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

Author

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  • 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
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    References listed on IDEAS

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    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.

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