INVESTIGATION OF OPTIMAL OFF-PEAK TARIFF RIDER (OPTR) BY USING PARTICLE SWARM OPTIMIZATION FOR SELECTED COMMERCIAL AND INDUSTRIAL CONSUMERS IN MALAYSIA

Abstract

In Peninsular Malaysia, Off-Peak Tariff Rider (OPTR) is offered to all medium voltage commercial and industrial consumers who are currently not enjoying any off-peak usage tariff rates. In this program, the consumers are able to obtain a discount rate of 20% with the condition that the load factor must be more significant than the average baseline set. Nevertheless, not all consumers are willing to commit. In this paper, a superior bio-inspired algorithm, Particle Swarm Optimization (PSO), is used to optimize the industrial and commercial load profile while adapting the simultaneous demand-side management (DSM) strategies such as peak clipping, valley filling, and load shifting. The proposed combination technique has shown a reduction of electricity cost and improvement of load factor for all optimal cases from the forecasting result when the different percentages of DSM strategies are applied accordingly. Hopefully, the findings of this study will help the consumers to manage their energy consumption wisely and obtain the full benefit from the DR program.