MIND - Modelling in Industry for Increased Energy Efficiency and Reduced Greenhouse Gas Emissions
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
In industry, energy efficiency reduces system cost and emissions to the environment. Energy audits are carried out in industry to identify measures that would increase energy efficiency. However, the usual case is that low-cost measures are implemented while capital intensive measures receive less attention possibly due to, example, inadequate information available to study risks involved.
Decisions support tools have been identified as a means of supporting complex production related investment decision. The aim of this paper is to investigate profitability and potential global CO2 emission reduction of energy conversion investments in a small energy intensive industry by using an optimisation method as a decision support tool.
The investments are evaluated using consistent future energy market scenarios with interdependent parameters. An optimisation model is developed with reMIND optimisation tool which is used to optimise the system cost of each scenario. The reduction in system cost and global CO2 emissions of the new investments and results from sensitivity analysis are evaluated to determine the optimal investment solution.
In the report, it is established that optimisation methods provide a structured means of studying the risk involved in capital intensive investments. The optimisation results show that investment in a small-scale steam turbine combined heat and power production is a profitable and robust investment. The net reduction of global CO2 emission is substantial compared with the reference system. Furthermore, it is shown that biofuel policies alone may not make cost intensive biofuel investments attractive, further reduction in investment cost is required.
The energy savings and global CO2 emission reductions achieved in this study can play an important role in achieving the aims of the European Union to reduce greenhouse gas emissions by 20% and save 20 % of energy by the year 2020.
Place, publisher, year, edition, pages
2010. , 51 p.
Optimisation, Energy efficiency, MIND modelling; Investment decision support
IdentifiersURN: urn:nbn:se:liu:diva-58042ISRN: LIU-IEI-TEK-A—10/00809—SEOAI: oai:DiVA.org:liu-58042DiVA: diva2:331727
2010-05-31, A36, A-huset, Linkoping University, 10:15 (English)
Thollander, Patrik, Assistant Professor
Söderström, Mats, Professor