Operation optimization of Combined Heat and Power microgrid in buildings consider renewable energy, electric vehicles and hydrogen fuel

This paper introduces a forward-thinking framework that integrates renewable energy, Electric Vehicles (EVs), and hydrogen within Combined Heat and Power (CHP) microgrids (MGs) for effective building energy management. By utilizing Particle Swarm Optimization (PSO) to find the optimal solution and incorporating Chance-Constrained Programming (CCP) to handle uncertainties in renewable energy generation and EV loads, this framework addresses the complexities of modern energy systems. The study employs Monte Carlo (MC) to simulate the EV load profile, applies K-means clustering to categorize load and renewable generation patterns, and uses a Sigmoid function-based model for Real-Time Pricing (RTP). The combination of PSO and CCP is used to optimize the system’s operating strategy. This evaluates the system’s economic benefits and impact on carbon emissions by analyzing different scenarios, such as weekdays versus weekends and various weather conditions (sunny, cloudy, rainy). The results show that due to the high price of hydrogen, it is currently costly to replace hydrogen completely. However, this integrated approach not only improves energy efficiency and reduces carbon footprint but also ensures system reliability under uncertain conditions, contributing to broader environmental sustainability.