Chance constrained optimization of energy intensive production as beneficial power units

TL;DR

This paper develops a chance-constrained optimization approach for managing an energy-intensive industrial system with uncertain renewable energy and demand, balancing efficiency and reliability.

Contribution

It introduces a novel risk-aware decision-making framework using chance constraints and a modified cost function for efficient computation in complex energy systems.

Findings

Effective trade-off between efficiency and reliability demonstrated

Method reduces computational effort compared to traditional approaches

Validated approach in a realistic industrial case study

Abstract

We study linear policy approximations for the risk-conscious operation of an industrial energy system with uncertain wind power, significant and variable electricity demand, and high thermal output, as found in a modern foundry. The system incorporates thermal storage and operates under rolling forecasts, leading to a sequential decision-making framework. To address uncertainty in key parameters, we formulate chance-constrained optimization problems that limit the probability of critical constraint violations, such as unmet demand requirements or the exceedance of system boundaries. To reduce computational effort, we replace direct uncertainty handling with a parameter-modified cost function that approximates the underlying risk structure. We validate our method through a numerical case study, demonstrating the trade-offs between operational efficiency and reliability in a stochastic environment.