Collaborative demand response management is an effective method to lower the peak-to-average ratio of demand and to facilitate the integration of locally distributed renewable energy resources to the electricity grid. The aggregator needs a holistic and privacy-preserving demand response management scheme to involve residential customers in a dynamic pricing market scenario. Using a quadratic function to model dynamic pricing, we propose a two-level distributed energy management scheme for a residential community to exploit the benefits of coordination among customers at the aggregator level and the smart devices at the customer level. In the proposed scheme, each customer wants to optimize the scheduling of its smart appliances, demand flexibility of air conditioning load, and energy storage strategies to minimize their expected cost, discomfort and appliance interruption. The aggregator, on the other hand, seeks to minimize the overall expected cost by optimizing customers energy demand and its energy storage strategies. The aggregator level optimization is formulated as a noncooperative Stackelberg equilibrium problem with shared constraints. Meanwhile, the customer level problem is formulated as a multiobjective optimization using different discomfort and interruption indicators to characterize various appliance preferences. We formulate iterative algorithms to obtain the appliance scheduling and storage strategies of the customers using genetic algorithm and to reach convergence. Simulation results indicate that the proposed scheme converges while enforcing the shared constraints and reduces the electricity cost to the customers with a quantifiable tradeoff between multiple objectives.