Development of a Hybrid Modeling Framework for the Optimal Operation of Microgrids

This paper presents a study on the development of a hybrid modeling framework for the optimal operation of microgrids based on renewable energy resources. Accurate prediction of both renewable energy generation and consumer demand is crucial for the efficient management of renewable energy-based microgrids. The proposed hybrid modeling framework integrates a high-resolution physical model for forecasting renewable energy sources (solar and wind), a data-driven model for renewable energy prediction, and a hybrid forecasting model that combines both physical and data-driven approaches. Additionally, the framework incorporates a consumer demand model to further optimize grid operations. In this research, a hybrid prediction model was developed to enhance the accuracy of solar and wind power generation forecasts. The hybrid model leverages the complementary strengths of both physical and data-driven models. When historical data are insufficient, the physical model generates synthetic training data to improve the learning process of the data-driven model. Moreover, in cases where the data-driven model exhibits limited predictive accuracy due to insufficient training data, the physical model provides reliable forecasts, ensuring robust performance under various conditions. When sufficient real-world data are available, the Weighted Inverse Error Weighting (WIEW) strategy is applied to dynamically integrate the outputs of both models, significantly enhancing forecasting accuracy. Furthermore, a digital twin platform was implemented to operate and simulate each model, and a validation system for the digital twin platform and models was established using Software-in-the-Loop Simulation (SILS) and Power Hardware-in-the-Loop Simulation (PHILS) techniques. This study focuses on the development and validation of a hybrid model designed to improve the accuracy of solar and wind power generation forecasts for renewable energy microgrids.