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Description
The subject of this thesis is distribution level load management using a pricing signal in a Smart Grid infrastructure. The Smart Grid implements advanced meters, sensory devices and near real time communication between the elements of the system, including the distribution operator and the customer. A stated objective of the

The subject of this thesis is distribution level load management using a pricing signal in a Smart Grid infrastructure. The Smart Grid implements advanced meters, sensory devices and near real time communication between the elements of the system, including the distribution operator and the customer. A stated objective of the Smart Grid is to use sensory information to operate the electrical power grid more efficiently and cost effectively. One potential function of the Smart Grid is energy management at the distribution level, namely at the individual customer. The Smart Grid allows control of distribution level devices, including distributed energy storage and distributed generation, in operational real time. One method of load control uses an electric energy price as a control signal. The control is achieved through customer preference as the customer allows loads to respond to a dynamic pricing signal. In this thesis, a pricing signal is used to control loads for energy management at the distribution level. The model for the energy management system is created and analyzed in the z-domain due to the envisioned discrete time implementation. Test cases are used to illustrate stability and performance by analytic calculations using Mathcad and by simulation using Matlab Simulink. The envisioned control strategy is applied to the Future Renewable Electric Energy Distribution Management (FREEDM) system. The FREEDM system implements electronic (semiconductor) controls and therefore makes the proposed energy management feasible. The pricing control strategy is demonstrated to be an effective method of performing energy management in a distribution system. It is also shown that stability and near optimal response can be achieved by controlling the parameters of the system. Addition-ally, the communication bandwidth requirements for a pricing control signal are evaluated.
ContributorsBoyd, Jesse (Author) / Heydt, Gerald T (Thesis advisor) / Datta, Rajib (Committee member) / Sankar, Lalitha (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Due to great challenges from aggressive environmental regulations, increased demand due to new technologies and the integration of renewable energy sources, the energy industry may radically change the way the power system is operated and designed. With the motivation of studying and planning the future power system under these new

Due to great challenges from aggressive environmental regulations, increased demand due to new technologies and the integration of renewable energy sources, the energy industry may radically change the way the power system is operated and designed. With the motivation of studying and planning the future power system under these new challenges, the development of the new tools is required. A network equivalent that can be used in such planning tools needs to be generated based on an accurate power flow model and an equivalencing procedure that preserves the key characteristics of the original system. Considering the pervasive use of the dc power flow models, their accuracy is of great concern. The industry seems to be sanguine about the performance of dc power flow models, but recent research has shown that the performance of different formulations is highly variable. In this thesis, several dc power-flow models are analyzed theoretically and evaluated numerically in IEEE 118-bus system and Eastern Interconnection 62,000-bus system. As shown in the numerical example, the alpha-matching dc power flow model performs best in matching the original ac power flow solution. Also, the possibility of applying these dc models in the various applications has been explored and demonstrated. Furthermore, a novel hot-start optimal dc power-flow model based on ac power transfer distribution factors (PTDFs) is proposed, implemented and tested. This optimal-reactance-only dc model not only matches the original ac PF solution well, but also preserves the congestion pattern obtain from the OPF results of the original ac model. Three improved strategies were proposed for applying the bus-aggregation technique to the large-scale systems, like EI and ERCOT, to improve the execution time, and memory requirements when building a reduced equivalent model. Speed improvements of up to a factor of 200 were observed.
ContributorsQi, Yingying (Author) / Tylavsky, Daniel J (Thesis advisor) / Hedman, Kory W (Committee member) / Sankar, Lalitha (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Renewable portfolio standards prescribe for penetration of high amounts of re-newable energy sources (RES) that may change the structure of existing power systems. The load growth and changes in power flow caused by RES integration may result in re-quirements of new available transmission capabilities and upgrades of existing transmis-sion paths.

Renewable portfolio standards prescribe for penetration of high amounts of re-newable energy sources (RES) that may change the structure of existing power systems. The load growth and changes in power flow caused by RES integration may result in re-quirements of new available transmission capabilities and upgrades of existing transmis-sion paths. Construction difficulties of new transmission lines can become a problem in certain locations. The increase of transmission line thermal ratings by reconductoring using High Temperature Low Sag (HTLS) conductors is a comparatively new technology introduced to transmission expansion. A special design permits HTLS conductors to operate at high temperatures (e.g., 200oC), thereby allowing passage of higher current. The higher temperature capability increases the steady state and emergency thermal ratings of the transmission line. The main disadvantage of HTLS technology is high cost. The high cost may place special emphasis on a thorough analysis of cost to benefit of HTLS technology im-plementation. Increased transmission losses in HTLS conductors due to higher current may be a disadvantage that can reduce the attractiveness of this method. Studies described in this thesis evaluate the expenditures for transmission line re-conductoring using HTLS and the consequent benefits obtained from the potential decrease in operating cost for thermally limited transmission systems. Studies performed consider the load growth and penetration of distributed renewable energy sources according to the renewable portfolio standards for power systems. An evaluation of payback period is suggested to assess the cost to benefit ratio of HTLS upgrades. The thesis also considers the probabilistic nature of transmission upgrades. The well-known Chebyshev inequality is discussed with an application to transmission up-grades. The Chebyshev inequality is proposed to calculate minimum payback period ob-tained from the upgrades of certain transmission lines. The cost to benefit evaluation of HTLS upgrades is performed using a 225 bus equivalent of the 2012 summer peak Arizona portion of the Western Electricity Coordi-nating Council (WECC).
ContributorsTokombayev, Askhat (Author) / Heydt, Gerald T. (Thesis advisor) / Sankar, Lalitha (Committee member) / Karady, George G. (Committee member) / Arizona State University (Publisher)
Created2014
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Description
This thesis focuses on developing an integrated transmission and distribution framework that couples the two sub-systems together with due consideration to conventional demand flexibility. The proposed framework ensures accurate representation of the system resources and the network conditions when modeling the distribution system in the transmission OPF and vice-versa. It

This thesis focuses on developing an integrated transmission and distribution framework that couples the two sub-systems together with due consideration to conventional demand flexibility. The proposed framework ensures accurate representation of the system resources and the network conditions when modeling the distribution system in the transmission OPF and vice-versa. It is further used to develop an accurate pricing mechanism (Distribution-based Location Marginal Pricing), which is reflective of the moment-to-moment costs of generating and delivering electrical energy, for the distribution system. By accurately modeling the two sub-systems, we can improve the economic efficiency and the system reliability, as the price sensitive resources can be controlled to behave in a way that benefits the power system as a whole.
ContributorsSinghal, Nikita G (Author) / Hedman, Kory W (Thesis advisor) / Tylavsky, Daniel J (Committee member) / Sankar, Lalitha (Committee member) / Arizona State University (Publisher)
Created2014
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Description
The electric power system is one of the largest, most complicated, and most important cyber-physical systems in the world. The link between the cyber and physical level is the Supervisory Control and Data Acquisition (SCADA) systems and Energy Management Systems (EMS). Their functions include monitoring the real-time system operation

The electric power system is one of the largest, most complicated, and most important cyber-physical systems in the world. The link between the cyber and physical level is the Supervisory Control and Data Acquisition (SCADA) systems and Energy Management Systems (EMS). Their functions include monitoring the real-time system operation through state estimation (SE), controlling the system to operate reliably, and optimizing the system operation efficiency. The SCADA acquires the noisy measurements, such as voltage angle and magnitude, line power flows, and line current magnitude, from the remote terminal units (RTUs). These raw data are firstly sent to the SE, which filters all the noisy data and derives the best estimate of the system state. Then the estimated states are used for other EMS functions, such as contingency analysis, optimal power flow, etc.

In the existing state estimation process, there is no defense mechanism for any malicious attacks. Once the communication channel between the SCADA and RTUs is hijacked by the attacker, the attacker can perform a man-in-middle attack and send data of its choice. The only step that can possibly detect the attack during the state estimation process is the bad data detector. Unfortunately, even the bad data detector is unable to detect a certain type of attack, known as the false data injection (FDI) attacks.

Diagnosing the physical consequences of such attacks, therefore, is very important to understand system stability. In this thesis, theoretical general attack models for AC and DC attacks are given and an optimization problem for the worst-case overload attack is formulated. Furthermore, physical consequences of FDI attacks, based on both DC and AC model, are addressed. Various scenarios with different attack targets and system configurations are simulated. The details of the research, results obtained and conclusions drawn are presented in this document.
ContributorsLiang, Jingwen (Author) / Sankar, Lalitha (Thesis advisor) / Kosut, Oliver (Thesis advisor) / Hedman, Kory (Committee member) / Arizona State University (Publisher)
Created2015
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Description
The objective of this thesis is to detect certain cyber attacks in a power distribution ener-gy management system in a Smart Grid infrastructure. In the Smart Grid, signals are sent be-tween the distribution operator and the customer on a real-time basis. Signals are used for auto-mated energy management, protection and

The objective of this thesis is to detect certain cyber attacks in a power distribution ener-gy management system in a Smart Grid infrastructure. In the Smart Grid, signals are sent be-tween the distribution operator and the customer on a real-time basis. Signals are used for auto-mated energy management, protection and energy metering. This thesis aims at making use of various signals in the system to detect cyber attacks. The focus of the thesis is on a cyber attack that changes the parameters of the energy management system. The attacks considered change the set points, thresholds for energy management decisions, signal multipliers, and other digitally stored parameters that ultimately determine the transfer functions of the components. Since the distribution energy management system is assumed to be in a Smart Grid infrastructure, customer demand is elastic to the price of energy. The energy pricing is represented by a distribution loca-tional marginal price. A closed loop control system is utilized as representative of the energy management system. Each element of the system is represented by a linear transfer function. Studies are done via simulations and these simulations are performed in Matlab SimuLink. The analytical calculations are done using Matlab.

Signals from the system are used to obtain the frequency response of the component transfer functions. The magnitude and phase angle of the transfer functions are obtained using the fast Fourier transform. The transfer function phase angles of base cases (no attack) are stored and are compared with the phase angles calculated at regular time intervals. If the difference in the phase characteristics is greater than a set threshold, an alarm is issued indicating the detection of a cyber attack.

The developed algorithm is designed for use in the envisioned Future Renewable Electric Energy Delivery and Management (FREEDM) system. Examples are shown for the noise free and noisy cases.
ContributorsRavi, Vaithinathan (Author) / Heydt, Gerald T (Thesis advisor) / Karady, George G. (Committee member) / Sankar, Lalitha (Committee member) / Arizona State University (Publisher)
Created2014
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Description
The large distributed electric power system is a hierarchical network involving the

transportation of power from the sources of power generation via an intermediate

densely connected transmission network to a large distribution network of end-users

at the lowest level of the hierarchy. At each level of the hierarchy (generation/ trans-

mission/ distribution), the system

The large distributed electric power system is a hierarchical network involving the

transportation of power from the sources of power generation via an intermediate

densely connected transmission network to a large distribution network of end-users

at the lowest level of the hierarchy. At each level of the hierarchy (generation/ trans-

mission/ distribution), the system is managed and monitored with a combination of

(a) supervisory control and data acquisition (SCADA); and (b) energy management

systems (EMSs) that process the collected data and make control and actuation de-

cisions using the collected data. However, at all levels of the hierarchy, both SCADA

and EMSs are vulnerable to cyber attacks. Furthermore, given the criticality of the

electric power infrastructure, cyber attacks can have severe economic and social con-

sequences.

This thesis focuses on cyber attacks on SCADA and EMS at the transmission

level of the electric power system. The goal is to study the consequences of three

classes of cyber attacks that can change topology data. These classes include: (i)

unobservable state-preserving cyber attacks that only change the topology data; (ii)

unobservable state-and-topology cyber-physical attacks that change both states and

topology data to enable a coordinated physical and cyber attack; and (iii) topology-

targeted man-in-the-middle (MitM) communication attacks that alter topology data

shared during inter-EMS communication. Specically, attack class (i) and (ii) focus on

the unobservable attacks on single regional EMS while class (iii) focuses on the MitM

attacks on communication links between regional EMSs. For each class of attacks,

the theoretical attack model and the implementation of attacks are provided, and the

worst-case attack and its consequences are exhaustively studied. In particularly, for

class (ii), a two-stage optimization problem is introduced to study worst-case attacks

that can cause a physical line over

ow that is unobservable in the cyber layer. The long-term implication and the system anomalies are demonstrated via simulation.

For attack classes (i) and (ii), both mathematical and experimental analyses sug-

gest that these unobservable attacks can be limited or even detected with resiliency

mechanisms including load monitoring, anomalous re-dispatches checking, and his-

torical data comparison. For attack class (iii), countermeasures including anomalous

tie-line interchange verication, anomalous re-dispatch alarms, and external contin-

gency lists sharing are needed to thwart such attacks.
ContributorsZhang, Jiazi (Author) / Sankar, Lalitha (Thesis advisor) / Hedman, Kory (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2015
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Description
Understanding the graphical structure of the electric power system is important

in assessing reliability, robustness, and the risk of failure of operations of this criti-

cal infrastructure network. Statistical graph models of complex networks yield much

insight into the underlying processes that are supported by the network. Such gen-

erative graph models are also

Understanding the graphical structure of the electric power system is important

in assessing reliability, robustness, and the risk of failure of operations of this criti-

cal infrastructure network. Statistical graph models of complex networks yield much

insight into the underlying processes that are supported by the network. Such gen-

erative graph models are also capable of generating synthetic graphs representative

of the real network. This is particularly important since the smaller number of tradi-

tionally available test systems, such as the IEEE systems, have been largely deemed

to be insucient for supporting large-scale simulation studies and commercial-grade

algorithm development. Thus, there is a need for statistical generative models of

electric power network that capture both topological and electrical properties of the

network and are scalable.

Generating synthetic network graphs that capture key topological and electrical

characteristics of real-world electric power systems is important in aiding widespread

and accurate analysis of these systems. Classical statistical models of graphs, such as

small-world networks or Erd}os-Renyi graphs, are unable to generate synthetic graphs

that accurately represent the topology of real electric power networks { networks

characterized by highly dense local connectivity and clustering and sparse long-haul

links.

This thesis presents a parametrized model that captures the above-mentioned

unique topological properties of electric power networks. Specically, a new Cluster-

and-Connect model is introduced to generate synthetic graphs using these parameters.

Using a uniform set of metrics proposed in the literature, the accuracy of the proposed

model is evaluated by comparing the synthetic models generated for specic real

electric network graphs. In addition to topological properties, the electrical properties

are captured via line impedances that have been shown to be modeled reliably by well-studied heavy tailed distributions. The details of the research, results obtained and

conclusions drawn are presented in this document.
ContributorsHu, Jiale (Author) / Sankar, Lalitha (Thesis advisor) / Vittal, Vijay (Committee member) / Scaglione, Anna (Committee member) / Arizona State University (Publisher)
Created2015
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Description
In order to meet the world’s growing energy need, it is necessary to create a reliable, robust, and resilient electric power grid. One way to ensure the creation of such a grid is through the extensive use of synchrophasor technology that is based on devices called phasor measurement units (PMUs),

In order to meet the world’s growing energy need, it is necessary to create a reliable, robust, and resilient electric power grid. One way to ensure the creation of such a grid is through the extensive use of synchrophasor technology that is based on devices called phasor measurement units (PMUs), and their derivatives, such as μPMUs. Global positioning system (GPS) time-synchronized wide-area monitoring, protection, and control enabled by PMUs has opened up new ways in which the power grid can tackle the problems it faces today. However, with implementation of new technologies comes new challenges, and one of those challenges when it comes to PMUs is the misuse of GPS as a method to obtain a time reference.The use of GPS in PMUs is very intuitive as it is a convenient method to time stamp electrical signals, which in turn helps provide an accurate snapshot of the performance of the PMU-monitored section of the grid. However, GPS is susceptible to different types of signal interruptions due to natural (such as weather) or unnatural (jamming, spoofing) causes. The focus of this thesis is on demonstrating the practical feasibility of GPS spoofing attacks on PMUs, as well as developing novel countermeasures for them. Prior research has demonstrated that GPS spoofing attacks on PMUs can cripple power system operation. The research conducted here first provides an experimental evidence of the feasibility of such an attack using commonly available digital radios known as software defined radio (SDR). Next, it introduces a new countermeasure against such attacks using GPS signal redundancy and low power long range (LoRa) spread spectrum modulation technique. The proposed approach checks the integrity of the GPS signal at remote locations and compares the data with the PMU’s current output. This countermeasure is a steppingstone towards developing a ready-to-deploy system that can provide an instant solution to the GPS spoofing detection problem for PMUs already placed in the power grid.
ContributorsSaadedeen, Fakhri G (Author) / Pal, Anamitra (Thesis advisor) / Sankar, Lalitha (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Signal compressed using classical compression methods can be acquired using brute force (i.e. searching for non-zero entries in component-wise). However, sparse solutions require combinatorial searches of high computations. In this thesis, instead, two Bayesian approaches are considered to recover a sparse vector from underdetermined noisy measurements. The first is constructed

Signal compressed using classical compression methods can be acquired using brute force (i.e. searching for non-zero entries in component-wise). However, sparse solutions require combinatorial searches of high computations. In this thesis, instead, two Bayesian approaches are considered to recover a sparse vector from underdetermined noisy measurements. The first is constructed using a Bernoulli-Gaussian (BG) prior distribution and is assumed to be the true generative model. The second is constructed using a Gamma-Normal (GN) prior distribution and is, therefore, a different (i.e. misspecified) model. To estimate the posterior distribution for the correctly specified scenario, an algorithm based on generalized approximated message passing (GAMP) is constructed, while an algorithm based on sparse Bayesian learning (SBL) is used for the misspecified scenario. Recovering sparse signal using Bayesian framework is one class of algorithms to solve the sparse problem. All classes of algorithms aim to get around the high computations associated with the combinatorial searches. Compressive sensing (CS) is a widely-used terminology attributed to optimize the sparse problem and its applications. Applications such as magnetic resonance imaging (MRI), image acquisition in radar imaging, and facial recognition. In CS literature, the target vector can be recovered either by optimizing an objective function using point estimation, or recovering a distribution of the sparse vector using Bayesian estimation. Although Bayesian framework provides an extra degree of freedom to assume a distribution that is directly applicable to the problem of interest, it is hard to find a theoretical guarantee of convergence. This limitation has shifted some of researches to use a non-Bayesian framework. This thesis tries to close this gab by proposing a Bayesian framework with a suggested theoretical bound for the assumed, not necessarily correct, distribution. In the simulation study, a general lower Bayesian Cram\'er-Rao bound (BCRB) bound is extracted along with misspecified Bayesian Cram\'er-Rao bound (MBCRB) for GN model. Both bounds are validated using mean square error (MSE) performances of the aforementioned algorithms. Also, a quantification of the performance in terms of gains versus losses is introduced as one main finding of this report.
ContributorsAlhowaish, Abdulhakim (Author) / Richmond, Christ D (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Sankar, Lalitha (Committee member) / Arizona State University (Publisher)
Created2019