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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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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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