A stochastic evaluation of investments in combined cooling, heat, and power systems

CCHP (Combined Cooling, Heat, and Power) systems, by their nature, work under uncertainties during their economic life. This study aims to use stochastic methods to forecast whether or not a CCHP system with long-term uncertainties will be feasible. To understand how uncertain parameters that affect profitability unfold over time, the system was analyzed with four different simulation methods, the results of which were compared: the parametric method, the Monte Carlo method, the historical trend method, and the scenario-based method. The parametric method gave the widest range of probabilities for the objective function, which provided an unclear prediction about the possible results of the projected years. The Monte Carlo method gave the highest mean value, while the historical trend method gave probabilities in a narrower range. The scenario-based method, which offered a broader prediction than the historical trend method, can be considered to be the most appropriate method to adopt given the comparisons and contrasts it provides. The methods proposed in this study provide decision-makers with a broader point of view to evaluate the amortization of CCHP systems.