Hierarchical decentralized optimization architecture for economic dispatch of large-scale power system

Abstract

In order to solve the economic dispatch problem, this dissertation project focuses on establishing an improved hierarchical decentralized algorithm for the large-scale power system, based on the previous hierarchical decentralized optimization architecture and method. The conventional and renewable power sources including the wind and solar power are considered in the economic dispatch model. Traditionally, such a large-scale economic dispatch problem is handled in a centralized manner, which is generally not flexible and has a high computation cost. Compared with different centralized algorithms, the improved hierarchical decentralized algorithm decomposes the centralized problem into the local sub-problems. The local cost function and constraint set are solved iteratively in each local generator. In order to coordinate local generators and handle the global constraint, an extra coordinator agent is introduced. Subject to the global constraints and local constraints separately, the improved hierarchical decentralized method minimizes the total cost to obtain the optimal solution. Besides, it can simplify the computational complexity and protect the privacy. The simulation results can illustrate that the improved hierarchical decentralized economic dispatch scheme is implemented by discussing different cases studies including the IEEE 30-bus and the IEEE 118-bus systems and the corresponding algorithm can acquire the optimal solution in a decentralized mode.