DER Day-Ahead Offering: A Neural Network Column-and-Constraint Generation Approach

TL;DR

This paper introduces a neural network-accelerated column-and-constraint generation approach to solve the day-ahead energy market offering problem for DER aggregators, significantly improving computational speed while maintaining solution quality.

Contribution

It develops a novel neural network-based method to efficiently solve a complex two-stage robust stochastic optimization model for DER offering strategies.

Findings

Method achieves up to 100x faster solutions than Gurobi.

Method is up to 33x faster than classical approaches.

Numerical results show high-quality solutions for large networks.

Abstract

In the day-ahead energy market, the offering strategy of distributed energy resource (DER) aggregators must be submitted before the uncertainty realization in the form of price-quantity pairs. This work addresses the day-ahead offering problem through a two-stage adaptive robust stochastic optimization model, wherein the first-stage price-quantity pairs and second-stage operational commitment decisions are made before and after DER uncertainty is realized, respectively. Uncertainty in day-ahead price is addressed using a stochastic programming-based approach, while uncertainty of DER generation is handled through robust optimization. To address the max-min structure of the second-stage problem, a neural network-accelerated column-and-constraint generation method is developed. A dedicated neural network is trained to approximate the value function, while optimality is maintained by the design of the network architecture. Numerical studies indicate that the proposed method yields high-quality solutions and is up to 100 times faster than Gurobi and 33 times faster than classical column-and-constraint generation on the same 1028-node synthetic distribution network.