Electrical load forecasting is one of the important parts for smart grid system. The reliable prediction of the load demand contributes to the efficient and economical operations and planning. The artificial neural network is used extensively in load demand forecasting. The nonlinear nature of the electrical load demand conforms to the ability of the artificial neural network in calculating the nonlinear relationship of inputs and outputs. Among many models of neural networks, radial basis neural networks yield superior performance in small error and fast simulation time.However, it is challenge to design the radial basis neural networks. The excessive numbers of hidden neurons lead to lacking of generalization or so called overfitting problems. This paper proposes an approach to design the radial basis neural networks that use as least numbers of hidden neurons as possible. The error criterion is optimized based on modified genetic algorithm as the numbers of hidden neurons are incrementally increased. Simulation results of short term load forecasting are calculated in Matlab, and compared to the orthogonal least square error method. The proposed approach gives better results with the same numbers of hidden neurons.

No abstract

No abstract

The penetration level of renewable energy sources is increasing worldwide with incentives and subsidies for declining greenhouse gas emissions. Nevertheless, determining the optimal location and size of renewable distributed generators (RDGs) remains a challenging task, owing to the uncontrollable reactance that dominates power distribution networks in voltage control and its sensitivity to weather conditions. Hence, without considering the reactive compensation of generators, RDG integration incurs undesired total power losses and puts the system at risk for voltage instability and collapse. This research proposes Load Disabling Nodal Analysis for Robust Voltage Stability (LDNA-RVS), a method that determines the optimal location and size of RDGs and aims to improve robust voltage stability by considering reactive compensation while enhancing the loss reduction efficiency (LRE) of the RDG integration. The proposed LDNA-RVS method has been successfully applied to the IEEE 33-bus and IEEE 69-bus test distribution systems, demonstrating its suitability for small-scale systems with a limited number of RDGs. Finally, LDNA-RVS outperforms other methods in six out of eight categories for robust voltage stability and achieves the top rank in all eight categories for LRE. These findings prove the effectiveness of LDNA-RVS in terms of robust voltage stability and LRE against the uncontrollable reactive compensation.

No abstract