Deep learning-based demand response for short-term operation of renewable-based microgrids

This paper introduces a cutting-edge deep learning-based model aimed at enhancing the short-term performance of microgrids by simultaneously minimizing operational costs and emissions in the presence of distributed energy resources. The primary focus of this research is to harness the potential of demand response programs (DRPs), which actively engage a diverse range of consumers to mitigate uncertainties associated with renewable energy sources (RES). To facilitate an effective demand response, this study presents a novel incentive-based payment strategy packaged as a pricing offer. This approach incentivizes consumers to actively participate in DRPs, thereby contributing to overall microgrid optimization. The research conducts a comprehensive comparative analysis by evaluating the operational costs and emissions under scenarios with and without the integration of DRPs. The problem is formulated as a challenging mixed-integer nonlinear programming problem, demanding a robust optimization technique for resolution. In this regard, the multi-objective particle swarm optimization algorithm is employed to efficiently address this intricate problem. To showcase the efficacy and proficiency of the proposed methodology, a real-world smart microgrid case study is chosen as a representative example. The obtained results demonstrate that the integration of deep learning-based demand response with the incentive-based pricing offer leads to significant improvements in microgrid performance, emphasizing its potential to revolutionize sustainable and cost-effective energy management in modern power systems. Key numerical results demonstrate the efficacy of our approach. In the case study, the implementation of our demand response strategy results in a cost reduction of 12.5% and a decrease in carbon emissions of 14.3% compared to baseline scenarios without DR integration. Furthermore, the optimization model shows a notable increase in RES utilization by 22.7%, which significantly reduces reliance on fossil fuel-based generation.