Performance Enhancement of Solar Panels Using Adaptive Velocity-Particle Swarm Optimization (AVPSO) Algorithm for Charging Station as an Effort for Energy Security

Abstract

The growth of public awareness of the environment is directly proportional to the development of the use of electric cars. Electric cars operate by consuming electrical energy from battery storage, which must be recharged periodically at the charging station. Solar panels are one source of energy that is environmentally friendly and has the potential to be applied to charging stations. The use of solar panels causes the charging station to no longer depend on conventional electricity networks, which the majority of it still use fossil fuel power plants. Solar panels have a problem that is not optimal electrical power output so that it has the potential to affect the charging parameters of the battery charging station. Adaptive Velocity-Particle Swarm Optimization (AV-PSO) is an artificial intelligence type MPPT optimization algorithm that can solve the problem of solar panel power optimization. This study also uses the Coulomb Counting method as a battery capacity estimator. The results showed that the average sensor accuracy is more than 91% with a DC-DC SEPIC converter which has an efficiency of 69.54%. In general, the proposed charging station system has been proven capable to enhance the energy security by optimizing the output power of solar panels up to 22.30% more than using conventional systems.

Evaluasi Hasil Kinerja Tekno-Ekonomi Pembangkit Listrik Tenaga Surya (PLTS) Pada Bangunan Perguruan Tinggi di Indonesia

“…PLTS memanfaatkan modul photovoltaic (PV) yang mengubah energi matahari menjadi energi listrik (Luthfansyah et al, 2020;Magribi et al, n.d.; 498 BRILIANT: Jurnal Riset dan Konseptual Volume 8 Nomor 2, Mei 2023 Mohammad et al, 2020). Sistem PLTS membutuhkan sebuah inverter untuk mengubah arus DC yang keluar dari sel surya menjadi arus AC sehingga dapat membantu memenuhi suplai energi dalam sebuah gedung (Hankins, 2010;Hart, 2011;RASHID, 2001).…”

“…Profil beban penggunaan energi listrik merupakan faktor yang paling penting untuk ditentukan sebelum merancang dan merencanakan sistem pembangkit listrik (Istiqomah et al, 2022;Mohammad et al, 2020;Prasetyono et al, 2020). Untuk mendefinisikan kebutuhan tersebut, persamaan (2) dapat digunakan; 499 P load−hours = P load x T load…”