Distributed Control, Optimization, and State Estimation for Renewable Power System

<p>The traditional power systems are usually centralized systems, in which the control, operation and monitoring are performed by the centralized control center, e.g., SCADA. However, with the development of renewable energy, power systems are getting more and more distributed. So, it becomes necessary to establish the distributed power system operation methods for these power systems. In this research, the distributed techniques for the renewable power systems are proposed based on the consensus protocol technique from graph theory. These techniques cover the three important problems in power systems, i.e., economic dispatch, state estimation, and optimal power flow. First, the Distributed Economic Dispatch (DED) approach is proposed. In this part, both the PI controller and Neural Network (NN) controller are utilized to design the distributed algorithm to minimize the power system’s operational cost in a distributed way. The communication-failure-tolerant DED algorithm is proposed to improve the robustness of the approach during communication failure. Also, the DED algorithm considering line loss model is proposed. On the other hand, an information propagation method is provided to develop the Distributed State Estimation (DSE) algorithm. Then, the bad data detection and measurement accuracy improvement topics in state estimation are discussed. Then, based on the proposed DED algorithm and DSE algorithm, the Distributed Optimal Power Flow (DOPF) method is developed. Finally, the AC power flow model is considered to build the distributed AC State Estmiation method and distributed AC Optimal Power Flow method. At the end, the proposed methods are verified in the MATLAB/simulation software. The 4-generator system model, IEEE 10-generator 39-bus system model, WSCC 9-Bus system model, and some specially designed power system models are employed in the tests. The results of the simulation show that the proposed methods reach the desired performance.</p>

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Year dc:date.available

2021

Author dc:creator

• Li, Qiao

Contributors dc:contributor

• David Gao
• Yunbo Yi
• Margareta Stefanovic
• Mohammad Matin

• Consensus protocol
• Economic dispatch
• Optimal power flow
• Renewable energy
• State estimation
• Electrical and Computer Engineering
• Electrical and Electronics
• Engineering
• Power and Energy

Statement dc:rights

• <p>Copyright is held by the author. User is responsible for all copyright compliance.</p>

Language dc:language

en

Repository record dc:identifier

https://digitalcommons.du.edu/etd/1944

OAI identifier oai:identifier

oai:digitalcommons.du.edu:etd-2943