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.

翻译：在新兴智能电网中，分布式能源资源（DER）数量的持续增长，使得能够高效利用这些资产的多智能体框架成为迫切需求。本文提出了一种新颖的分布式能源聚合框架，该框架包含多智能体架构及多种机制，用于实现电网中分布式能源的有效管理与高效集成。该架构的关键组件是本地灵活性估计器（LFE）智能体，其核心作用在于减轻聚合器在隐私保护、分布式能源报价准确性预测等重负荷任务上的负担。所提框架支持构建高效的LFE合作体系。为此，我们开发并部署了多种合作成员选择机制，包括（a）评分规则与（b）（深度）强化学习。利用知名PowerTAC模拟器数据进行系统性评估，实验验证了该框架在高效整合异构分布式能源方面的有效性。值得注意的是，当采用广泛应用的概率预测精度激励型CRPS评分规则作为选择机制时，与传统商业聚合器相比，本框架显著提升了参与者的平均收益。