Optimal Composition of Electricity Generation for Power Plants of Different Technologies in Austria

Abstract

This thesis addresses the optimal economic deployment of various technologies of power plants under current regulatory and market conditions. Using a Mixed-Integer Nonlinear Programming (MINLP) approach, a mathematical optimization model is developed to maximize the profit from electricity generation while incorporating real-world constraints and parameters. Although the model optimizes solely for profit, the results clearly demonstrate how political instruments such as CO2 certificate pricing, emission taxes, and subsidies for low-emission technologies can effectively guide the energy transition by influencing economic decisions. The work begins with a characterization of different power plant technologies and a discussion of relevant environmentalpolicy mechanisms. The core of the work involves the formulation and solution of the optimization problem using realistic input data. The results are analyzed to reveal both economic and environmental implications of various policy scenarios. The findings highlight how regulatory frameworks can indirectly steer energy system decarbonization through market-based incentives. This research contributes to the understanding of how profitability-oriented models can serve as tools for aligning economic and environmental objectives in energy policy.