Robust Optimization of the Energy Concept of an Industrial Plant w.r.t. Uncertain Energy Costs and Environmental Conditions

Kurzfassung

Due to the required CO2 reduction to achieve global climate goals, the political, social, and economic pressure for decarbonizing industry increases rapidly. An essential step towards this goal is the replacement of fossil fuels by renewable energy sources making changes in technology for generating electricity, steam, and process heat an inevitable requirement. This raises the question about the combination of energy supply technologies (such as photovoltaic systems, wind turbines or solar thermal systems) with energy conversion units (such as heat pumps or electric boilers) to cover the demand of an industrial process at minimal cost. Optimization methods are increasingly used for the selection and dimensioning of such units. These methods can systematically and efficiently determine optimal energy concepts according to the multicriterial requirements of a specific industrial process. The results of such deterministic optimizations depend heavily on assumptions of environmental conditions such as solar radiation and wind speed, the cost of purchasing and selling revenue of electric power, local infrastructure, and the demand of the industrial plant. Changes in these assumptions can result in significantly different costs or lead to an energy system, which is eventually incapable of covering the process demand. In this paper, the modelling of the required components is briefly described and a robust optimization approach is presented taking uncertainties of the assumptions into account during the optimization process. After a robust optimization for an industrial process is performed, the results are compared and discussed to those of a deterministic optimization. It can be shown, that the robust optimization allows to find energy concepts with less sensitivity and higher reliability when uncertainties are considered.