Evaluation of Optimal Net Load Management in Microgrids Using Hardware-in-the-Loop Simulation

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This paper presents the performance evaluation of a net load management (NLM) engine that balances load and generation in an isolated community to power a critical facility after a grid interruption event (e.g., the loss of a large generation unit). This NLM engine is particularly important for microgrid systems because it provides a high-speed, cost-optimal control solution to coordinate grid-forming inverters and to dispatch grid-following inverters and deferrable loads in microgrid systems to enhance grid resilience and reliability. The NLM algorithm cost-optimally dispatches the grid-following inverters and deferrable loads based on the demanded power and load priorities, and the grid-forming inverters use droop control to form system voltages and share active and reactive power. A controller-hardware-in-the-loop platform is developed to evaluate the control performance of the NLM algorithm with two sequential contingency events of lost generation units. The experimental results indicate that the NLM engine can maintain system stability, achieve the targeted system voltage and frequency, and balance load and generation to serve the critical facility with improved system resilience and reliability.

Citation Formats

TY - DATA AB - This paper presents the performance evaluation of a net load management (NLM) engine that balances load and generation in an isolated community to power a critical facility after a grid interruption event (e.g., the loss of a large generation unit). This NLM engine is particularly important for microgrid systems because it provides a high-speed, cost-optimal control solution to coordinate grid-forming inverters and to dispatch grid-following inverters and deferrable loads in microgrid systems to enhance grid resilience and reliability. The NLM algorithm cost-optimally dispatches the grid-following inverters and deferrable loads based on the demanded power and load priorities, and the grid-forming inverters use droop control to form system voltages and share active and reactive power. A controller-hardware-in-the-loop platform is developed to evaluate the control performance of the NLM algorithm with two sequential contingency events of lost generation units. The experimental results indicate that the NLM engine can maintain system stability, achieve the targeted system voltage and frequency, and balance load and generation to serve the critical facility with improved system resilience and reliability. AU - Wang, Jing A2 - Chakraborty, Soham A3 - Khatana, Vivek A4 - Lundstrom, Blake A5 - Saraswat, Govind A6 - Salapaka, Murti DB - C-MIX - Community Microgrid Information Exchange DP - Open EI | National Laboratory of the Rockies DO - 10.1109/ISGT50606.2022.9817515 KW - Power electronics and inverters KW - Power electronics KW - Inverters KW - Battery energy storage KW - Solar KW - Photovoltaics KW - PV KW - Diesel generators KW - Other liquid-fuel generators KW - Case studies KW - Performance KW - Power plant controls KW - SCADA KW - Resilience KW - Extreme weather KW - Standards KW - Interconnection KW - Protection LA - English DA - 2022/01/01 PY - 2022 PB - NLR T1 - Evaluation of Optimal Net Load Management in Microgrids Using Hardware-in-the-Loop Simulation UR - https://doi.org/10.1109/ISGT50606.2022.9817515 ER -
Export Citation to RIS
Wang, Jing, et al. Evaluation of Optimal Net Load Management in Microgrids Using Hardware-in-the-Loop Simulation. NLR, 1 January, 2022, C-MIX - Community Microgrid Information Exchange. https://doi.org/10.1109/ISGT50606.2022.9817515.
Wang, J., Chakraborty, S., Khatana, V., Lundstrom, B., Saraswat, G., & Salapaka, M. (2022). Evaluation of Optimal Net Load Management in Microgrids Using Hardware-in-the-Loop Simulation. [Data set]. C-MIX - Community Microgrid Information Exchange. NLR. https://doi.org/10.1109/ISGT50606.2022.9817515
Wang, Jing, Soham Chakraborty, Vivek Khatana, Blake Lundstrom, Govind Saraswat, and Murti Salapaka. Evaluation of Optimal Net Load Management in Microgrids Using Hardware-in-the-Loop Simulation. NLR, January, 1, 2022. Distributed by C-MIX - Community Microgrid Information Exchange. https://doi.org/10.1109/ISGT50606.2022.9817515
@misc{CMIX_Dataset_94, title = {Evaluation of Optimal Net Load Management in Microgrids Using Hardware-in-the-Loop Simulation}, author = {Wang, Jing and Chakraborty, Soham and Khatana, Vivek and Lundstrom, Blake and Saraswat, Govind and Salapaka, Murti}, abstractNote = {This paper presents the performance evaluation of a net load management (NLM) engine that balances load and generation in an isolated community to power a critical facility after a grid interruption event (e.g., the loss of a large generation unit). This NLM engine is particularly important for microgrid systems because it provides a high-speed, cost-optimal control solution to coordinate grid-forming inverters and to dispatch grid-following inverters and deferrable loads in microgrid systems to enhance grid resilience and reliability. The NLM algorithm cost-optimally dispatches the grid-following inverters and deferrable loads based on the demanded power and load priorities, and the grid-forming inverters use droop control to form system voltages and share active and reactive power. A controller-hardware-in-the-loop platform is developed to evaluate the control performance of the NLM algorithm with two sequential contingency events of lost generation units. The experimental results indicate that the NLM engine can maintain system stability, achieve the targeted system voltage and frequency, and balance load and generation to serve the critical facility with improved system resilience and reliability.}, url = {https://cmix.openei.org/submissions/94}, year = {2022}, howpublished = {C-MIX - Community Microgrid Information Exchange, NLR, https://doi.org/10.1109/ISGT50606.2022.9817515}, note = {Accessed: 2026-08-06}, doi = {10.1109/ISGT50606.2022.9817515} }
https://dx.doi.org/10.1109/ISGT50606.2022.9817515

Details

Data from Jan 1, 2022

Last updated Mar 30, 2026

Submitted Jun 2, 2026

Organization

NLR

Contact

Jing Wang

Authors

Jing Wang

NLR

Soham Chakraborty

NLR

Vivek Khatana

University of Minnesota

Blake Lundstrom

Enphase Energy

Govind Saraswat

NLR

Murti Salapaka

University of Minnesota
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