Self-adaptive system state optimization based on nonlinear affine transformation for renewable energy volatility

The short-term power variations of renewable energy sources result in system state fluctuations and deviations from the scheduled operating point. Tracking short-term power variations and maintaining the system's optimality is challenging for traditional optimization methods due to their highly...

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Bibliographic Details
Published in:Renewable energy Vol. 230; p. 120846
Main Authors: Zhang, Zhaoyi, Han, Zixi, Hu, Hao, Fan, Youping, Fan, Jianbin, Shu, Yinbiao
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.09.2024
Subjects:
algorithms
bus transportation
electric potential difference
Energy storage devices
Nonlinear affine transformation
Renewable energy sources
Self-adaptive algorithm
Short-term power fluctuations
Taylor series
temporal variation
Renewable energy sources
Taylor series
Energy storage devices
Short-term power fluctuations
Nonlinear affine transformation
Self-adaptive algorithm
ISSN:0960-1481
Online Access:Get full text
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Summary:The short-term power variations of renewable energy sources result in system state fluctuations and deviations from the scheduled operating point. Tracking short-term power variations and maintaining the system's optimality is challenging for traditional optimization methods due to their highly time-consuming nature. This paper proposes a short-timescale self-adaptive optimization strategy based on the nonlinear affine transformation to deal with this problem. Firstly, the multi-period optimization model is established and solved by considering static power-frequency characteristics to obtain an optimal operating point on a longer temporal scale, with the state security margins reserved using chance constraint programming. Next, analogously with the Taylor series, the nonlinear relationship between the system state variable and short-term power fluctuations is revealed through an analytic expression of the nonlinear affine transformation. Then, a self-adaptive optimization algorithm based on the nonlinear affine transformation is proposed to achieve frequency and voltage optimizations on a shorter temporal scale. With less communication, self-adaptive optimization is implemented at the local bus level to achieve more optimal states for short-term renewable power variations rapidly. Finally, simulations demonstrate that the proposed optimization strategy can effectively enhance frequency and voltage qualities, and decrease objective function, thereby improving the operation safety and economy.
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ISSN:0960-1481
DOI:10.1016/j.renene.2024.120846