Modeling Framework and Validation of a Smart Grid and Demand Response System for Wind Power Integration
Abstract
Electricity generation from wind power and other renewable energy sources is increasing, and their variability introduces new challenges to the power system. The emergence of smart grid technologies in recent years has seen a paradigm shift in redefining the electrical system of the future, in which controlled response of the demand side is used to balance fluctuations and intermittencies from the generation side. This paper presents a modeling framework for an integrated electricity system where loads become an additional resource. The agent-based model represents a smart grid power system integrating generators, transmission, distribution, loads and market. The model incorporates generator and load controllers, allowing suppliers and demanders to bid into a Real-Time Pricing (RTP) electricity market. The modeling framework is applied to represent a physical demonstration project conducted on the Olympic Peninsula, Washington, USA, and validation simulations are performed using actual dynamic data. Wind power is then introduced into the power generation mix illustrating the potential of demand response to mitigate the impact of wind power variability, primarily through thermostatically controlled loads. The results also indicate that effective implementation of Demand Response (DR) to assist integration of variable renewable energy resources requires a diversity of loads to ensure functionality ofmore »
- Authors:
- Publication Date:
- Research Org.:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1123229
- Report Number(s):
- PNNL-SA-93446
TE1201000
- DOE Contract Number:
- AC05-76RL01830
- Resource Type:
- Journal Article
- Journal Name:
- Applied Energy, 113:199-207
- Additional Journal Information:
- Journal Name: Applied Energy, 113:199-207
- Country of Publication:
- United States
- Language:
- English
- Subject:
- GridLAB-D; Demand Response; Olympic Peninsula; Transactive
Citation Formats
Broeer, Torsten, Fuller, Jason C., Tuffner, Francis K., Chassin, David P., and Djilali, Ned. Modeling Framework and Validation of a Smart Grid and Demand Response System for Wind Power Integration. United States: N. p., 2014.
Web. doi:10.1016/j.apenergy.2013.06.058.
Broeer, Torsten, Fuller, Jason C., Tuffner, Francis K., Chassin, David P., & Djilali, Ned. Modeling Framework and Validation of a Smart Grid and Demand Response System for Wind Power Integration. United States. https://doi.org/10.1016/j.apenergy.2013.06.058
Broeer, Torsten, Fuller, Jason C., Tuffner, Francis K., Chassin, David P., and Djilali, Ned. 2014.
"Modeling Framework and Validation of a Smart Grid and Demand Response System for Wind Power Integration". United States. https://doi.org/10.1016/j.apenergy.2013.06.058.
@article{osti_1123229,
title = {Modeling Framework and Validation of a Smart Grid and Demand Response System for Wind Power Integration},
author = {Broeer, Torsten and Fuller, Jason C. and Tuffner, Francis K. and Chassin, David P. and Djilali, Ned},
abstractNote = {Electricity generation from wind power and other renewable energy sources is increasing, and their variability introduces new challenges to the power system. The emergence of smart grid technologies in recent years has seen a paradigm shift in redefining the electrical system of the future, in which controlled response of the demand side is used to balance fluctuations and intermittencies from the generation side. This paper presents a modeling framework for an integrated electricity system where loads become an additional resource. The agent-based model represents a smart grid power system integrating generators, transmission, distribution, loads and market. The model incorporates generator and load controllers, allowing suppliers and demanders to bid into a Real-Time Pricing (RTP) electricity market. The modeling framework is applied to represent a physical demonstration project conducted on the Olympic Peninsula, Washington, USA, and validation simulations are performed using actual dynamic data. Wind power is then introduced into the power generation mix illustrating the potential of demand response to mitigate the impact of wind power variability, primarily through thermostatically controlled loads. The results also indicate that effective implementation of Demand Response (DR) to assist integration of variable renewable energy resources requires a diversity of loads to ensure functionality of the overall system.},
doi = {10.1016/j.apenergy.2013.06.058},
url = {https://www.osti.gov/biblio/1123229},
journal = {Applied Energy, 113:199-207},
number = ,
volume = ,
place = {United States},
year = {Fri Jan 31 00:00:00 EST 2014},
month = {Fri Jan 31 00:00:00 EST 2014}
}