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Smart Grid Control
Abstract
This chapter offers a comprehensive overview of the progressive developments in power system control within the dynamic landscape of smart grids. It discusses in detail the various control systems present in a power system and the importance of frequency control. Load Frequency Control (LFC) is used as the representative control system to analyze the various proposed algorithms. A detailed analysis of the Multi-Area LFC system model is derived based on the machine and system equations, and a state-space model is developed to represent the MA-LFC system. Since the system is a linearized one, we also introduce the nonlinearities. The model derived in this chapter will further be used as a representative system to build and analyze various algorithms in future chapters.
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Cyber-Physical System (CPS) is a new kind of digital technology that increases its attention across academia, government, and industry sectors and covers a wide range of applications like agriculture, energy, medical, transportation, etc. The traditional power systems with physical equipment as a core element are more integrated with information and communication technology, which evolves into the Cyber-Physical Power System (CPPS). The CPPS consists of a physical system tightly integrated with cyber systems (control, computing, and communication functions) and allows the two-way flows of electricity and information for enabling smart grid technologies. Even though the digital technologies monitoring and controlling the electric power grid more efficiently and reliably, the power grid is vulnerable to cybersecurity risk and involves the complex interdependency between cyber and physical systems. Analyzing and resolving the problems in CPPS needs the modelling methods and systematic investigation of a complex interaction between cyber and physical systems. The conventional way of modelling, simulation, and analysis involves the separation of physical domain and cyber domain, which is not suitable for the modern CPPS. Therefore, an integrated framework needed to analyze the practical scenario of the unification of physical and cyber systems. A comprehensive review of different modelling, simulation, and analysis methods and different types of cyber-attacks, cybersecurity measures for modern CPPS is explored in this paper. A review of different types of cyber-attack detection and mitigation control schemes for the practical power system is presented in this paper. The status of the research in CPPS around the world and a new path for recommendations and research directions for the researchers working in the CPPS are finally presented.
Distributed Network Protocol (DNP3) is the predominant SCADA protocol in the energy sector – more than 75% of North American
electric utilities currently use DNP3 for industrial control applications. This paper presents a taxonomy of attacks on the
protocol. The attacks are classified based on targets (control center, outstation devices and network/communication paths)
and threat categories (interception, interruption, modification and fabrication). To facilitate risk analysis and mitigation
strategies, the attacks are associated with the specific DNP3 protocol layers they exploit. Also, the operational impact of
the attacks is categorized in terms of three key SCADA objectives: process confi- dentiality, process awareness and process
control. The attack taxonomy clarifies the nature and scope of the threats to DNP3 systems, and can provide insights into
the relative costs and benefits of implementing mitigation strategies.
With deregulation of the power generation sector, the necessity for an enhanced and open communication infrastructure to support an increasing variety of ancillary services is apparent. A duplex and distributed communication system seems to be the most suitable solution to meet and ensure good quality of these services. Parameters needed and additional limits introduced by this new communication topology must be investigated and defined. This paper focuses on the communication network requirements for a third party load frequency control service. Data communication models are proposed based on queuing theory. Simulation is performed to model the effects of certain types of signal delays on this ancillary service.
Third edition. "Since publication of the second edition, there have been extensive changes in the algorithms, methods, and assumptions in energy management systems that analyze and control power generation. This edition is updated to acquaint electrical engineering students and professionals with current power generation systems. Algorithms and methods for solving integrated economic, network, and generating system analysis are provided. Also included are the state-of-the-art topics undergoing evolutionary change, including market simulation, multiple market analysis, multiple interchange contract analysis, contract and market bidding, and asset valuation under various portfolio combinations"-- "Online video course with powerpoint slides for each chapter at www.cusp.umn.edu; site also contains links to important research reports, an entire set of student programs in MATLAB, and sets of power system sample data sets for use in student exercises"-- Preface to the third edition -- Preface to the second edition -- Preface to the first edition -- Acknowledgment -- Introduction -- Industrial organization, managerial economics, and finance -- Economic dispatch of thermal units and methods of solution -- Unit commitment -- Generation with limited energy supply --Transmission system effects -- Power system security -- Optimal power flow -- Introduction to state estimation in power systems -- Control of generation -- Interchange, pooling, brokers, and auctions -- Short-term demand forecasting -- Index.
This document highlights the role of control systems in the evolution of the Smart Grid. It includes an overview of research investigations that are needed for renewable integration, reliability, self-healing, energy efficiency, and resilience to physical and cyber attacks. These investigations are encapsulated in several loci of control including: new methodologies for transmission, distribution, and renewable energy, and storage; new roles in emerging topics such as electricity markets, demand-response, microgrids, and virtual power plants; and new solutions for efficiency, heating and cooling, and security. Together, they usher in new horizons for control, such as architecting a system of distributed systems, building interfaces to social sciences such as economics, sociology, and psychology, and providing a blueprint for critical infrastructure systems. While the emerging role of control and its implication on grid architectures have been articulated in various papers, a comprehensive discourse on the evolution of Smart Grid and the opportunities and challenges that it presents for control, ranging from generators to consumers, from planning to real-time operation, from current practice to scenarios in 2050 in the grid and all of its subsystems, has not been undertaken hitherto and is the purpose of this document. For Corporate or Institutional Access, request a custom quote for your organization at www.ieee.org/smartgridresearch
The paper deals with some aspects of automatic generation control of the two-area hydrothermal system provided with classical controllers. A comprehensive procedure for continuous-and discrete-mode optimisation of integral controllers using an integral squared error criterion is suggested. Investigations reveal that the optimal integral gains achieved through continuous-mode analysis are totally unacceptable in the discrete mode for the sampling periods used in practice. Moreover, for all practical purposes, the optimum integral gains in the discrete mode can be achieved by neglecting the generation rate constraints from the mathematical model, in contrast to the case in the continuous mode. Analysis also highlights significant differences in the dynamic performance of the hydrothermal system due to step-load perturbation in either of the areas. An attempt is also made to recommend an optimum sampling period.