Research on Grid Load Optimization Scheduling Algorithm Based on Line Loss Data

Abstract

In recent times, power grid optimization has incorporated ML and optimization algorithms to improve the power grid and reduce losses. Graph Neural Networks (GNN) are efficient in modelling complex dependencies, while Particle Swarm Optimization (PSO) optimizes line losses and operating costs. However, the application of the above approaches together in a single framework has received attention from researchers. The current approaches, like GA and LSTM, face difficulties in scaling and accuracy, which affect the power grid optimization. This study proposes a novel integrated approach for grid load scheduling and line loss reduction using the GNN + PSO approach. The framework is grounded on the IEEE 123-Bus system dataset for training the GNN model for load prediction, and optimized load distribution is determined using PSO to minimize line losses while satisfying grid constraints. The GNN+PSO model resulted in a MAE of 0.005, Mean Squared Error (MSE) of 0.0003, RMSE of 0.008, and R2 OF 0.99. In addition, GNN+PSO approach resulted in a reduction of line losses by 77.1%. This shows that the model outperforms other models like Bi-LSTM+GA, LSTM+GO, and GAT. This approach provides better accuracy and efficiency, which can be used to solve smart grid applications and can be extended to future dynamic grid management.