Economic optimization of solar systems in uncertain economic conditions using the Monte Carlo method

Solar energy is an environmentally sustainable energy source as it is clean and inexhaustible. Solar systems are very common and cost-effective, thus, can be used for many home applications. In this paper, a new method is presented to optimize solar systems economically, regarding to energy cost fluctuations. In spite of conventional analyses, in which the inflation is considered constant, this method considers a probability distribution for inflation. The probability function of the life cycle solar saving (LCS) is then estimated by the Monte Carlo method. The expected value of LCS is used as the objective function. The standard domestic solar system is considered as a benchmark to show capability of the method. Three most important parameters of a solar water heating system are considered as manipulated variables. The optimal value of each parameter was found based on the proposed procedure, and employing the particle swarm optimization (PSO) algorithm as the optimization method. The results show that the collector area of 17 m2, collector angle of 42o, and storage tank of 100 l/m2 maximize LCS to the mean value of 9930 USD for the selected case study. Also, the probability distribution of LCS shows that the mean value of the payback time is 4.1138 years with standard deviation of 1.3182.