Hierarchical Distributed Scheme for Demand Estimation and Power Reallocation in a Future Power Grid

The classical power allocation/reallocation faces difficult challenges in a future power grid with a great many distributed generators and fast power fluctuations caused by high percentage of renewable energy. To perform power reallocation fast in a future power grid with a large number of participants and disturbances, a hierarchical distributed scheme based on a partition framework is proposed. In the proposed scheme, the power grid is naturally partitioned into a certain number of regions, and the total energy demand in the power grid with disturbances is automatically estimated rather than given in advance. Besides, the centralized local optimizations in regions and the distributed global optimization among regions are coupled to solve the power reallocation problem, in which each region performs as a single agent. Thus, the agents in the proposed scheme are much fewer than the purely distributed ones, hence the communication load is greatly relieved and the reallocation process is significantly simplified. Effectiveness of the proposed scheme is verified by the cases.