Consumer operational comfort level based power demand management in the smart grid

In the smart grid, peak-load shifting allows smart homes to limit their peak hour demand to reduce electricity cost. By means of balancing the demand and supply, efficiency and stability are achieved in the power grid. While most existing Demand Response (DR) programs only use pricing signals to encourage consumers to alter their power consumption patterns, the impacts on consumers have been overlooked. In this paper, we propose a novel demand management scheme that takes into account of the consumer comfort level. We define the concept of Operational Comfort Level (OCL), and construct the OCL models for a range of smart appliances. These OCL models are integrated into our load management scheme. We develop a Min-Max Load Scheduling (MMLS) algorithm to minimize the peak-to-average ratio (PAR), while maximize the OCL of consumers. Simulation results confirm that our proposed MMLS algorithm is able to achieve both peak-load shifting and energy cost saving with minimal impact on consumers' comfort levels.