Load flow with uncertain loading and generation in future smart grids

Krause, Olav and Lehnhoff, Sebastian (2011). Load flow with uncertain loading and generation in future smart grids. In Kasthurirangan Gopalakrishnan, Siddhartha Kumar Khaitan and Soteris Kalogirou (Ed.), Soft computing in green and renewable energy systems (pp. 117-156) Berlin, Germany: Springer. doi:10.1007/978-3-642-22176-7_5

Author Krause, Olav
Lehnhoff, Sebastian
Title of chapter Load flow with uncertain loading and generation in future smart grids
Title of book Soft computing in green and renewable energy systems
Place of Publication Berlin, Germany
Publisher Springer
Publication Year 2011
Sub-type Research book chapter (original research)
DOI 10.1007/978-3-642-22176-7_5
Open Access Status
Series Studies in Fuzziness and Soft Computing Series
ISBN 9783642221750
ISSN 1434-9922
Editor Kasthurirangan Gopalakrishnan
Siddhartha Kumar Khaitan
Soteris Kalogirou
Volume number 269
Chapter number 5
Start page 117
End page 156
Total pages 40
Total chapters 11
Collection year 2012
Language eng
Formatted Abstract/Summary
The growing amount of renewable and fluctuating energy sources for the production of electrical energy increases the volatility and level of uncertainty in the operation of power systems. Whether it is the growing number of photovoltaic installations harnessing solar energy or large-scale wind farms, these new class of environmentally dependent appliances increase the unpredictability of load situations hitherto known only from consumer behavior. One of the mayor concerns in grid operation under increasing feed-in from unpredictable generation and consumption is the detection of peaks in network strain. In order to limit investments into grid infrastructure to a reasonable level node-specific limitations for power injections are introduced to reduce the probability of such peaks that may pose a threat to a stable operation of the power system. In order to support the ongoing integration of renewable generation into the grid, a trade-off has to be found between investment costs and imposed operational constraints. In order to determine the probability of congestions under these unpredictable conditions, mathematical algorithms are employed that are able to estimate the probability of certain line loading levels from the probabilistic data derived from the appliances’ behavior.
This chapter will cover a variety of approaches to solve (probabilistic) load flow problems, ranging from currently deployed state-of-the-art procedures to the newest advances in probabilistic load flow calculation and determination. Advantages and drawbacks of those methods are discussed in detail.
Q-Index Code B1
Q-Index Status Confirmed Code
Institutional Status UQ

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Created: Thu, 01 Mar 2012, 11:23:36 EST by Dr Olav Krause on behalf of School of Information Technol and Elec Engineering