Optimizing Industrial Energy Systems: A Multi-Objective Approach to Decarbonization and Cost Efficiency

The transition to a renewable energy (RE) based future necessitates transformative changes in the industrial sector's energy infrastructure. As industries strive to maintain economic viability while achieving decarbonization goals, there is a pressing need for innovative strategies that integrate renewable energy sources, enhance energy efficiency, and optimize energy procurement. This paper presents a comprehensive approach that leverages a combination of simulation and optimization methods to address the challenges of designing and operating complex future industrial energy systems. A nested optimization approach is proposed, which combines Mixed-Integer Linear Programming (MILP) for operational optimization with black-box optimization techniques. The approach enables exploration of solutions for optimal infrastructure investment as well as operational strategies, allowing companies to benchmark a wide range of infrastructure and contractual options. This provides these companies with the means to determine long-term, cost-effective pathways that minimize CO2 emissions, avoid malinvestments, and ensure the sustainable operation of energy systems. A case study involving a German waste processing company illustrates the practical application of our approach, highlighting how strategic decisions in asset dimensioning, energy flexibility utilization, and renewable integration can significantly impact both costs and emissions.