Uncertainty analysis of energy and economic performances of hybrid solar photovoltaic and combined cooling, heating, and power (CCHP + PV) systems using a Monte-Carlo method

This study examines the impacts of uncertainties in energy demands and solar resources on the energy and economic performances of hybrid solar photovoltaic and combined cooling, heating and power (CCHP + PV) systems with variations in PV penetration levels. This study investigates two models: a deterministic and stochastic model. The deterministic model uses hourly demands of the U.S. Department of Energy (DOE) reference large office building in San Francisco, CA and solar irradiance profiles in the Typical Meteorological Year (TMY) data as the independent variables. The stochastic model accounts for uncertainties in these independent variables using a Monte-Carlo method. The results show that regardless of PV penetration levels, the uncertainties in building energy demands and solar irradiance marginally influence the energy performance of CCHP + PV systems; however, they can notably increase annual operating costs up to $75,000 per year (13%). The annual cost increase is mainly attributed to a significant increase in demand charges (up to $79,000 per year). The demand charges tend to increase with higher uncertainties in the peak demand. The results suggest that in cases of the demand charge being responsible for a large portion in electricity bills (i.e., demand tariffs), a deterministic model tends to underestimate operating costs of CCHP + PV systems or other analogous distributed energy systems compared to a stochastic model. The errors with the deterministic model can become more extreme when demand charges outweigh energy charges.