Improved Air-Conditioning Demand Response of Connected Communities over Individually Optimized Buildings
Autor: | Miguel A. Peinado-Guerrero, Patrick E. Phelan, Nicolas A. Campbell, Jesus Villalobos |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Technology
Control and Optimization Optimization problem Linear programming Computer science MathematicsofComputing_GENERAL Energy Engineering and Power Technology Demand response Peak demand connected communities Range (statistics) Electrical and Electronic Engineering Engineering (miscellaneous) Flexibility (engineering) coincidence factor peak demand reduction Renewable Energy Sustainability and the Environment business.industry air-conditioning electricity cost reduction building energy management systems Reliability engineering Air conditioning demand response Management system business Energy (miscellaneous) |
Zdroj: | Energies, Vol 14, Iss 5926, p 5926 (2021) Energies Volume 14 Issue 18 Pages: 5926 |
ISSN: | 1996-1073 |
Popis: | Connected communities potentially offer much greater demand response capabilities over singular building energy management systems (BEMS) through an increase of connectivity. The potential increase in benefits from this next step in connectivity is still under investigation, especially when applied to existing buildings. This work utilizes EnergyPlus simulation results on eight different commercial prototype buildings to estimate the potential savings on peak demand and energy costs using a mixed-integer linear programming model. This model is used in two cases: a fully connected community and eight separate buildings with BEMS. The connected community is optimized using all zones as variables, while the individual buildings are optimized separately and then aggregated. These optimization problems are run for a range of individual zone flexibility values. The results indicate that a connected community offered 60.0% and 24.8% more peak demand savings for low and high flexibility scenarios, relative to individually optimized buildings. Energy cost optimization results show only marginally better savings of 2.9% and 6.1% for low and high flexibility, respectively. |
Databáze: | OpenAIRE |
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