Abstract
Input-output mapping for a given power system problem, such as loads versus economic dispatch (ED) results, has been demonstrated to be learnable through artificial intelligence (AI) techniques, including neural networks. However, the process of identifying and constructing a comprehensive dataset for the training of such input-output mapping remains a challenge to be solved. Conventionally, load samples are generated by a pre-defined distribution, and then ED is solved based on those load samples to form training datasets, but this paper demonstrates that such dataset generation is biased regarding load-ED mapping. The marginal unit and line congestion (i.e., marginal pattern) exhibit a unique characteristic called "step change" in which the marginal pattern changes when the load goes from one critical loading level (CLL) to another, and there is no change of marginal units within the interval of the two adjacent CLLs. Those loading intervals differ significantly in size. The randomly generated training dataset overfills intervals with large sizes and underfits intervals with small sizes, so it is biased. In this paper, three algorithms are proposed to construct a marginal pattern library to examine this bias according to different computational needs, and an enhancement algorithm is proposed to eliminate the bias for the load-ED dataset generation. Three illustrative test cases demonstrate the proposed algorithms, and comparative studies are constructed to show the superiority of the enhanced, unbiased dataset.
Recommended Citation
Q. Zhang et al., "Building Marginal Pattern Library with Unbiased Training Dataset for Enhancing Model-Free Load-ED Mapping," IEEE Open Access Journal of Power and Energy, Institute of Electrical and Electronics Engineers (IEEE), Feb 2022.
The definitive version is available at https://doi.org/10.1109/OAJPE.2022.3149308
Department(s)
Electrical and Computer Engineering
Publication Status
Early Access
Keywords and Phrases
Artificial Intelligence; Critical Load Level (CLL); Economic Dispatch (ED); Electricity Market; Libraries; Load Modeling; Loading; Locational Marginal Price (LMP); Neural Networks; Neural Networks; Optimal Power Flow (OPF); Prediction Algorithms; Training
International Standard Serial Number (ISSN)
2687-7910
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2022 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
07 Feb 2022
Comments
The authors would like to acknowledge the support in part by the US Department of Energy CEDS Project “Watching Grid Infrastructure Stealthily Through Proxies (WISP)” under award number DE-OE0000899 and in part by the CURENT which is a US NSF/DOE Engineering Research Center funded by NSF award EEC-1041877.