Unbalanced operation of integrated power distribution system for optimal energy flow using LSO-vCANNs approach

The integrated energy distribution system (IEDS) integrates the natural gas, thermal, and electrical networks at the distribution level. Because of its high cost and complicated operation of the imbalanced electrical distribution network, the load flow problem of the IEDS was disregarded in the operation of integrated energy distribution systems (IESs). This paper focuses on the operation optimization of IEDSs by introducing a novel hybrid approach for an unbalanced operation of the power distribution system for optimal energy flow. The proposed hybrid approach is the combination of Light Spectrum Optimizer (LSO) and Viscoelastic Constitutive Artificial Neural Networks vCANNs commonly named as LSO-vCANNs approach. LSO is utilized to optimize the load flow of the unbalanced radial distribution electrical network. vCANN is used to predict the energy flow of natural gas and heat networks. The aim of the paper is to minimize the total operation cost the most cost-effective way to distribute energy across the integrated system while meeting demand and network constraints. The proposed model is implemented in the MATLAB platform and compared to different existing approaches like teaching–learning based optimization algorithm (TLBO), particle swarm optimization (PSO), and deep neural networks (DNN). The cost of the system using the proposed method is 20$ which is lower, and the computational time is 0.8 s which is lower than the existing methods. This suggests that adopting the proposed methodology could lead to improved performance and efficiency in energy distribution operations.