Advanced energy-management and sizing techniques for renewable microgrids with electric-vehicle integration: A review

Abstract

Rising worldwide energy consumption and environmental concerns have expedited the transition to hybrid renewable energy systems (HRES) and electric vehicles (EVs). EVs continue to be viewed as completely compliant passive batteries, which ignore owner behaviors. Owners may deny unloading for a number of reasons, such as cheap prices, long commutes outside of town, or battery lifespan issues. EMS are divided into three categories: rule-based EMS, optimization-based EMS, and learning-based EMS. Rule-based EMS uses deterministic control techniques, such as fuzzy logic and frequency decoupling, which makes it simple and computationally efficient but restricts its flexibility. Optimization-based EMS achieves worldwide effectiveness by using complex mathematical models such as predictive control and game theory, but it demands a significant amount of processing power. Each form of EMS has advantages and disadvantages, with rule-based EMS being less expensive and simpler to implement. The study provided and analyzed a complete literature evaluation of recently published papers by numerous HRES scholars. This study further investigates energy management techniques (EMS) for EVs and HRES, with an emphasis on demand-side management, economic policies, and techno-economic strategies for optimal grid interaction. Furthermore, the combination of V2G technology is presented as a bidirectional transmission of electricity solution that enables EVs to function as mobile energy storage units, thus contributing to grid stability as well as demand control. Future research paths stress the need for hybrid AI-powered algorithms and blockchain for safe energy transactions. The future possibilities of PV, wind, and other HESs are finally covered in this study, along with their control, power management, optimization, and ideal size.