Transmission switching (TS) has shown to be an ef-fective power flow control tool. TS can reduce the system cost, improve system reliability, and enhance the management of in-termittent renewable resources. This paper addresses the state of the art problem of…
Transmission switching (TS) has shown to be an ef-fective power flow control tool. TS can reduce the system cost, improve system reliability, and enhance the management of in-termittent renewable resources. This paper addresses the state of the art problem of TS by developing an AC-based real-time con-tingency analysis (RTCA) package with TS. The package is tested on real power system data, taken from energy management sys-tems of PJM, TVA, and ERCOT. The results show that post-contingency corrective switching is a ready to be implemented transformational technology that provides substantial reliability gains. The computational time and the performance of the devel-oped RTCA package, reported in the paper, are promising.
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Reserve requirements serve as a proxy for N-1 reli-ability in the security-constrained unit commitment (SCUC) problem. However, there is no guarantee that the reserve is deliv-erable for all scenarios (post-contingency states). One cheap way to improve reserve deliverability is to…
Reserve requirements serve as a proxy for N-1 reli-ability in the security-constrained unit commitment (SCUC) problem. However, there is no guarantee that the reserve is deliv-erable for all scenarios (post-contingency states). One cheap way to improve reserve deliverability is to harness the flexibility of the transmission network. Flexible AC transmission system (FACTS) devices are able to significantly improve the transfer capability. However, FACTS utilization is limited today due to the complexi-ties these devices introduce to the DC optimal power flow prob-lem (DCOPF). With a linear objective, the traditional DCOPF is a linear program (LP); when variable impedance based FACTS devices are taken into consideration, the problem becomes a non-linear program (NLP). A reformulation of the NLP to a mixed integer linear program, for day-ahead corrective operation of FACTS devices, is presented in this paper. Engineering insight is then introduced to further reduce the complexity to an LP. Alt-hough optimality is not guaranteed, the simulation studies on the IEEE 118-bus system show that the method finds the globally optimal solution in 98.8% of the cases. Even when the method did not find the optimal solution, it was able to converge to a near-optimal solution, which substantially improved the reliability, very quickly.
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Transmission systems are under stress and need to be upgraded. Better utilization of the existing grid provides a fast and cheap alternative to building new transmission. One way to improve the utilization of the transmission network is power flow control…
Transmission systems are under stress and need to be upgraded. Better utilization of the existing grid provides a fast and cheap alternative to building new transmission. One way to improve the utilization of the transmission network is power flow control via flexible AC transmission system (FACTS) devices. While FACTS devices are used today, the utilization of these devices is limited; traditional dispatch models (e.g., security con-strained economic dispatch) assume a fixed, static transmission grid even though it is rather flexible. The primary barrier is the complexity that is added to the power flow problem. The mathe-matical representation of the DC optimal power flow, with the added modeling of FACTS devices, is a nonlinear program (NLP). This paper presents a method to convert this NLP into a mixed-integer linear program (MILP). The MILP is reformulat-ed as a two-stage linear program, which enforces the same sign for the voltage angle differences for the lines equipped with FACTS. While this approximation does not guarantee optimality, more than 98% of the presented empirical results, based on the IEEE 118 bus and Polish system, achieved global optimality. In the case of suboptimal solutions, the savings were still significant and the solution time was dramatically reduced.
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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…
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.
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Reserve requirements promote reliability by ensuring resources are available to rebalance the power system following random disturbances. However, reliability is not guaranteed when dispatch is limited by transmission constraints. In this work, we propose a modified form of reserve requirement…
Reserve requirements promote reliability by ensuring resources are available to rebalance the power system following random disturbances. However, reliability is not guaranteed when dispatch is limited by transmission constraints. In this work, we propose a modified form of reserve requirement that identifies response sets for distinct contingency scenarios. The approach disqualifies reserve from counting towards a particular scenario if transmission constraints are likely to render that reserve undeliverable. A decomposition algorithm for security-constrained unit commitment dynamically updates the response sets to address changing conditions. Testing on the RTS 96 test case demonstrates the approach applied in tandem with existing reserve policies to avoid situations where reserve is not deliverable due to transmission constraints. Operational implications of the proposed method are discussed.
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The main objective of this research is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in engineered complex adaptive systems (ECASs). A multi-layer conceptual framework and modeling approach including behavioral and structural aspects…
The main objective of this research is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in engineered complex adaptive systems (ECASs). A multi-layer conceptual framework and modeling approach including behavioral and structural aspects is provided to describe the structure of a class of engineered complex systems and predict their future adaptive patterns. The approach allows the examination of complexity in the structure and the behavior of components as a result of their connections and in relation to their environment. This research describes and uses the major differences of natural complex adaptive systems (CASs) with artificial/engineered CASs to build a framework and platform for ECAS. While this framework focuses on the critical factors of an engineered system, it also enables one to synthetically employ engineering and mathematical models to analyze and measure complexity in such systems. In this way concepts of complex systems science are adapted to management science and system of systems engineering. In particular an integrated consumer-based optimization and agent-based modeling (ABM) platform is presented that enables managers to predict and partially control patterns of behaviors in ECASs. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). The approach integrates social networks, social science, complexity theory, and diffusion theory. Furthermore, it has unique and significant contribution in exploring and representing concrete managerial insights for ECASs and offering new optimized actions and modeling paradigms in agent-based simulation.
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Under the framework of intelligent management of power grids by leveraging advanced information, communication and control technologies, a primary objective of this study is to develop novel data mining and data processing schemes for several critical applications that can enhance…
Under the framework of intelligent management of power grids by leveraging advanced information, communication and control technologies, a primary objective of this study is to develop novel data mining and data processing schemes for several critical applications that can enhance the reliability of power systems. Specifically, this study is broadly organized into the following two parts: I) spatio-temporal wind power analysis for wind generation forecast and integration, and II) data mining and information fusion of synchrophasor measurements toward secure power grids. Part I is centered around wind power generation forecast and integration. First, a spatio-temporal analysis approach for short-term wind farm generation forecasting is proposed. Specifically, using extensive measurement data from an actual wind farm, the probability distribution and the level crossing rate of wind farm generation are characterized using tools from graphical learning and time-series analysis. Built on these spatial and temporal characterizations, finite state Markov chain models are developed, and a point forecast of wind farm generation is derived using the Markov chains. Then, multi-timescale scheduling and dispatch with stochastic wind generation and opportunistic demand response is investigated. Part II focuses on incorporating the emerging synchrophasor technology into the security assessment and the post-disturbance fault diagnosis of power systems. First, a data-mining framework is developed for on-line dynamic security assessment by using adaptive ensemble decision tree learning of real-time synchrophasor measurements. Under this framework, novel on-line dynamic security assessment schemes are devised, aiming to handle various factors (including variations of operating conditions, forced system topology change, and loss of critical synchrophasor measurements) that can have significant impact on the performance of conventional data-mining based on-line DSA schemes. Then, in the context of post-disturbance analysis, fault detection and localization of line outage is investigated using a dependency graph approach. It is shown that a dependency graph for voltage phase angles can be built according to the interconnection structure of power system, and line outage events can be detected and localized through networked data fusion of the synchrophasor measurements collected from multiple locations of power grids. Along a more practical avenue, a decentralized networked data fusion scheme is proposed for efficient fault detection and localization.
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This thesis presents an overview of the calculation and application of locational marginal prices in electric power systems particularly pertaining to the distribution system. The terminology proposed is a distribution locational marginal price or DLMP. The calculation of locational process…
This thesis presents an overview of the calculation and application of locational marginal prices in electric power systems particularly pertaining to the distribution system. The terminology proposed is a distribution locational marginal price or DLMP. The calculation of locational process in distribution engineering is conjectured and discussed. The use of quadratic programming for this calculation is proposed and illustrated. A small four bus test bed exemplifies the concept and then the concept is expanded to the IEEE 34 bus distribution system. Alternatives for the calculation are presented, and approximations are reviewed. Active power losses in the system are modeled and incorporated by two different methods. These calculation methods are also applied to the 34 bus system. The results from each method are compared to results found using the PowerWorld simulator. The application of energy management using the DLMP to control load is analyzed as well. This analysis entails the use of the DLMP to cause certain controllable loads to decrease when the DLMP is high, and vice-versa. Tests are done to illustrate the impact of energy management using DLMPs for residential, commercial, and industrial controllable loads. Results showing the dynamics of the loads are shown. The use and characteristics of Matlab function FMINCON are presented in an appendix.
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This dissertation presents a new hybrid fault current limiter (FCL) topology that is primarily intended to protect single-phase power equipment. It can however be extended to protect three phase systems but would need three devices to protect each individual phase.…
This dissertation presents a new hybrid fault current limiter (FCL) topology that is primarily intended to protect single-phase power equipment. It can however be extended to protect three phase systems but would need three devices to protect each individual phase. In comparison against the existing fault current limiter technology, the salient fea-tures of the proposed topology are: a) provides variable impedance that provides a 50% reduction in prospective fault current; b) near instantaneous response time which is with-in the first half cycle (1-4 ms); c) the use of semiconductor switches as the commutating switch which produces reduced leakage current, reduced losses, improved reliability, and a faster switch time (ns-µs); d) zero losses in steady-state operation; e) use of a Neodym-ium (NdFeB) permanent magnet as the limiting impedance which reduces size, cost, weight, eliminates DC biasing and cooling costs; f) use of Pulse Width Modulation (PWM) to control the magnitude of the fault current to a user's desired level. g) experi-mental test system is developed and tested to prove the concepts of the proposed FCL. This dissertation presents the proposed topology and its working principle backed up with numerical verifications, simulation results, and hardware implementation results. Conclu-sions and future work are also presented.
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