Non-Negative Least Square Optimization Model for Industrial Peak Load Estimation

Load research is the study of load characteristics on a power distribution system which helps planning engineer make decisions about equipment ratings and future expansion decisions. As it is expensive to collect and maintain data across the entire system, data is collected only for a sample of customers, where the sample is divided into groups based upon the customer class. These sample measurements are used to calculate the load research factors like kWHr-to-peak kW conversion factors, diversity factors and 24 hour average consumption as a function of class, month and day type. These factors are applied to the commonly available monthly billing kW data to estimate load on the system.

Among various customers on a power system, industrial customers form an important group for study as their annual kWHr consumption is among the highest. Also the errors with which the estimates are calculated are also highest for this class. Hence we choose the industrial class to demonstrate the Lawson-Hanson Non-Negative Least Square (NNLS) optimization technique to minimize the residual squared error between the estimated loads and the SCADA currents on the system. Five feeders with industrial dominant customers are chosen to demonstrate the improvement provided by the NNLS model. The results showed significant improvement over the Nonlinear Load Research Estimation (NLRE) method.