Robust Unit Commitment With Wind Power and Pumped Storage Hydro
As renewable energy increasingly penetrates into power grid systems, new challenges arise for system operators to keep the systems reliable under uncertain circumstances, while ensuring high utilization of renewable energy. With the naturally intermittent renewable energy, such as wind energy, playing more important roles, system robustness becomes a must. In this paper, we propose a robust optimization approach to accommodate wind output uncertainty, with the objective of providing a robust unit commitment schedule for the thermal generators in the day-ahead market that minimizes the total cost under the worst wind power output scenario. Robust optimization models the randomness using an uncertainty set which includes the worst-case scenario, and protects this scenario under the minimal increment of costs. In our approach, the power system will be more reliable because the worst-case scenario has been considered. In addition, we introduce a variable to control the conservatism of our model, by which we can avoid over-protection. By considering pumped-storage units, the total cost is reduced significantly.
Future on Power Electronics for Wind Turbine Systems
Wind power is still the most promising renewable energy in the year of 2013. The wind turbine system (WTS) started with a few tens of kilowatt power in the 1980s. Now, multimegawatt wind turbines are widely installed even up to 6-8 MW. There is a widespread use of wind turbines in the distribution networks and more and more wind power stations, acting as power plants, are connected directly to the transmission networks. As the grid penetration and power level of the wind turbines increase steadily, the wind power starts to have significant impacts to the power grid system. Therefore, more advanced generators, power electronic systems, and control solutions have to be introduced to improve the characteristics of the wind power plant and make it more suitable to be integrated into the power grid. Meanwhile, there are also some emerging technology challenges, which need to be further clarified and investigated. This paper gives an overview and discusses some development trends in the technologies used for wind power systems. First, the developments of technology and market are generally discussed. Next, several state-of-the-art wind turbine concepts, as well as the corresponding power electronic converters and control structures, are reviewed, respectively. Furthermore, grid requirements and the technology challenges for the future WTS are also addressed.
On False Data-Injection Attacks against Power System State Estimation: Modeling and Countermeasures
It is critical for a power system to estimate its operation state based on meter measurements in the field and the configuration of power grid networks. Recent studies show that the adversary can bypass the existing bad data detection schemes, posing dangerous threats to the operation of power grid systems. Nevertheless, two critical issues remain open: 1) how can an adversary choose the meters to compromise to cause the most significant deviation of the system state estimation, and 2) how can a system operator defend against such attacks? To address these issues, we first study the problem of finding the optimal attack strategy--i.e., a data-injection attacking strategy that selects a set of meters to manipulate so as to cause the maximum damage. We formalize the problem and develop efficient algorithms to identify the optimal meter set. We implement and test our attack strategy on various IEEE standard bus systems, and demonstrate its superiority over a baseline strategy of random selections. To defend against false data-injection attacks, we propose a protection-based defense and a detection-based defense, respectively. For the protection-based defense, we identify and protect critical sensors and make the system more resilient to attacks. For the detection-based defense, we develop the spatial-based and temporal-based detection schemes to accurately identify data-injection attacks.
Design Of Smart Power Grid Renewable Energy Systems
FOREWORD. PREFACE. ACKNOWLEDGMENTS. 1 ENERGY AND CIVILIZATION. 1.1 Introduction. 1.2 Fossil Fuel. 1.3 Depletion of Energy Resources. 1.4 An Alternative Energy Source: Nuclear Energy. 1.5 Global Warming. 1.6 The Age of the Electric Power System. 1.7 Green and Renewable Energy Sources. 1.8 Energy Units and Conversions. 1.9 Estimating the Cost of Energy. 1.10 Conclusion. 2 POWER GRIDS. 2.1 Introduction. 2.2 Electric Power Grids. 2.3 The Basic Concepts of Power Grids. 2.4 Load Models. 2.5 Transformers in Electric Power Grids. 2.6 Modeling a Microgrid System. 2.7 Modeling Three-Phase Transformers. 2.8 Tap Changing Transformers. 2.9 Modeling Transmission Lines. 3 MODELING CONVERTERS IN MICROGRID POWER SYSTEMS. 3.1 Introduction. 3.2 Single-Phase DC/AC Inverters with Two Switches. 3.3 Single-Phase DC/AC Inverters with a Four-Switch Bipolar Switching Method. 3.3.1 Pulse Width Modulation with Unipolar Voltage Switching for a Single-Phase Full-Bridge Inverter. 3.4 Three-Phase DC/AC Inverters. 3.5 Pulse Width Modulation Methods. 3.6 Analysis of DC/AC Three-Phase Inverters. 3.7 Microgrid of Renewable Energy Systems. 3.8 The DC/DC Converters in Green Energy Systems. 3.9 Rectifiers. 3.10 Pulse Width Modulation Rectifiers. 3.11 A Three-Phase Voltage Source Rectifier Utilizing Sinusoidal PWM Switching. 3.12 The Sizing of an Inverter for Microgrid Operation. 3.13 The Sizing of a Rectifi er for Microgrid Operation. 3.14 The Sizing of DC/DC Converters for Microgrid Operation. 4 SMART POWER GRID SYSTEMS. 4.1 Introduction. 4.2 Power Grid Operation. 4.3 The Vertically and Market-Structured Utility. 4.4 Power Grid Operations Control. 4.5 Load-Frequency Control. 4.6 Automatic Generation Control. 4.7 Operating Reserve Calculation. 4.8 The Basic Concepts of a Smart Power Grid. 4.9 The Load Factor. 4.10 A Cyber-Controlled Smart Grid. 4.11 Smart Grid Development. 4.12 Smart Microgrid Renewable Green Energy Systems. 4.13 A Power Grid Steam Generator. 4.14 Power Grid Modeling. 5 MICROGRID SOLAR ENERGY SYSTEMS. 5.1 Introduction. 5.2 The Solar Energy Conversion Process: Thermal Power Plants. 5.3 Photovoltaic Power Conversion. 5.4 Photovoltaic Materials. 5.5 Photovoltaic Characteristics. 5.6 Photovoltaic Effi ciency. 5.7 The Design of Photovoltaic Systems. 5.8 The Modeling of a Photovoltaic Module. 5.9 The Measurement of Photovoltaic Performance. 5.10 The Maximum Power Point of a Photovoltaic Array. 5.11 A Battery Storage System. 5.12 A Storage System Based on a Single-Cell Battery. 5.13 The Energy Yield of a Photovoltaic Module and the Angle of Incidence. 5.14 The State of Photovoltaic Generation Technology. 5.15 The Estimation of Photovoltaic Module Model Parameters. 6 MICROGRID WIND ENERGY SYSTEMS. 6.1 Introduction. 6.2 Wind Power. 6.3 Wind Turbine Generators. 6.4 The Modeling of Induction Machines. 6.5 Power Flow Analysis of an Induction Machine. 6.6 The Operation of an Induction Generator. 6.7 Dynamic Performance. 6.8 The Doubly-Fed Induction Generator. 6.9 Brushless Doubly-Fed Induction Generator Systems. 6.10 Variable-Speed Permanent Magnet Generators. 6.11 A Variable-Speed Synchronous Generator. 6.12 A Variable-Speed Generator with a Converter Isolated from the Grid. 7 LOAD FLOW ANALYSIS OF POWER GRIDS AND MICROGRIDS. 7.1 Introduction. 7.2 Voltage Calculation in Power Grid Analysis. 7.3 The Power Flow Problem. 7.4 Load Flow Study as a Power System Engineering Tool. 7.5 Bus Types. 7.6 General Formulation of the Power Flow Problem. 7.7 The Bus Admittance Model. 7.8 The Bus Impedance Matrix Model. 7.9 Formulation of the Load Flow Problem. 7.10 The Gauss Seidel YBus Algorithm. 7.11 The Gauss Seidel ZBus Algorithm. 7.12 Comparison of the YBus and ZBus Power Flow Solution Methods. 7.13 The Synchronous and Asynchronous Operation of Microgrids. 7.14 An Advanced Power Flow Solution Method: The Newton Raphson Algorithm. 7.15 The Fast Decoupled Load Flow Algorithm. 7.16 Analysis of a Power Flow Problem. 8 POWER GRID AND MICROGRID FAULT STUDIES. 8.1 Introduction. 8.2 Power Grid Fault Current Calculation. 8.3 Symmetrical Components. 8.4 Sequence Networks for Power Generators. 8.5 The Modeling of a Photovoltaic Generating Station. 8.6 Sequence Networks for Balanced Three-Phase Transmission Lines. 8.7 Ground Current Flow in Balanced Three-Phase Transformers. 8.8 Zero Sequence Network. 8.9 Fault Studies. APPENDIX A COMPLEX NUMBERS. APPENDIX B TRANSMISSION LINE AND DISTRIBUTION TYPICAL DATA. APPENDIX C ENERGY YIELD OF A PHOTOVOLTAIC MODULE AND ITS ANGLE OF INCIDENCE. APPENDIX D WIND POWER. INDEX.
Fog Computing for Smart Grid Systems in the 5G Environment: Challenges and Solutions
Currently, the demand for electricity is increasing day by day, which necessitates upgrading of the existing power grid system. The conventional power grid has already been replaced with modern ICT-based infrastructure, which is known as smart grid (SG). In SG, smart meters generate a huge amount of data, and it is a challenging task to store, process, and analyze the data, which varies with respect to volume, velocity, and variety. The data generated in an SG system is generally stored and analyzed using cloud computing (CC), which provides real-time response for various applications. However, to handle the latency issue during the data analytics in SG, fog computing (FC) has emerged as a new technology that provides most of the computing resources in proximity of the end users. It acts as a bridge between SG and CC to fill the gap between processing power of remote data centers and smart devices in SG systems. To handle the aforementioned issues, there is a requirement to set up advanced sensors and measurement systems having communication network backbones in the upcoming fifth generation (5G). In this article, we discuss the architecture of SG in the context of FC for making the decision about energy requirements by the smart devices at the fog layer. Moreover, the communication and computing aspects are also explored in the context of 5G network infrastructure. We examine the influence of FC on response time, transmission delay, and energy management costs for delay-sensitive applications.
Smart Meter Based on Real Time Pricing
Sudden, unprecedented electric blackouts and outages come as a dampener for a system that thrives on electrical energy. Reliability and efficiency are essential features for any power grid system. The conventional power grid systems are incapable of making use of real-time information from consumer side as well as the supply end for a more efficient transmission. Smart grid system, in which tremendous research has been done and which has been improvised manifold over the past decade, helps in meeting the incessant fluctuations in power demand from a huge consumer population. Smart grid brings about automation in managing the energy requirements using a two way interactive system, i.e. its ability to fetch information from the user and supply ends and utilize it to improve the overall reliability and efficiency of the transmission lines. Incorporating smart energy meters along with smart grid could come a long way in conserving electricity apart from removing manual energy meter reading from the scene. Smart meters supply the required data for the smart grid which helps the grid in providing an automated response. The paper elucidates how smart meters help in digitally tackling the issue of energy conservation in the consumer end and the utility end.
Experimental implementation of Multi-Agent System algorithm for distributed restoration of a Smart Grid System
There have been numerous simulation works done on Grid Restoration using Multi-Agent System but almost no experimental work to ascertain the duplicity of these simulation results. This work aims to experimentally perform distributed restoration of a smart power grid system. The concept used in this research is based on the distributed and intelligent multi-agent system technology where multiple smart entities are geographically spread and if equipped with two-way communication capability these entities are able to reach goals or solutions that would have been impossible to reach with non-smart entities. The technology is implemented through the use of a six-bus experimental test bed set up using Tennessee Technological University Smart Grid Laboratory which is undergoing rapid development. The experimental results obtained align with simulation results published earlier and show that the proposed system can restore power in a timely manner without violating any constraints.
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