Technology pathways for industrial cogeneration systems: Optimal investment planning considering long-term trends. (15th October 2022)

• Record Type: Journal Article
• Authors: Hoettecke, Lukas; Schuetz, Thomas; Thiem, Sebastian; Niessen, Stefan
• Is Part Of: Applied energy. Volume 324(2022)
• Publication Date: 2022-10-15
• Subjects: Energy systems -- Industrial cogeneration -- Multi-year planning -- Mixed-integer linear programming; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.119675
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: Integrated design approach for energy supply systems in three industrial sites. Multi-year optimization to integrate long-term trends. Sequential and strategic planning approaches enable adaptive decision making. Discussion of rising carbon prices and cooperate decarbonization targets. Abstract: Cogeneration plants appear highly attractive for industrial companies as production sites typically combine demands for electricity, heating, and cooling. However, gas-fired cogeneration might become economically less attractive with further increasing carbon prices and companies' commitment to decarbonization targets. This paper proposes a methodology to determine technology pathways from an optimization model. Technology pathways describe the evolution of an energy supply system over a multi-decade planning horizon. Therefore, projected long-term trends are integrated into the decision-making process for energy supply infrastructure. Three investment strategies are compared regarding their accuracy and computational complexity. The methodology is illustrated for industrial sites from dairy, pharmaceutical and automotive industry in Southern Germany. Results highlight the advantages of technology pathways compared to optimization approaches based on a representative year. A strategic planning approach identifies optimal decarbonization pathways with economic saving potentials of up to 20 %. For most of the considered case studies, a sequential decision approach with a rolling horizon heuristic yields similar results in 89 % less solving time.