Resilience-oriented design and proactive preparedness of electrical distribution system

Extreme weather events, such as hurricanes and ice storms, pose a top threat to power distribution systems as their frequency and severity increase over time. Recent severe power outages caused by extreme weather events, such as Hurricane Harvey and Hurricane Irma, have highlighted the importance and urgency to enhance the resilience of electric power distribution systems. The goal of enhancing the resilience of distribution systems against extreme weather events can be fulfilled through upgrading and operating measures. This work focuses on investigating the impacts of upgrading measures and preventive operational measures on distribution system resilience. The objective of this dissertation is to develop a multi-timescale optimization framework to provide some actionable resilience-enhancing strategies for utility companies to harden/upgrade power distribution systems in the long-term and do proactive preparation management in the short-term. In the long-term resilience-oriented design (ROD) of distribution system, the main challenges are i) modeling the spatio-temporal correlation among ROD decisions and uncertainties, ii) capturing the entire failure-recovery-cost process, and iii) solving the resultant large-scale mixed-integer stochastic problem efficiently. To deal with these challenges, we propose a hybrid stochastic process with a deterministic casual structure to model the spatio-temporal correlations of uncertainties. A new two-stage stochastic mixed-integer linear program (MILP) is formulated to capture the impacts of ROD decisions and uncertainties on system responses to extreme weather events. The objective is to minimize the ROD investment cost in the first stage and the expected costs of loss of load, DG operation, and damage repairs in the second stage. A dual decomposition (DD) algorithm with branch-and-bound is developed to solve the proposed model with binary variables in both stages. Case studies on the IEEE 123-bus test feeder have shown the proposed approach can improve the system resilience at minimum costs. For an upcoming extreme weather event, we develop a pre-event proactive energy management and preparation strategy such that flexible resources can be prepared in advance. In order to explicitly materialize the trade-off between the pre-event resource allocation cost and the damage loss risk associated with an event, the strategy is modeled a two-stage stochastic mixed-integer linear programming (SMILP) and Conditional Value at-Risk (CVaR). The progressive algorithm is used to solve the proposed model and obtain the optimal proactive energy management and preparation strategy. Numerical studies on the modified IEEE 123-bus test feeder show the effectiveness of the proposed approach to improve the system resilience at different risk levels

Studies of Uncertainties in Smart Grid: Wind Power Generation and Wide-Area Communication

This research work investigates the uncertainties in Smart Grid, with special focus on the uncertain wind power generation in wind energy conversion systems (WECSs) and the uncertain wide-area communication in wide-area measurement systems (WAMSs). For the uncertain wind power generation in WECSs, a new wind speed modeling method and an improved WECS control method are proposed, respectively. The modeling method considers the spatial and temporal distributions of wind speed disturbances and deploys a box uncertain set in wind speed models, which is more realistic for practicing engineers. The control method takes maximum power point tracking, wind speed forecasting, and wind turbine dynamics into account, and achieves a balance between power output maximization and operating cost minimization to further improve the overall efficiency of wind power generation. Specifically, through the proposed modeling and control methods, the wind power control problem is developed as a min-max optimal problem and efficiently solved with semi-definite programming. For the uncertain communication delay and communication loss (i.e. data loss) in WAMSs, the corresponding solutions are presented. First, the real-world communication delay is measured and analyzed, and the bounded modeling method for the communication delay is proposed for widearea applications and further applied for system-area and substation-area protection applications, respectively. The proposed bounded modeling method is expected to be an important tool in the planning, design, and operation of time-critical wide-area applications. Second, the real synchronization signal loss and synchrophasor data loss events are measured and analyzed. For the synchronization signal loss, the potential reasons and solutions are explored. For the synchrophasor data loss, a set of estimation methods are presented, including substitution, interpolation, and forecasting. The estimation methods aim to improve the accuracy and availability of WAMSs, and mitigate the effect of communication failure and data loss on wide-area applications

Real-time Voltage Stability Monitoring and Control for Load Areas: A Hybrid Approach

This dissertation proposes a hybrid approach for real-time monitoring and controlling voltage stability of a load area fed by N tie lines. This hybrid approach integrates both simulation-based and measurement-based approaches for voltage stability assessment (VSA). First, for measurement-based VSA (MBVSA), a new method is proposed for monitoring and control of load areas, which adopts an N+1 buses equivalent system so as to model and monitor individual tie lines of a load area compared to a traditional MBVSA method adopting a Thevenin equivalent. For each tie line, the new method solves the power transfer limit against voltage instability analytically as a function of all parameters of that equivalent, which is online identified from real-time synchronized measurements on boundary buses of the load area. Thus, this new MBVSA method can directly calculate the real-time power transfer limit on each tie line. Second, in order to assess the voltage stability margins under an n-1 contingency, based on the proposed MBVSA method, two sensitivity analyses have been performed, which are respectively for the parameter sensitivity of the equivalent system and the sensitivity of the tie line flow under an n-1 contingency. Third, the proposed MBVSA method implemented for both the real-time condition and potential n-1 contingencies is integrated with the simulation-based VSA approach to form a hybrid approach. The MBVSA method helps reduce the computation burden by eliminating the unimportant contingencies while the simulation-based method provides accurate information for specific “what if” scenarios such as stability limit and margin indices under n-1 contingency conditions. In addition, simulation using the model of the system can provide recommendations for preventive control if potential voltage instability is identified. This proposed hybrid VSA approach has been validated on the NPCC (Northeast Power Coordinating Council) Large-scale Test Bed (LTB) system developed by the CURENT (Center for Ultra-Wide-Area Resilient Electric Energy Transmission Networks), and also implemented on the CURENT Hardware Test Bed (HTB) system. The effectiveness of the MBVSA in real-time monitoring and closed-loop control against voltage instability has been validated

Co-Optimization of Gas-Electricity Integrated Energy Systems Under Uncertainties

In the United States, natural gas-fired generators have gained increasing popularity in recent years due to low fuel cost and emission, as well as the needed large gas reserves. Consequently, it is worthwhile to consider the high interdependency between the gas and electricity networks. In this dissertation, several co-optimization models for the optimal operation and planning of gas-electricity integrated energy systems (IES) are proposed and investigated considering uncertainties from wind power and load demands. For the coordinated operation of gas-electricity IES: 1) an interval optimization based coordinated operating strategy for the gas-electricity IES is proposed to improve the overall system energy efficiency and optimize the energy flow. The gas and electricity infrastructures are modeled in detail and their operation constraints are fully considered. Then, a demand response program is incorporated into the optimization model, and its effects on the IES operation are investigated. Interval optimization is applied to address wind power uncertainty in IES. 2) a stochastic optimal operating strategy for gas-electricity IES is proposed considering N-1 contingencies in both gas and electricity networks. Since gas pipeline contingencies limit the fuel deliverability to gas-fired units, N-1 contingencies in both gas and electricity networks are considered to ensure that the system operation is able to sustain any possible power transmission or gas pipeline failure. Moreover, wind power uncertainty is addressed by stochastic programming. 3) a robust scheduling model is proposed for gas-electricity IES with uncertain wind power considering both gas and electricity N-1 contingencies. The proposed method is robust against wind power uncertainty to ensure that the system can sustain possible N-1 contingency event of gas pipeline or power transmission. Case studies demonstrate the effectiveness of the proposed models. For the co-optimization planning of gas-electricity IES: a two-stage robust optimization model is proposed for expansion co-planning of gas-electricity IES. The proposed model is solved by the column and constraint generation (C&CG) algorithm. The locations and capacities of new gas-fired generators, power transmission lines, and gas pipelines are optimally determined, which is robust against the uncertainties from electric and gas load growth as well as wind power

Modern Power System Dynamic Performance Improvement through Big Data Analysis

Higher penetration of Renewable Energy (RE) is causing generation uncertainty and reduction of system inertia for the modern power system. This phenomenon brings more challenges on the power system dynamic behavior, especially the frequency oscillation and excursion, voltage and transient stability problems. This dissertation work extracts the most useful information from the power system features and improves the system dynamic behavior by big data analysis through three aspects: inertia distribution estimation, actuator placement, and operational studies.First of all, a pioneer work for finding the physical location of COI in the system and creating accurate and useful inertia distribution map is presented. Theoretical proof and dynamic simulation validation have been provided to support the proposed method for inertia distribution estimation based on measurement PMU data. Estimation results are obtained for a radial system, a meshed system, IEEE 39 bus-test system, the Chilean system, and a real utility system in the US. Then, this work provided two control actuator placement strategy using measurement data samples and machine learning algorithms. The first strategy is for the system with single oscillation mode. Control actuators should be placed at the bus that are far away from the COI bus. This rule increased damping ratio of eamples systems up to 14\% and hugely reduced the computational complexity from the simulation results of the Chilean system. The second rule is created for system with multiple dynamic problems. General and effective guidance for planners is obtained for IEEE 39-bus system and IEEE 118-bus system using machine learning algorithms by finding the relationship between system most significant features and system dynamic performance. Lastly, it studied the real-time voltage security assessment and key link identification in cascading failure analysis. A proposed deep-learning framework has Achieved the highest accuracy and lower computational time for real-time security analysis. In addition, key links are identified through distance matrix calculation and probability tree generation using 400,000 data samples from the Western Electricity Coordinating Council (WECC) system

Gain tuning of proportional integral controller based on multiobjective optimization and controller hardware-in-loop microgrid setup

Proportional integral (PI) control is a commonly used industrial controller framework. This PI controller needs to be tuned to obtain desired response from the process under control. Tuning methods available in literature by and large need sophisticated mathematical modelling, and simplifications in the plant model to perform gain tuning. The process of obtaining approximate plant model conceivably become time consuming and produce less accurate results. This is due to the simplifications desired by the power system applications especially when power electronics based inverters are used in it. Optimal gain selection for PI controllers becomes crucial for microgrid application. Because of the presence of inverter based distributed energy resources. In the proposed approach, a multi-objective genetic algorithm is used to tune the controller to obtain expected step response characteristics. The proposed approach do not need simplified mathematical models. This prevents the need for obtaining unfailing plant models to maintain the fidelity of modelling. Microgrid system and the PI controller are modelled in different software, hardware platform and tuned using the proposed approach. Gain values for PI controller in these different platform are tuned using the same objective function and multi-objective optimization. This proves the re-usability, scalability, and modularity of the proposed tuning algorithm. Three different combination of software, hardware platform are proposed. First, the process and the PI controller are modelled in a computer based hardware. In order to increase the speed of the multi-objective optimization in the computer based hardware parallel computing is employed. This is a natural fit for paralleling the GA based optimization. Second, both the plant and control representation are modelled in the real time digital simulator (RTDS). Finally, a controller hardware in loop platform is used. In this platform, the plant will be modelled in RTDS and the PI controller will be modelled in an FPGA based hardware platform. Results indicate that the proposed approach has promising potentials since it does not need to simplify the switching model and can effectively solve the complicated tuning procedure using parallel computing. Similar advantage could be said for RTDS based tuning because RTDS simulates the models in real time

A Study on Wide-area Measurement-based Approaches for Power System Voltage Stability

With the development of wide-area monitoring system (WAMS) enabled by the synchrophasor technology, measurement-based approaches for power system voltage stability and control have been widely discussed in recent years. Based on high-frequency synchronized measurement signals collected from phasor measurement units (PMUs), these approaches have great potentials to significantly improve the situational awareness and to effectively guide the controls of interconnected modern power systems. If compared with conventional model-based voltage stability assessment (VSA) and control methods, the measurement-based methods are relatively new. Although their simplicity and independence of system models make them suitable for online deployment, the applications of these measurement-based methods are not as well explored as their model-based counterparts, which have been improved and matured over several decades. Therefore, the motivation of this dissertation is to explore new applications of measurement-based voltage stability assessment and control. In this dissertation, first, a comparative study on existing measurement-based approaches is provided; second, a hybrid VSA approach for N-1 contingency is proposed; third, measurement-based wide-area loading margin sensitivity suitable for voltage stability control is presented with a sample control study; fourth, mitigation approaches for overestimation of voltage stability margin when using coupled single-port circuit are proposed; and fifth, voltage dependent load model is integrated into measurement-based voltage stability analysis to provide a practical and accurate assessment of voltage stability margin