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Title: Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM: Preprint

Abstract

Demand response (DR) serves an important role in improving the efficiency and stability of power systems. In recent years, with advances in communication and smart device technologies, many aggregators have emerged to facilitate end customer participation in DR programs. These aggregators, equipped with customized optimal control algorithms, are capable of providing various grid services. Among them is load scheduling during DR events, namely following a load signal provided by the utility company while minimizing overall customer discomfort. However, as the number of aggregators keeps increasing, it becomes challenging for utility companies to conduct load scheduling for multiple aggregators and generate reference signals for each of them. This paper proposes an optimization framework using hierarchical alternating direction method of multipliers (H-ADMM) to optimally generate load following signals for multiple aggregators. Under this framework, utility and multiple aggregators work in a cooperative manner, aiming at minimizing an overall system cost from different levels of the power system hierarchy, while protecting user privacy. A case study has been conducted in a system with multiple aggregators, based on control of HVAC loads. Experimental results validate the effectiveness of the proposed algorithm.

Authors:
 [1];  [1];  [1]; ORCiD logo [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1605697
Report Number(s):
NREL/CP-2C00-76096
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2020 IEEE Conference on Innovative Smart Grid Technologies (IEEE ISGT), 17-20 February 2020, Washington, D.C.
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; demand response; aggregator; load scheduling; ADMM

Citation Formats

Zhang, Xiangyu, Biagioni, David J, Graf, Peter A, and King, Jennifer R. Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM: Preprint. United States: N. p., 2020. Web.
Zhang, Xiangyu, Biagioni, David J, Graf, Peter A, & King, Jennifer R. Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM: Preprint. United States.
Zhang, Xiangyu, Biagioni, David J, Graf, Peter A, and King, Jennifer R. Mon . "Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM: Preprint". United States. https://www.osti.gov/servlets/purl/1605697.
@article{osti_1605697,
title = {Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM: Preprint},
author = {Zhang, Xiangyu and Biagioni, David J and Graf, Peter A and King, Jennifer R},
abstractNote = {Demand response (DR) serves an important role in improving the efficiency and stability of power systems. In recent years, with advances in communication and smart device technologies, many aggregators have emerged to facilitate end customer participation in DR programs. These aggregators, equipped with customized optimal control algorithms, are capable of providing various grid services. Among them is load scheduling during DR events, namely following a load signal provided by the utility company while minimizing overall customer discomfort. However, as the number of aggregators keeps increasing, it becomes challenging for utility companies to conduct load scheduling for multiple aggregators and generate reference signals for each of them. This paper proposes an optimization framework using hierarchical alternating direction method of multipliers (H-ADMM) to optimally generate load following signals for multiple aggregators. Under this framework, utility and multiple aggregators work in a cooperative manner, aiming at minimizing an overall system cost from different levels of the power system hierarchy, while protecting user privacy. A case study has been conducted in a system with multiple aggregators, based on control of HVAC loads. Experimental results validate the effectiveness of the proposed algorithm.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {3}
}

Conference:
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