Power System Scada And Smart Grids Pdf Free Download

Mini S. Thomas is a professor in the Department of Electrical Engineering at Jamia Millia Islamia, New Delhi, India (JMI), with 29 years of teaching and research experience in the field of power systems. She was the head of the Department of Electrical Engineering and currently is the director of the Center for Innovation and Entrepreneurship. She graduated from the University of Kerala, India and obtained her M.Tech from the Indian Institute of Technology Madras, both with Gold Medals. She also holds a Ph.D from the Indian Institute of Technology Delhi, New Delhi. Dr. Thomas conceived, designed, and implemented the first-of-their-kind supervisory control and data acquisition (SCADA) and substation automation (SA) laboratories and has done extensive research work in SCADA systems, substation and distribution automation, and smart grids. She has published more than 100 research papers in international journals and conferences of repute, and is the coordinator of the special assistance program (SAP) on power system automation from the University Grants Commission, Government of India.

A testbed for evaluating if and how process-aware monitoring may increase the security of decentralized SCADA networks in power grids is presented. The testbed builds on the co-simulation framework Mosaik, and co-simulates in an integrated way, the power distribution network on different voltage levels, as well as the control network (Modbus/TCP). The existing simulators were extended to allow topology changes, and a controller (RTU) simulator connected to a SCADA server enabling remote control was implemented. Using the developed testbed, a recently proposed local monitoring approach was investigated. The results show that for so-called interlocks the proposed monitoring approach prevents the execution of 33.3% of the commands, that would result in an unsafe state of the power distribution grid. Furthermore, it is shown that unsafe transformer tap positions can also be avoided. To illustrate the relevance and importance of the proposed testbed, a detailed comparison of related work on process-aware intrusion detection approaches and testbeds combining (parts of) the control network and the power grid is provided.

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The ongoing integration of more renewable energy resources and new technology, like energy storage systems, into smart grids requires the full integration of ICT into power transmission and distribution systems (Smart Grids in Distribution Networks 2015). To guarantee a stable power grid, many approaches propose Decentralized Energy Management (DEM), which relies on Supervisory Control and Data Acquisition (SCADA) networks to communicate sensor readings and commands between the individual components and their control server. Due to the increasing number of Distributed Energy Resources (DERs) such as Photo Voltaic (PV) panels, real-time monitoring and control is required also at medium and low voltage levels (Lu et al. 2015). While DEM is promising, recent events, such as disconnecting the Ukrainian distribution substations (ICS-CERT 2018b) through cyber attacks, have shown that also these control networks need to be improved w.r.t. their security and reliability. Moreover, reports show that breaches in the energy domain account for 20% of the reported cyber security incidents in 2016 (ICS-CERT 2016), and new hacking tools are being developed with the energy sector in mind (CRASHOVERRIDE 2017), e.g., abusing vulnerabilities of protocols used in the energy sector.

In contrast, current co-simulation environments focus on simulating the entire network using Omnet++ (Awad et al. 2016; Lvesque et al. 2012), ns2 (Lin et al. 2011), RINSE (Davis et al. 2006) or OPNET (Sadi et al. 2015), and evaluate, e.g., denial of service attacks on the control network only. These fully simulated approaches are highly flexible, while more advanced testbeds (Koutsandria et al. 2015; Kang et al. 2015; Gunathilaka et al. 2016; Sadi et al. 2015), may require a connection to emulate real hardware or the use of proprietary software. Non-virtualized testbeds at Distribution System Operators (DSOs) are less flexible and often difficult to access. All simulation-based approaches require a power simulator, like Power World (Davis et al. 2006; Gunathilaka et al. 2016), OpenDSS (Awad et al. 2016; Lvesque et al. 2012), PSFL (Lin et al. 2011), or MATPOWER-based Matlab/Simulink (Sadi et al. 2015; Koutsandria et al. 2015) or Mosaik (Schloegl et al. 2015). The latter easily integrates existing simulators in the smart grid co-simulation framework. Moreover, if needed, new simulators can be attached to the Mosaik co-simulation framework by using the provided API. This is the main reason why Mosaik was chosen in the proposed testbed and integrated with (part of) the Modbus/TCP based control network and the monitoring tool, as shown previously.

This paper presents a co-simulation testbed that can be used to implement and evaluate local monitoring approaches for SCADA systems as proposed before, e.g., (Chromik et al. 2016a; Koutsandria et al. 2014; Urbina et al. 2016; Chromik et al. 2017; Meliopoulos et al. 2017). The presented testbed environment is based on the co-simulation framework Mosaik and simulates both the power distribution system and a control network implementing the communication protocol Modbus/TCP. Moreover, a monitoring system based on process-aware policies implemented using the Bro monitoring tool is presented. For better reference, the paper also provides an extensive overview on the related work on approaches for process-aware intrusion detection systems and on testbed environments for power grids.

When dealing with smart grids for electric utilities, managing critical infrastructure is more efficient when SCADA systems are leveraged for the control of Remote Terminal Units (RTUs), Programmable Logic Controllers (PLCs), Intelligent Electronic Devices (IEDs), automatic breaker reclosers, and various other hardware applications.

For many electric and power utilities, implementing full smart grid technology has been a learning curve. Applying cost-effective, high-performance wireless networking solutions to increase automation capabilities and effectively monitor data has challenged many utilities, forcing them to consider how to best leverage IoT hardware such as RTUs for their SCADA systems.

The smart grid represents the full suite of current and proposed responses to the challenges of electricity supply. Numerous contributions to the overall improvement of the efficiency of energy infrastructure are anticipated from the deployment of smart grid technology, in particular including demand-side management. The improved flexibility of the smart grid permits greater penetration of highly variable renewable energy sources such as solar power and wind power, even without the addition of energy storage. Smart grids could also monitor/control residential devices that are noncritical during periods of peak power consumption, and return their function during nonpeak hours.[3]

In the 20th century, local grids grew over time and were eventually interconnected for economic and reliability reasons. By the 1960s, the electric grids of developed countries had become very large, mature, and highly interconnected, with thousands of 'central' generation power stations delivering power to major load centres via high capacity power lines which were then branched and divided to provide power to smaller industrial and domestic users over the entire supply area. The topology of the 1960s grid was a result of the strong economies of scale: large coal-, gas- and oil-fired power stations in the 1 GW (1000 MW) to 3 GW scale are still found to be cost-effective, due to efficiency-boosting features that can be cost-effective only when the stations become very large.

A common element to most definitions is the application of digital processing and communications to the power grid, making data flow and information management central to the smart grid. Various capabilities result from the deeply integrated use of digital technology with power grids. Integration of the new grid information is one of the key issues in the design of smart grids. Electric utilities now find themselves making three classes of transformations: improvement of infrastructure, called the strong grid in China; addition of the digital layer, which is the essence of the smart grid; and business process transformation, necessary to capitalize on the investments in smart technology. Much of the work that has been going on in electric grid modernization, especially substation and distribution automation, is now included in the general concept of the smart grid.[17]

Smart grid technologies emerged from earlier attempts at using electronic control, metering, and monitoring. In the 1980s, automatic meter reading was used for monitoring loads from large customers and evolved into the Advanced Metering Infrastructure of the 1990s, whose meters could store how electricity was used at different times of the day.[18] Smart meters add continuous communications so that monitoring can be done in real-time, and can be used as a gateway to demand response-aware devices and "smart sockets" in the home. Early forms of such demand side management technologies were dynamic demand aware devices that passively sensed the load on the grid by monitoring changes in the power supply frequency. Devices such as industrial and domestic air conditioners, refrigerators, and heaters adjusted their duty cycle to avoid activation during times the grid was suffering a peak condition. Beginning in 2000, Italy's Telegestore Project was the first to network large numbers (27 million) of homes using smart meters connected via low bandwidth power line communication.[19] Some experiments used the term broadband over power lines (BPL), while others used wireless technologies such as mesh networking promoted for more reliable connections to disparate devices in the home as well as supporting metering of other utilities such as gas and water.[11] ff782bc1db