Wide-Area Time-Synchronized Closed-Loop Control of Power Systems And Decentralized Active Distribution Networks

The rapidly expanding power system grid infrastructure and the need to reduce the occurrence of major blackouts and prevention or hardening of systems against cyber-attacks, have led to increased interest in the improved resilience of the electrical grid. Distributed and decentralized control have been widely applied to computer science research. However, for power system applications, the real-time application of decentralized and distributed control algorithms introduce several challenges. In this dissertation, new algorithms and methods for decentralized control, protection and energy management of Wide Area Monitoring, Protection and Control (WAMPAC) and the Active Distribution Network (ADN) are developed to improve the resiliency of the power system. To evaluate the findings of this dissertation, a laboratory-scale integrated Wide WAMPAC and ADN control platform was designed and implemented. The developed platform consists of phasor measurement units (PMU), intelligent electronic devices (IED) and programmable logic controllers (PLC). On top of the designed hardware control platform, a multi-agent cyber-physical interoperability viii framework was developed for real-time verification of the developed decentralized and distributed algorithms using local wireless and Internet-based cloud communication. A novel real-time multiagent system interoperability testbed was developed to enable utility independent private microgrids standardized interoperability framework and define behavioral models for expandability and plug-and-play operation. The state-of-theart power system multiagent framework is improved by providing specific attributes and a deliberative behavior modeling capability. The proposed multi-agent framework is validated in a laboratory based testbed involving developed intelligent electronic device prototypes and actual microgrid setups. Experimental results are demonstrated for both decentralized and distributed control approaches. A new adaptive real-time protection and remedial action scheme (RAS) method using agent-based distributed communication was developed for autonomous hybrid AC/DC microgrids to increase resiliency and continuous operability after fault conditions. Unlike the conventional consecutive time delay-based overcurrent protection schemes, the developed technique defines a selectivity mechanism considering the RAS of the microgrid after fault instant based on feeder characteristics and the location of the IEDs. The experimental results showed a significant improvement in terms of resiliency of microgrids through protection using agent-based distributed communication

Smart integrated adaptive centralized controller for islanded microgrids under minimized load shedding

In this paper, a smart integrated adaptive centralized controller is proposed for monitoring and controlling integrated renewable energy sources (RESs), both for intentional and unintentional islanding modes of operation for microgrids, as well as, for a variable range of transient load shedding and fault scenarios corresponding to electrical power system outages. It is demonstrated that the proposed smart adaptive controller is capable of instructing fast frequency response by proper coordination of the dispatch of RESs units such as, mini-hydro, Photovoltaic (PV), Battery Energy Storage System (BESS) and standby diesel generators. In particular, the BESS used as power reserve, at the early stage of fault events can prevent detrimental and uncontrollable system frequency decline and the extent of load shedding. In summary, the performance of a centralized controller in terms of a fast frequency response recovery feature is validated for an actual microgrid distribution network of Malaysia. The demonstration of this intelligent control scheme highlights the advantage of utilizing the fast power recovery response of energy storage and standby generator, which fulfil the criteria for minimal load shedding from the main grid, during the unintentional microgrid islanding conditions

An enhanced predictive hierarchical power management framework for islanded microgrids

This paper proposes an enhanced three-layer predictive hierarchical power management framework for secure and economic operation of islanded microgrids. The tertiary control, guaranteeing the microgrid economic operation, is built upon the semi-definite programming-based AC optimal power flow model, which periodically sends power references to secondary control. To mitigate uncertainties arising from renewable generations and loads, a centralized linear model predictive control (MPC) controller is proposed and implemented for secondary control. The MPC controller can effectively regulate the microgrid system frequency by closely tracking reference signals from the tertiary controller with low computational complexity. Droop-based primary controllers are implemented to coordinate with the secondary MPC controller to balance the system in real time. Both synchronous generators (SGs) and solar photovoltaics (PVs) are simulated in the microgrid power management framework. A unified linear input-state estimator (ULISE) is proposed for SG state variable estimation and control anomaly detection due to compromised cyber-physical system components, etc. Simulation results demonstrated that SG states can be accurately estimated, while inconsistency in control signals can be effectively detected for an enhanced MPC. Furthermore, comparing with conventional proportional-integral (PI) control, the proposed hierarchical power management scheme exhibits superior frequency regulation capability whilst maintaining lower system operating costs

An Online Data-Driven Method for Microgrid Secondary Voltage and Frequency Control with Ensemble Koopman Modeling

Low inertia, nonlinearity and a high level of uncertainty (varying topologies and operating conditions) pose challenges to microgrid (MG) systemwide operation. This paper proposes an online adaptive Koopman operator optimal control (AKOOC) method for MG secondary voltage and frequency control. Unlike typical data-driven methods that are data-hungry and lack guaranteed stability, the proposed AKOOC requires no warm-up training yet with guaranteed bounded-input-bounded-output (BIBO) stability and even asymptotical stability under some mild conditions. The proposed AKOOC is developed based on an ensemble Koopman state space modeling with full basis functions that combines both linear and nonlinear bases without the need of event detection or switching. An iterative learning method is also developed to exploit model parameters, ensuring the effectiveness and the adaptiveness of the designed control. Simulation studies in the 4-bus (with detailed inner-loop control) MG system and the 34-bus MG system showed improved modeling accuracy and control, verifying the effectiveness of the proposed method subject to various changes of operating conditions even with time delay, measurement noise, and missing measurements.Comment: Accepted by IEEE Transactions on Smart Grid for future publicatio

Optimized Two-Level Control of Islanded Microgrids to Reduce Fluctuations

The main problem in the operation of micro-grids is controlling the voltage and frequency. The inertia of the whole grid is low, so the operation of the system is interrupted by sudden changes in load or incidence in the absence of a proper control system. In order to solve this issue, various control structures have been proposed. In this paper, an optimal distributed control strategy for coordinating multiple distributed generation instances is presented in an islanded microgrid. A secondary frequency control method is implemented in order to eliminate voltage deviation and reduce the small signal error. In this layer, an optimized PID controller is used. PID controller optimization is carried out via the Honey Badger Algorithm, and results are obtained using the MATLAB software. According to the results, inadequate adjustment of a secondary loop leads to poor and unacceptable outcomes, and the necessary power quality is not achieved. However, by using the proposed method, a proper performance of the microgrid in the face of disturbances is achieved

Synchrophasor Data Analytics for Control and Protection Applications in Smart Grids

RÉSUMÉ Des réseaux intelligents sont des réseaux d’énergie fortement distribués où les technologies d’énergie et des services sont intégrés avec des informations, des communications et contrôlent des technologies. Puisque les sources d’énergie renouvelable deviennent plus efficaces et rentables, les réseaux intelligents peuvent livrer la puissance propre, durable, sécuritaire, et fiable aux consommateurs. Cependant, l’utilisation rapide de sources d’énergie renouvelable provoque des défis techniques en termes de surveillance, le contrôle et la protection des réseaux électriques. En fait, l’énergie renouvelable implique les phénomènes qui sont naturellement stochastiques comme la lumière du soleil et le vent. Donc, les réseaux intelligents devraient être capables de surveiller et répondre aux changements tant dans fournisseur d’énergie que dans la demande. L’évolution des réseaux électriques provoque aussi le déploiement de nombreuses unités de mesure sans précédent et d’intelligents appareils de mesure. En vertu des systèmes de communications, les signaux en temps réel et les données peuvent être échangés entre les composants des réseaux intelligents. Le flux de données en temps réel fournit une occasion unique pour des applications axées sur les données et des outils pour démultiplier la modernisation de réseaux et la résilience. Les unités de mesure de phaseur sont les dispositifs spécialisés qui acquièrent le phaseur synchronisé (synchrophasor) des données des réseaux électriques. L’analytique de données Synchrophasor peut potentiellement étre plus performant que des méthodes traditionnelles en termes de prise de décisions. Spécifiquement, l’analytique de données est des approches qualitatives/quantitatives et les algorithmes qui rassemblent et traitent des données pour en fin de compte améliorer la conscience situationnelle dans des réseaux électriques. Motivé par ce fait, cette thèse présente des solutions viables pour l’analytique de données synchrophasor dans le but d’améliorer la surveillance, le contrôle et la protection de réseaux de distribution. La thèse se concentre sur trois fonctionnalités qui sont portées de basé sur l’analytique de données synchrophasor: Détection de perturbation centralisée, surveillance de production décentralisée (PD) et la protection “backup” coordonnée. L’objectif de surveillance de perturbation est de réaliser la détection rapide et fiable de tension/des déviations de fréquence qui affectent la stabilité de réseau. La surveillance de PD est liée à la détection de la présence/absence de ressources énergétiques pour la gestion du flux de puissance.----------ABSTRACT Smart grids are highly distributed energy networks where energy technologies and services are integrated with information, communications and control technologies. As renewable energy sources are becoming more efficient and cost–effective, the smart grids can deliver safe, clean, sustainable and reliable power to consumers. However, the rapid utilization of renewable energy sources brings about technical challenges in terms of monitoring, control, and protection of power systems. In fact, renewable energy involves phenomena which are naturally stochastic such as sunlight and wind. Therefore, the smart grids should be capable of monitoring and responding to changes in both power supply and demand. The evolution of the power systems also gives rise to deployment of unprecedented number of measurement units and smart meters. By virtue of communications systems, real-time signals and data can be exchanged between components of the smart grids. The flow of real-time data provides a unique opportunity for data-driven applications and tools to leverage grid modernization and resiliency. Phasor measurement units are specialized devices that acquire synchronized phasor (synchrophasor) data from the power systems. Synchrophasor data analytics can potentially outperform traditional methods in terms of decision making. Specifically, data analytics are qualitative/quantitative approaches and algorithms that collect and process data to ultimately improve situational awareness in the power systems. Motivated by this fact, this thesis presents viable solutions for synchrophasor data analytics with the aim of improving monitoring, control and protection of power distribution grids. The thesis focuses on three functionalities that are carried out based on synchrophasor data analytics: Centralized disturbance detection, monitoring of distributed generation (DG) systems, and coordinated backup protection. The objective of disturbance monitoring is to achieve fast and reliable detection of voltage/frequency deviations that affect the network stability. The DG monitoring is concerned with detecting presence/absence of energy resources for management of the flow of power. Disturbance and DG monitoring tools pave the way for adaptive backup protection of active distribution networks. The adaptive backup protection scheme ensures the post-fault stability by detecting line faults within a permissible tolerance time. The coordination between control and backup protection systems leads to fast recovery of voltage/frequency and minimizes power outage. The efficacy and reliability of the developed methods and algorithms are validated by extensive computer simulations based on different benchmarks