IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i7p1884-d158851.html
   My bibliography  Save this article

A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

Author

Listed:
  • Saeid Esmaeili

    (Department of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran)

  • Amjad Anvari-Moghaddam

    (Department of Energy Technology, Aalborg University, 9220 Aalborg East, Denmark)

  • Shahram Jadid

    (Department of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran)

  • Josep M. Guerrero

    (Department of Energy Technology, Aalborg University, 9220 Aalborg East, Denmark)

Abstract

Due to the recent developments in the practical implementation of remotely controlled switches (RCSs) in the smart distribution system infrastructure, distribution system operators face operational challenges in the hourly reconfigurable environment. This paper develops a stochastic Model Predictive Control (MPC) framework for operational scheduling of distribution systems with dynamic and adaptive hourly reconfiguration. The effect of coordinated integration of energy storage systems and demand response programs through hourly reconfiguration on the total costs (including cost of total loss, switching cost, cost of bilateral contract with power generation owners and responsive loads, and cost of exchanging power with the wholesale market) is investigated. A novel Switching Index (SI) based on the RCS ages and critical points in the network along with the maximum number of switching actions is introduced. Due to nonlinear nature of the problem and several existing binary variables, it is basically considered as a Mixed Integer Non-Linear Programming (MINLP) problem, which is transformed into a Mixed Integer Linear Programming (MILP) problem. The satisfactory performance of the proposed model is demonstrated through its application on a modified IEEE 33-bus distribution system.

Suggested Citation

  • Saeid Esmaeili & Amjad Anvari-Moghaddam & Shahram Jadid & Josep M. Guerrero, 2018. "A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems," Energies, MDPI, vol. 11(7), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1884-:d:158851
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/7/1884/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/7/1884/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nwulu, Nnamdi I. & Xia, Xiaohua, 2017. "Optimal dispatch for a microgrid incorporating renewables and demand response," Renewable Energy, Elsevier, vol. 101(C), pages 16-28.
    2. Fares, Robert L. & Webber, Michael E., 2014. "A flexible model for economic operational management of grid battery energy storage," Energy, Elsevier, vol. 78(C), pages 768-776.
    3. Ryusuke Konishi & Akiko Takeda & Masaki Takahashi, 2018. "Optimal Sizing of Energy Storage Systems for the Energy Procurement Problem in Multi-Period Markets under Uncertainties," Energies, MDPI, vol. 11(1), pages 1-19, January.
    4. Anand, M.P. & Golshannavaz, Sajjad & Ongsakul, Weerakorn & Rajapakse, Athula, 2016. "Incorporating short-term topological variations in optimal energy management of MGs considering ancillary services by electric vehicles," Energy, Elsevier, vol. 112(C), pages 241-253.
    5. Gutiérrez-Alcaraz, G. & Galván, E. & González-Cabrera, N. & Javadi, M.S., 2015. "Renewable energy resources short-term scheduling and dynamic network reconfiguration for sustainable energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 256-264.
    6. Kavousi-Fard, Abdollah & Khodaei, Amin, 2016. "Efficient integration of plug-in electric vehicles via reconfigurable microgrids," Energy, Elsevier, vol. 111(C), pages 653-663.
    7. Aghajani, G.R. & Shayanfar, H.A. & Shayeghi, H., 2017. "Demand side management in a smart micro-grid in the presence of renewable generation and demand response," Energy, Elsevier, vol. 126(C), pages 622-637.
    8. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    9. Mazidi, Mohammadreza & Monsef, Hassan & Siano, Pierluigi, 2016. "Robust day-ahead scheduling of smart distribution networks considering demand response programs," Applied Energy, Elsevier, vol. 178(C), pages 929-942.
    10. Bendato, Ilaria & Bonfiglio, Andrea & Brignone, Massimo & Delfino, Federico & Pampararo, Fabio & Procopio, Renato, 2017. "A real-time Energy Management System for the integration of economical aspects and system operator requirements: Definition and validation," Renewable Energy, Elsevier, vol. 102(PB), pages 406-416.
    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. Saeid Esmaeili & Amjad Anvari-Moghaddam & Shahram Jadid, 2019. "Optimal Operational Scheduling of Reconfigurable Multi-Microgrids Considering Energy Storage Systems," Energies, MDPI, vol. 12(9), pages 1-23, May.
    2. Mingyue He & Zahra Soltani & Mojdeh Khorsand & Aaron Dock & Patrick Malaty & Masoud Esmaili, 2022. "Behavior-Aware Aggregation of Distributed Energy Resources for Risk-Aware Operational Scheduling of Distribution Systems," Energies, MDPI, vol. 15(24), pages 1-18, December.
    3. Chan-Hyeok Oh & Joon-Ho Choi & Sang-Yun Yun & Seon-Ju Ahn, 2021. "Short-Term Cooperative Operational Scheme of Distribution System with High Hosting Capacity of Renewable-Energy-Based Distributed Generations," Energies, MDPI, vol. 14(19), pages 1-25, October.
    4. Fatma Yaprakdal & Mustafa Baysal & Amjad Anvari-Moghaddam, 2019. "Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources," Energies, MDPI, vol. 12(10), pages 1-17, May.

    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. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    2. 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).
    3. Ricardo Faia & Pedro Faria & Zita Vale & João Spinola, 2019. "Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House," Energies, MDPI, vol. 12(9), pages 1-18, April.
    4. Lin, Haiyang & Fu, Kun & Wang, Yu & Sun, Qie & Li, Hailong & Hu, Yukun & Sun, Bo & Wennersten, Ronald, 2019. "Characteristics of electric vehicle charging demand at multiple types of location - Application of an agent-based trip chain model," Energy, Elsevier, vol. 188(C).
    5. Cruz, Marco R.M. & Fitiwi, Desta Z. & Santos, Sérgio F. & Catalão, João P.S., 2018. "A comprehensive survey of flexibility options for supporting the low-carbon energy future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 338-353.
    6. 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.
    7. 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.
    8. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    9. Huang, Pei & Sun, Yongjun, 2019. "A robust control of nZEBs for performance optimization at cluster level under demand prediction uncertainty," Renewable Energy, Elsevier, vol. 134(C), pages 215-227.
    10. Torkan, Ramin & Ilinca, Adrian & Ghorbanzadeh, Milad, 2022. "A genetic algorithm optimization approach for smart energy management of microgrids," Renewable Energy, Elsevier, vol. 197(C), pages 852-863.
    11. Saeid Esmaeili & Amjad Anvari-Moghaddam & Shahram Jadid, 2019. "Optimal Operational Scheduling of Reconfigurable Multi-Microgrids Considering Energy Storage Systems," Energies, MDPI, vol. 12(9), pages 1-23, May.
    12. Jordehi, A. Rezaee, 2019. "Optimisation of demand response in electric power systems, a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 308-319.
    13. Haddadian, Hossein & Noroozian, Reza, 2017. "Optimal operation of active distribution systems based on microgrid structure," Renewable Energy, Elsevier, vol. 104(C), pages 197-210.
    14. Alqahtani, Mohammed & Hu, Mengqi, 2022. "Dynamic energy scheduling and routing of multiple electric vehicles using deep reinforcement learning," Energy, Elsevier, vol. 244(PA).
    15. Paulo M. De Oliveira-De Jesus & Jose M. Yusta, 2024. "Optimal Power Dispatch for Maximum Energy Community Welfare by Considering Closed Distribution Systems and Renewable Sources," Energies, MDPI, vol. 17(18), pages 1-21, September.
    16. Luiz Almeida & Ana Soares & Pedro Moura, 2023. "A Systematic Review of Optimization Approaches for the Integration of Electric Vehicles in Public Buildings," Energies, MDPI, vol. 16(13), pages 1-26, June.
    17. Norouzi, Mohammadali & Aghaei, Jamshid & Niknam, Taher & Alipour, Mohammadali & Pirouzi, Sasan & Lehtonen, Matti, 2023. "Risk-averse and flexi-intelligent scheduling of microgrids based on hybrid Boltzmann machines and cascade neural network forecasting," Applied Energy, Elsevier, vol. 348(C).
    18. Xu, Ying & Ren, Li & Zhang, Zhongping & Tang, Yuejin & Shi, Jing & Xu, Chen & Li, Jingdong & Pu, Dongsheng & Wang, Zhuang & Liu, Huajun & Chen, Lei, 2018. "Analysis of the loss and thermal characteristics of a SMES (Superconducting Magnetic Energy Storage) magnet with three practical operating conditions," Energy, Elsevier, vol. 143(C), pages 372-384.
    19. Ceran, Bartosz, 2019. "The concept of use of PV/WT/FC hybrid power generation system for smoothing the energy profile of the consumer," Energy, Elsevier, vol. 167(C), pages 853-865.
    20. Hernández, J.C. & Ruiz-Rodriguez, F.J. & Jurado, F., 2017. "Modelling and assessment of the combined technical impact of electric vehicles and photovoltaic generation in radial distribution systems," Energy, Elsevier, vol. 141(C), pages 316-332.

    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:11:y:2018:i:7:p:1884-:d:158851. 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.