Performance Enhancement of Solar Panels Using Adaptive Velocity-Particle Swarm Optimization (AVPSO) Algorithm for Charging Station as an Effort for Energy Security
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.
Akila, A., Akila, E., Akila, S., Anu, K., & Elzalet, J. (2019). Charging station for e-vehicle using solar with IOT. 2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019, 785–791. Retrieved from: https://doi.org/10.1109/ICACCS.2019.8728391
Arun, P. S., & Mohanrajan, S. R. (2019). Effect of partial shading on vehicle integrated PV system. Proceedings of the 3rd International Conference on Electronics and Communication and Aerospace Technology, ICECA 2019, 1262–1267. Retrieved from: https://doi.org/10.1109/ICECA.2019.8821888
Gong, L., Cao, W., & Zhao, J. (2017). Load modeling method for EV charging stations based on trip chain. 2017 IEEE Conference on Energy Internet and Energy System Integration, EI2 2017 - Proceedings, 2018-January, 1–5. Retrieved from: https://doi.org/10.1109/EI2.2017.8245572
Gurfude, S. S., & Kulkarni, P. S. (2020). Energy yield of tracking type floating solar PV plant. 2019 National Power Electronics Conference (NPEC), 1–6. Retrieved from: https://doi.org/10.1109/npec47332.2019.9034846
Hankins, M. (2010). Stand-alone solar electric systems: The earthscan expert handbook for planning, design and installation. Earthscan.
Hart, D. W. (2011). Power electronic. Tata McGraw-Hill Education.
He, L., & Guo, D. (2019). An improved coulomb counting approach based on numerical iteration for SOC estimation with real-time error correction ability. IEEE Access, 7, 74274–74282. Retrieved from: https://doi.org/10.1109/ACCESS.2019.2921105
Ilayaraja, S., & Narmadha, T. V. (2016). Modeling of an e-vehicle charging station using DC-DC self-lift SEPIC converter. 2016 2nd International Conference on Science Technology Engineering and Management, ICONSTEM 2016, 526–531. Retrieved from: https://doi.org/10.1109/ICONSTEM.2016.7560949
IRENA. (2019). Climate change and renewable energy: National policies and the role of communities, cities and regions (Report to the G20 climate sustainability working group (CSWG)). Retrieved from: www.irena.org
Kumar, S. (2017). Ant colony optimization for less power consumption and fast charging of battery in solar grid system. 244–249.
Moeini, A., & Wang, S. (2018). Design of fast charging technique for electrical vehicle charging stations with grid-tied cascaded H-bridge multilevel converters. Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC, 2018-March(1540118), 3583–3590. Retrieved from: https://doi.org/10.1109/APEC.2018.8341621
Mohammad, L., Prasetyono, E., & Murdianto, F. D. (2019). Performance evaluation of ACO-MPPT and constant voltage method for street lighting charging system. Proceedings - 2019 International Seminar on Application for Technology of Information and Communication: Industry 4.0: Retrospect, Prospect, and Challenges, ISemantic 2019. Retrieved from: https://doi.org/10.1109/ISEMANTIC.2019.8884303
P, D., & Kumar, K. R. (2017). MPPT based control of sepic converter using firefly algorithm for solar PV system under partial shaded conditions. 1–8.
Pavkovic, D., Lobrovic, M., Hrgetic, M., Komljenovic, A., & Smetko, V. (2014). Battery current and voltage control system design with charging application. 2014 IEEE Conference on Control Applications, CCA 2014, 1133–1138. Retrieved from: https://doi.org/10.1109/CCA.2014.6981481
Pragallapati, N., Sen, T., & Agarwal, V. (2017). Adaptive velocity PSO for global maximum power control of a PV array under nonuniform irradiation conditions. IEEE Journal of Photovoltaics, 7(2), 624–639. Retrieved from: https://doi.org/10.1109/JPHOTOV.2016.2629844
Suyanto, S., Mohammad, L., Setiadi, I. C., & Roekmono, R. (2019). Analysis and evaluation performance of MPPT algorithms: Perturb observe (PO), firefly, and flower pollination (FPA) in smart microgrid solar panel systems. 2019 International Conference on Technologies and Policies in Electric Power and Energy, TPEPE 2019. Retrieved from: https://doi.org/10.1109/IEEECONF48524.2019.9102532
Tairov, S., & Stevanatto, L. C. (2011). The novel method for estimating VRLA battery state of charge. Proceedings - 2011 IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2011, 211–215. Retrieved from: https://doi.org/10.1109/CERMA.2011.40