Embedding Reservoirs in Industrial Models to Exploit their Flexibility

TL;DR

This paper introduces a reservoir-based modeling methodology for industrial energy processes, enabling quick, interpretable, and flexible optimization to enhance operational responsiveness and cost savings amid energy price volatility.

Contribution

The paper presents a novel reservoir modeling approach that simplifies the representation of industrial processes for flexible energy management and easy integration into optimization models.

Findings

Significant cost savings in case studies.

Models are quick to develop and solve.

Approach is accessible to nontechnical users.

Abstract

In the context of energy transition, industrial plants that heavily rely on electricity face more and more price volatility. To continue operating in these conditions, the directors become continually more willing to increase their flexibility, i.e. their ability to react to price fluctuations. This work proposes an intuitive methodology to mathematically model electro-intensive processes in order to assess their flexibility potential. To this end, we introduce the notion of reservoir, a storage of either material or energy, that allows models based on this paradigm to have interpretations close to the physics of the processes. The design of the reservoir methodology has three distinct goals: (i) to be easy and quick to build by an energy-sector consultant; (ii) to be effortlessly converted into mixed-integer linear or nonlinear programs; (iii) to be straightforward to understand by nontechnical people, thanks to their graphic nature. We apply this methodology to two industrial case studies, namely an induction furnace (linear model) and an industrial cooling installation (nonlinear model), where we can achieve significant cost savings. In both cases, the models can be quickly written using our method and solved by appropriate solver technologies.