A novel optimal planning methodology of an autonomous Photovoltaic/Wind/Battery hybrid power system by minimizing economic, energetic and environmental objectives.

Generated electricity from renewable sources such as solar panels and wind turbines is considered, until now, as clean and nonpolluting energy. However, these systems are responsible and directly tied to greenhouse gases (GHG) emissions when considering their different steps of manufacturing, transportation, operation, maintenance, and decommissioning. This paper describes a new sizing optimization methodology of a stand-alone hybrid Photovoltaic/Wind/Battery system, minimizing the Levelized Cost of Energy (LCOE), the Loss of Power Supply Probability (LPSP), and the Equivalent Carbon Dioxide (CO₂-eq) life cycle emission. An elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to solve this constrained nonlinear multi-objective optimization problem taking the expected photovoltaic peak power, wind turbine output power, batteries' energy capacity as decision variables and embodied carbon dioxide per unit of electricity consumed as a constraint. Different combinations of PV/Wind/Battery systems are optimized and compared to identify the cost-effective, reliable, and environmentally friendly optimal architecture. Finally, a sensitivity analysis is applied to the proposed algorithm; only the batteries' state of charge (SOC) setpoint is considered to examine its impact on the system sizing procedure. The proposed algorithm is used for optimal planning of a stand-alone hybrid renewable power system expected to be installed in Borj Cedria Science and Technology Park (latitude = 36.71ºN, longitude = 10.42ºE). Simulation results proved the effectiveness of the proposed method to achieve economic, energetic, and environmental objectives. [ABSTRACT FROM AUTHOR]