Data-Driven Pricing Strategy for Demand-Side Resource Aggregators

Journal Article · Mon Jan 01 04:00:00 EST 2018 · IEEE Transactions on Smart Grid

DOI:https://doi.org/10.1109/tsg.2016.2544939· OSTI ID:1465139

Xu, Zhiwei; Deng, Tianhu; Hu, Zechun; Song, Yonghua; Wang, Jianhui

We consider a utility who seeks to coordinate the energy consumption of multiple demand-side flexible resource aggregators. For the purpose of privacy protection, the utility has no access to the detailed information of loads of resource aggregators. Instead, we assume that the utility can directly observe each aggregator's aggregate energy consumption outcomes. Furthermore, the utility can leverage resource aggregator energy consumption via time-varying electricity price profiles. Based on inverse optimization technique, we propose an estimation method for the utility to infer the energy requirement information of aggregators. Subsequently, we design a data-driven pricing scheme to help the utility achieve system-level control objectives (e.g., minimizing peak demand) by combining hybrid particle swarm optimizer with mutation algorithm and an iterative algorithm. Case studies have demonstrated the effectiveness of the proposed approach against two benchmark pricing strategies-a flat-rate scheme and a time-of-use scheme.

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: National Natural Science Foundation of China
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1465139
• Journal Information: IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 1 Vol. 9; ISSN 1949-3053
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

data-driven optimization
demand response
dynamic pricing
inverse optimization
particle swarm optimization (PSO)