Matching Items (5)
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- All Subjects: Electric power systems
- Creators: Kosut, Oliver
- Creators: Scaglione, Anna
- Resource Type: Text
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 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.
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
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 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.
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
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 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.
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
Description
Lossy compression is a form of compression that slightly degrades a signal in ways that are ideally not detectable to the human ear. This is opposite to lossless compression, in which the sample is not degraded at all. While lossless compression may seem like the best option, lossy compression, which is used in most audio and video, reduces transmission time and results in much smaller file sizes. However, this compression can affect quality if it goes too far. The more compression there is on a waveform, the more degradation there is, and once a file is lossy compressed, this process is not reversible. This project will observe the degradation of an audio signal after the application of Singular Value Decomposition compression, a lossy compression that eliminates singular values from a signal’s matrix.
ContributorsHirte, Amanda (Author) / Kosut, Oliver (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Two thirds of the U.S. power systems are operated under market structures. A good market design should maximize social welfare and give market participants proper incentives to follow market solutions. Pricing schemes play very important roles in market design.
Locational marginal pricing scheme is the core pricing scheme in energy markets. Locational marginal prices are good pricing signals for dispatch marginal costs. However, the locational marginal prices alone are not incentive compatible since energy markets are non-convex markets. Locational marginal prices capture dispatch costs but fail to capture commitment costs such as startup cost, no-load cost, and shutdown cost. As a result, uplift payments are paid to generators in markets in order to provide incentives for generators to follow market solutions. The uplift payments distort pricing signals.
In this thesis, pricing schemes in electric energy markets are studied. In the first part, convex hull pricing scheme is studied and the pricing model is extended with network constraints. The subgradient algorithm is applied to solve the pricing model. In the second part, a stochastic dispatchable pricing model is proposed to better address the non-convexity and uncertainty issues in day-ahead energy markets. In the third part, an energy storage arbitrage model with the current locational marginal price scheme is studied. Numerical test cases are studied to show the arguments in this thesis.
The overall market and pricing scheme design is a very complex problem. This thesis gives a thorough overview of pricing schemes in day-ahead energy markets and addressed several key issues in the markets. New pricing schemes are proposed to improve market efficiency.
Locational marginal pricing scheme is the core pricing scheme in energy markets. Locational marginal prices are good pricing signals for dispatch marginal costs. However, the locational marginal prices alone are not incentive compatible since energy markets are non-convex markets. Locational marginal prices capture dispatch costs but fail to capture commitment costs such as startup cost, no-load cost, and shutdown cost. As a result, uplift payments are paid to generators in markets in order to provide incentives for generators to follow market solutions. The uplift payments distort pricing signals.
In this thesis, pricing schemes in electric energy markets are studied. In the first part, convex hull pricing scheme is studied and the pricing model is extended with network constraints. The subgradient algorithm is applied to solve the pricing model. In the second part, a stochastic dispatchable pricing model is proposed to better address the non-convexity and uncertainty issues in day-ahead energy markets. In the third part, an energy storage arbitrage model with the current locational marginal price scheme is studied. Numerical test cases are studied to show the arguments in this thesis.
The overall market and pricing scheme design is a very complex problem. This thesis gives a thorough overview of pricing schemes in day-ahead energy markets and addressed several key issues in the markets. New pricing schemes are proposed to improve market efficiency.
ContributorsLi, Chao (Author) / Hedman, Kory (Thesis advisor) / Sankar, Lalitha (Committee member) / Scaglione, Anna (Committee member) / Arizona State University (Publisher)
Created2016