Implementation of a New Solar-Powered Street Lighting System: Optimization and Technical-Economic Analysis Using Artificial Intelligence

Abstract

Public lighting system represents a key role in the energy transition process, considering the high electricity consumptions related to this sector. The integration of renewables could be suitable for this application and many solutions of solar-based lamps for street lighting are spreading. In this research work, a specific application of a PV-integrated lighting system was installed in the center of Italy along a footpath and monitored for several months, both in terms of electricity parameters and lighting behavior. It is equipped with monocrystalline photovoltaic cells, a lithium-based battery, and a LED lamp. The measured data allow the development of an optimization algorithm in Python program for the correct management of the solar streetlamp and the forecasting of electricity consumptions for different boundary conditions (e.g. Italian geographical position). The energy taken from grid in a year is very low with respect to a traditional lamp not powered by renewables: only 43-46% in central-northern regions and 35% in south region. Data are also used to study the possible substitution of all the traditional lamppost of the walkway with the novel proposed system. A technical-economic analysis is carried out to analyze the effectiveness of this solution not only in terms of electricity consumptions reduction, but also costs savings. The suitability of the investment and the payback time depend on Italian national price of electricity and initial cost of the solar lamp. The results can be applied to similar case studies in Italy to save electricity consumptions and reduce CO2 emissions.

Keywords

• Artificial intelligence
• electricity consumptions
• LED
• optimization algorithm
• photovoltaic (PV)
• storage