A novel load profile generation method based on the estimation of regional usage habit parameters with genetic algorithm

Abstract

Considering the millions of end-users in a medium-scale city, even achieving a smart price improvement or imposing incentives for flattening the demands makes millions of saving. Hence, load profiling for electricity companies has recently emerged as a crucial requirement for commitment agreements, maintenance programming, and efficient energy management within demand response applications. Those facilities require load type classification in the customer data set to apply the deterministic and stochastic techniques in the planning and operation processes. In this manner, we have presented a novel procedure with a customized genetic algorithm (GA) to generate load profiles without adopting costly techniques. Using the regional survey or statistical data for generating the profiles, we have considered the real-time scenarios in terms of living people and household appliances for the residential sector. Namely, the usage habit parameters are estimated recursively by GA, then those parameters are used to generate near-realistic profiles. The overall scheme is tested on MATLAB (R) and remarkable results are illustrated.