Distributed Control Methods for Integrating Renewable Generations and ICT Systems

With increased energy demand and decreased fossil fuels usages, the penetration of distributed generators (DGs) attracts more and more attention. Currently centralized control approaches can no longer meet real-time requirements for future power system. A proper decentralized control strategy needs to be proposed in order to enhance system voltage stability, reduce system power loss and increase operational security. This thesis has three key contributions: Firstly, a decentralized coordinated reactive power control strategy is proposed to tackle voltage fluctuation issues due to the uncertainty of output of DG. Case study shows results of coordinated control methods which can regulate the voltage level effectively whilst also enlarging the total reactive power capability to reduce the possibility of active power curtailment. Subsequently, the communication system time-delay is considered when analyzing the impact of voltage regulation. Secondly, a consensus distributed alternating direction multiplier method (ADMM) algorithm is improved to solve the optimal power ow (OPF) problem. Both synchronous and asynchronous algorithms are proposed to study the performance of convergence rate. Four different strategies are proposed to mitigate the impact of time-delay. Simulation results show that the optimization of reactive power allocation can minimize system power loss effectively and the proposed weighted autoregressive (AR) strategies can achieve an effective convergence result. Thirdly, a neighboring monitoring scheme based on the reputation rating is proposed to detect and mitigate the potential false data injection attack. The simulation results show that the predictive value can effectively replace the manipulated data. The convergence results based on the predictive value can be very close to the results of normal case without cyber attack

Newton-Raphson Consensus for Distributed Convex Optimization

We address the problem of distributed uncon- strained convex optimization under separability assumptions, i.e., the framework where each agent of a network is endowed with a local private multidimensional convex cost, is subject to communication constraints, and wants to collaborate to compute the minimizer of the sum of the local costs. We propose a design methodology that combines average consensus algorithms and separation of time-scales ideas. This strategy is proved, under suitable hypotheses, to be globally convergent to the true minimizer. Intuitively, the procedure lets the agents distributedly compute and sequentially update an approximated Newton- Raphson direction by means of suitable average consensus ratios. We show with numerical simulations that the speed of convergence of this strategy is comparable with alternative optimization strategies such as the Alternating Direction Method of Multipliers. Finally, we propose some alternative strategies which trade-off communication and computational requirements with convergence speed.Comment: 18 pages, preprint with proof

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

Decentralized optimization approach for power distribution network and microgrid controls

The smart grid vision has led to the development of advanced control and management frameworks using distributed generation (DG) and storage resources, commonly referred to together as distributed energy resources (DERs). Albeit environment-friendly, these DERs in distribution networks including microgrids (MGs) could greatly challenge the operational goal of maintaining adequate power system reliability standards because of their high intermittency, uncertainty, and lack of physical inertia. Meanwhile, these networks are inherently unbalanced and lack high-quality communications to a centralized entity as compared to the bulk transmission grid. Both aspects contribute to the challenge of designing voltage and frequency control frameworks therein. To tackle these problems, we propose decentralized control strategies, which account for cyber-physical network interactions automatically and dynamically while being either cognizant of various communication scenarios or resilient to malicious cyber intrusions. By treating the transmission grid as an infinity bus, voltage stability is the main concern in distribution networks where more DERs are being installed in the near future. Thanks to advances in power electronics, DERs can also be excellent sources of reactive power (VAR), a quantity that is known to have a significant impact on the network voltage level. Accordingly, we first formulate the local VAR-based voltage control design by minimizing a weighted quadratic voltage mismatch error objective using gradient-projection (GP) updates. The step-size design under both static and dynamic settings is further analyzed for practical implementation purposes. Nonetheless, such local design suffers degraded performance due to lack of information exchanges, especially under limited VAR resources. To address this issue, we develop the distributed voltage control (DVC) design based on the alternating direction method of multipliers (ADMM) algorithm. The DVC design has simple node-to-node communication architecture while seamlessly adapting to dynamically varying system operating conditions and being robust against random communication link failures. To further reduce communication complexity and enhance robustness to imperfect communications, especially under the worst-case scenarios of a total communication outage, we integrate both local and distributed control designs to a hybrid voltage control (HVC) scheme that can achieve the dual objectives in terms of flexible adaptivity to variable rate of communications and global optimality of voltage regulation performance. Such an innovative design aims to unify the separated framework of either local or distributed control design. Numerical tests using realistic feeders and real time-series data have been demonstrated for the voltage control designs. The aforementioned decentralized voltage control designs can improve the power system stability while distribution feeders are interconnecting to the bulk transmission grids. With a high penetration of DERs in the networks, it is possible to build a discrete energy system, namely, a microgrid (MG), that is capable of operating in parallel with, or independently from, the transmission grids. Henceforth, MGs are likely to emerge as a means to advance power and cyber physical resiliency in future grid systems. As MGs may operate independently, these mostly power electronics-interfaced DERs exhibiting low-inertia characteristic have raised significant concern over the frequency stability issues. To tackle this problem, we introduce the concept of virtual inertia of DERs and cast the secondary frequency control design for isolated MGs as a consensus optimization problem. We solve it distributively by adopting the partial primal-dual (PPD) algorithm. Interestingly, parts of our specially designed control algorithm turn out to mimic the dynamics of network power flow and virtual synchronous generator-based inverter. Thus, such dynamics is seamlessly governed by the physical system itself. Given a proper control parameter choice, the convergence of the consensus is guaranteed without assuming the time-scale separation of the hierarchical control design methodologies. By extending this work to a practical industrial MG network that follows the IEC 61850 communication protocol, similar frequency regulation objective is introduced and solved by a decentralized ADMM-based algorithm. The countermeasures for malicious attacks on the communication network for both PPD- and ADMM-based control designs are also investigated. Specifically, we analyze two types of malicious attacks on the communication network, namely, the link and node attacks. Meanwhile, anomaly detection and localization strategies are developed based on the metrics of optimization-related variables. We showcase the microgrid frequency regulation operation to demonstrate the effectiveness of the proposed frequency control designs under a real-time simulation environment

Distributed Compressive Sensing Algorithm for Photoacoustic Tomography

Biomedical imaging techniques are playing an essential role in diagnosing different kinds of diseases, which always motivates the search for improving their sensitivity and accuracy. Photoacoustic Tomography (PAT) is one of the most powerful techniques. PAT has many advantages as it is less expensive and faster than Magnetic Resonance Imaging (MRI). It combines the advantages of optical imaging and ultrasound imaging as it provides high contrast, high penetration, and high-resolution images for biological tissues. Also, it uses non-ionizing radiation which is very safe for human health. The main challenge in PAT is that human tissues can be exposed only to a limited amount of radiation, so a full-view of PAT requires many transducers and a great number of measurements. This thesis aims to develop an efficient reconstruction algorithm of Photoacoustic (PA) images that uses a few number of transducers, a few number of measurements, and offers low computational complexity while maintaining a high quality of recovered images. The proposed reconstruction algorithm depends on the Compressive Sensing (CS) theory which is a signal processing technique that is capable of forming a full view PAT images (under certain prerequisites) with a few number of measurements. The proposed algorithm solves the CS problem using a distributed and parallel implementation of the Alternating Direction Method of Multipliers (ADMM). ADMM is a well-known method for solving convex optimization problems. A group of local processors that work in parallel with one global processor is used to form the images. The iterative algorithm of ADMM is distributed over local processors in such a way perfect reconstruction of images is possible. Simulation results show that the proposed algorithm is powerful and successful in reconstructing different kinds of PA images with very high quality and significantly reduced computational complexity. Reducing the computational complexity is reflected in a much lower reconstruction time. Also, the algorithm requires lower cost and shorter acquisition time since the CS theory is used which allows the recovery of images from a few number of samples and sensors. Although the idea of distributed ADMM has been introduced before in literature but to the best of our knowledge, this is the first work to apply distributed ADMM method in recovering photoacoustic images by distributing the iterative algorithm among multiple processors working in parallel