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Distributed real-time power management for virtual energy storage systems using dynamic price

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  • Kang, Wenfa
  • Chen, Minyou
  • Lai, Wei
  • Luo, Yanyu

Abstract

Energy storage systems (ESS) are widely used in active distribution networks (ADN) to smoothen the drastic fluctuation of renewable energy sources (RES). In order to enhance the scalability and flexibility of ESS, a virtual energy storage system (VESS), which is composed of battery energy storage system (BESS), RES as well as flexible loads (FL), is developed in this paper to realize the functionalities of ESS in more cost-effective way in ADN. Aiming at achieving voltage regulation, dynamic pricing strategies based on system voltage condition are designed for VESS. A distributed real-time power management model containing dynamic pricing strategies is proposed to accomplish the voltage regulation and economic power sharing in VESS. Moreover, a set of distributed algorithms, over time-varying unbalanced directed networks, are designed for dynamic pricing strategies and optimal power management model. Furthermore, the convergence property, optimality and system voltage stability are explained by detailed mathematical analysis. Three various case studies which were ran on a real time digital simulator (OPAL-RT OP5600) were designed to validate the effectiveness of the strategy. Finally, simulation results show that the economic power dispatch and voltage regulation are achieved among VESS simultaneously, even in the presence of time-varying directed and unbalanced communication networks.

Suggested Citation

  • Kang, Wenfa & Chen, Minyou & Lai, Wei & Luo, Yanyu, 2021. "Distributed real-time power management for virtual energy storage systems using dynamic price," Energy, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:energy:v:216:y:2021:i:c:s0360544220321769
    DOI: 10.1016/j.energy.2020.119069
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    2. Mostafa Darvishi & Mehrdad Tahmasebi & Ehsan Shokouhmand & Jagadeesh Pasupuleti & Pitshou Bokoro & Jwan Satei Raafat, 2023. "Optimal Operation of Sustainable Virtual Power Plant Considering the Amount of Emission in the Presence of Renewable Energy Sources and Demand Response," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
    3. He, Yi & Guo, Su & Dong, Peixin & Huang, Jing & Zhou, Jianxu, 2023. "Hierarchical optimization of policy and design for standalone hybrid power systems considering lifecycle carbon reduction subsidy," Energy, Elsevier, vol. 262(PA).
    4. Ajay Shetgaonkar & Aleksandra Lekić & José Luis Rueda Torres & Peter Palensky, 2021. "Microsecond Enhanced Indirect Model Predictive Control for Dynamic Power Management in MMC Units," Energies, MDPI, vol. 14(11), pages 1-26, June.
    5. Yin, Linfei & Lu, Yuejiang, 2021. "Expandable deep width learning for voltage control of three-state energy model based smart grids containing flexible energy sources," Energy, Elsevier, vol. 226(C).
    6. Aghdam, Farid Hamzeh & Mudiyanselage, Manthila Wijesooriya & Mohammadi-Ivatloo, Behnam & Marzband, Mousa, 2023. "Optimal scheduling of multi-energy type virtual energy storage system in reconfigurable distribution networks for congestion management," Applied Energy, Elsevier, vol. 333(C).

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