Technical and economic optimization of energy investments wih evolutionary algorithms

Ivan Lorencin (2010) Technical and economic optimization of energy investments wih evolutionary algorithms. MSc thesis.

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Abstract

Energy is an area of human activities that contains production, trading, transmission, distribution, supply and consumption of energy. Deregulation and restructuring of energy sector have brought tectonic movements. Therefore, the preparation of information for decision making is gaining the importance. In the current situation power systems is becoming more than ever depended on timely and quality decisions. As soon as resources become insufficient, the need for timely and quality information for decision-making comes to the forefront. Therefore, the energy sector needs modern IT tools and systems based on high-quality and highly accurate mathematical models, thereby enabling timely and optimal decisions to ensure stability of the energy system as well as reliability, adequacy and continuity of energy supply. In the thesis we therefore analyze the techno-economical decision and information support that is used to generate information for decision making on investments in the energy sector. We also analyzed the characteristics of multiobjective decision-making in the energy sector, the criteria specific to energy, the problems and possible solutions to optimize the investments. In addition to general observations about optimization, we examined the dominance relation and Pareto optimality, evolutionary algorithms, differential evolution and multiobjective optimization using evolutionary algorithms in the energy sector. Using a mathematical model, we tested the applicability of multiobjective optimization techniques with evolutionary algorithms in case of a decision to invest in an alternative system of power supply. An alternative energy supply system based on a combination of photovoltaic systems, diesel generator, which provides the security of supply, and battery pack to ensure the maximum use of both energy sources, was optimized according to two criteria, the cost of investment, operation and maintenance of the system in entire life-cycle, and reliability of electricity supply which is measured by the percentage of nonsupplied energy.

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