Access to energy has always played a significant role in economic, social, and human development. However, nowadays 1.3 billion people still do not have access to electricity. The majority of these communities live in rural areas of underdeveloped countries. To face this issue, decentralized PV systems are suitable solutions for household electrification and water pumping. In addition, with the recent drop of PV panel’s and batteries cost, more sophisticated systems that integrate power management control strategies appear.
To support this implementation, different tools and programs helping a precise and optimal sizing of this system were made available. PV SOL, PV SYST, HOMER or iHOGA can be cited as example of prediction programs. Nonetheless, in spite of the level of sophistication of these different programs, a free access program allowing an accurate, precise and optimal design of PV-battery systems is still needed.
This thesis presents a program that uses genetic algorithm for the optimal sizing of PV-battery systems. The program was developed in Python. It simulates the behaviour of PV-battery systems installed in any location where meteorological data is available. The optimal number and the type of PV modules, batteries and inverters are provided as a result. In addition, an evolutive, adaptable, alterable data bank of the different components of the system is readily available.
In this thesis, a house, a school, floors lamps and a health care center for the remote community of ELDORET in Kenya are analysed as a case study. The optimal PV-Battery systems designed by the program were compared to the result of a classical sizing method and other software like iHOGA and HOMER. The validation process revealed a maximum gap of 12% between the program and iHOGA. A gap that can be attributed to a modelling difference.
The results were found to be in fair agreement with other predictions and therefore it is believe that the novel, free and adaptable program meets the need of remote communities when it comes to design optimal PV solar systems
