A Novel Probabilistic Approach to Optimize Stand-Alone Hybrid Wind-Photovoltaic Renewable Energy System

Abstract

In the present study, a novel probabilistic approach is proposed to optimize a stand-alone hybrid wind-photovoltaic renewable energy system installed in the South China Sea. In detail, the probability distribution of power generated from a hybrid wind-photovoltaic system is estimated based on the probabilistic descriptions of wind and solar energy resources in the South China Sea. In addition, the present study proposed a battery level coefficient model to calculate the battery capacity of the hybrid system. As the battery level coefficient implies the expected power deficit in a specific continuous duration, it reflects the reliability of the battery system and, hence, the performance of the system under the power deficit condition. Given the probabilistic models estimated the stability of power generations, a genetic algorithm is applied to optimize the sizes of the system components (the installed capacities of wind turbines and photovoltaic modules and the load) when the levelized cost of energy (LCOE) is used as the indicator. The optimization verifies that the proposed probabilistic approach provides reasonable estimates of the power generation of a hybrid system in an optimization process. In addition, the comparisons with the conventional deterministic approach implies that the widely used loss of power supply probability (LPSP) could be interpreted, in a statistical sense, as the expected duration of power deficit. More importantly, the LPSP is connected to the localized sea condition, and therefore, this stability assessment criterion should be specified according to the location where the system is installed.