Using Distributed Energy Resources to Improve Power System Stability and Voltage Unbalance

The increasing penetration of renewables has driven power systems to operate closer to their stability boundaries and makes maintaining power quality more difficult. The goals of this dissertation are to develop methods to control distributed energy resources to improve power system stability and voltage unbalance. Specifically, demand response (DR) is used to realize the former goal, and solar photovoltaic (PV) systems are used to achieve the latter. We present a new DR strategy to change the consumption of flexible loads while keeping the total load constant, improving voltage or small-signal stability without affecting frequency stability. The new loading pattern is only maintained temporarily until the generators can be re-dispatched. Additionally, an energy payback period maintains the total energy consumption of each load at its nominal value. Multiple optimization problems are proposed for determining the optimal loading pattern to improve different voltage or small-signal stability margins. The impact of different system models on the optimal solution is also investigated. To quantify voltage stability, we choose the smallest singular value (SSV) of the power flow Jacobian matrix and the distance to the closest saddle-node bifurcation (SNB) of the power flow as the stability margins. We develop an iterative linear programming (ILP) algorithm using singular value sensitivities to obtain the loading pattern with the maximum SSV. We also compare our algorithm's performance to that of an iterative nonlinear programming algorithm from the literature. Results show that our ILP algorithm is more computationally scalable. We formulate another problem to maximize the distance to the closest SNB, derive the Karush–Kuhn–Tucker conditions, and solve them using the Newton-Raphson method. We also explore the possibility of using DR to improve small-signal stability. The results indicate that DR actions can improve small-signal characteristics and sometimes achieve better performance than generation actions. Renewables can also cause power quality problems in distribution systems. To address this issue, we develop a reactive power compensation strategy that uses distributed PV systems to mitigate voltage unbalance. The proposed strategy takes advantage of Steinmetz design and is implemented via both decentralized and distributed control. We demonstrate the performance of the controllers on the IEEE 13-node feeder and a much larger feeder, considering different connections of loads and PV systems. Simulation results demonstrate the trade-offs between the controllers. It is observed that the distributed controller achieves greater voltage unbalance reduction than the decentralized controller, but requires communication infrastructure. Furthermore, we extend our distributed controller to handle inverter reactive power limits, noisy/erroneous measurements, and delayed inputs. We find that the Steinmetz controller can sometimes have adverse impacts on feeder voltages and unbalance at noncritical nodes. A centralized controller from the literature can explicitly account for these factors, but requires significantly more information from the system and longer computational times. We compare the performance of the Steinmetz controller to that of the centralized controller and propose a new controller that integrates centralized controller results into the Steinmetz controller. Results show that the integrated controller achieves better unbalance improvement compared with that of the centralized controller running infrequently. In summary, this dissertation presents two demand-side strategies to deal with the issues caused by the renewables and contributes to the growing body of literature that shows that distributed energy resources have the potential to play a key role in improving the operation of the future power system.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162969/1/mqyao_1.pd

Developing and deploying enhanced algorithms to enable operational stability control systems with embedded high voltage DC links

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe increasing penetration of renewable energy resources within the Great Britain (GB) transmission system has created much greater variability of power flows within the transmission network. Consequently, modern transmission networks are presented with an ever increasing range of operating conditions. As a result, decision making in the Electricity National Control Centre (ENCC) of the GB electrical power transmission system is becoming more complex and control room actions are required for reducing timescales in the future so as to enable optimum operation of the system. To maximise utilisation of the electricity transmission system there is a requirement for fast transient and dynamic stability control. In this regard, GB electrical power transmissions system reinforcement using new technology, such as High Voltage Direct Current (HVDC) links and Thyristor-Controlled Series Compensation (TCSC), is planned to come into operation. The research aim of this PhD thesis is to fully investigate the effects of HVDC lines on power system small-disturbance stability in the presence of operational uncertainties. The main research outcome is the comprehensive probabilistic assessment of the stability improvements that can be achieved through the use of supplementary damping control when applied to HVDC systems. In this thesis, two control schemes for small-signal dynamic stability enhancement of an embedded HVDC link are proposed: Modal Linear Quadratic Gaussian (MLQG) controller and Model Predictive Controller (MPC). Following these studies, probabilistic methodologies are developed in order to test of the robustness of HVDC based damping controllers, which involves using classification techniques to identify possible mitigation options for power system operators. The Monte Carlo (MC) and Point Estimated Method (PEM) are developed in order to identify the statistical distributions of critical modes of a power system in the presence of uncertainties. In addition, eigenvalue sensitivity analysis is devised and demonstrated to ensure accurate results when the PEM is used with test systems. Finally, the concepts and techniques introduced in the thesis are combined to investigate robustness for the widely adopted MLQG controller and the recently introduced MPC, which are designed as the supplementary controls of an embedded HVDC link for damping inter-area oscillations. Power system controllers are designed using a linearised model of the system and tuned for a nominal operating point. The assumption is made that the system will be operating within an acceptable proximity range of its nominal operating condition and that the uncertainty created by changes within each operating point can possibly have an adverse effect on the controller’s performance

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Novel techniques of computational intelligence for analysis of astronomical structures

Gravitational forces cause the formation and evolution of a variety of cosmological structures. The detailed investigation and study of these structures is a crucial step towards our understanding of the universe. This thesis provides several solutions for the detection and classification of such structures. In the first part of the thesis, we focus on astronomical simulations, and we propose two algorithms to extract stellar structures. Although they follow different strategies (while the first one is a downsampling method, the second one keeps all samples), both techniques help to build more effective probabilistic models. In the second part, we consider observational data, and the goal is to overcome some of the common challenges in observational data such as noisy features and imbalanced classes. For instance, when not enough examples are present in the training set, two different strategies are used: a) nearest neighbor technique and b) outlier detection technique. In summary, both parts of the thesis show the effectiveness of automated algorithms in extracting valuable information from astronomical databases

A Practical Method for Power Systems Transient Stability and Security

Stability analysis methods may be categorized by two major stability analysis methods: small-signal stability and transient stability analyses. Transient stability methods are further categorized into two major categories: numerical methods based on numerical integration, and direct methods. The purpose of this thesis is to study and investigate transient stability analysis using a combination of step-by-step and direct methods using Equal Area Criterion. The proposed method is extended for transient stability analysis of multi machine power systems. The proposed method calculates the potential and kinetic energies for all machines in a power system and then compares the largest group of kinetic energies to the smallest groups of potential energies. A decision based on the comparison can be made to determine stability of the power system. The proposed method is used to simulate the IEEE 39 Bus system to verify its effectiveness by comparison to the results obtained by pure numerical methods