TY  - EJOU
AU  - M’hamed, Rebhi 
AU  - Youcef, Himri 
AU  - Bousmaha, Bouchiba 
AU  - Mouaadh, Yaichi 

TI  - Energy Management of Photovoltaic Plant for Smart Street Lighting System
T2  - Energy Engineering

PY  - 2025
VL  - 122
IS  - 12
SN  - 1546-0118

AB  - Currently, most conventional street lighting systems use a constant light mode throughout the entire night, from sunset to sunrise, which results in high energy consumption and maintenance costs. Furthermore, scientific research predicts that energy consumption for street lighting will increase in the coming years due to growing demand and rising electricity prices. The dimming strategy is a current trend and a key concept in smart street lighting systems. It involves turning on the road lights only when a vehicle or pedestrian is detected; otherwise, the control system reduces the light intensity of the lamps. Power control is generally implemented using artificial intelligence algorithms such as fuzzy logic, artificial neural networks, or swarm intelligence to manage different events. In our project, the dimming strategy was utilized to reduce costs and energy consumption by at least 48%. This research proposes a standalone photovoltaic plant (SPP) to power a smart street lighting system, which can replace individual solar street lights in a small neighborhood in southwestern Algeria. This design offers advantages such as lower costs and the option to add additional loads a DC pump, a domestic power supply, or technical services to operate the photovoltaic system during the day after charging the storage batteries, especially when there is monthly energy surplus. The energy surplus analyzed in our project ranges from 804 W in January to 4 kW in June, generated by the entire PV installation. To demonstrate the feasibility and reliability of this system, we studied the implementation of the standalone photovoltaic plant, energy management, and economic analysis using the Particle Swarm Optimization technique under MATLAB software.
KW  - Plant; smart street lighting; energy management; fuzzy logic control; PSO

DO  - 10.32604/ee.2025.070806