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Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch

Author

Listed:
  • Vo-Van Thanh

    (Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Wencong Su

    (Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Bin Wang

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

Abstract

Recently, the integration of optimal battery dispatch and demand response has received much attention in improving DC microgrid operation under uncertainties in the grid-connect condition and distributed generations. However, the majority of prior studies on demand response considered the characteristics of global frequency variable instead of the local voltage for adjusting loads, which has led to obstacles in operating DC microgrids in the context of increasingly rising power electronic loads. Moreover, the consideration of voltage-dependent demand response and optimal battery dispatch has posed challenges for the traditional planning methods, such as stochastic programming, because of nonlinear constraints. Considering these facts, this paper proposes a model predictive control-based integrated voltage-based demand response and batteries’ optimal dispatch operation for minimizing the entire DC microgrid’s operating cost. In the proposed model predictive control approach, the binary decisions about voltage-dependent demand response and charging or discharging status of storage batteries are determined using a deep-Q network-based reinforcement learning method to handle uncertainties in various operating conditions (e.g., AC grid-connect faults and DC sources variations). It also helps to improve the DC microgrid operation efficiency in the two aspects: continuously avoiding load shedding or shifting and reducing the batteries’ charge and discharge cycles to prolong their service life. Finally, the proposed method is validated by comparing to the stochastic programming-based model predictive control method. Simulation results show that the proposed method obtains convergence with approximately 41.95% smaller operating cost than the stochastic optimization-based model predictive control method.

Suggested Citation

  • Vo-Van Thanh & Wencong Su & Bin Wang, 2022. "Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch," Energies, MDPI, vol. 15(6), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2140-:d:771582
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    References listed on IDEAS

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    Cited by:

    1. Shivam Chaturvedi & Mengqi Wang & Yaoyu Fan & Deepak Fulwani & Guilherme Vieira Hollweg & Shahid Aziz Khan & Wencong Su, 2023. "Control Methodologies to Mitigate and Regulate Second-Order Ripples in DC–AC Conversions and Microgrids: A Brief Review," Energies, MDPI, vol. 16(2), pages 1-34, January.
    2. Trinadh Pamulapati & Muhammed Cavus & Ishioma Odigwe & Adib Allahham & Sara Walker & Damian Giaouris, 2022. "A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective," Energies, MDPI, vol. 16(1), pages 1-34, December.
    3. Marvin Lema & Wilson Pavon & Leony Ortiz & Ama Baduba Asiedu-Asante & Silvio Simani, 2022. "Controller Coordination Strategy for DC Microgrid Using Distributed Predictive Control Improving Voltage Stability," Energies, MDPI, vol. 15(15), pages 1-15, July.
    4. Juan Moreno-Castro & Victor Samuel Ocaña Guevara & Lesyani Teresa León Viltre & Yandi Gallego Landera & Oscar Cuaresma Zevallos & Miguel Aybar-Mejía, 2023. "Microgrid Management Strategies for Economic Dispatch of Electricity Using Model Predictive Control Techniques: A Review," Energies, MDPI, vol. 16(16), pages 1-24, August.

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