/Demand Side Energy Management via Multiagent Coordination in Consumer Cooperatives

Demand Side Energy Management via Multiagent Coordination in Consumer Cooperatives

Andreas Veit, Ying Xu, Ronghuo Zheng, Nilanjan Chakraborty and Katia Sycara
Tech. Report, CMU-RI-TR-13-27, Robotics Institute, Carnegie Mellon University, October, 2013

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Abstract

A key challenge in creating a sustainable and energy-efficient society is to make consumer demand adaptive to the supply of energy, especially to renewable supply. In this paper, we propose a partially-centralized organization of consumers (or agents), namely, a consumer cooperative for purchasing electricity from the market. We propose a novel multiagent coordination algorithm, to shape the energy consumption of the cooperative. In the cooperative, a central coordinator buys the electricity for the whole group and consumers make their own consumption decisions, based on their private consumption constraints and preferences. To coordinate individual consumers under incomplete information, we propose an iterative algorithm, in which a virtual price signal is sent by the coordinator to induce consumers to shift their demands when required. This algorithm provably converges to the central optimal solution and minimizes the electric energy cost of the cooperative. Additionally, we perform simulations based on real world consumption data to characterize (a) the convergence properties of our algorithm and (b) understand the effect of different parameters that characterize the electricity consumption profile on the potential cost reduction through coordination by our algorithm. The results show that as the participants’ flexibility of shifting their demands increases, cost reduction increases. We also observe that the cost reduction is not very sensitive to the variation in consumption patterns of the consumers (e.g., whether the consumers use more electricity during the evening or during the day). Finally, our simulations indicate that the convergence time of the algorithm scales linearly with the agent population size.

BibTeX Reference
@techreport{Veit-2013-7778,
author = {Andreas Veit and Ying Xu and Ronghuo Zheng and Nilanjan Chakraborty and Katia Sycara},
title = {Demand Side Energy Management via Multiagent Coordination in Consumer Cooperatives},
year = {2013},
month = {October},
institution = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-13-27},
keywords = {automation for energy management; computational sustainability; distributed AI; multiagent coordination; distributed optimization;},
}
2017-09-13T10:39:15+00:00