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Dhiraj Ramesh Kshirsagar,
Rahul R. Bibave,
- Student, Department of Electrical Engineering, Sanjivani College of Engineering, Kopargaon, Maharashtra, India
- Lecturer, Department of Electrical Engineering, Sanjivani College of Engineering, Kopargaon, Maharashtra, India
Abstract
The fast growth of Distributed Energy Resources (DERs) like solar photovoltaics, wind power, and energy storage devices requires enhanced optimization methods to manage energy efficiently and stabilize operations in contemporary smart grids. A significant challenge is the dynamic optimization of the energy storage systems (ESS) and the distribution of the energy among DERs in conditions of uncertainty of loads and generation. The conventional control and optimization methods generally find it difficult to handle nonlinearities, real-time responsiveness, and computational complexity. This article suggests a new hybrid intelligent optimization approach that couples the Sine Whale Optimization Algorithm (SWOA) with a Modified Spiking Neural Network (MSNN) for improved energy storage maximization and DER optimization. SWOA, an upgraded version of the classical Whale Optimization Algorithm (WOA), includes sine-based movement principles for improved convergence speed and exploration-exploitation trade-off during optimization. In the meantime, MSNN exploits the temporal and spiking behavior of biological neurons to provide reliable forecasting, adaptive control, and pattern recognition abilities in a nonlinear, real-time setting. The SWOA-MSNN model is demonstrated with a simulation of a smart microgrid comprising solar, wind, battery storage, and controllable loads. Results show that the hybrid framework greatly enhances energy storage utilization efficiency, minimizes power losses, and reduces operational expense over individual WOA and traditional neural networks.
Keywords: Distributed Energy Resources (DERs), Energy Storage Systems (ESS), Smart Grid, Sine Whale Optimization Algorithm (SWOA), Modified Spiking Neural Network (MSNN), Renewable Energy Integration, Intelligent Energy Management, Optimization Algorithms
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International Journal of Advanced Control and System Engineering
| Volume | 04 | |
| 02 | ||
| Received | 23/07/2026 | |
| Accepted | 29/07/2026 | |
| Published | 17/08/2026 | |
| Publication Time | 25 Days |