A novel intelligent optimal control methodology for energy balancing of microgrids with renewable energy and storage batteries

A price-based demand response (DR) program is essential for maintaining energy balance in a smart power grid (SPG). Given the uncertainty and stochastic nature of renewable energy sources (RESs) and loads, dynamic pricing strategies are required to minimize instant energy shortage risks and ensure energy balancing. This study introduces an optimal adaptive control methodology based on an elastic demand control mechanism using dynamic pricing to address energy balancing in renewable smart microgrids. The proposed optimal adaptive controller, referred to as the ant colony optimization algorithm tuned super-twisting sliding mode controller (ACO-STSMC), effectively handles system nonlinearities and enhances the response of the system to uncertainties and variability of RESs and loads. The ACO-STSMC regulates energy price signals, manages the net load demand, and responds to RESs generation fluctuations, ultimately achieving and maintaining an energy balance in renewable energy smart microgrids. The system exhibits a minimal mismatch between generation and demand, avoids instant demand overshots, and maintains low-energy pricing signal volatility. The findings demonstrate that the developed ACO-STSMC outperforms benchmark controllers such as PSO-PI, PSO-FOPI, PSO-STSMC, ACO-PI, and ACO-FOPI in terms of energy balancing in renewable-energy smart microgrids. The results also confirm that the elastic DR based on dynamic energy pricing with the ACO-STSMC can effectively track the generation of renewable energy smart microgrids.