Load Flow Analysis in Smart Grids

Load Flow Analysis in Smart Grids

Copyright: © 2018 |Pages: 11
DOI: 10.4018/978-1-5225-2255-3.ch271
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Load flow analysis is widely used to estimate the flow of various electrical parameters, such as the voltage, current and power, in power grids. These estimates allow us to effectively and reliably manage the given grid under random and uncertain conditions. Given the enormous amount of randomness and uncertainties in the factors that affect the smart grids, compared to traditional power grids, a complete and rigorous load flow analysis holds a vital role in ensuring the reliability of this safety-critical domain. In this chapter, we describe smart grids in terms of their basic components and then categorize the factors that affect the loads in smart grids. This is followed by a comprehensive survey of various existing load flow analysis techniques, i.e., numerical, computational intelligence and probabilistic.
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With 19320 TW-hr/yr consumption of electrical energy in the entire world nowadays, the traditional unidirectional power transmission grids are struggling to survive as the number of fluctuations, blackouts and outages is tremendously growing since the last decade (Gao et al., 2012). More reliable and safe distribution networks have become a dire requirement due to the safety and financial-critical nature of electricity these days. For example, a blackout per minute across Silicon Valley costs 75 million and 1 million dollars for Sun Microsystems alone. There are numerous environmental concerns with the present-age power generation methods as well since these methods are largely dependent on fossil fuels, which result in global warming and carbon-dioxide emissions. For example, the United States power system alone is responsible for 40 percent of carbon emission nationwide (Hledik, 2009). Thus renewable energy resources, like solar and wind based solutions, are extensively being advocated throughout the world but the traditional grid does not facilitate their integration in the national grids. Moreover, the traditional power grids are not very efficient in terms of distribution loss management as well. For example, about 17 percent of electrical energy generated in the year 2011 by Pakistan was wasted in distribution systems. Similarly, the problem of electricity theft is also a growing concern in traditional grids.

Smart grids can overcome the above mentioned shortcomings by providing an alternative electric power transmission framework that comprises of Intelligence based Electronic Devices (IED) (Momoh, 2012) for detecting and correcting faults, and advanced metering infrastructure (AMI), to facilitate the integration of multiple renewable energy sources. Some of the distinguishing characteristics of smart girds compared to traditional power grids include:

  • Safety and Reliability: Smart grids can predict unforeseen situations and autonomously react accordingly to prevent them (e.g., isolating the faulty component of the grid from the entire system (Farhangi, 2010)) and hence improve the safety and reliability (Moslehi and Kumar, 2010) of power distribution and save millions of dollars.

  • Cost-Effectiveness: Smart grids provide real-time tariff information to the consumers so that they can manage their loads to save energy and costs (Li et al., 2010).

  • Efficiency: Smart grids allow optional usage of the assets to maximize the efficiency of the grid and thus can have a major performance impact. For example, according to the US Department of Energy (DOE), just a 5% increase in grid efficiency can have the same impact as if fuel and greenhouse gas emissions are eliminated from 53 million cars.

  • Security: Smart grids allow more secure electrical networks, by using tools like smart meters, and thus electricity theft can be minimized (Khurana et al., 2010, Metke and Ekl, 2010).

  • Environmental Friendliness: Smart grid allows the integration of environmental friendly generation methods and is inline with the recent advancements in renewable energy research (RER) (Ipakchi and Albuyeh, 2009).

Based on above-mentioned capabilities, the National Academy of Engineering listed “electrification as made possible by the grid” as the most significant engineering achievement of the 20th Century.

Key Terms in this Chapter

Renewable Energy Integration: Integrating various renewable energy sources is the most desirable feature of smart grids. However, this component faces various challenges. Advanced energy storage at the transmission, distribution, and residential levels, Static VAR compensators and synchro-phasors within the transmission grid, dynamic pricing demand response, micro grids, virtual power plants, and smart wind and solar technologies are some of the tools for Renewable Energy Integration (REI).

Energy Management System: The Energy Management System (EMS) is used for monitoring and controlling the performance of the generation and transmission system. It allows getting real-time updates from power plants about their conditions and generation parameters. The monitor and control functions are implemented through Supervisory Control and Data Acquisition (SCADA).

Distribution Management System: The Distribution Management System (DMS) may be regarded as the control center of the smart grid. The DMS mainly uses the fault location, Geographic Information Systems (GIS) and Outage Management System (OMS) to improve the reliability of the smart grid by reducing outages and sustaining the frequency and voltage levels. The most important role of DMS is to check the faults and isolate the faulty part out of the system. The “intelligent nodes” of the DMS can communicate with one-another periodically and if a fault occurs then they work together to reconfigure the system.

Asset Management Systems: Asset management of smart grids is a core requirement due to the huge investments involved. For example, according to the US Department Of Energy (DOE), around 1.5 trillion dollars have been invested in the US electricity infrastructure so far. Asset management applies to both tangible and intangible assets.

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