Cost-effective decarbonization of industrial energy systems through hybrid renewable integration: A long-term MILP optimization

Limiting global warming to 1.5 °C requires immediate action. Although allowable global greenhouse gas emissions are well-defined, translating these targets into actionable strategies at the company-level remains challenging. This study addresses critical gaps in understanding transition cost implications and the influence of science-based emission reduction targets on industrial energy systems. We present a novel mixed-integer linear programming (MILP) model that optimizes industrial energy systems until 2050 under climate mitigation constraints. Three scenarios were analyzed: a fossil-fuel reference case, an optimized system without emission constraints, and an optimized system adhering to science-based emission reduction targets. Stochastic programming ensures robust analysis. Three case studies from China, Germany, and the USA illustrated the modeling framework's application. Results indicated that, within the selected case studies and underlying assumptions, optimized hybrid renewable energy systems can achieve substantial cost and emission reductions compared with conventional reference systems. The model demonstrates the feasibility of integrating renewable energy to balance sustainability goals with financial performance. Comparative analysis across international contexts underscores how macroeconomic and regulatory factors shape the economic viability of hybrid renewable energy systems. The findings illustrate the potential of industrial energy system optimization to reduce emissions under favorable techno-economic and regulatory conditions, while highlighting the strong dependence of outcomes on location-specific assumptions.