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

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

Distributed Optimization for Power Systems with Radial Partitioning

This paper proposes group-based distributed optimization algorithms on top of intelligent partitioning for the optimal power flow (OPF) problem. Radial partitioning of the graph of a network is introduced as a systematic way to split a large-scale problem into more tractable sub-problems, which can potentially be solved efficiently with methods such as convex relaxations. The simple implementation of a Distributed Consensus Algorithm (DiCA) with very few parameters makes it viable for different parameter selection methods, which are crucial for the fast convergence of the distributed algorithms. The DiCA algorithm returns more accurate solutions to the tested problems with fewer iterations than component-based algorithms. Our numerical results show the performance of the algorithms for different power network instances and the effect of parameter selection. A software package DiCARP is created, which is implemented in Python using the Pyomo optimization package.Comment: 7 pages, 4 figure

Advanced Control and Optimization for Future Grid with Energy Storage Devices

In the future grid environment, more sustainable resources will be increasing steadily. Their inherent unpredictable and intermittent characteristics will inevitably cause adverse impacts on the system static, dynamic and economic performance simultaneously. In this context, energy storage (ES) devices have been receiving growing attention because of their significant falling prices. Therefore, how to utilize these ES to help alleviate the problem of renewable energy (RE) sources integration has become more and more attractive. In my thesis, I will try to resolve some of the related problems from several perspectives. First of all, a comprehensive Future Australian transmission network simulation platform is constructed in the software DIgSILENT. Then in-depth research has been done on the aspect of frequency controller design. Based on mathematical reasoning, an advanced robust H∞ Load Frequency Controller (LFC) is developed, which can be used to assist the power system to maintain a stable frequency when accommodating more renewables. Afterwards, I develop a power system sensitivity analysis based-Enhanced Optimal Distributed Consensus Algorithm (EODCA). In the following study, a Modified Consensus Alternating Direction Method of Multipliers (MC-ADMM) is proposed, with this approach it can be verified that the convergence speed is notably accelerated even for complex large dimensional systems. Overall, in the Master thesis, I successfully provide several novel and practical solutions, algorithms and methodologies in regards to tackling both the frequency, voltage and the power flow issues in a future grid with the assistance of energy storage devices. The scientific control and optimal dispatch of these facilities could provide us with a promising approach to mitigate the potential threats that the intermittent renewables posed on the power system in the following decades