LAUSR

Demand-side management and incentive-based optimization have the potential to improve energy efficiency of modern smart homes and smart communities. Existing approaches only refer to consumers’ comfort level to thermal-related electric appliances. Other controllable appliances may not be included in the incentive designs, and the total community power consumption is somehow neglected. Thus, the involvement of residents’ participation is limited. To address this issue, we propose a new incentive-based residential energy optimization system to manage community demand reduction requests efficiently and, meanwhile, reward consumers with multi-level financial incentives and guaranteed comfort. A new design of comfort indicator is used, which considers both thermal and major controllable electric appliances based on the consumers’ comfort level. We integrate a genetic algorithm to solve this optimization problem, i.e., to minimize the reward costs for the utility (according to maximize the consumers’ comfort level). As an alternative approach, the mixed integer programming technique is also employed if the objective function includes a certain piecewise linear decision variable. Simulation studies on both 10-house and 100-house cases show that the proposed approach is outperformed two existing approaches in terms of reward incentives, comfort levels, and the number of active appliances.