A Novel Multiagent Flexibility Aggregation Framework

TL;DR

This paper introduces a multiagent DER aggregation framework for smart grids, utilizing local flexibility estimators and cooperative mechanisms to efficiently integrate diverse distributed energy resources, verified through PowerTAC simulations.

Contribution

It presents a novel multiagent architecture with innovative cooperative member selection mechanisms, including scoring rules and reinforcement learning, for efficient DER aggregation.

Findings

Improved integration of heterogeneous DERs into the grid.

Enhanced participant payments using the CRPS scoring rule.

Effective deployment of cooperative selection mechanisms.

Abstract

The increasing number of Distributed Energy Resources (DERs) in the emerging Smart Grid, has created an imminent need for intelligent multiagent frameworks able to utilize these assets efficiently. In this paper, we propose a novel DER aggregation framework, encompassing a multiagent architecture and various types of mechanisms for the effective management and efficient integration of DERs in the Grid. One critical component of our architecture is the Local Flexibility Estimators (LFEs) agents, which are key for offloading the Aggregator from serious or resource-intensive responsibilities -- such as addressing privacy concerns and predicting the accuracy of DER statements regarding their offered demand response services. The proposed framework allows the formation of efficient LFE cooperatives. To this end, we developed and deployed a variety of cooperative member selection mechanisms, including (a) scoring rules, and (b) (deep) reinforcement learning. We use data from the well-known PowerTAC simulator to systematically evaluate our framework. Our experiments verify its effectiveness for incorporating heterogeneous DERs into the Grid in an efficient manner. In particular, when using the well-known probabilistic prediction accuracy-incentivizing CRPS scoring rule as a selection mechanism, our framework results in increased average payments for participants, when compared with traditional commercial aggregators.