosa & Rodrigo Palma-Behnke & Oscar Núñez-Mata, 2020. "Energy Management Systems for Microgrids: Main Existing Trends in Centralized Control Architectures," Energies, MDPI, vol. 13(3), pages 1-32, January.
    20. Ghatikar, Girish & Mashayekh, Salman & Stadler, Michael & Yin, Rongxin & Liu, Zhenhua, 2016. "Distributed energy systems integration and demand optimization for autonomous operations and electric grid transactions," Applied Energy, Elsevier, vol. 167(C), pages 432-448.

    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:7:p:499-:d:73088. 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.