Short-time fluctuations and their impact on waste-to-energy conceptual design optimized by multi-stage stochastic model

The paper focuses on the development of a computational tool for effective investment planning in the field of waste-to-energy. The expected return on investment is only achieved if the plant operates successfully in the future. Generally, the operation of any plant can be analysed at various levels of detail, which differ in the basic time step. We may distinguish short-term, mid-term and long-term operation planning. The construction period of any waste-to-energy plant, including legislative processes usually lasts 5 to 10 y. The following operation is planned for next 20 - 30 y. Such a long project duration requires a robust design with low sensitivity to future changes in the key parameters. So here, multi-stage optimization model, which proposes a conceptual design of the plant based on plant’s performance under different future scenarios can serve as an effective support tool. So future operation modelling represents an integral part of such a tool. The objective of the paper is to discuss a proper time interval in the context of the complex multi-stage model and the influence of different time steps. The proper length of the time step varies depending on the required accuracy of the model. This paper aims to determine the susceptibility of different models depending on the time step. Correction coefficients for a more accurate model, which take into account the fluctuation of the data on the shorter time intervals, are proposed. The paper reviews aspects, which substantially fluctuate and vary with time and thus their prediction for long periods of time is very difficult, e.g. heat demand or waste supply. They are described and classified and their influence on the future economy of the plant is analysed. Data analysis of these parameters over shorter periods of time can also help to include auxiliary equipment such as heat accumulators or reserve gas or coal- fired boilers into the investment planning phase. Given the scope of the model is to design a new project, which uses data on an annual basis. For a more realistic model outputs, daily fluctuations in many parameters should be mentioned as well. For this purpose system of correction coefficients is proposed where applicable.