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Optimization framework for coordinated operation of home energy management system and Volt-VAR optimization in unbalanced active distribution networks considering uncertainties

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  • Mak, Davye
  • Choi, Dae-Hyun

Abstract

This study proposes an optimization framework that coordinates the operations of a home energy management system (HEMS) in a low-voltage (LV) distribution network and Volt/VAR optimization (VVO) in a medium-voltage (MV) distribution network through flexible electricity consumption and production of prosumers. The proposed framework consists of a three-level optimization problem, which corresponds to the HEMS for a prosumer at the first level, HEMS aggregator at the second level, and VVO at the third level. The optimal operations of home appliances and distributed energy resources are scheduled in the HEMS according to the prosumer’s preferred appliance scheduling and comfort level. Given the optimal energy consumption schedules from multiple HEMSs, the HEMS aggregator recalculates them while interacting with the VVO, which monitors and controls the MV distribution network efficiently. Furthermore, to incorporate the uncertainty for the predicted errors of residential solar photovoltaic generation and outdoor temperature into the proposed framework, the deterministic optimization (DO)-based HEMS aggregator model is reformulated into a chance constrained optimization (CCO)-based model. Numerical examples tested in IEEE 13-node MV and CIGRE 18-node LV distribution systems show that, in contrast with a method without coordination of HEMS and VVO, the proposed DO-based approach reduces the total energy losses, active energy consumption, and reactive energy consumption by 21.03%,7.62%, and 115.51%, respectively, in the LV system, and 2.34%,1.36%, and 4.07%, respectively, in the MV system. In addition, the performance of the proposed CCO-based approach was validated in terms of probability level of chance constraints.

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  • Mak, Davye & Choi, Dae-Hyun, 2020. "Optimization framework for coordinated operation of home energy management system and Volt-VAR optimization in unbalanced active distribution networks considering uncertainties," Applied Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:appene:v:276:y:2020:i:c:s0306261920310072
    DOI: 10.1016/j.apenergy.2020.115495
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    References listed on IDEAS

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    1. Rastegar, Mohammad & Fotuhi-Firuzabad, Mahmud & Aminifar, Farrokh, 2012. "Load commitment in a smart home," Applied Energy, Elsevier, vol. 96(C), pages 45-54.
    2. Davye Mak & Dae-Hyun Choi, 2018. "Hierarchical Look-Ahead Conservation Voltage Reduction Framework Considering Distributed Energy Resources and Demand Reduction," Energies, MDPI, vol. 11(12), pages 1-20, November.
    3. Iria, José & Soares, Filipe & Matos, Manuel, 2018. "Optimal supply and demand bidding strategy for an aggregator of small prosumers," Applied Energy, Elsevier, vol. 213(C), pages 658-669.
    4. Killian, M. & Zauner, M. & Kozek, M., 2018. "Comprehensive smart home energy management system using mixed-integer quadratic-programming," Applied Energy, Elsevier, vol. 222(C), pages 662-672.
    5. Ayón, X. & Gruber, J.K. & Hayes, B.P. & Usaola, J. & Prodanović, M., 2017. "An optimal day-ahead load scheduling approach based on the flexibility of aggregate demands," Applied Energy, Elsevier, vol. 198(C), pages 1-11.
    6. Rieger, Alexander & Thummert, Robert & Fridgen, Gilbert & Kahlen, Micha & Ketter, Wolfgang, 2016. "Estimating the benefits of cooperation in a residential microgrid: A data-driven approach," Applied Energy, Elsevier, vol. 180(C), pages 130-141.
    7. Gonçalves, Ivo & Gomes, Álvaro & Henggeler Antunes, Carlos, 2019. "Optimizing the management of smart home energy resources under different power cost scenarios," Applied Energy, Elsevier, vol. 242(C), pages 351-363.
    8. Nan, Sibo & Zhou, Ming & Li, Gengyin, 2018. "Optimal residential community demand response scheduling in smart grid," Applied Energy, Elsevier, vol. 210(C), pages 1280-1289.
    9. Correa-Florez, Carlos Adrian & Michiorri, Andrea & Kariniotakis, Georges, 2018. "Robust optimization for day-ahead market participation of smart-home aggregators," Applied Energy, Elsevier, vol. 229(C), pages 433-445.
    10. Jin, Xin & Baker, Kyri & Christensen, Dane & Isley, Steven, 2017. "Foresee: A user-centric home energy management system for energy efficiency and demand response," Applied Energy, Elsevier, vol. 205(C), pages 1583-1595.
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    Cited by:

    1. Jeddi, Babak & Mishra, Yateendra & Ledwich, Gerard, 2021. "Distributed load scheduling in residential neighborhoods for coordinated operation of multiple home energy management systems," Applied Energy, Elsevier, vol. 300(C).
    2. Jeon, Soi & Choi, Dae-Hyun, 2022. "Joint optimization of Volt/VAR control and mobile energy storage system scheduling in active power distribution networks under PV prediction uncertainty," Applied Energy, Elsevier, vol. 310(C).
    3. Vijayan, Vineeth & Mohapatra, Abheejeet & Singh, S.N., 2021. "Demand Response with Volt/Var Optimization for unbalanced active distribution systems," Applied Energy, Elsevier, vol. 300(C).
    4. Niphon Kaewdornhan & Chitchai Srithapon & Rittichai Liemthong & Rongrit Chatthaworn, 2023. "Real-Time Multi-Home Energy Management with EV Charging Scheduling Using Multi-Agent Deep Reinforcement Learning Optimization," Energies, MDPI, vol. 16(5), pages 1-25, March.
    5. Etedadi, Farshad & Kelouwani, Sousso & Agbossou, Kodjo & Henao, Nilson & Laurencelle, François, 2023. "Consensus and sharing based distributed coordination of home energy management systems with demand response enabled baseboard heaters," Applied Energy, Elsevier, vol. 336(C).
    6. Kabir, Farzana & Yu, Nanpeng & Gao, Yuanqi & Wang, Wenyu, 2023. "Deep reinforcement learning-based two-timescale Volt-VAR control with degradation-aware smart inverters in power distribution systems," Applied Energy, Elsevier, vol. 335(C).
    7. Ruan, Hebin & Gao, Hongjun & Qiu, Haifeng & Gooi, Hoay Beng & Liu, Junyong, 2023. "Distributed operation optimization of active distribution network with P2P electricity trading in blockchain environment," Applied Energy, Elsevier, vol. 331(C).
    8. Tostado-Véliz, Marcos & Kamel, Salah & Aymen, Flah & Jurado, Francisco, 2022. "A novel hybrid lexicographic-IGDT methodology for robust multi-objective solution of home energy management systems," Energy, Elsevier, vol. 253(C).
    9. Binghui Han & Younes Zahraoui & Marizan Mubin & Saad Mekhilef & Mehdi Seyedmahmoudian & Alex Stojcevski, 2023. "Optimal Strategy for Comfort-Based Home Energy Management System Considering Impact of Battery Degradation Cost Model," Mathematics, MDPI, vol. 11(6), pages 1-26, March.

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