Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
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.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1602182
- Report Number(s):
- NREL/PO-2C00-76083
- Country of Publication:
- United States
- Language:
- English
Similar Records
Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM: Preprint
A Comprehensive Scheduling Framework using SP-ADMM for Residential Demand Response with Weather and Consumer Uncertainties