A Spatio-Temporal Graph Learning Approach to Real-Time Economic Dispatch with Multi-Transmission-Node DER Aggregation

TL;DR

This work addresses the scalability challenge of real-time economic dispatch (RTED) with large-scale multi-node DER participation mandated by FERC Order 2222. It introduces a spatio-temporal graph convolutional network (ST-GCN) to predict dynamic distribution factors (DFs) that map distributed DER outputs to nodal injections across multiple transmission nodes, paired with an iterative crucial constraints identification (ICCI) method to prune the transmission-security constraints during RTED. The approach achieves near-optimal operational costs on large-scale 118-, 2383-, and 3012-bus systems while substantially reducing computation time, demonstrating strong scalability and practical viability for real-time markets. This framework enables efficient participation of DER aggregators (DERAs) under current market paradigms and maintains transmission feasibility, offering a principled path toward robust, scalable DER integration in wholesale electricity markets.

Abstract

The integration of distributed energy resources (DERs) into wholesale electricity markets, as mandated by FERC Order 2222, imposes new challenges on system operations. To remain consistent with existing market structures, regional transmission organizations (RTOs) have advanced the aggregation of transmission-node-level DERs (T-DERs), where a nodal virtual power plant (VPP) represents the mapping of all distribution-level DERs to their respective transmission nodes. This paper develops a real-time economic dispatch (RTED) framework that enables multi-transmission-node DER aggregation while addressing computational efficiency. To this end, we introduce a spatio-temporal graph convolutional network (ST-GCN) for adaptive prediction of distribution factors (DFs), thereby capturing the dynamic influence of individual T-DERs across the transmission system. Furthermore, an iterative constraint identification strategy is incorporated to alleviate transmission security constraints without compromising system reliability. Together, these innovations accelerate the market clearing process and support the effective participation of T-DER aggregators under current market paradigms. The proposed approach is validated on large-scale test systems, including modified 118-, 2383-, and 3012-bus networks under a rolling RTED setting with real demand data. Numerical results demonstrate significant improvements in reducing operational costs and maintaining transmission network feasibility, underscoring the scalability and practicality of the proposed framework.

Figures (8)

• Figure 1: Illustration of multi-transmission-node T-DER aggregation.
• Figure 2: The timeline of RTED and DERA's self-dispatch.
• Figure 3: Graph-based representation of system physical profiles
• Figure 4: Architecture of the proposed PI-GCN framework
• Figure 5: Timeline of the proposed enhanced DF-based RTED.