The phrase water-energy nexus is commonly used to describe the inherent and critical interdependencies between the electric power system and the water supply systems (WSS). The key interdependencies between the two systems are the power plant’s requirement of water for the cooling cycle and the water system’s need of electricity for pumping for water supply. While previous work has considered the dependency of WSS on the electrical power, this work incorporates into an optimization-simulation framework, consideration of the impact of short and long-term limited availability of water and/or electrical energy.

This research focuses on the water supply system (WSS) facet of the multi-faceted optimization and control mechanism developed for an integrated water – energy nexus system under U.S. National Science Foundation (NSF) project 029013-0010 CRISP Type 2 – Resilient cyber-enabled electric energy and water infrastructures modeling and control under extreme mega drought scenarios. A water supply system (WSS) conveys water from sources (such as lakes, rivers, dams etc.) to the treatment plants and then to users via the water distribution systems (WDS) and/or water supply canal systems (WSCS). Optimization-simulation methodologies are developed for the real-time operation of water supply systems (WSS) under critical conditions of limited electrical energy and/or water availability due to emergencies such as extreme drought conditions, electric grid failure, and other severe conditions including natural and manmade disasters. The coupling between WSS and the power system was done through alternatively exchanging data between the power system and WSS simulations via a program control overlay developed in python.

A new methodology for WDS infrastructural-operational resilience (IOR) computation was developed as a part of this research to assess the real-time performance of the WDS under emergency conditions. The methodology combines operational resilience and component level infrastructural robustness to provide a comprehensive performance assessment tool.

The optimization-simulation and resilience computation methodologies developed were tested for both hypothetical and real example WDS and WSCS, with results depicting improved resilience for operations of the WSS under normal and emergency conditions.

Until late 1970’s the primary focus in power system modeling has been largely directed towards power system generation and transmission. Over the years, the importance of load modeling grew and having an accurate representation of load played an important role in the planning and operation studies. With an emphasis on tackling the topic of load modeling, this thesis presents the following intermediary steps in developing accurate load models:

1. Synthesis of a three-phase standard feeder and load model using the measured voltages and currents, for events such as faults and feeder pickup cases, obtained at the head of the feeder.

2. Investigated the impact of the synthesized standard feeder and load model on the sub-transmission system for a feeder pick-up case.

In the first phase of this project, a standard feeder and load model had been synthesized by capturing the current transients when three-phase voltage measurements (obtained from a local electric utility) are played-in as input to the synthesized model. The comparison between the measured currents and the simulated currents obtained using an electromagnetic transient analysis software (PSCAD) are made at the head of the designed feeder. The synthesized load model has a load composition which includes impedance loads, single-phase induction motor loads and three-phase induction motor loads. The parameters of the motor models are adjusted to obtain a good correspondence between measured three-phase currents and simulated current responses at the head of the feeder when subjected to events under which measurements were obtained on the feeder. These events include faults which occurred upstream of the feeder at a higher voltage level and a feeder pickup event that occurred downstream from the head of the feeder. Two different load compositions have been obtained for this feeder and load model depending on the types of load present in the surrounding area (residential or industrial/commercial).

The second phase of this project examines the impact of the feeder pick-up event on the 69 kV sub-transmission system using the obtained standard feeder and load model. Using the 69 kV network data obtained from a local utility, a sub-transmission network has been built in PSCAD. The main difference between the first and second phase of this project is that no measurements are played-in to the model in the latter case. Instead, the feeder pick-up event at a particular substation is simulated using the reduced equivalent of the 69 kV sub-transmission circuit together with the synthesized three-phase models of the feeder and the loads obtained in the first phase of the project. Using this analysis, it is observed that a good correspondence between the PSCAD simulated values of both three-phase voltages and currents with their corresponding measured responses at the substation is achieved.

Distributed energy resources have experienced dramatic growth and are beginning to support a significant amount of customer loads. Power electronic converters are the primary interface between the grid and the distributed energy resources/storage and offer several advantages including fast control, flexibility and high efficiency. The efficiency and the power density by volume are important performance metrics of a power converter. Compact and high efficiency power converter is beneficial to the cost-effectiveness of the converter interfaced generations. In this thesis, a soft-switching technique is proposed to reduce the size of passive components in a grid-connected converter while maintaining a high power conversion efficiency. The dynamic impact of the grid-connected converters on the power system is causing concerns as the penetration level of the converter interfaced generation increases, necessitating a detailed dynamic analysis. The unbalanced nature of distribution systems makes the conventional transient stability simulation based on positive sequence components unsuitable for this purpose. Methods suitable for the dynamic simulation of grid-connected converters in large scale unbalanced and single-phase systems are presented in this thesis to provide an effective way to study the dynamic interactions between the grid and the converters. Dynamic-link library (DLL) of converter dynamic models are developed by which converter dynamic simulations can be easily conducted in OpenDSS. To extend the converter controls testing beyond pure simulation, real-time simulation can be utilized where partial realistic scenarios can be created by including realistic components in the simulation loop. In this work, a multi-platform, real-time simulation testbed including actual digital controller platforms, communication networks and inverters has been developed for validating the microgrid concepts and implementations. A hierarchical converted based microgrid control scheme is proposed which enables the islanded microgrid operation with 100% penetration level of converter interfaced generation. Impact of the load side dynamic modeling on the converter response is also discussed in this thesis.

The reliable operation of critical infrastructure systems is of significant importance to society. The power grid and the water distribution system are two critical infrastructure systems, each of which is facilitated by a cyber-based supervisory control and data acquisition (SCADA) system. Although critical infrastructure systems are interdependent with each other due to coupling (a power grid may be the electrical supply for a water distribution system), the corresponding SCADA systems operated independently and did not share information with each other. Modern critical infrastructure systems tend to cover a larger geographic area, indicating that a SCADA control station supervising a small area is far from meeting the demands.

In this thesis, the above-mentioned problem is addressed by building a middleware to facilitate reliable and flexible communications between two or more SCADA systems. Software Defined Networking (SDN), an emerging technology providing programmable networking, is introduced to assist the middleware. In traditional networks, network configurations required highly skilled personnel for configuring many network elements. However, SDN separates the control plane from the data plane, making network intelligence logically centralized, and leaving the forwarding switches with easy commands to follow. In this way, the underlying network infrastructures can be easily manipulated by programming, supporting the future dynamic network functions.

In this work, an SDN-assisted middleware is designed and implemented with open source platforms Open Network Operating System (ONOS) and Mininet, connecting the power grids emulator and water delivery and treatment system (WDTS) emulator EPANet. Since the focus of this work is on facilitating communications between dedicated networks, data transmissions in backbone networks are emulated. For the interfaces, a multithreaded communication module is developed. It not only enables real-time information exchange between two SCADA control centers but also supports multiple-to-multiple communications simultaneously. Human intervention is allowed in case of emergency.

SDN has many attractive benefits, however, there are still obstacles like high upgrade costs when implementing this technique. Therefore, rather than replace all the routers at once, incremental deployment of hybrid SDN networks consisting of both legacy routers and programmable SDN switches is adopted in this work. We emulate on the ratio of SDN deployment against the performance of the middleware and the results on the real dataset show that a higher fraction of SDN results in a higher reliability and flexibility of data transmissions. The middleware developed may contribute to the development of the next-generation SCADA systems.

The uncertainty and variability associated with stochastic resources, such as wind and solar, coupled with the stringent reliability requirements and constantly changing system operating conditions (e.g., generator and transmission outages) introduce new challenges to power systems. Contemporary approaches to model reserve requirements within the conventional security-constrained unit commitment (SCUC) models may not be satisfactory with increasing penetration levels of stochastic resources; such conventional models pro-cure reserves in accordance with deterministic criteria whose deliverability, in the event of an uncertain realization, is not guaranteed. Smart, well-designed reserve policies are needed to assist system operators in maintaining reliability at least cost.

Contemporary market models do not satisfy the minimum stipulated N-1 mandate for generator contingencies adequately. This research enhances the traditional market practices to handle generator contingencies more appropriately. In addition, this research employs stochastic optimization that leverages statistical information of an ensemble of uncertain scenarios and data analytics-based algorithms to design and develop cohesive reserve policies. The proposed approaches modify the classical SCUC problem to include reserve policies that aim to preemptively anticipate post-contingency congestion patterns and account for resource uncertainty, simultaneously. The hypothesis is to integrate data-mining, reserve requirement determination, and stochastic optimization in a holistic manner without compromising on efficiency, performance, and scalability. The enhanced reserve procurement policies use contingency-based response sets and post-contingency transmission constraints to appropriately predict the influence of recourse actions, i.e., nodal reserve deployment, on critical transmission elements.

This research improves the conventional deterministic models, including reserve scheduling decisions, and facilitates the transition to stochastic models by addressing the reserve allocation issue. The performance of the enhanced SCUC model is compared against con-temporary deterministic models and a stochastic unit commitment model. Numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. Test results illustrate that the proposed reserve models consistently outperform the benchmark reserve policies by improving the market efficiency and enhancing the reliability of the market solution at reduced costs while maintaining scalability and market transparency. The proposed approaches require fewer ISO discretionary adjustments and can be employed by present-day solvers with minimal disruption to existing market procedures.

The flexibility in power system networks is not fully modeled in existing real-time contingency analysis (RTCA) and real-time security-constrained economic dispatch (RT SCED) applications. Thus, corrective transmission switching (CTS) is proposed in this dissertation to enable RTCA and RT SCED to take advantage of the flexibility in the transmission system in a practical way.

RTCA is first conducted to identify critical contingencies that may cause violations. Then, for each critical contingency, CTS is performed to determine the beneficial switching actions that can reduce post-contingency violations. To reduce computational burden, fast heuristic algorithms are proposed to generate candidate switching lists. Numerical simulations performed on three large-scale realistic power systems (TVA, ERCOT, and PJM) demonstrate that CTS can significantly reduce post-contingency violations. Parallel computing can further reduce the solution time.

RT SCED is to eliminate the actual overloads and potential post-contingency overloads identified by RTCA. Procedure-A, which is consistent with existing industry practices, is proposed to connect RTCA and RT SCED. As CTS can reduce post-contingency violations, higher branch limits, referred to as pseudo limits, may be available for some contingency-case network constraints. Thus, Procedure-B is proposed to take advantage of the reliability benefits provided by CTS. With the proposed Procedure-B, CTS can be modeled in RT SCED implicitly through the proposed pseudo limits for contingency-case network constraints, which requires no change to existing RT SCED tools. Numerical simulations demonstrate that the proposed Procedure-A can effectively eliminate the flow violations reported by RTCA and that the proposed Procedure-B can reduce most of the congestion cost with consideration of CTS.

The system status may be inaccurately estimated due to false data injection (FDI) cyber-attacks, which may mislead operators to adjust the system improperly and cause network violations. Thus, a two-stage FDI detection (FDID) approach, along with several metrics and an alert system, is proposed in this dissertation to detect FDI attacks. The first stage is to determine whether the system is under attack and the second stage would identify the target branch. Numerical simulations demonstrate the effectiveness of the proposed two-stage FDID approach.

The electric power system is monitored via an extensive network of sensors in tandem with data processing algorithms, i.e., an intelligent cyber layer, that enables continual observation and control of the physical system to ensure reliable operations. This data collection and processing system is vulnerable to cyber-attacks that impact the system operation status and lead to serious physical consequences, including systematic problems and failures.

This dissertation studies the physical consequences of unobservable false data injection (FDI) attacks wherein the attacker maliciously changes supervisory control and data acquisition (SCADA) or phasor measurement unit (PMU) measurements, on the electric power system. In this context, the dissertation is divided into three parts, in which the first two parts focus on FDI attacks on SCADA and the last part focuses on FDI attacks on PMUs.

The first part studies the physical consequences of FDI attacks on SCADA measurements designed with limited system information. The attacker is assumed to have perfect knowledge inside a sub-network of the entire system. Two classes of attacks with different assumptions on the attacker's knowledge outside of the sub-network are introduced. In particular, for the second class of attacks, the attacker is assumed to have no information outside of the attack sub-network, but can perform multiple linear regression to learn the relationship between the external network and the attack sub-network with historical data. To determine the worst possible consequences of both classes of attacks, a bi-level optimization problem wherein the first level models the attacker's goal and the second level models the system response is introduced.

The second part of the dissertation concentrates on analyzing the vulnerability of systems to FDI attacks from the perspective of the system. To this end, an off-line vulnerability analysis framework is proposed to identify the subsets of the test system that are more prone to FDI attacks.

The third part studies the vulnerability of PMUs to FDI attacks. Two classes of more sophisticated FDI attacks that capture the temporal correlation of PMU data are introduced. Such attacks are designed with a convex optimization problem and can always bypass both the bad data detector and the low-rank decomposition (LD) detector.

This research develops decision support mechanisms for power system operation and planning practices. Contemporary industry practices rely on deterministic approaches to approximate system conditions and handle growing uncertainties from renewable resources. The primary purpose of this research is to identify soft spots of the contemporary industry practices and propose innovative algorithms, methodologies, and tools to improve economics and reliability in power systems.

First, this dissertation focuses on transmission thermal constraint relaxation practices. Most system operators employ constraint relaxation practices, which allow certain constraints to be relaxed for penalty prices, in their market models. A proper selection of penalty prices is imperative due to the influence that penalty prices have on generation scheduling and market settlements. However, penalty prices are primarily decided today based on stakeholder negotiations or system operator’s judgments. There is little to no methodology or engineered approach around the determination of these penalty prices. This work proposes new methods that determine the penalty prices for thermal constraint relaxations based on the impact overloading can have on the residual life of the line. This study evaluates the effectiveness of the proposed methods in the short-term operational planning and long-term transmission expansion planning studies.

The second part of this dissertation investigates an advanced methodology to handle uncertainties associated with high penetration of renewable resources, which poses new challenges to power system reliability and calls attention to include stochastic modeling within resource scheduling applications. However, the inclusion of stochastic modeling within mathematical programs has been a challenge due to computational complexities. Moreover, market design issues due to the stochastic market environment make it more challenging. Given the importance of reliable and affordable electric power, such a challenge to advance existing deterministic resource scheduling applications is critical. This ongoing and joint research attempts to overcome these hurdles by developing a stochastic look-ahead commitment tool, which is a stand-alone advisory tool. This dissertation contributes to the derivation of a mathematical formulation for the extensive form two-stage stochastic programming model, the utilization of Progressive Hedging decomposition algorithm, and the initial implementation of the Progressive Hedging subproblem along with various heuristic strategies to enhance the computational performance.

Year after year, babies are dying after being left behind in cars that reach dangerous levels of heat. This project, conducted by the Hot Babies Senior Design Team, aims to solve this growing issue with the development of a hot car baby monitor. This device is integrated with multiple sensors: temperature, sound, carbon dioxide, and motion in order to detect life inside of a hot car. By using different sensors, a combination of threshold activated signals can be used to provide high quality monitoring and reduce false alarms from outside noise. Once the algorithms predict the presence of a living being inside a dangerously hot vehicle, the baby car monitor will send out text messages warning designated parents and/or guardians of the issue. The baby car monitor is further optimized with a low battery indicator and a sleep mode feature. The schedule of the project is separated into the fall and spring semesters. For the fall semester, all of the sensors and the microcontroller were purchased and tested individually. For the spring semester, all of the sensors were integrated together on a PCB and tested under hot car environments. Additionally, features such as the text messaging interface and the sleep mode were added. The budget of the final working product is roughly ~ $200. The cost includes the different sensors, microcontroller, data plan, text messaging module, and PCB. When mass produced, the cost is expected to go down.

The holomorphic embedding method (HEM) applied to the power-flow problem (HEPF) has been used in the past to obtain the voltages and flows for power systems. The incentives for using this method over the traditional Newton-Raphson based nu-merical methods lie in the claim that the method is theoretically guaranteed to converge to the operable solution, if one exists.

In this report, HEPF will be used for two power system analysis purposes:

a. Estimating the saddle-node bifurcation point (SNBP) of a system

b. Developing reduced-order network equivalents for distribution systems.

Typically, the continuation power flow (CPF) is used to estimate the SNBP of a system, which involves solving multiple power-flow problems. One of the advantages of HEPF is that the solution is obtained as an analytical expression of the embedding parameter, and using this property, three of the proposed HEPF-based methods can es-timate the SNBP of a given power system without solving multiple power-flow prob-lems (if generator VAr limits are ignored). If VAr limits are considered, the mathemat-ical representation of the power-flow problem changes and thus an iterative process would have to be performed in order to estimate the SNBP of the system. This would typically still require fewer power-flow problems to be solved than CPF in order to estimate the SNBP.

Another proposed application is to develop reduced order network equivalents for radial distribution networks that retain the nonlinearities of the eliminated portion of the network and hence remain more accurate than traditional Ward-type reductions (which linearize about the given operating point) when the operating condition changes.

Different ways of accelerating the convergence of the power series obtained as a part of HEPF, are explored and it is shown that the eta method is the most efficient of all methods tested.

The local-measurement-based methods of estimating the SNBP are studied. Non-linear Thévenin-like networks as well as multi-bus networks are built using model data to estimate the SNBP and it is shown that the structure of these networks can be made arbitrary by appropriately modifying the nonlinear current injections, which can sim-plify the process of building such networks from measurements.