This dissertation focuses on developing an integrated hybrid model for studying the thermal dynamic operations of passive buildings considering active power management. For this, the following methodology is designed. First, the hybrid model,including a procedure for identifying model parameters, is established. Second, the model is simulated, and results compared with EnergyPlus counterparts for validation using three different climatic zones. Third, the energy optimization framework considering all the active energy sources in the building is illustrated. Fourth, the model is utilized within Model Predictive Control (MPC) and optimization framework to demonstrate its capability for extensive applications in complex demand management programs and advanced transactive operations. For this, test cases were implemented, including energy management with Time of Use rates, power reference tracking, and demand response with load scheduling capabilities. Finally, a distributed energy resource aggregation framework that limits aggregate demand for multiple homes was formulated, to enforce grid limits and simultaneously achieve energy cost savings. The results show that the model has an average of 4% error compared with EnergyPlus results, and the framework intuitively prioritizes natural ventilation operation while effectively coordinating building energy resources for an average of 63% reduction in peak electricity usage during the time of use event, an average of 1.2% error in power reference tracking, and a 49% to 56% gain on incentives during a load scheduling strategy in the evaluated zones. The aggregator framework proves efficient in reducing the aggregate demand of ten passive buildings from 160 kW (without aggregator oversight) to 97.5 kW (with aggregator oversight).