Power systems are undergoing a significant transformation as a result of the retirements of conventional coal-fired generation units and the increasing integration of converter interfaced renewable resources. The instantaneous renewable generation penetration as a percentage of the load served in megawatt (MW), in some areas of the United States (U.S.) sometimes approaches over 50 percent. These changes have introduced new challenges for reliability studies considering the two functional reliability aspects, i.e., adequacy and the dynamic security or operating reliability.

Adequacy assessment becomes more complex due to the variability introduced by renewable energy generation. The traditionally used reserve margin only considers projected peak demand and would be inadequate since it does not consider an evaluation of off-peak conditions that could also be critical due to the variable renewable generation. Therefore, in order to address the impact of variable renewable generation, a probabilistic evaluation that studies all hours of a year based on statistical characteristics is a necessity to identify the adequacy risks. On the other hand, the system dynamic behavior is also changing. Converter interfaced generation resources have different dynamic characteristics from the conventional synchronous units and inherently do not participate in grid regulation functions such as frequency control and voltage control that are vital to maintaining operating reliability. In order to evaluate these evolving grid characteristics, comprehensive reliability evaluation approaches that consider system stochasticity and evaluate both adequacy and dynamic security are important to identify potential system risks in this transforming environment.

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 government support towards green energy sources for the better future of the planet has changed the perspective of the people towards the usage of green energy. Among renewables, solar is one of the important and easily accessible resources to convert energy from the sun directly into electricity and this system has gained fame since the past three decades.

SRP has set up a 6.36 kW PV and 19.4 kWh battery system on the rooftop of Engineering Research Center (ERC). The system is grid-connected and ASU (Arizona State University) has developed two load banks with a minimum step of 72 watts to simulate different residential load profiles and perform other research objectives.

A customer benefit analysis is performed for residential customers with photovoltaic (PV) systems and energy storage particularly in the state of Arizona. By optimizing the use of energy storage device, the algorithm aims at maximizing the profit and minimizing utility bills in accordance with the demand charge algorithm of the local utility. This part of the research has been published as a conference paper in IEEE PES General Meeting 2017.

A transient test is performed on the PV-battery during the on-grid mode and the off-grid mode to study the system behaviour during the transients. An algorithm is developed by the ASU research team to minimize the demand charge tariff for the residential customers. A statistical analysis is performed on the data collected from the system using a MATLAB algorithm.

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.

Electric power system security assessment is one of the most important requirements for operational and resource planning of the bulk power system ensuring safe operation of the power system for all credible contingencies. This deterministic approach usually provides a conservative criterion and can result in expensive bulk system expansion plans or conservative operating limits. Furthermore, with increased penetration of converter-based renewable generation in the electric grid, the dynamics of the grid are changing. In addition, the variability and intermittency associated with the renewable energy sources introduce uncertainty in the electricity grid. Since security margins have direct economic impact on the utilities; more clarity is required regarding the basis on which security decisions are made. The main objective of this work is to provide an approach for risk-based security assessment (RBSA) to define dynamic reliability standards in future electricity grids. RBSA provides a measure of the security of the power system that combines both the likelihood and the consequence of an event.

A novel approach to estimate the impact of transient stability is presented by modeling several important protection systems within the transient stability analysis. A robust operational metric to quantify the impact of transient instability event is proposed that incorporates the effort required to stabilize any transiently unstable event. The effect of converter-interfaced renewable energy injection on system reliability is investigated us-ing RBSA. A robust RBSA diagnostics tool is developed which provides an interactive user interface where the RBSA results and contingency ranking reports can be explored and compared based on specific user inputs without executing time domain simulations or risk calculations, hence providing a fast and robust approach for handling large time domain simulation and risk assessment data. The results show that RBSA can be used effectively in system planning to select security limits. Comparison of RBSA with deterministic methods show that RBSA not only provides less conservative results, it also illustrates the bases on which such security decisions are made. RBSA helps in identifying critical aspects of system reliability that is not possible using the deterministic reliability techniques.

Large-scale integration of wind generation introduces planning and operational difficulties due to the intermittent and highly variable nature of wind. In particular, the generation from non-hydro renewable resources is inherently variable and often times difficult to predict. Integrating significant amounts of renewable generation, thus, presents a challenge to the power systems operators, requiring additional flexibility, which may incur a decrease of conventional generation capacity.

This research investigates the algorithms employing emerging computational advances in system operation policies that can improve the flexibility of the electricity industry. The focus of this study is on flexible operation policies for renewable generation, particularly wind generation. Specifically, distributional forecasts of windfarm generation are used to dispatch a “discounted” amount of the wind generation, leaving a reserve margin that can be used for reserve if needed. This study presents systematic mathematic formulations that allow the operator incorporate this flexibility into the operation optimization model to increase the benefits in the energy and reserve scheduling procedure. Incorporating this formulation into the dispatch optimization problem provides the operator with the ability of using forecasted probability distributions as well as the off-line generated policies to choose proper approaches for operating the system in real-time. Methods to generate such policies are discussed and a forecast-based approach for developing wind margin policies is presented. The impacts of incorporating such policies in the electricity market models are also investigated.

Present distribution infrastructure is designed mainly for uni-directional power flow with well-controlled generation. An increase in the inverter-interfaced photovoltaic (PV) systems requires a thorough re-examination of the design, operation, protection and control of distribution systems. In order to understand the impact of high penetration of PV generation, this work conducts an automated and detailed modeling of a power distribution system. The simulation results of the modeled distribution feeder have been verified with the field measurements.

Based on the feeder model, this work studies the impact of the PV systems on voltage profiles under various scenarios, including reallocation of the PV systems, reactive power support from the PV inverters, and settings of the load-tap changing transformers in coordination with the PV penetration. Design recommendations have been made based on the simulation results to improve the voltage profiles in the feeder studied.

To carry out dynamic studies related to high penetration of PV systems, this work proposes a differential algebraic equation (DAE) based dynamic modeling and analysis method. Different controllers including inverter current controllers, anti-islanding controllers and droop controllers, are designed and tested in large systems. The method extends the capability of the distribution system analysis tools, to help conduct dynamic analyses in large unbalanced distribution systems.

Another main contribution of this work is related to the investigation of the PV impacts on the feeder protection coordination. Various protection coordination types, including fuse-fuse, recloser-fuse, relay-fuse and relay-recloser have been studied. The analyses provide a better understanding of the relay and recloser settings under different configurations of the PV interconnection transformers, PV penetration levels, and fault types.

A decision tree and fuzzy logic based fault location identification process has also been proposed in this work. The process is composed of the off-line training of the decision tree, and the on-line analysis of the fault events. Fault current contribution from the PV systems, as well as the variation of the fault resistance have been taken into consideration. Two actual fault cases with the event data recorded were used to examine the effectiveness of the fault identification process.

With the growing importance of underground power systems and the need for greater reliability of the power supply, cable monitoring and accurate fault location detection has become an increasingly important issue. The presence of inherent random fluctuations in power system signals can be used to extract valuable information about the condition of system equipment. One such component is the power cable, which is the primary focus of this research.

This thesis investigates a unique methodology that allows online monitoring of an underground power cable. The methodology analyzes conventional power signals in the frequency domain to monitor the condition of a power cable.

First, the proposed approach is analyzed theoretically with the help of mathematical computations. Frequency domain analysis techniques are then used to compute the power spectral density (PSD) of the system signals. The importance of inherent noise in the system, a key requirement of this methodology, is also explained. The behavior of resonant frequencies, which are unique to every system, are then analyzed under different system conditions with the help of mathematical expressions.

Another important aspect of this methodology is its ability to accurately estimate cable fault location. The process is online and hence does not require the system to be disconnected from the grid. A single line to ground fault case is considered and the trend followed by the resonant frequencies for different fault positions is observed.

The approach is initially explained using theoretical calculations followed by simulations in MATLAB/Simulink. The validity of this technique is proved by comparing the results obtained from theory and simulation to actual measurement data.

Constraint relaxation by definition means that certain security, operational, or financial constraints are allowed to be violated in the energy market model for a predetermined penalty price. System operators utilize this mechanism in an effort to impose a price-cap on shadow prices throughout the market. In addition, constraint relaxations can serve as corrective approximations that help in reducing the occurrence of infeasible or extreme solutions in the day-ahead markets. This work aims to capture the impact constraint relaxations have on system operational security. Moreover, this analysis also provides a better understanding of the correlation between DC market models and AC real-time systems and analyzes how relaxations in market models propagate to real-time systems. This information can be used not only to assess the criticality of constraint relaxations, but also as a basis for determining penalty prices more accurately.

Constraint relaxations practice was replicated in this work using a test case and a real-life large-scale system, while capturing both energy market aspects and AC real-time system performance. System performance investigation included static and dynamic security analysis for base-case and post-contingency operating conditions. PJM peak hour loads were dynamically modeled in order to capture delayed voltage recovery and sustained depressed voltage profiles as a result of reactive power deficiency caused by constraint relaxations. Moreover, impacts of constraint relaxations on operational system security were investigated when risk based penalty prices are used. Transmission lines in the PJM system were categorized according to their risk index and each category was as-signed a different penalty price accordingly in order to avoid real-time overloads on high risk lines.

This work also extends the investigation of constraint relaxations to post-contingency relaxations, where emergency limits are allowed to be relaxed in energy market models. Various scenarios were investigated to capture and compare between the impacts of base-case and post-contingency relaxations on real-time system performance, including the presence of both relaxations simultaneously. The effect of penalty prices on the number and magnitude of relaxations was investigated as well.

ABSTRACT

This dissertation introduces a real-time topology monitoring scheme for power systems intended to provide enhanced situational awareness during major system disturbances. The topology monitoring scheme requires accurate real-time topology information to be effective. This scheme is supported by advances in transmission line outage detection based on data-mining phasor measurement unit (PMU) measurements.

A network flow analysis scheme is proposed to track changes in user defined minimal cut sets within the system. This work introduces a new algorithm used to update a previous network flow solution after the loss of a single system branch. The proposed new algorithm provides a significantly decreased solution time that is desired in a real- time environment. This method of topology monitoring can provide system operators with visual indications of potential problems in the system caused by changes in topology.

This work also presents a method of determining all singleton cut sets within a given network topology called the one line remaining (OLR) algorithm. During operation, if a singleton cut set exists, then the system cannot withstand the loss of any one line and still remain connected. The OLR algorithm activates after the loss of a transmission line and determines if any singleton cut sets were created. These cut sets are found using properties of power transfer distribution factors and minimal cut sets.

The topology analysis algorithms proposed in this work are supported by line outage detection using PMU measurements aimed at providing accurate real-time topology information. This process uses a decision tree (DT) based data-mining approach to characterize a lost tie line in simulation. The trained DT is then used to analyze PMU measurements to detect line outages. The trained decision tree was applied to real PMU measurements to detect the loss of a 500 kV line and had no misclassifications.

The work presented has the objective of enhancing situational awareness during significant system disturbances in real time. This dissertation presents all parts of the proposed topology monitoring scheme and justifies and validates the methodology using a real system event.