Intelligent Control Strategies for Peak Load Management of Domestic Consumers in Developing Nations

The constantly rising demand for electricity in the residential sector can cause blackout problems unless the demand is met. Many demand response programs are discussed in the existing literature, in which the peak load of houses is shifted by managing the time of use of appliances. In these programs, data exchange between supply and demand is required, and achieved by the integration between these programs and smart grid systems. However, in many developing nations conventional electric grids are still used where there is no data exchange. In the present research, intelligent control strategies are proposed to reshape domestic power demand profiles without requiring the grid to be “smart”. Two case studies are conducted to demonstrate the effectiveness of the proposed strategies in reducing and shifting the peak demand to off-peak times. In the first case study, a control strategy is developed in which the operating frequency of domestic water pumps is reduced during peak hours. It is shown that the controller reduces the operating frequency of domestic pumps by 90% during peak hours while increasing their work during off-peak times by 55%. At the same time, the controller does not let the water level drop below 30% of the storage tank’s maximum capacity, thereby maintaining an acceptable inside-house water supply pressure that ensures the end-users’ water usage comfort. The controller is validated against a MPC controller that reduces the domestic pump operating frequency by 100% but it permits the water level to drop to a critical level that reduces the inside-house water supply pressure. In the second case study, a fuzzy logic control strategy that operates six home appliances is developed, such that the overall consumption peak current is not exceeded. Additionally, a regional fuzzy controller is used to monitor the overall current consumption of the neighbourhood, helping to control the operation of the fuzzy controllers inside the houses and prevent a regional current consumption overload. The simulations reveals that the fuzzy control strategy shifts 43% of the load from peak hours to other hours while at the same time the occupants’ electric usage comfort is preserved. Cost analysis is conducted to compare the economic feasibility of these control strategies against purchasing new peak diesel generators. If the utility company were to promote the new controllers for pumps in 25,000 houses for free, the utility could avoid investing an extra 773,500US$ for a peak generation of capacity of 2.85MW. On the other hand, the investment avoided by the fuzzy control strategy is approximately 5,000US$ in the case of implementing it in 40 houses. The reduction in peak energy consumption would be approximately 63% and in peak load 52kW. In order to estimate the power profiles required in the simulations, a power consumption model is developed. The model is validated against actual measurements of domestic power profiles, and the average percentage error between the model and the measurements is found to be 8%. In addition, the model is constructed in Matlab GUI, in order to generalize it and make it easy to use for non-professional people. A survey is conducted to investigate the effect of some socio-demographic factors on domestic power consumption. The multi-regression results show that domestic power consumption is significantly affected by family income and the surface area of homes. Keywords: Load shifting, pumping work, algorithmic control, fuzzy control, random operation modelling, power modelling, socio-demographic factors.