Optimal Trading of a Charging-Station Company in Auction Markets for Electricity

TL;DR

This paper develops a stochastic optimization framework for a charging-station company's electricity trading in auction markets, utilizing advanced machine learning and decomposition techniques to improve solution efficiency and decision-making.

Contribution

It introduces a novel stochastic MILP model for charging-station trading, employing GANs, random forests, and an improved decomposition algorithm to enhance computational performance.

Findings

The proposed ILSD algorithm reduces computational complexity.

GAN-based scenario clustering improves stochastic modeling.

Numerical results demonstrate effective trading strategies for Chargco.

Abstract

This paper addresses a charging-station company (Chargco) for electric and hydrogen vehicles. The optimal trading of the Chargco in day-ahead and intraday auction markets for electricity is modeled as a stochastic Mixed-Integer Quadratic Program (MIQP). We propose a series of linearization and reformulation techniques to reformulate the stochastic MIQP as a mixed-integer linear program (MILP). To model stochasticity, we utilize generative adversarial networks to cluster electricity market price scenarios. Additionally, a combination of random forests and linear regression is employed to model the relationship between Chargco electricity and hydrogen loads and their selling prices. Finally, we propose an Improved L-Shaped Decomposition (ILSD) algorithm to solve our stochastic MILP. Our ILSD algorithm not only addresses infeasibilities through an innovative approach but also incorporates warm starts, valid inequalities and multiple generation cuts, thereby reducing computational complexity. Numerical experiments illustrate the Chargco trading using our proposed stochastic MILP and its solution algorithm.