Work

Feature-based scenario clustering and selective degrees of freedom reduction for two-stage stochastic optimization of Power-to-Methanol

Year: 2025

Type: article

Abstract: Power-to-X processes can be operated flexibly to exploit electricity price fluctuations or renewable power availability, reducing production costs. Ideally, the process design should be optimized simultaneously with the scheduling or operation. To address the uncertainty in electricity prices or availability, several scheduling scenarios can be simultaneously considered in two-stage stochastic optimization problems. However, these problems, especially when nonlinear, become challenging to solve when the scenario number and scheduling horizon increase. To tackle this challenge, we combine feature-based scenario clustering with reducing the degrees of freedom (DoF) related to the scheduling variables. The latter maintains time step chronology memory, thus ensuring optimal solution feasibility even if storage is present. For a Power-to-Methanol process, we show that DoF reduction should not be applied to all scheduling variables when there is intermediate storage, since it strongly correlates the variables, thus affecting the optimal process design. In a sensitivity analysis concerning the scenario cluster number, we show that scenario clustering based on interpretable features can represent the original space and estimate the objective function with a few scenarios, performing similarly to k-means and k-medoids clustering regarding the objective function. However, despite similar objective values, different optimal designs are identified for different scenario cluster numbers. • Simultaneous process design and scheduling optimization under uncertainty. • Feature-based scenario clustering via k-medoids. • Central moments and principal components as features. • Selective degrees of freedom reduction maintaining time step chronology. • Optimal designs can vary significantly despite leading to similar objective values

Source: Process Safety and Environmental Protection

DOI: 10.1016/j.cherd.2025.09.046

Authors: Simone Mucci, Dominik Bongartz

Institutions: RWTH Aachen University, KU Leuven

Language: English

Topic: Process Optimization and Integration

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: hybrid

Estimated APC paid: $3,500