International Journal of Engineering and Technology

The target of any load management is to maintain a constant level of load. The important benefits of load management are reduction in maximum demand, reduction in power loss, better equipment utilization and saving through reduced maximum demand charges. Load shifting, one of the simplest methods of load management, is to reduce customer demand during the peak period by shifting the use of appliances and equipment to partial peak and on-peak periods. This paper proposes an application of artificial intelligent (AI) optimization methods i.e. genetic algorithm (GA), particle swarm optimization (PSO) and bee algorithm (BA) to develop the load shifting and the same has been tried with the actual load data collected from the textile industry plant. The objective is to minimize the total electricity tariff cost. The methodology proposed can be used for determining the optimal response for textile industry under time varying tariffs such as flat rate and time of use (TOU).To show its efficiency, the AI methods are applied to solve the case studies in case of single process multi-jobs (SPMJ). The results show that the proposed methods are able to achieve the best solution efficiently and easy to implement.