Synopsis of Phasor Monitoring Applications for Wide Area Control and Protection

The phenomenon of voltage collapse in electric power systems has received a considerable amount of attention in the last decade. Although the occurrences of the voltage blackouts have decreased in the recent years, the problem of voltage stability still poses a considerable threat to the security and the reliability of modern electricity grids. The ultimate objective of this thesis is to further investigate and expand upon the existing body of knowledge on the voltage collapse phenomenon and develop protection and control schemes for mitigating such undesirable events in modern electric grids. In doing so, the research done in this thesis work builds on an earlier work done in the area of Voltage Instability Prediction (VIP). Although proven to be a successful metric in determining the proximity of large nonlinear systems to a potential voltage instability event, much remains to be explored in the area of Voltage Instability Prediction. In particular the issues of estimating the static stability margins, the locational dependence of accuracy of such VIP derived margins, the exploitation of redundant local measurements and a compelling argument in favor of combining/fusing the individual VIP margins into a single system-wide measure of voltage collapse margin form the main focus of investigation of this work. To lower the individual entropy of the VIP derived margins, a data fusion algorithm built on the foundations of Dempster-Shafer’s evidential reasoning method is proposed. The research is concluded on a positive note with the final results further pushing the envelope of knowledge in the field of voltage stability studies in power systems

On the Environmental and Economic Impact of Utility-Scale Renewable Energy Deployment

As per the U.S Energy Information Administration’s latest inventory of electricity generators, renewable energy, most notably solar and wind, will account for roughly 70% of nearly 40 gigawatts of new electricity generating capacity to start commercial operation in 2021. The year 2021 will also set a record in the deployment of utility-scale solar capacity by adding 15.4 gigawatts of capacity to the grid, which surpasses the 12 gigawatts increase in 2020. The rapid increase of renewable energy is expected to significantly decrease emissions of greenhouse gases and change the load profile in the power grid by suppressing production from conventional generators. This paper aims to propose a framework to study the impact of utility-scale solar PV deployment on the generation resource allocation and investigate the economics and policy of electricity generation and carbon emissions. The investigation is carried on the generation resource pool of the southeast region of the U.S augmented by a substantial amount of utility-scale solar generation

Capacity usage determination of a Capacitor-less D-STATCOM considering Power System Uncertainties

The increasing adoption of distributed energy resources (DERs), particularly solar generation and the use of unconventional loads such as plug-in electric vehicles (PHEVs), has a profound impact on the planning and operation of electric distribution systems. In particular, PHEV charging introduces stochastic peaks in energy consumption, while solar generation is fraught with variability during intermittent clouds. The stochastic nature of such DERs renders the operation of mechanical assets such as on-load tap changers and switched capacitor banks ineffective. A possible solution to mitigate the undesirable effects of DERs is using solid-state-based devices such as a distribution static synchronous compensator (D-STATCOM). This paper examines the capacity usage of a capacitor-less D-STATCOM in distribution systems while considering the uncertainties associated with using the aforementioned DERs. We propose a Monte Carlo simulation to study the capacity usage problem with DER inputs sampled from the proposed underlying distributions

On Accelerated Aging of Mechanical Assets in Distribution Systems with Renewable Generation

The integration challenges associated with the widespread adoption of the photovoltaic generation can be divided into operational and the maintenance issues. Work done in recent years has addressed issues like voltage rise and unbalance. Less attention was directed to the maintenance challenges like accelerated aging of mechanically controlled voltage support assets under rapidly changing conditions. In particular, there is need for analysis on the mechanism of accelerated wear and tear of devices such as on-load tap changers and capacitor banks exposed to rapid voltage fluctuations. Such an analysis relies on development of lifetime models of switching devices to study the impact of increased stress, whether electrical or mechanical, on operational life. This article focuses on developing such models and proposes the framework to study the impact of non-scheduled distributed generation on aging of mechanically-switched devices commonly used in distribution feeders

A Probabilistic Modeling Framework for Integration of Distributed Energy Resources

The emergence of distributed energy resources has led to new challenges in the operation and planning of power networks. Of particular significance is the introduction of a new layer of complexity that manifests in the form of new uncertainties that could severely limit the resiliency and reliability of modern power networks. Some of the new uncertainties that emerge as a direct consequence of the integration of distributed energy resources include generation uncertainties typical of solar and wind power, uncertain consumer demand patterns due to the increasing adoption of un-conventional loads such as Plug-in Hybrid Electric Vehicles, topological uncertainties that include outages of one or many components of a power system. To facilitate the widespread adoption of distributed energy resources, it is thus essential to develop robust methodologies that would adequately capture and quantify the uncertainties associated with using them. Such decision-making problems that involve uncertainties embedded in the input data naturally lend themselves to be addressed by statistical decision-theoretic methods. A natural advantage of using statistical decision theory in addressing power system uncertainties is using sample information to make inferences about the unknown quantities. Thus, using computational methods like Bayesian statistics and Markov Chain Monte Carlo is natural within the context of decision-making under uncertainty in energy systems. This research proposes the use of computational methods such as scenario generation techniques, probabilistic mixture models, Bayesian analysis, and Markov Chain Monte Carlo to model the complex stochastic processes such as solar generation, power system load, and various topological uncertainties like accelerated aging and the premature failing of components such as On-load Tap Changers and switched capacitor banks. Furthermore, this research work is also concerned with investigating the impact of modern reactive power compensation in the form of a solid-state-based capacitor-less power quality compensator to further the integration of distributed resources, particularly distributed roof-top solar generation in low voltage distribution networks. To that end, a stochastic cost-benefit equation is developed, considering the uncertainties associated with distributed energy resources. The cost-benefit study investigates the economic viability of deploying such power electronics-based reactive power compensation devices in low-voltage distribution networks

On Estimation of Equipment Failures in Electric Distribution Systems Using Bayesian Inference

This paper presents a new statistical parametric model to predict the times-to-failure of broad classes of identical devices such as on-load tap changers, switched capacitors, breakers, etc. A two-parameter Weibull distribution with scale parameter given by the inverse power law is employed to model the survivor functions and hazard rates of on-load tap changers. The resulting three-parameter distribution, referred to as IPL-Weibull, is flexible enough to assume right, left, and even symmetrical modal distribution. In this work, we propose an inferential method based on Bayes’ rule to derive the point estimates of model parameters from the past right-censored failure data. Using the Monte Carlo integration technique, it is possible to obtain such parameter estimates with high accuracy

Locational Accuracy of VIP Indices for Voltage Collapse Margin Estimation

Nearly two decades of work on VIP (Voltage Instability Prediction) has enhanced the ability to obtain information on system vulnerability to voltage collapse based on minimum local information. Several indices have been developed over the past 20 years for local monitoring of voltage collapse problems, each with a different level of complexity with respect to computational and communicational infrastructure needed. This article addresses the disparity found in the VIP-derived margins and attempts to study the allocation of critical (more accurate) VIP locations across a power network during system changes. Additionally, a sensitivity metric is proposed to track the accurate VIP locations in real-time under increased system loading which could also lead to a meaningful data fusion of the more accurate VIP-derived margins

Impact of large distributed solar PV generation on distribution voltage

by Miroslav Begovic, Aaqib Peerzada, Shipra Mohan, Wesam Rohouma and Robert Balo