The objective of this paper is to propose a methodology for designing a stand-alone hybrid PV/wind/diesel/battery minimizing the Levelized Cost of Energy (LCE) and the CO2 emission using genetic algorithm. The methodology developed was applied using solar radiation, temperature and wind speed data, collected on the site of Gandon, located in the northwestern of Senegal. Obtained results were presented as optimal Pareto front. The optimal number of devices, the Levelized Cost of Energy (LCE) and the CO2 emission were determined for each solution. An influence study of the size of diesel generator on the optimal configuration was carried

This paper presents a methodology to size and to optimize a stand-alone hybrid PV/Wind/Diesel/Battery bank minimizing the Levelized Cost of Energy (LCE) and the CO2 emission using a Multi-Objectives Genetic Algorithm approach. The main objective of this work is to study the influence of the load profile variation on the optimal configuration. The methodology developed was applied using solar radiation, temperature and wind speed collected on the site of Gandon located in the northwestern coast of Senegal. Results show that, for the all load profiles, as the LCE increases the CO2 emission decreases. For the all solutions, the LCE was lower for the load profile 3 than for the load profiles 1 and 2

The main objective of this paper is to propose a methodology to design and optimize a stand-alone hybrid PV/wind/diesel/battery minimizing the Levelized Cost of Energy (LCE) and the CO2 emission using a Multi-Objectives Genetic Algorithm approach. The methodology developed was applied using the solar radiation, temperature and the wind speed collected on the site of Potou located in the northwestern coast of Senegal. The LCE and the CO2 emission were computed for each solution and the results were presented as a Pareto front between LCE and the CO2 emission. These results show that as the LCE increases the CO2 emission decreases. For example, the solution A (left solution on the Pareto front) presents 2.05 €/kWh and 11.89 kgCO2/year, however the solution E (right solution on the Pareto front) shows 0.77 €/kWh and 10,839.55 kgCO2 /year. It was also noted that the only PV/battery or Wind/ battery was not an optimal configuration for this application on the site of Potou with the use of the load profile and the specifications of the used devices. For all solutions, the PV generator was more adapted to supply the energy demand than the wind turbines. Key words: Hybrid system, optimization, genetic algorithm, cost of energy, CO2 emission.

This paper deals with a methodology of sizing hybrid systems solar/wind/battery optimized by minimizing the annualized cost system (ACS) and the loss of power supply probability (LPSP) using multi-objective genetic algorithm. The developed methodology was applied using the hourly solar, temperature and the wind speed data collected for one year on the site of Potou located in the northwestern coast of Senegal. The annual average hourly load profile of a typical remote village located in the northwestern coast of Senegal which energy is of 94 kWh/day has been used. The obtained results show that the cost of the optimal configuration strongly depends on the loss of power supply probability (LPSP). For example, the cost of the optimal configuration decreases by 25 % when the loss of power supply probability (LPSP) grows to 1% from 0%.