Reactive Power Management in Real-Time at Tennessee Valley Authority (TVA)

Author(s):  
Tim Fritch ◽  
Ulyana Pugina Elliott ◽  
Josh Shultz ◽  
Patrick Causgrove ◽  
Gilburt Chiang
2021 ◽  
Vol 7 ◽  
pp. 126-133
Author(s):  
Peilin Xie ◽  
Sen Tan ◽  
Josep M. Guerrero ◽  
Juan C. Vasquez

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Faisal Iqbal ◽  
Muhammad Zahid ◽  
Durdana Habib ◽  
Lizy Kurian John

Accurate real-time traffic prediction is required in many networking applications like dynamic resource allocation and power management. This paper explores a number of predictors and searches for a predictor which has high accuracy and low computation complexity and power consumption. Many predictors from three different classes, including classic time series, artificial neural networks, and wavelet transform-based predictors, are compared. These predictors are evaluated using real network traces. Comparison of accuracy and cost, both in terms of computation complexity and power consumption, is presented. It is observed that a double exponential smoothing predictor provides a reasonable tradeoff between performance and cost overhead.


Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1327 ◽  
Author(s):  
Thiago Soares ◽  
Ubiratan Bezerra ◽  
Maria Tostes

This paper proposes the development of a three-phase state estimation algorithm, which ensures complete observability for the electric network and a low investment cost for application in typical electric power distribution systems, which usually exhibit low levels of supervision facilities and measurement redundancy. Using the customers´ energy bills to calculate average demands, a three-phase load flow algorithm is run to generate pseudo-measurements of voltage magnitudes, active and reactive power injections, as well as current injections which are used to ensure the electrical network is full-observable, even with measurements available at only one point, the substation-feeder coupling point. The estimation process begins with a load flow solution for the customers´ average demand and uses an adjustment mechanism to track the real-time operating state to calculate the pseudo-measurements successively. Besides estimating the real-time operation state the proposed methodology also generates nontechnical losses estimation for each operation state. The effectiveness of the state estimation procedure is demonstrated by simulation results obtained for the IEEE 13-bus test network and for a real urban feeder.


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