Browsing by Author "Khodaei, Amin"
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Item A Quickest Detection Framework for Smart Grid(2013-05) Huang, Yi 1984-; Han, Zhu; Ogmen, Haluk; Khodaei, Amin; Zheng, Rong; Qian, LijunThe smart grid technology has significantly enhanced the robustness and efficiency of the traditional power grid network. The integration of such smart functionalities into the power grid also poses many risks such as increasing system complexity, network security risk, end-user data privacy issues, uncertainty of the renewable energy generation, and etc. Although the smart grid has been investigated heavily in many directions and aspects when it was raised for the first time, the research on the power system issues and the quickest detection techniques on smart grid networks are still limited. In this dissertation, we explore specifically in three areas: system status, security issue, and resource management in smart grid networks. First, we propose a CUSUM-based defense strategy against the false data injection attack in smart grid networks. In comparison to classical approaches, the advantages of the proposed CUSUM-based defense mechanism include the low complexity approach of solving unknown parameters in the probability density function of post change distribution, and the development of Markov chain based model for analyzing the proposed approach for performance guarantee. Second, we propose a quickest estimation scheme to determine the network topology with minimum detection/decision delay while maintaining a given accuracy constraints from the dispersive environment. The conventional topology estimation requires a long process of network status analysis for ensuring the normality. The proposed algorithm helps detect and identify the topological error efficiently and promptly for smart grid state estimation via just using online power measurement, and furthermore, reduce on vulnerability on system failure. Finally, we investigate the energy profile allocation scheme for end-user that is capable of determining the best choice of energy profiles as few samples as possible for long-term usage under the accuracy constraint while balancing the exploration and exploitation. In other words, an online learning technique is developed to learn the evolution of the power pattern in terms of reliability over time. We derive the close form for the confident interval and obtain an upper bound for the expected regret for the proposed scheme. In conclusion, the proposed technologies concerning different aspects of smart grid issues, such as cyber security issues, network topology problem, alternative renewable energy resource allocation, can provide a lot of benefits to a power grid society, and will enhance the grid reliability and stability, utility services, emission control, and end-user experience in enabling better communications access to the grid, which could potentially translate into effective efficient utility operations and better living environment for human beings.Item Asset Analytics of Smart Grid Infrastructure for Resiliency Enhancement(2015-05) Arab, Ali; Khator, Suresh K.; Han, Zhu; Lim, Gino J.; Tekin, Eylem; Khodaei, AminFirst, a post-hurricane restoration model for power grid which considers the economics of disaster is introduced. The physical and economic constraints of the system, including unit commitment and restoration constraints, are incorporated in the proposed model. The aim is to restore the hurricane-related damages to electric power system infrastructure in an economic and customer-centered manner, without violating the physics of the system, in order to mitigate the aftermath of natural disasters. Second, a proactive resource allocation model for repair and restoration of potential damages to the power system infrastructure located on the path of an upcoming hurricane is proposed. The objective is to develop an efficient framework for system operators to restore potential damages to power system components in a cost-effective manner. The problem is modeled as a two-stage stochastic integer program with recourse. This model can improve proactive preparedness of the decision makers to cope with emergencies, especially those of nature origins, in order to minimize the restoration cost, and enhance the resilience of the power system. Third, a model is proposed to incorporate the impact of potential damage due to hurricane in the maintenance scheduling of the power infrastructure components located in hurricane prone areas. The power infrastructure deterioration process, as well as two competing and independent failure modes, i.e., failure due to loss of reliability and failure due to hurricane damages are integrated into the model. Moreover, the interrelationship between the component, the grid, and the associated downtime cost dynamics are analyzed. The problem is modeled as a Markov decision process with perfect state information. Fourth, the impact of El Nino/La Nina phenomenon which has shown to induce seasonal effects on hurricane arrivals in long-term climatological horizon is considered in asset management strategies of the electric power systems. An integrated infrastructure hardening and condition-based maintenance scheduling model for critical components of the power systems is developed. The partially observable Markov decision processes are used to formulate the problem. The survival function against hurricane is derived as a dynamic stress-strength model, and is incorporated in the proposed framework.Item Attack and Defend Mechanisms for State Estimation in Smart Grid(2013-08) Esmalifalak, Mohammad; Han, Zhu; Ogmen, Haluk; Zheng, Rong; Khodaei, Amin; Xie, LeAging power industries together with an increase in the demand from industrial and residential customers are the main incentive for policy makers to define a road map to the next generation power system called the smart grid. Changing the traditional structure of power systems and integrating communication devices are beneficial for better monitoring and decision making by the system operators, but at the same time it increases the risk of cyber attacks. Power system blackout in 2003 created serious problems for customers in the eastern US and Canada. Although different investigations report reasons other than cyber attack for the blackout, many researchers believe a similar tragedy could happen with targeted cyber attacks. Later in 2007, researchers at the Idaho National Lab tried to attack a synchronous generator. The attack was successful and the generator was self-destroyed in a couple of minutes. This attack alarmed cyber-security decision makers, motivating them to define a critical infrastructure that is vulnerable to cyber-attack. An example of this vulnerability is the current bad data detection routine in state estimation, which is not able to detect a certain type of cyber attack called \emph{stealth attack}. Stealth attacks are able to manipulate the state estimation results in order to take economical advantages or make technical problems for power grid. In this dissertation, we analyze the cyber attack against state estimation, from both the attacker and defender points of views. We first review the structure of the electricity market, and then we present the way that the attacker alters the congestion in the ex--post market (in the desired direction) and makes financial profits. We investigate the case that attackers without prior knowledge of the power grid topology, try to make inferences through phasor observations. The inferred structural information is used to launch stealth attacks. This attack is formulated to change the price of electricity in the real-time market. Second, we look at the false data injection from the defender point of view. Because of a huge number of measurements in the network, attacking and defending all measurements are impossible for the attacker and defender, respectively. This situation is modeled as a zero-sum game between the attacker and the defender, and we describe how the interest of one party (attacker or defender) can influence the other's interest. The results of this game defines the proportion of times that the attacker and defender will attack and defend different measurements, respectively. Finally, we illustrate how the normal operations of power networks can be statistically distinguished from the case under stealthy attacks. We first propose two machine learning based techniques for stealthy attack detection. The first method utilizes the supervised learning over labeled data and trains a support vector machine. The second method requires no labeled outputs for training data and detects deviation in the measurements. In both methods, principle component analysis is used to reduce the dimensionality of the data to be processed, which leads to lower computational complexities.Item Big Data Optimization for Distributed Resource Management in Smart Grid(2017-05) Nguyen, Hung Khanh; Han, Zhu; Rajashekara, Kaushik; Pan, Miao; Khodaei, Amin; Mohsenian-Rad, HamedElectric power grids are experiencing the increasing adoption of distributed energy resources, which can bring huge economical and environmental benefit. However, the large-scale penetration of distributed energy resources will make both operations and long-term planning to be more and more complex due to the higher degree of output variability than traditional centralized sources. This variability creates irresistible challenges for grid operators to ensure system security and reliability. In addition, traditional optimization algorithms are no longer applicable for such integrated and complex systems in which economic efficiency, grid reliability, and privacy need to be simultaneously satisfied. Therefore, an innovative optimization framework is critical to tackle the emerging challenges due to the large-scale and independent decision-making nature of distributed resource management problem in the future power system. In this dissertation, we focus on the application of big data optimization methods for distributed resource management problem in smart grid to improve the reliability and security of the distribution system. First, we propose an incentive mechanism design to motivate microgrids to participate in the peak ramp minimization problem for the system to mitigate the ramping effect due to the high penetration of distributed renewable generations. Distributed algorithms to achieve the optimal operation point are proposed, which allow microgrids to execute their computation in either synchronous fashion or asynchronous fashion. Second, a large-scale optimization problem for microgrid optimal scheduling and the load curtailment problem is formulated. We propose a decomposition algorithm and implement parallel computation for the proposed algorithm to run on a computer cluster using the Hadoop MapReduce software framework. Third, a decentralized reactive power compensation model is studied to reduce the power losses and improve the voltage profile for distribution networks. Finally, we consider big data optimization methods for resource allocation problem in wireless network virtualization to prevent traffic disruption against physical network failures.Item Electric Power Grid Restoration Considering Disaster Economics(IEEE Access, 2/1/2016) Arab, Ali; Khodaei, Amin; Khator, Suresh K.; Han, ZhuThis paper presents a cost-effective system-level restoration scheme to improve power grids resilience by efficient response to the damages due to natural or manmade disasters. A post-disaster decision making model is developed to find the optimal repair schedule, unit commitment solution, and system configuration in restoration of the damaged power grid. The physical constraints of the power grid, associated with the unit commitment and restoration, are considered in the proposed model. The value of lost load is used as a viable measure to represent the criticality of each load in the power grid. The model is formulated as a mixed-integer program and, then, is decomposed into an integer master problem and a dual linear subproblem to be solved using Benders decomposition algorithm. Different scenarios are developed to analyze the proposed model on the standard IEEE 118-bus test system. This paper provides a prototype and a proof of concept for utility companies to consider economics of disaster and include unit commitment model into the post-disaster restoration process.Item Game Theoretical Framework for Distributed Dynamic Control in Smart Grids(2013-12) Forouzandehmehr, Najmeh 1982-; Han, Zhu; Ogmen, Haluk; Mohsenian-Rad, Hamed; Khodaei, Amin; Shih, Wei-ChuanIn the emerging smart grids, production increasingly relies on a greater number of decentralized generation sites based on renewable energy sources. The variable nature of the new renewable energy sources will require a certain form of distributed energy storage, such as batteries, flywheels, compressed air and so on to help maintain supply security. Moreover, integration of demand response programs in conjunction with distrusted generation makes an economic and environmental advantage by altering end-users’ normal consumption patterns in response to changes in the electricity price. These new techniques change the way we consume and produce energy also enable the possibility to reduce the greenhouse effect and improve grid stability by optimizing energy streams. In order to accommodate these technologies, solid mathematical tools are essential to ensure robust operation of heterogeneous and distributed nature of smart grids. In this context, game theory could constitute a robust framework that can address relevant and timely open problems in the emerging smart grid networks. In this dissertation, three dynamic game-theoretical approaches are proposed for distributed control of generation and storage units and demand response applications in smart grid networks. We first study the competitive interactions between an autonomous pumpedstorage hydropower plant and a thermal power plant in order to optimize power generation and storage. Each type of power plant individually tries to maximize its own profit by adjusting its strategy: both types of plants can sell their power to the market; or alternatively, the thermal-power plant can sell its power at a fixed price to the pumped-storage hydropower plant by storing the energy in the reservoir. A stochastic differential game is formulated to characterize this competition. The solutions are derived using the stochastic Hamilton-Jacobi-Bellman equations. Based on the effect of real-time pricing on users’ daily demand profile, the simulation results demonstrate the properties of the proposed game and show how we can optimize consumers’ electricity cost in presence of time-varying prices. Second, we focus on controllable load types in energy-smart buildings that are associated with dynamic systems. In this regard, we propose a new demand response model based on a two-level differential game framework. At the beginning of each demand response interval, the price is decided by the upper level (aggregator, utility, or market) given the total demand of lower level users. Given the price from the upper level, the electricity usage of air conditioning unit and the battery storage charging/discharging schedules are controlled for each player (buildings that are equipped with automated load control systems and local renewable generators), in order to minimize the user’s total electricity cost. The optimal user strategies are derived, and we also show that the proposed game can converge to a feedback Nash equilibrium. Finally, the problem of distributed control of the heating, ventilation and air conditioning (HVAC) system for multiple zones in an energy-smart building is addressed. This analysis is based on the idea of satisfaction equilibrium, where the players are exclusively interested in the satisfaction of their individual constraints instead of individual performance optimization. This configuration enables a HVAC unit as a player to make stochastically stable decisions with limited information from the rest of players. To achieve satisfaction equilibrium, a learning dynamics based on trialand- error learning is proposed. In particular, it is shown that this algorithm reaches stochastically stable states that are equilibria and maximizers of the global welfare of the corresponding game.Item Proactive Recovery of Electric Power Assets for Resiliency Enhancement(IEEE Access, 2/16/2015) Arab, Ali; Khodaei, Amin; Han, Zhu; Khator, Suresh K.This paper presents a significant change in current electric power grid response and recovery schemes by developing a framework for proactive recovery of electric power assets with the primary objective of resiliency enhancement. Within the proposed framework, which can potentially present the next generation decision-making tool for proactive recovery, several coordinated models will be developed including: 1) the outage models to indicate the impact of hurricanes on power system components; 2) a stochastic pre-hurricane crew mobilization model for managing resources before the event; and 3) a deterministic post-hurricane recovery model for managing resources after the event. Proposed models will be extended to ensure applicability to a variety of electric power grids with different technologies and regulatory issues. The theoretical and practical implications of the developed models will push the research frontier of proactive response and recovery schemes in electric power grids, while its flexibility will support application to a variety of infrastructures, in response to a wide range of extreme weather events and natural disasters.