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Optimal demand side management scheduling-based bidirectional regulation of energy distribution network for multi-residential demand response with self-produced renewable energy

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  • Nguyen, Hai-Tra
  • Safder, Usman
  • Loy-Benitez, Jorge
  • Yoo, ChangKyoo

Abstract

Global transactive energy has become more complex due to the extension of its efficiency. Thus, multi-residential demand responses pose a new challenge in the critical infrastructure of power transition networks in energy planning and management. By encompassing optimal scheduling with strategic energy decentralization, a reliable distribution network can suggest a resolution that contributes to optimize energy efficiency. This research proposed a holistic bidirectional distribution network with optimal scheduling-based demand-side management (DSM) for superior power sharing and beneficial performance. A DSM was developed to maintain equal power sharing between various mix-energy sources, providing a sub-level residential demand response program. Among several scenarios considered, the DSM-based scheduling distribution network resulted in optimum and equivalent sharing of power loads for residential demands. Moreover, the application of self-production-based renewable energy source (RES) is a novel strategy to improve imbalanced power sharing. The energy deficit and surplus can be solved by establishing a conservation strategy with self-installation-based photovoltaic panels by end-users. CO2 emissions were also estimated to elucidate the effect of the energy distribution network on the environment according to different category of energy sources. Considering satisfaction analysis based on appliance energy consumption, the energy distribution network was efficient in improving the end-user expenditure, sustaining a clean energy loading profile, and maximizing the benefit of cost.

Suggested Citation

  • Nguyen, Hai-Tra & Safder, Usman & Loy-Benitez, Jorge & Yoo, ChangKyoo, 2022. "Optimal demand side management scheduling-based bidirectional regulation of energy distribution network for multi-residential demand response with self-produced renewable energy," Applied Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:appene:v:322:y:2022:i:c:s0306261922007590
    DOI: 10.1016/j.apenergy.2022.119425
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    2. Elsir, Mohamed & Al-Sumaiti, Ameena Saad & El Moursi, Mohamed Shawky & Al-Awami, Ali Taleb, 2023. "Coordinating the day-ahead operation scheduling for demand response and water desalination plants in smart grid," Applied Energy, Elsevier, vol. 335(C).
    3. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    4. Li, Yanxue & Wang, Zixuan & Xu, Wenya & Gao, Weijun & Xu, Yang & Xiao, Fu, 2023. "Modeling and energy dynamic control for a ZEH via hybrid model-based deep reinforcement learning," Energy, Elsevier, vol. 277(C).
    5. Liu, Hongtao & Zhai, Rongrong & Patchigolla, Kumar & Turner, Peter & Yu, Xiaohan & Wang, Peng, 2023. "Multi-objective optimisation of a thermal-storage PV-CSP-wind hybrid power system in three operation modes," Energy, Elsevier, vol. 284(C).
    6. Qadeer Akbar Sial & Usman Safder & Shahid Iqbal & Rana Basit Ali, 2024. "Advancement in Supercapacitors for IoT Applications by Using Machine Learning: Current Trends and Future Technology," Sustainability, MDPI, vol. 16(4), pages 1-26, February.

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