/Multi-agent coordination for demand management with energy generation and storage

Multi-agent coordination for demand management with energy generation and storage

Ronghuo Zheng, Ying Xu, Nilanjan Chakraborty, Michael Lewis and Katia Sycara
Conference Paper, Conference on Group Decision and Negotiation (GDN), June, 2015

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

In this paper, we focus on demand side management in consumer collectives with community owned renewable energy generation and storage facilities for effective integration of renewable energy with the existing fossil fuelbased power supply system. The collective buys energy as a group through a central coordinator who also decides about the storage and usage of renewable energy. produced by the collective. Our objective is to design coordination algorithms to minimize the cost of electricity consumption of the consumer collective while allowing the consumers to make their own consumption decisions based on their private consumption constraints and preferences. Minimizing the cost is not only of interest to the consumers but is also socially desirable because it reduces the consumption at times of peak demand (since differential pricing mechanisms like time-of-use pricing is usually used by electricity companies to discourage consumption at times of peak demand). We develop an iterative coordination algorithm in which the coordinator makes the storage decision and shapes the demands of the consumers by designing a virtual price signal for the agents. We prove that our algorithm converges, and it achieves the optimal solution under realistic conditions We also present simulation results based on real world consumption data to quantify the performance of our algorithm.

BibTeX Reference
@conference{Zheng-2015-5988,
author = {Ronghuo Zheng and Ying Xu and Nilanjan Chakraborty and Michael Lewis and Katia Sycara},
title = {Multi-agent coordination for demand management with energy generation and storage},
booktitle = {Conference on Group Decision and Negotiation (GDN)},
year = {2015},
month = {June},
}
2017-09-13T10:38:38+00:00