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Adaptive cold-load pickup considerations in 2-stage microgrid unit commitment for enhancing microgrid resilience

Author

Listed:
  • Hu, Rongxing
  • Shirsat, Ashwin
  • Muthukaruppan, Valliappan
  • Li, Yiyan
  • Zhang, Si
  • Tang, Wenyuan
  • Baran, Mesut
  • Lu, Ning

Abstract

In an extended main grid outage spanning multiple days, load shedding serves as a critical mechanism for islanded microgrids to maintain essential power and energy reserves that are indispensable for fulfilling reliability and resiliency mandates. However, using load shedding for such purposes leads to increasing occurrence of cold load pickup (CLPU) events. This study presents an innovative adaptive CLPU model that introduces a method for determining and incorporating parameters related to CLPU power and energy requirements into a two-stage microgrid unit commitment (MGUC) algorithm. In contrast to the traditional fixed-CLPU-curve approach, this model calculates CLPU duration, power, and energy demands by considering outage durations and ambient temperature variations within the MGUC process. By integrating the adaptive CLPU model into the MGUC problem formulation, it allows for the optimal allocation of energy resources throughout the entire scheduling horizon to fulfill the CLPU requirements when scheduling multiple CLPU events. The performance of the enhanced MGUC algorithm considering CLPU needs is assessed using actual load and photovoltaic (PV) data. Simulation results demonstrate significant improvements in dispatch optimality evaluated by the amount of load served, customer comfort, energy storage operation, and adherence to energy schedules. These enhancements collectively contribute to reliable and resilient microgrid operation.

Suggested Citation

  • Hu, Rongxing & Shirsat, Ashwin & Muthukaruppan, Valliappan & Li, Yiyan & Zhang, Si & Tang, Wenyuan & Baran, Mesut & Lu, Ning, 2024. "Adaptive cold-load pickup considerations in 2-stage microgrid unit commitment for enhancing microgrid resilience," Applied Energy, Elsevier, vol. 356(C).
    • Handle: RePEc:eee:appene:v:356:y:2024:i:c:s0306261923017889
    DOI: 10.1016/j.apenergy.2023.122424
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    References listed on IDEAS

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    1. Li, Yiyan & Zhang, Si & Hu, Rongxing & Lu, Ning, 2021. "A meta-learning based distribution system load forecasting model selection framework," Applied Energy, Elsevier, vol. 294(C).
    2. Mousavizadeh, Saeed & Haghifam, Mahmoud-Reza & Shariatkhah, Mohammad-Hossein, 2018. "A linear two-stage method for resiliency analysis in distribution systems considering renewable energy and demand response resources," Applied Energy, Elsevier, vol. 211(C), pages 443-460.
    3. Hussain, Akhtar & Bui, Van-Hai & Kim, Hak-Man, 2019. "Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience," Applied Energy, Elsevier, vol. 240(C), pages 56-72.
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