Optimizing Smart Grid Performance: A GRU Approach to Electric Power Forecasting and Demand Side Management

• Optimizing GRU hyperparameters enhances EPC forecasting accuracy in smart grids. • Day-ahead dynamic pricing reduces substation peak demand effectively. • Demand response schemes allow EV users to customize the charging schedules. • Improved forecasting and DSM enhance grid stability, and reduce peak-time costs. The significance of smart grids is growing alongside the popularity of Renewable Energy Sources (RES) to achieve zero carbon emissions and the increasing demand for reliable electricity. Smart grid systems use the Gated Recurrent Unit (GRU) approach to enhance the performance of Demand Side Management (DSM) and the accuracy of Electric Power Consumption (EPC) forecasting. The existing smart grid models are struggling to predict the electricity consumption accurately and properly control the peak demand especially as more renewable energy sources as well as Electric Vehicle (EV) charging loads are integrated to the grid. This research aims to improve the performance of smart grids by tuning the hyperparameters of GRU networks and DSM. This work emphasises optimising GRU hyperparameters to forecast EPC in the distribution network accurately. This study investigates the impact of hyperparameter selection on the performance of forecasting models for smart grids. The optimised hyperparameter with the GRU model exhibited superior predictive performance, achieving an RMSE of 0.4688, an MSE of 0.2198, a MAE of 0.3485, and an R 2 of 0.9652, indicating excellent agreement between predicted and actual values. The above forecasted outcomes are used to compute the feeder-wise Day-ahead Dynamic Power Pricing (DDEP) system, which is designed to reduce peak demand at the distribution substation by implementing the Demand Response Scheme (DRS). This DRS helps Consumers optimise their Electric Vehicle (EV) charging schedule. Enhance the efficient utilisation of generated renewable energy and reduce peak demand in the smart substation; similarly, consumers reduce electricity tariffs through the proposed DRS. This leads to enhanced grid stability, a more balanced allocation of demand and supply, and a decreased peak-to-average ratio across all feeders, thereby improving overall system performance.