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 in the load-ED training dataset generation. Three illustrative test cases demonstrate the proposed algorithms, and comparative studies are constructed to show the superiority of the enhanced, unbiased training dataset.
Highlights
THE 2016 victory of AlphaGo, a computer program that defeated the strongest human Go player in the world, demonstrated the potential of artificial intelligence (AI) for solving complex decision-making problems [1]
This section consists of two parts: (1) three algorithms are proposed to collect marginal patterns, which construct a marginal pattern library examining the training dataset; (2) if the marginal pattern library contains patterns that are missing in the training dataset, an enhancement algorithm is proposed to eliminate the bias in samples by generating samples for those marginal patterns
In general, this phenomenon exists in any model-free application for economic dispatch (ED)-based problems due to the step change nature shown in Fig. 1, and this paper uses the neural networks for load-Locational marginal price (LMP) mapping as an example
Summary
THE 2016 victory of AlphaGo, a computer program that defeated the strongest human Go player in the world, demonstrated the potential of artificial intelligence (AI) for solving complex decision-making problems [1]. Recent research has attempted to directly predict the results of the OPF-based ED problem through neural networks without solving the optimization model. [9] developed the DeepOPF approach, which applies a deep neural network to predict the dispatch result of a linearized OPF problem. Previous research has applied different types of neural networks to predict the following four outputs, direct or indirect, of the ED problem: (1) the optimal dispatch results; (2) the optimal cost; (3) the reliability index/status; and (4) the characteristic of the optimal solutions. In this paper, we demonstrate that the randomly generated load samples are biased in relation to ED outputs, such as generator dispatches and LMPs. Here, three algorithms are proposed to construct the marginal pattern library, and another algorithm is proposed to enhance the dataset for model-free applications in ED and LMP calculations.
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