Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第18989号 / 工博第4031号 / 新制||工||1621 / 31940 / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 引原 隆士, 教授 木本 恒暢, 教授 松尾 哲司 / 学位規則第4条第1項該当

Successful development of the electric power grid of the future, hereinafter referred to as a smart grid, implicitly demands the capability to model the behavior, performance, and cost of distribution-level smart grid components. The modeling and simulation of such individual components, together with their overall interaction, will provide a foundation for the design and configuration of a smart grid. It is the primary intent of this thesis, to provide a basic insight into the energy transfer of various distribution-level components by modeling and simulating their dynamic behavior. The principal operations of a smart grid must be considered, including variable renewable generation, energy storage, power electronic interfaces, variable load, and plug-in electric vehicles. The methodology involves deriving the mathematical equations of components, and, using the MATLAB/Simulink environment, creating modules for each component. Ultimately, these individual modules may be connected together via a voltage interface to perform various analyses, such as the treatment of harmonics, or to acquire an understanding of design parameters such as capacity, runtime, and optimal asset utilization. / text

The ability of typical utility meters and advanced meters including sub-circuit metering to disaggregate residential electric loads and determine what appliances a homeowner is using at a given time in investigated. The basics of residential electricity systems, instrumentation options, and characteristics of selected residential loads are presented. This information informs a discussion on present and future disaggregation algorithms. The conclusions highlight the importance of reactive power and current harmonics in determining power consumed and identifying modern electrical devices, and raise concerns related to the ability of typical 15 minute interval utility smart meters to disaggregate loads. / text

Fault detection and location algorithms have allowed for the power industry to alter the power grid from the traditional model to becoming a smart grid. This thesis implements an already established algorithm for detecting faults, as well as an impedance-based algorithm for detecting where on the line the fault has occurred and develops a smart algorithm for future HDL conversion using Simulink. Using the algorithms, the ways in which this implementation can be used to create a smarter grid are the fundamental basis for this research. Simulink was used to create a two-bus power system, create environment variables, and then Matlab was used to program the algorithm such that it could be FPGA-implementable, where the ways in which one can retrieve the data from a power line has been theorized. This novel approach to creating a smarter grid was theorized and created such that real-world applications may be further implemented in the future.

Cyber Physical System (CPS) is a system where cyber and physical components work in a complex co-ordination to provide better performance. By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of a CPS. In this dissertation, security measures for different types of attacks/ faults in two CPSs, water supply system (WSS) and smart grid system, are presented. In this context, I also present my study on energy management in Smart Grid. The techniques for detecting attacks/faults in both WSS and Smart grid system adopt Kalman Filter (KF) and χ2 detector. The χ2 -detector can detect myriad of system fault- s/attacks such as Denial of Service (DoS) attack, short term and long term random attacks. However, the study shows that the χ2 -detector is unable to detect the intelligent False Data Injection attack (FDI). To overcome this limitation, I present a Euclidean detector for smart grid which can effectively detect such injection attacks. Along with detecting attack/faults I also present the isolation of the attacked/faulty nodes for smart grid. For isolation the Gen- eralized Observer Scheme (GOS) implementing Kalman Filter is used. As GOS is effective in isolating attacks/faults on a single sensor, it is unable to isolate simultaneous attacks/faults on multiple sensors. To address this issue, an Iterative Observer Scheme (IOS) is presented which is able to detect attack on multiple sensors. Since network is an integral part of the future CPSs, I also present a scheme for pre- serving privacy in the future Internet architecture, namely MobilityFirst architecture. The proposed scheme, called Anonymity in MobilityFirst (AMF), utilizes the three-tiered ap- proach to effectively exploit the inherent properties of MF Network such as Globally Unique Flat Identifier (GUID) and Global Name Resolution Service (GNRS) to provide anonymity to the users. While employing new proposed schemes in exchanging of keys between different tiers of routers to alleviate trust issues, the proposed scheme uses multiple routers in each tier to avoid collaboration amongst the routers in the three tiers to expose the end users.

Electricity generation from wind power and other renewable energy sources is increasing, and their variability introduces new challenges to the existing power system, which cannot cope effectively with highly variable and distributed energy resources. The emergence of smart grid technologies in recent year has seen a paradigm shift in redefining the electrical system of the future, in which controlled response of the demand side is used to balance fluctuations and intermittencies from the generation side. This thesis investigates the impact of smart grid technologies on the integration of wind power into the power system. A smart grid power system model is developed and validated by comparison with a real-life smart grid experiment: the Olympic Peninsula Demonstration Experiment. The smart grid system model is then expanded to include 1000 houses and a generic generation mix of nuclear, hydro, coal, gas and oil based generators. The effect of super-imposing varying levels of wind penetration are then investigated in conjunction with a market model whereby suppliers and demanders bid into a Real-Time Pricing (RTP) electricity market. The results demonstrate and quantify the effectiveness of DR in mitigating the variability of renewable generation. It is also found that the degree to which Greenhouse Gas (GHG) emissions can be mitigated is highly dependent on the generation mix. A displacement of natural gas based generation during peak demand can for instance lead to an increase in GHG emissions. Of practical significance to power system operators, the simulations also demonstrate that Demand Response (DR) can reduce generator cycling and improve generator efficiency, thus potentially lowering GHG emissions while also reducing wear and tear on generating equipment. / Graduate

The Energy Independence and Security Act of 2007 established that the current electric grid was inadequate to serve the United States needs. Congress mandated that the U.S. transition to a more intelligent grid for the future. The Department of Energy was tasked with making this goal a reality. Six years later in 2013, only marginal progress has been made. Outside of smart meter rollouts and pilots programs funded through the American Recovery and Reinvestment Act of 2009 (ARRA), many issues still need to be addressed in order to realize the U.S. Smart Grid vision. Most of the barriers to progress are not technological; the research and business community are rising to the occasion and meeting the challenge through innovation. However, policy issues present a large barrier to overcome. With issues ranging from vague Smart Grids goals issued by the Department of Energy to a general lack of consumer knowledge about the Smart Grid. This paper seeks to identify the gaps in the current electric grid and policy schema are inadequate and suggest recommendations to encourage and expedite the growth of the U.S. Smart Grid.

The total electricity consumption of plugged-in electric loads (PELs) currently accounts for more usage than any other single end-use service in residential and commercial buildings. Compared with other categories of electric loads, PELs possess significant potential to be efficiently controlled and managed in buildings. Therefore, accurate and reliable PEL identification methods that are used to collect identity and performance information are desired for many purposes. However, few existing electric load identification methods are designed for PELs to handle unique challenges such as the diversity within each type of PEL and similarity between different types of PELs equipped by similar front-end power supply units. The objective of this dissertation is to develop non-intrusive, accurate, robust, and applicable PEL identification algorithms utilizing voltage and current measurements. Based on the literature review of almost all existing features that describe electric loads and five types of existing methods for electric load identification, a two-level framework for PELs classification and identification is proposed. First, the supervised self-organizing map (SSOM) is adopted to classify a large number of PELs of different models and brands into several groups by their inherent similarities. Therefore, PELs with similar front-end power supply units or characteristics fall into the same group. The partitioned groups are verified by their power supply unit topology. That is, different groups should have different topologies. This dissertation proposes a novel combination of the SSOM framework and the Bayesian framework. Such a hybrid identifier can provide the probability of an unknown PEL belonging to a specific type of load. Within each classified group by the SSOM, both static and dynamic methods are proposed to distinguish PELs with similar characteristics. Static methods extract steady-state features from the voltage and current waveforms to train different computational intelligence algorithms such as the SSOM itself and the support vector machine (SVM). An unknown PEL is then presented to the trained algorithm for identification. In contrast to static methods, dynamic methods take into consideration the dynamics of long-term (minutes instead of milliseconds) waveforms of PELs and extract elements such as spikes, oscillations, steady-state operations, as well as similarly repeated patterns.

During the last century, the significant increase in electricity demand, and its consequences, has appeared as a serious concern for the utility companies, but no essential change has been applied to the conventional power grid infrastructure till now. Recently, researchers have identified efficient control and power distribution mechanisms as the immediate challenges for conventional power grids. Hence, the next step for conventional power grid toward Smart Grid is to provide energy efficiency management along with higher reliability via smart services, in which the application of Information and Communication Technology (ICT) is inevitable. ICT introduces powerful tools to comply with the smart grid requirements. Among various ICT properties, the telecommunication network plays a key role for providing a secure infrastructure. The two-way digital communication system provides an interaction between energy suppliers and consumers for managing, controlling and optimizing energy distribution. We can also define the smart grid as a two-way flow of energy and control information, where the electricity consumers can generate energy using green energy resources. The main objective of this thesis is to select an effective communication infrastructure to support the smart grid services by considering wireless and optical communication technologies. Fiber-wireless (FiWi) networks are considered as a potential solution to provide a fast and reliable network backbone with the optical access network integration and the flexibility and mobility of the wireless network. Therefore, we adopt the integration of the wireless sensor network (WSN) to Ethernet Passive Optical Network (EPON) as a broadband access network to transmit smart meter data along with the Fiber To The Home/Building/Curb (FTTX) traffic through the shared fiber. Finally, we present and analyze the simulation results for the aforementioned infrastructure based on our adopted priority-based FTTX-WSN integration model.

Wireless sensor network plays a significant role in the design of future Smart Grid, mainly for the purpose of environment monitoring, data acquisition and remote control. Sensors deployed on the utility poles on the power transmission line are used to collect environment information and send them to the substations for analysis and management. However, the transmission is suffered from erasures and errors along the transmission channels. In this thesis, we consider a line network model proposed in [1] and [2]. We first analyze several different erasure codes in terms of overhead and encoding/decoding costs, followed by proposing two different coding schemes for our line network. To deal with both erasures and errors, we combine the erasure codes and the traditional error control codes, where an RS code is used as an outer codes in addition to the erasure codes. Furthermore, an adaptive RS coding scheme is proposed to improve the overall coding efficiency over all SNR regions. In the end, we apply network coding with error correction of network errors and erasures and examine our model from the mathematical perspective. / Graduate