Scenario-MPC-based energy system scheduling considering controllable air conditioning load and partially-controllable load

Due to the limited controllable devices in an individual microgrid, there exist performance limits to the flexibility and reliability of an individual microgrid. A cluster of microgrids in close geographical area can enhance the flexibility and operational robustness of the microgrid through self-sufficiency of individual microgrid and collaborative operation among them. Consequently, the problem of collaborative optimization and scheduling among multi-microgrid has attracted much attention in recent years. This paper considers this problem and makes the following contributions. First, collaborative scheduling model is formulated for multi-microgrids that consider controllable air conditioning loads, partially-controllable uncertain charging loads, controllable energy storage, and uncertain distributed renewable energy. This model improves the overall operational efficiency of the multi-microgrid through energy exchange among microgrids and between microgrids and the main grid. Second, a scenario-based MPC (Model Predictive Control) method is proposed to solve this multi-stage stochastic optimization model by linearization where multiple scenarios are generated to represent uncertain supply and demand. Then, scenario reduction technique is applied to select key scenarios, thereby speeding up the generation of optimal scheduling strategies for the multi-microgrid. Finally, the effectiveness of this method is validated based on a cluster of microgrids composed of multiple buildings.