Dynamic pricing scheme for energy balancing in microgrid using an intelligent system.

The random nature of both renewable energy supply and demand patterns poses a significant challenge to energy balancing in microgrids. This unpredictability impacts key power system parameters, reflecting real-time imbalances between supply and demand. Such fluctuations can affect the performance of dynamic pricing, which plays a crucial role in optimizing real-time energy usage within a microgrid environment. Traditional pricing models fail to adapt to these variations, resulting in inefficiencies such as overconsumption during peak hours and underutilization during low-demand periods. These limitations hinder efforts to maintain grid stability, cost-effectiveness, and resource sustainability. To address these challenges, an intelligent pricing approach is essential for effectively responding to fluctuating system conditions. Therefore, this research proposes a dynamic pricing scheme based on real-time variations in supply, demand, and the state of charge (SOC) of a battery. An exponential pricing model is developed using key factors such as the supply–demand ratio and SOC. To enable price forecasting, training data is generated using this model under a 5 kW load during peak and off-peak periods. The data is then used to train and test machine learning algorithms, including Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANN). Among these, ANN demonstrated the highest accuracy and effectiveness in predicting energy prices. To implement and validate the proposed AI-based pricing approach; a MATLAB-based hybrid microgrid system has been developed. This system consists of a 2 kW solar source, a 240 V, 100 Ah lithium-ion battery, and a 3 kW grid connection. The results show that this approach enables faster, more accurate and more efficient pricing, thereby facilitating improved energy balance and benefiting both suppliers and consumers.