A time of use tariff scheme for demand side management of residential energy consumers in Bangladesh

Abstract

The aim of this paper is to develop a new Time of Use (TOU) tariff scheme for Demand Side Management (DSM) of residential consumers in Bangladesh. In this advanced era of technology, the requirement of electrical energy is continuously increasing. However, the overall growth of electricity generation is comparatively slower due to depletion of fuel sources. In this situation, a strategic plan needs to be implemented at consumer end to mitigate the energy crisis. As such, the concept of DSM is introduced where consumers can save electricity bills by changing their consumption pattern. It subsequently reduces the generation requirement during peak hours, which offers operational flexibility and financial benefits to utility. In the literature, DSM has been used in residential, commercial, and industrial sectors. However, the existing techniques do not explicitly consider the low-income consumers when TOU scheme is utilised. It may result in financial repercussion for these consumers. To overcome this challenge, a TOU scheme is proposed in this paper by ensuring financial benefits for all types of residential consumers and utility. To this end, an optimisation model is formulated by taking into account the various consumer groups such as low-income, middle-income and high-income. Two different meters (meter 1 and meter 2) are prudently allocated to these consumers. Using genetic algorithm technique, optimisation model is solved to provide electricity tariff and block sizes for various consumers under two meters. The proposed algorithm is applied to the residential inhabitants of a practical distribution system in Bangladesh. The results suggest that significant amount of annual savings can be achieved simultaneously by consumers and utility. The low-income consumers remain financially least affected while the proposed TOU tariff scheme is executed. In addition, the proposed TOU approach is compared to existing methods and found that optimisation based TOU outperforms the conventional ones.