Predictive algorithm for Volt/VAR optimization of distribution networks using Neural Networks

Abstract

Smart Grid functions such as Advanced Metering Infrastructure, Pervasive Control and Distribution Management Systems have brought numerous control and optimization opportunities for distribution networks through more accurate and reliable techniques. This paper presents a new predictive approach for Volt/VAr Optimization (VVO) of smart distribution systems using Neural Networks (NN) and Genetic Algorithm (GA). The proposed predictive algorithm is capable of predicting the load profile of target nodes a day ahead by employing the historical metrology data of Smart Meters, It can further perform a comprehensive VVO in order to minimize distribution network loss/operating costs and run Conservation Voltage Reduction (CVR) to conserve more energy. To test the merits of the proposed algorithm, British Columbia Institute of Technology north campus distribution grid is used as research case study.

Quasi real-time ZIP load modeling for Conservation Voltage Reduction of smart distribution networks using disaggregated AMI data

“…Therefore, as explained quasi real-time AMI-based VVO engine solves the optimization problem through its optimization technique (here, improved Genetic Algorithm is used as the algorithm programmed in MATLAB environment [65]) at each quasi real-time stage. In next step, VVO engine sends control commands to VVCCs to optimize the distribution grid.…”