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

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.

💡 Research Summary

The paper tackles the day‑ahead offering problem faced by distributed energy resource (DER) aggregators, where price‑quantity pairs must be submitted before the realization of both market price and DER generation uncertainties. The authors formulate the problem as a two‑stage adaptive robust stochastic optimization (2S‑ARSO) model. In the first stage, a set of price‑quantity offers (x) is decided “here‑and‑now” under a collection of stochastic price scenarios (\omega) sampled via a Markov process, with scenario weights (\rho_\omega). The second stage occurs after DER generation uncertainty (\xi) is revealed; the aggregator then dispatches DERs (decision vector (y)) subject to device limits, network power‑flow constraints, and a polyhedral uncertainty set (U). The overall objective is

\