Francesco Ghionda

Industrial refineries operate as complex multi-energy systems where electricity, process fuels, steam, and multiple energy carriers interact across tightly coupled processes. This work presents a data-driven optimisation framework implemented in PyPSA to analyse operational dispatch and evaluate alternative decarbonisation technologies in a refinery energy system. The model integrates detailed historical operational data and hourly efficiency profiles to replicate assets’ behaviour and explore different technology configurations over a reference year. The objective function includes annualised capital expenditures and operating costs, including maintenance and energy carrier costs, enabling a consistent comparison of candidate technologies within the operational optimisation. The analysis provides insights into the potential contributions of existing assets’ flexibility and sector coupling to the identification of cost-efficient refinery decarbonisation strategies. Key performance indicators include total system cost, technology utilisation factors, and the resulting configuration of the energy system. The proposed full-year, hourly resolved LP approach offers a data-driven tool for exploring operational improvements and evaluating technology options under realistic operating conditions in industrial energy systems. The identified cost-optimal solutions for the multi-energy sub-system object of the study achieve annual emissions reductions of up to 7.2% of around 600 ktonCO_2eq/y while maintaining annual system costs comparable to those of the reference case. Across all scenarios, flexible scheduling of the existing generation assets consistently emerges as a key driver of operational savings, highlighting the potential of industrial multi-energy systems to provide market-responsive demand-side flexibility through the adaptation of energy dispatch in response to electricity market signals.

In the contemporary landscape, roughly one-fourth of worldwide carbon dioxide emissions stem from industrial energy usage. In the industrial sector, improving the efficient and flexible coupling among different energy demands (electricity, heating, and cooling) and exploiting the integration of Renewable Energy Sources (RESs) and waste heat can lead to a drastic reduction in CO2 emissions, these are also the goals of the EU founded Horizon Europe FLEXIndustries project. This study aims to establish a cost-optimized decarbonization strategy for an energy-intensive industry, focusing on an Italian pharmaceutical company. It delves into the exploration of potential pathways and diverse energy mix configurations. The approach undertaken involves coupling a customized energy system simulation framework, specifically designed for the industrial site, with a Multi-Objective Evolutionary Algorithm (MOEA). The study, conducted with a focus on the year 2024, involves a comparative analysis of three distinct scenarios. Within the intricate and challenging constraints of the industrial demo site, 13 technologies were investigated. The outcomes of each scenario identify a set of 500 Pareto optimal solutions, obtained through 40,000 simulations. These results shed light on the compelling potential of hybrid solutions, showcasing the feasibility of achieving substantial decarbonization with only moderate increases in costs. The availability of land for RES technologies, along with the existence of a biomass supply chain in the region, emerge as pivotal determinants.