This paper presents a futuristic framework combining renewable energy, EVs, and hydrogen in the broader context of Combined Heat and Power (CHP) microgrids in buildings. While challenging, integrating these technologies is becoming increasingly practical with advances in control techniques, energy storage, and smart grid technologies. The study employs popular computational algorithms to navigate the complexities of this integration. It utilizes Monte Carlo (MC) methods for simulating EV load profiles, K-means clustering for categorizing load and renewable energy generation patterns, and a Sigmoid function-based model for formulating Real-Time Pricing (RTP). A microgrid system, integrating EVs and renewable energy sources, is established. Particle Swarm Optimization (PSO) is applied for operational optimization. The study conducts comparative analyses for 6 scenarios, including working days versus rest days and varied weather conditions like sunny, cloudy, and rainy days. It evaluates the system’s efficiency, its economic advantages, and its impact on carbon emissions reduction. This integrated optimal operation approach elevates energy efficiency and significantly contributes to reducing carbon footprints, thereby bolstering environmental sustainability. Keywords: Renewable energy, Monte Carlo simulation, K-means, RTP, PSO, V2G & G2V, CHP, Microgrid operation.