A polygeneration system is an energy system capable of providing multiple energy outputs to meet local demands, by application of high process integration. In this paper, optimal configuration and capacity of a polygeneration system for an indoor swimming pool building is determined by application of a method based on TRNSYS simulation and GenOpt optimization software. Based on the applicability, a superstructure of the polygeneration system is integrated, consisting of the following polygeneration modules: an internal combustion engine cogeneration module, a vapor compression chiller, and adsorption chiller, a ground source heat pump, flat plate solar thermal collectors, photovoltaic collectors, and heat storage. Annual behavior of energy loads of the public swimming pool building during a typical meteorological year and the polygeneration system are modeled and simulated using TRNSYS software, whereas techno-economic optimization is performed by GenOpt optimization. The results indicated the optimal configuration of the polygeneration system for the modelled energy demands, as well as the optimal capacity of the polygeneration modules, thus defining the optimal capacity of the polygeneration system for the energy demands of the public swimming pool building.

Representation of probabilistic technique for evaluation of thermal power system reliability is the main subject of this paper. The system of thermal power plant under study consists of three subsystems and the reliability assessment is based on a sixteen-year failure database. By applying the mathematical theory of reliability to exploitation research data and using complex two-parameter Weibull distribution, the theoretical reliability functions of specified system have been determined. Obtained probabilistic laws of failure occurrence have confirmed a hypothesis that the distribution of the observed random variable fully describes behaviour of such a system in terms of reliability. Shown results make possible to acquire a better knowledge of current state of the system, as well as a more accurate estimation of its behavior during future exploitation. Final benefit is opportunity for potential improvement of complex system maintenance policies aimed at the reduction of unexpected failure occurrences.