DEMAND SIDE MANAGEMENT

Abstract

Peak demand is a growing concern in most of the electricity grid, especially when the demand of electricity is more than the supply. Over the last few decades a continuous growth in peak demand has raised frequent stressed condition in electrical power system and increases the risk of outages. The traditional approach to overcome the peak demand issues is by adding infrastructure at the supply end. This approach however may cause an increased level of environmental pollution and an extra cost in installation of equipment. To address the current peak demand problems and to seek sustainable effort in meeting future electricity demand, implementing DSM strategies emerged as a viable option. DSM aims to match the demand with respect to supply either by reducing the peak power consumption or reshaping the load profile. In many countries demand response programs have been implemented widely for industrial and commercial loads. On the other hand, for residential sectors very few DSM programs are implemented. The residential consumers across the world accounts for a very significant proportion of the total electricity use and hence to exploit the maximum benefit of a DSM program, active participation of residential sector is utmost important. The objective of this thesis is to develop an automated demand side management technique for residential consumers, which will manage the operation of non-critical power intensive household appliances based on a pre-defined comfort level and consumer priorities. The developed technique shifts the operation of non-critical loads such that the total load remains below a certain value called demand limit, which will be imposed by the utilities. The appliances are shifted as per the consumer preference and comfort so as to provide them more flexibility in the operation of these appliances. Simulation has been done for a typical house consisting of four non-critical household appliances viz, electric vehicles, clothes dryer, air-Conditioner and water heater under different value of the demand limit. The results obtained from simulation are supposed to give a perception of the possible level of load curtailments for domestic consumers, which will reflect the potential of DR in residential markets. The results obtained shows that there is a significant improvement in the peak load reduction using the technique. Also there is a huge saving in electricity cost under a dynamic tariff scheme. Thus the developed algorithm can be successfully used for peak load reduction in domestic sector and may benefit both utility and consumers.