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.

本文提出了一种新的分布式能源聚合框架，其中包括多智能体架构和各种机制，用于有效管理和高效整合 Smart Grid 中的分布式能源，特别是当使用 CRPS 评分规则作为选择机制时，在与传统商业聚合器比较时，我们的框架导致参与者的平均支付增加。