Emission-aware energy management and renewable forecasting in microgrids using machine learning and hippopotamus optimizer

The global transition toward sustainable energy is accelerating due to rising environmental concerns and the need to minimize fossil fuel dependency. Microgrids (MGs), which integrate distributed energy resources (DERs) such as solar and wind, provide a flexible and sustainable solution. However, renewable generation variability and dynamic operating conditions challenge energy balance and cost efficiency. This paper presents a machine learning (ML)-based energy management system (EMS) designed to improve forecasting accuracy, optimize scheduling, and enhance MG reliability in both grid-connected and islanded modes. The proposed EMS employs advanced ML models trained on historical data, weather conditions, and real-time parameters to accurately forecast solar and wind power generation. These forecasts feed into a dynamic optimization framework solved using the modified hippopotamus optimizer (MHO), which determines the optimal DER operation and grid interaction. Additionally, a real-time energy trading mechanism is incorporated to enable cost-effective energy exchange decisions based on market prices, load demand, and renewable availability. By maximizing renewable utilization and ensuring seamless operational transitions, the framework reduces reliance on external power sources, lowers operational costs and emissions, and enhances system resilience. Case studies demonstrate the system’s ability to maintain energy balance under varying operating conditions, highlighting its potential for advancing sustainable MG management.