Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM

Xiangyu Zhang, Dave Biagioni, Peter Graf, Jennifer King

Research output: Contribution to conferencePaperpeer-review

2 Scopus Citations

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.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - Feb 2020
Event2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020 - Washington, United States
Duration: 17 Feb 202020 Feb 2020

Conference

Conference2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020
Country/TerritoryUnited States
CityWashington
Period17/02/2020/02/20

Bibliographical note

See NREL/CP-2C00-76096 for preprint

NREL Publication Number

  • NREL/CP-2C00-77370

Keywords

  • ADMM
  • Aggregator
  • Demand response
  • Load scheduling

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