A Simulation-Based Experiential Learning Framework for Smart Pricing and Hybrid Storage Optimization in PV-Integrated EV Charging Systems

Abstract

The rapid development of photovoltaic (PV)-based electric vehicle (EV) charging systems with hybrid energy storage systems presents complicated operational and economic issues that are hard to communicate using conventional pedagogical strategies. In this research, an experiential learning framework that is introduced where students are able to learn about smart pricing and multi-objective optimization in EV charging systems. The framework proposed represents a PV-powered EV charger station in combination with a hybrid energy storage system (HESS) comprising of lithium-ion batteries and supercapacitors. A virtual learning environment is integrated with a dynamic electricity pricing system and a multi-objective optimization system based on Multi-Objective Oppositional Swordfish Optimization (MOOSFO). PV variability, temperature effects and EV charging demand are the parameters of the system that students interact with and observe how they affect the grid stability, cost and system performance. The framework is created as a virtual laboratory module whereby the learners will be able to experiment on the real-life problems like reduction of peak load, cost optimization, and energy management. Educational assessment shows better student knowledge of the integration of renewable energy, smart grid economics, and methods of optimization, as well as better analytical and problem-solving abilities. This research can be used in engineering education through offering a complex energy management problem to a student-centric interactive learning environment, which facilitates outcome-based education and interdisciplinary learning.