Individual Incremental loading factor based maximum loadability limit prediction using modern optimization tools

Abstract

Infrastructure innovation in the power system industry encourages more partakers to participate in the electricity market which improvises the load utilization level. So, the maintenance of power system’s agility with respect to any dynamic update in terms of load level is necessary. Precise prediction of maximum allowable loading point helps to enhance the power system agility and also improvises the total allowable power transfer capability which in turn helps to supply continuous eminent power supply at the minimal cost to the customers by means of encouraging more contracts. Considering the above potential benefits, in this papear by using individual incremental loading factor (IILF) the precise prediction of total loadability limit (TLL) of the system is manipulated with the help of newly evolved meta-heuristic optimization algorithm such as Grey Wolf (GRW) optimizer and Flower Pollination Algorithm (FPA). The allowable single line contingency scenario is considering along with base case scenario to extract the more realistic TLL which helps to maintain the power system balance with respect to the dynamic nature of the load. The proposed maximum loading point extraction manipulation solution problem is tested with the help of three standard IEEE systems such as 30 Bus, 57 Bus and 118 bus systems. The extracted test results show that the predicted maximum allowable loading point enhances the load utilization level without affecting the system securities. The statistical performance measures of GRW and FPA confirmed the better balance of exploration and exploitation in extracting the optimal results.