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Distributed model predictive control for joint coordination of demand response and optimal power flow with renewables in smart grid

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  • Shi, Ye
  • Tuan, Hoang Duong
  • Savkin, Andrey V.
  • Lin, Chin-Teng
  • Zhu, Jian Guo
  • Poor, H. Vincent

Abstract

Demand response is an emerging application of smart grid in exploiting timely interactions between utilities and their customers to improve the reliability and sustainability of power networks. This paper investigates the joint coordination of demand response and AC optimal power flow with curtailment of renewable energy resources to not only save the total amount of power generation costs, renewable energy curtailment costs and price-elastic demand costs but also manage the fluctuation of the overall power load under various types of demand response constraints and grid operational constraints. Its online implementation is very challenging since the future power demand is unpredictable with unknown statistics. Centralized and distributed model predictive control (CMPC and DMPC)-based methods are respectively proposed for the centralized and distributed computation of the online scheduling problem. The CMPC can provide a baseline solution for the DMPC. The DMPC is quite challenging that invokes distributed computation of a nonconvex optimization problem at each time slot. A novel alternating direction method of multipliers (ADMM)-based DMPC algorithm is proposed for this challenging DMPC. It involves an iterative subroutine computation during the update procedure of primal variables that can efficiently handle the difficult nonconvex constraints. Comprehensive experiments have been conducted to test the proposed methods. Simulation results show that the gap in objective values between the DMPC and its baseline counterpart (CMPC) are all within 1%, further verifying the effectiveness of the proposed ADMM-based DMPC algorithm.

Suggested Citation

  • Shi, Ye & Tuan, Hoang Duong & Savkin, Andrey V. & Lin, Chin-Teng & Zhu, Jian Guo & Poor, H. Vincent, 2021. "Distributed model predictive control for joint coordination of demand response and optimal power flow with renewables in smart grid," Applied Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:appene:v:290:y:2021:i:c:s0306261921002245
    DOI: 10.1016/j.apenergy.2021.116701
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    References listed on IDEAS

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    6. Zhu, Zheli & Guan, Guanghua & Wang, Kang, 2023. "Distributed model predictive control based on the alternating direction method of multipliers for branching open canal irrigation systems," Agricultural Water Management, Elsevier, vol. 285(C).
    7. Shi, Kaibo & Cai, Xiao & She, Kun & Zhong, Shouming & Soh, YengChai & Kwon, OhMin, 2022. "Quantized memory proportional–integral control of active power sharing and frequency regulation in island microgrid under abnormal cyber attacks," Applied Energy, Elsevier, vol. 322(C).
    8. Stennikov, Valery & Barakhtenko, Evgeny & Mayorov, Gleb & Sokolov, Dmitry & Zhou, Bin, 2022. "Coordinated management of centralized and distributed generation in an integrated energy system using a multi-agent approach," Applied Energy, Elsevier, vol. 309(C).
    9. 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).

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