Uncertainty-Aware Capacity Expansion for Real-World DER Deployment via End-to-End Network Integration

TL;DR

This paper presents a robust, end-to-end neural network integrated optimization framework for capacity expansion of distributed energy resources in unbalanced three-phase distribution networks, validated with real-world data.

Contribution

It introduces a novel two-stage robust optimization model combined with neural networks to explicitly handle model uncertainty in realistic unbalanced power systems.

Findings

Improved accuracy in capacity expansion planning for real-world grids.

Effective handling of model uncertainty with provable guarantees.

Validated framework demonstrates practical decision-making support.

Abstract

The deployment of distributed energy resource (DER) devices plays a critical role in distribution grids, offering multiple value streams, including decarbonization, provision of ancillary services, non-wire alternatives, and enhanced grid flexibility. However, existing research on capacity expansion suffers from two major limitations that undermine the realistic accuracy of the proposed models: (i) the lack of modeling of three-phase unbalanced AC distribution networks, and (ii) the absence of explicit treatment of model uncertainty. To address these challenges, we develop a two-stage robust optimization model that incorporates a 3-phase unbalanced power flow model for solving the capacity expansion problem. Furthermore, we integrate a predictive neural network with the optimization model in an end-to-end training framework to handle uncertain variables with provable guarantees. Finally, we validate the proposed framework using real-world power grid data collected from our partner distribution system operators. The experimental results demonstrate that our hybrid framework, which combines the strengths of optimization models and neural networks, provides tractable decision-making support for DER deployments in real-world scenarios.