Publications
2016
Li, Xiwang; Wen, Jin; Malkawi, Ali
An operation optimization and decision framework for a building cluster with distributed energy systems Journal Article
In: Applied Energy, vol. 178, pp. 98-109, 2016, ISSN: 0306-2619.
Abstract | Links | BibTeX | Tags: Demand response, Multi-objective optimization, Particle swarm optimization, Smart building, Smart grid
@article{LI201698,
title = {An operation optimization and decision framework for a building cluster with distributed energy systems},
author = {Xiwang Li and Jin Wen and Ali Malkawi},
url = {https://www.sciencedirect.com/science/article/pii/S0306261916308054},
doi = {https://doi.org/10.1016/j.apenergy.2016.06.030},
issn = {0306-2619},
year = {2016},
date = {2016-01-01},
journal = {Applied Energy},
volume = {178},
pages = {98-109},
abstract = {Driven by the development of smart buildings and smart grids, numerous of research has focused on developing optimal operation strategies for smart buildings with the aims of reducing energy consumption and cost, as well as improving the grid reliability. Unfortunately, most of the studies from smart building perspective only target on a single building with elaborated energy forecasting models. Few of them addresses the effects of multiple buildings on power grid operation. On the other hand, a few studies from smart grid area focus on multiple buildings and their influence on power grid, they usually, however, use simplified linear energy forecasting models, which are hard to guarantee the findings reflecting the cases in real fields. As a result, this research proposes to bridge this research gap, through developing and validating high fidelity energy forecasting models for a building cluster with multiple buildings and distributed energy systems, as well as creating a collaborative operation framework to determining the optimal operation strategies of this building cluster. The operation framework utilizes multi-objective optimizations to determine the operation strategies: building temperature setpoints, energy storage charging and discharging schedules, etc., using particle swarm optimization. Pareto curves for energy cost saving and thermal comfort maintaining are also derived with different thermal comfort requirements. The results from this study show that the developed building cluster collaborative operation framework is able to reduce the energy cost by 12.1–58.3% under different electricity pricing plans and thermal comfort requirements.},
keywords = {Demand response, Multi-objective optimization, Particle swarm optimization, Smart building, Smart grid},
pubstate = {published},
tppubtype = {article}
}
Driven by the development of smart buildings and smart grids, numerous of research has focused on developing optimal operation strategies for smart buildings with the aims of reducing energy consumption and cost, as well as improving the grid reliability. Unfortunately, most of the studies from smart building perspective only target on a single building with elaborated energy forecasting models. Few of them addresses the effects of multiple buildings on power grid operation. On the other hand, a few studies from smart grid area focus on multiple buildings and their influence on power grid, they usually, however, use simplified linear energy forecasting models, which are hard to guarantee the findings reflecting the cases in real fields. As a result, this research proposes to bridge this research gap, through developing and validating high fidelity energy forecasting models for a building cluster with multiple buildings and distributed energy systems, as well as creating a collaborative operation framework to determining the optimal operation strategies of this building cluster. The operation framework utilizes multi-objective optimizations to determine the operation strategies: building temperature setpoints, energy storage charging and discharging schedules, etc., using particle swarm optimization. Pareto curves for energy cost saving and thermal comfort maintaining are also derived with different thermal comfort requirements. The results from this study show that the developed building cluster collaborative operation framework is able to reduce the energy cost by 12.1–58.3% under different electricity pricing plans and thermal comfort requirements.