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Title: Improving BA Control Performance Through Advanced Regulation Requirements Prediction

Abstract

The paper presents a comprehensive approach to predict balancing authority (BA) regulation requirements in order to improve BAs control performance. The proposed probabilistic approach takes into account multiple uncertain and time dependent factors affecting the system balance (e.g., wind and solar generation, electrical loads). A set of methods to predict the regulation has been developed: (1) probabilistic analysis of regulation requirements with respect to time of the day, (2) linear or nonlinear regression models linking regulation requirements to different influencing factors, (3) adopting time series forecasting techniques (e.g., autoregressive integrated moving average (ARIMA)) to account for temporal continuity and auto-correlation in the regulation requirements data. Proposed methodology has been tested and validated using actual California Independent System Operator (ISO) data.

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1];  [1];  [2];  [2]
  1. BATTELLE (PACIFIC NW LAB)
  2. California Independent System Operator
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1511688
Report Number(s):
PNNL-SA-130443
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: IEEE Power & Energy Society General Meeting (PESGM 2018), August 5-10, 2018, Portland, OR
Country of Publication:
United States
Language:
English
Subject:
Solar Generation, Wind Generation, Area Control Error (ACE), Control Performance Standards, Balancing Authority

Citation Formats

Etingov, Pavel V., Miller, Laurie E., Hou, Zhangshuan, Makarov, Yuri V., Loutan, Clyde, and Katzenstein, Warren. Improving BA Control Performance Through Advanced Regulation Requirements Prediction. United States: N. p., 2018. Web. doi:10.1109/PESGM.2018.8586317.
Etingov, Pavel V., Miller, Laurie E., Hou, Zhangshuan, Makarov, Yuri V., Loutan, Clyde, & Katzenstein, Warren. Improving BA Control Performance Through Advanced Regulation Requirements Prediction. United States. doi:10.1109/PESGM.2018.8586317.
Etingov, Pavel V., Miller, Laurie E., Hou, Zhangshuan, Makarov, Yuri V., Loutan, Clyde, and Katzenstein, Warren. Fri . "Improving BA Control Performance Through Advanced Regulation Requirements Prediction". United States. doi:10.1109/PESGM.2018.8586317.
@article{osti_1511688,
title = {Improving BA Control Performance Through Advanced Regulation Requirements Prediction},
author = {Etingov, Pavel V. and Miller, Laurie E. and Hou, Zhangshuan and Makarov, Yuri V. and Loutan, Clyde and Katzenstein, Warren},
abstractNote = {The paper presents a comprehensive approach to predict balancing authority (BA) regulation requirements in order to improve BAs control performance. The proposed probabilistic approach takes into account multiple uncertain and time dependent factors affecting the system balance (e.g., wind and solar generation, electrical loads). A set of methods to predict the regulation has been developed: (1) probabilistic analysis of regulation requirements with respect to time of the day, (2) linear or nonlinear regression models linking regulation requirements to different influencing factors, (3) adopting time series forecasting techniques (e.g., autoregressive integrated moving average (ARIMA)) to account for temporal continuity and auto-correlation in the regulation requirements data. Proposed methodology has been tested and validated using actual California Independent System Operator (ISO) data.},
doi = {10.1109/PESGM.2018.8586317},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {8}
}

Conference:
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