Moore vs. Murphy: Tradeoffs between complexity and reliability in distributed energy system scheduling using software-as-a-service
Abstract
Software-based optimization of building control strategies, including scheduling, has the potential to improve the performance of existing complex heating, ventilation, and air conditioning (HVAC), storage, and other systems—especially if temporally variable energy production, such as solar thermal or photovoltaics, is included. If reductions in energy bills can be achieved using optimized control strategies that take advantage of cost-saving opportunities, such as time-of-use pricing, the additional bill savings can cover further efficiency investment costs. As computer processing becomes cheaper over time (Moore's Law), opportunities to perform complex control optimization become more abundant, and these can be performed remotely as software-as-a-service (SaaS). However, by “perfecting” our control strategies, we run an increased risk that when something unexpected happens (Murphy's Law), the consequences of failure are greater. This study used simulation to explore the potential benefits of HVAC schedule optimization, delivery, and implementation using a SaaS paradigm, at various levels of complexity. Implementing optimal schedules in a model of an efficient building's HVAC system, the study predicts energy cost savings of up to 10% compared to the naïve reference control strategy. Optimizing more system control variables increases the potential energy cost savings; however, these savings could be compromised by failures in communication inherentmore »
- Authors:
-
[1];
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Univ. of New Mexico, Albuquerque, NM (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
- OSTI Identifier:
- 1650052
- Alternate Identifier(s):
- OSTI ID: 1547810
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Applied Energy
- Additional Journal Information:
- Journal Volume: 238; Journal ID: ISSN 0306-2619
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING
Citation Formats
Dutton, Spencer, Marnay, Chris, Feng, Wei, Robinson, Matthew, and Mammoli, Andrea. Moore vs. Murphy: Tradeoffs between complexity and reliability in distributed energy system scheduling using software-as-a-service. United States: N. p., 2019.
Web. doi:10.1016/j.apenergy.2019.01.067.
Dutton, Spencer, Marnay, Chris, Feng, Wei, Robinson, Matthew, & Mammoli, Andrea. Moore vs. Murphy: Tradeoffs between complexity and reliability in distributed energy system scheduling using software-as-a-service. United States. https://doi.org/10.1016/j.apenergy.2019.01.067
Dutton, Spencer, Marnay, Chris, Feng, Wei, Robinson, Matthew, and Mammoli, Andrea. Mon .
"Moore vs. Murphy: Tradeoffs between complexity and reliability in distributed energy system scheduling using software-as-a-service". United States. https://doi.org/10.1016/j.apenergy.2019.01.067. https://www.osti.gov/servlets/purl/1650052.
@article{osti_1650052,
title = {Moore vs. Murphy: Tradeoffs between complexity and reliability in distributed energy system scheduling using software-as-a-service},
author = {Dutton, Spencer and Marnay, Chris and Feng, Wei and Robinson, Matthew and Mammoli, Andrea},
abstractNote = {Software-based optimization of building control strategies, including scheduling, has the potential to improve the performance of existing complex heating, ventilation, and air conditioning (HVAC), storage, and other systems—especially if temporally variable energy production, such as solar thermal or photovoltaics, is included. If reductions in energy bills can be achieved using optimized control strategies that take advantage of cost-saving opportunities, such as time-of-use pricing, the additional bill savings can cover further efficiency investment costs. As computer processing becomes cheaper over time (Moore's Law), opportunities to perform complex control optimization become more abundant, and these can be performed remotely as software-as-a-service (SaaS). However, by “perfecting” our control strategies, we run an increased risk that when something unexpected happens (Murphy's Law), the consequences of failure are greater. This study used simulation to explore the potential benefits of HVAC schedule optimization, delivery, and implementation using a SaaS paradigm, at various levels of complexity. Implementing optimal schedules in a model of an efficient building's HVAC system, the study predicts energy cost savings of up to 10% compared to the naïve reference control strategy. Optimizing more system control variables increases the potential energy cost savings; however, these savings could be compromised by failures in communication inherent in delivering schedules via SaaS. The additional cost of energy resulting from the risk of increased demand charges generally increased with increased communication failure to a much larger extent than the risk of increased energy use charges. This work suggests that moderate improvements in performance, achieved at low cost by simple means, may be more effective than highly optimized schemes, which are more susceptible to failure due to their dependence on complex interactions between systems.},
doi = {10.1016/j.apenergy.2019.01.067},
journal = {Applied Energy},
number = ,
volume = 238,
place = {United States},
year = {Mon Jan 28 00:00:00 EST 2019},
month = {Mon Jan 28 00:00:00 EST 2019}
}
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