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Model predictive control of microgrids for real-time ancillary service market participation
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“Although multiple studies have discussed the selection of appropriate prediction horizons from aspects such as computational cost [30], prediction accuracy [23], and control performance [31], targeted research is still needed based on the environmental differences of edible mushroom factories.”
From Energy-Saving Control Method for Factory Mushroom Room Air Conditioning Based on MPC (2023) · cites this paper as [31]
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Model predictive control: Theory and practice—A survey
García, C. E., Prett, D. M., & Morari, M. (1989) · Automatica · doi:10.1016/0005-1098(89)90002-2 · Cited by 5,392
AMPL : a modeling language for mathematical programming
Fourer, R., & Kernighan, B. W. (1993) · Cited by 1,860
Microgrids: A review of technologies, key drivers, and outstanding issues
Hirsch, A. I., Parag, Y., & Guerrero, J. M. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.03.040 · Cited by 1,508
Optimization in microgrids with hybrid energy systems – A review
Fathima, A. H., & Palanisamy, K. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.01.059 · Cited by 710
Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience
Hussain, A., Bui, V., & Kim, H. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.02.055 · Cited by 517
Microgrid control: A comprehensive survey
Sen, S., & Kumar, V. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.04.012 · Cited by 228
Use of model predictive control for experimental microgrid optimization
Parisio, A., Rikos, E., Tzamalis, G., & Glielmo, L. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.10.027 · Cited by 225
Metrics and quantitative framework for assessing microgrid resilience against windstorms
Amirioun, M. H., Aminifar, F., Lesani, H., & Shahidehpour, M. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2018.07.025 · Cited by 208
Wang, J., Zhong, H., Tang, W., Rajagopal, R., Xia, Q. L., Kang, C., & Wang, Y. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.07.047 · Cited by 189
Bidding strategy of microgrid with consideration of uncertainty for participating in power market
Shi, L., Luo, Y., & Tu, G. Y. (2014) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2014.01.033 · Cited by 184
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Choudhury, S. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.103966 · Cited by 298
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Golmohamadi, H. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111963 · Cited by 274
Salehi, N., García, H. M., Quesada, G. V., & Guerrero, J. M. (2022) · IEEE Access · doi:10.1109/access.2022.3142810 · Cited by 182
Smart grid evolution: Predictive control of distributed energy resources—A review
Babayomi, O. O., Zhang, Z., Dragičević, T., Hu, J., & Rodríguez, J. (2022) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2022.108812 · Cited by 149
Quantum computing in power systems
Zhou, Y., Tang, Z., Nikmehr, N., Babahajiani, P., Feng, F., Wei, T.-C., Zheng, H., & Zhang, P. (2022) · iEnergy · doi:10.23919/ien.2022.0021 · Cited by 105
Metadata
- Authors
- Nelson, J. R., & Johnson, N. G.
- Affiliations
- Arizona State University (US)
- Corresponding author
- Nelson, James R.
- ORCID
- Johnson, Nathan G.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 269
- Pages
- 114963
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W3025717283
- Cited by
- 81
- References
- 36
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 15 · 2023: 15 · 2022: 22 · 2021: 11 · 2020: 4
- FWCI
- 6.43
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Applied Energy (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- National Science Foundation
- Office of Naval Research
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Microgrid Control and Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- model predictive control, microgrids, ancillary services, real-time electricity market, microgrid dispatch, battery energy storage, photovoltaics, diesel generator, spinning reserve, battery state of charge, operational cost reduction, grid-connected microgrid
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- Nelson, J. R., & Johnson, N. G. (2020). Model predictive control of microgrids for real-time ancillary service market participation. Applied Energy. https://doi.org/10.1016/j.apenergy.2020.114963
