Swarm intelligence–based distributed stochastic model predictive control for transactive operation of networked building clusters
Abstract
To maximize potential energy and cost savings, networked clusters of local buildings are formed for energy transactions. Both centralized and distributed decision approaches were explored in past decades as a way to enable efficient transactive operations. However, online distributed stochastic transactive operation has been overlooked in the literature. To bridge gaps in the research, a bi-level distributed stochastic model predictive control framework was proposed to study the transactive operations of building clusters where a system-level agent is employed to coordinate multiple building agents at the subsystem level. The energy transaction is optimized by a marginal price-based particle swarm optimizer at the system level. Given the energy transaction decisions, each building can independently solve a scenario-based two-stage stochastic model to optimally dispatch the electricity and ancillary services for optimal energy performance. The effectiveness of the proposed framework and coordination algorithm are demonstrated in deterministic, stochastic, and online operations and compared to centralized decisions using several sets of experiments. Additionally, the proposed approach can realize autonomous transactive operation and be extended to community-level building clusters in a plug-and-play way.
- Authors:
-
[1];
[2]
- Univ. of Illinois at Chicago, Chicago, IL (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Univ. of Illinois at Chicago, Chicago, IL (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1542247
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Energy and Buildings
- Additional Journal Information:
- Journal Volume: 198; Journal Issue: C; Journal ID: ISSN 0378-7788
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 29 ENERGY PLANNING, POLICY, AND ECONOMY; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Building clusters; Smart grid; Transactive operation; Marginal price; Distributed stochastic model predictive control
Citation Formats
Chen, Yang, and Hu, Mengqi. Swarm intelligence–based distributed stochastic model predictive control for transactive operation of networked building clusters. United States: N. p., 2019.
Web. doi:10.1016/j.enbuild.2019.06.010.
Chen, Yang, & Hu, Mengqi. Swarm intelligence–based distributed stochastic model predictive control for transactive operation of networked building clusters. United States. https://doi.org/10.1016/j.enbuild.2019.06.010
Chen, Yang, and Hu, Mengqi. Wed .
"Swarm intelligence–based distributed stochastic model predictive control for transactive operation of networked building clusters". United States. https://doi.org/10.1016/j.enbuild.2019.06.010. https://www.osti.gov/servlets/purl/1542247.
@article{osti_1542247,
title = {Swarm intelligence–based distributed stochastic model predictive control for transactive operation of networked building clusters},
author = {Chen, Yang and Hu, Mengqi},
abstractNote = {To maximize potential energy and cost savings, networked clusters of local buildings are formed for energy transactions. Both centralized and distributed decision approaches were explored in past decades as a way to enable efficient transactive operations. However, online distributed stochastic transactive operation has been overlooked in the literature. To bridge gaps in the research, a bi-level distributed stochastic model predictive control framework was proposed to study the transactive operations of building clusters where a system-level agent is employed to coordinate multiple building agents at the subsystem level. The energy transaction is optimized by a marginal price-based particle swarm optimizer at the system level. Given the energy transaction decisions, each building can independently solve a scenario-based two-stage stochastic model to optimally dispatch the electricity and ancillary services for optimal energy performance. The effectiveness of the proposed framework and coordination algorithm are demonstrated in deterministic, stochastic, and online operations and compared to centralized decisions using several sets of experiments. Additionally, the proposed approach can realize autonomous transactive operation and be extended to community-level building clusters in a plug-and-play way.},
doi = {10.1016/j.enbuild.2019.06.010},
journal = {Energy and Buildings},
number = C,
volume = 198,
place = {United States},
year = {Wed Jun 05 00:00:00 EDT 2019},
month = {Wed Jun 05 00:00:00 EDT 2019}
}
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