A swarm intelligence and deep learning strategy for wind power and energy storage scheduling in smart grid

In today's world, rising energy demands are a significant challenge, and the smart grid emerges as a solution for sustainable energy management. An essential view of advancing the Smart Grid (SG) capabilities is the collaborative scheduling of Wind Power Generation (WPG) and energy storage. It plays a significant role in elevating SG efficiency, reliability, and environmental sustainability. This kind of strategic planning is essential to increase coordination between WPG and flexible deployment of Energy Storage Systems (ESS). Efficient SG functions will be maintained, and energy sources can be regulated with demand variations. Putting an emphasis on assumptions and empirical data is vital in conventional techniques. When it comes to the continuously shifting environment of SG and RE resources, traditional approaches aren't highly reliable or adaptable. The present article uses a hybrid model that integrates Deep Reinforcement Learning (DRL) and Particle Swarm Optimization (PSO) to address those drawbacks. The primary purpose of it is to help with the joint scheduling of WP and ESS. This technique is what permits DRL to reach selections rapidly in convoluted, ever-changing environments. The proposed approach, when combined with PSO's effectiveness for variable optimization, will result in improved scheduling findings. The framework additionally exploits the finest use of ESS, but it also effectively addresses the challenging task of integrating dynamic WP with the SG. Reliable and cost-effective supply is ensured by the system's design. The accuracy, stability, and versatility of the suggested approach to the dynamic features of Wind Energy (WE) and storage management are incomparable to traditional approaches. The findings indicate the method's actual validity and its significance for improving SG functions. Applying state-of-the-art statistical techniques for holistic optimization of RE resources and storage systems is emphasized by the framework. Owing to minimizing Energy Consumption (EC) and lowering greenhouse gas emissions, this study provides a significant step towards achieving the goal of effective and eco-friendly SG functions. • To address these constraints, this paper uses a hybrid model which combines Deep Reinforcement Learning with Particle Swarm Optimization. • It optimizes the use of Enegry Storage System, and also the model efficiently navigates the challenges of integrating variable Wind Power with the Smart Grid. • This proposed approach demonstrates exemplary performance in terms of accuracy, stability, and adaptability to the dynamic property of Wind Energy and storage management compared to existing methods.