Review of Learning-Assisted Power System Optimization

Ruan, Guangchun; Zhong, Haiwang; Zhang, Guanglun; He, Yiliu; Wang, Xuan; Pu, Tianjiao

doi:10.36227/techrxiv.12895337

Cited by 15 publications

(17 citation statements)

References 64 publications

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“…The proposed GP-based CFPF approximation supports model interpretability. A recent review [41] identifies interpretability as one of the big challenges for ML models applied to power system. Below we discuss the interpretability of the proposed analytical solution method and develop a qualitative measure of nonlinearity.…”

Section: Analytical Solution Of Power Flow Problemmentioning

confidence: 99%

A Framework for Analytical Power Flow Solution Using Gaussian Process Learning

Pareek

Nguyen

2022

IEEE Trans. Sustain. Energy

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This paper proposes a novel analytical solution framework for power flow (PF) solutions in active distribution networks under uncertainty. We use the Gaussian process (GP) regression to learn node voltage as a function of effective bus load or negative net-injection vector. The proposed approximation is valid over a subspace of load and provides an understanding of system behavior under uncertainty via GP interpretability. We interpret the relative variation extent of different node voltages using the quality ratio (QR) defined based on the hyper-parameters of GP. Further, the application of the proposed framework in calculation of voltage limit violation probability and dominant voltage influencer ranking has also been presented. Through test simulations for 33-bus and 56-bus systems, the proposed method achieves low mean absolute error (MAE) of order E-05 (pu) in voltage magnitude and E-04 (rad) in angle. The discussions on salient features of the proposed method and comparative analysis with large-scale Monte-Carlo simulations, and state-ofart methods is also presented for the proposed applications.

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Section: Analytical Solution Of Power Flow Problemmentioning

confidence: 99%

A Framework for Analytical Power Flow Solution Using Gaussian Process Learning

Pareek

Nguyen

2022

IEEE Trans. Sustain. Energy

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“…We modified it by connecting three wind farms with installed capacities of 400 MW, 300 MW, 300MW, and one photovoltaic power station with an installed capacity of 330 MW. Generator parameters used in this research are taken from [11] and [12].…”

Section: Case Study a Case Settingsmentioning

confidence: 99%

An Extreme Learning Machine-Based System Frequency Nadir Constraint Linearization Method

Liu¹,

Hu²,

Pathak³

et al. 2021

Preprint

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Large-scale integration of converter-based renewable energy sources (RESs) into the power system will lead to a higher risk of frequency nadir limit violation and even frequency instability after the large power disturbance. Therefore, it is essential to consider the frequency nadir constraint (FNC) in power system scheduling. Nevertheless, the FNC is highly nonlinear and nonconvex. The <a>state-of-the-art</a> method to simplify the constraint is to construct a low-order frequency response model at first, and then linearize the frequency nadir equation. In this letter, an extreme learning machine (ELM)-based network is built to <a>derive </a>the linear formulation of FNC, where the two-step fitting process is integrated into one training process and more details about the physical model of the generator are considered to reduce the fitting error. Simulation results show the superiority of the proposed method on the fitting accuracy.

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“…In addition, machine learning approaches become increasingly popular in analyzing the potential impacts on the operation or resilience of power systems [24]. Reference [25] used five classical machine learning approaches for electric load forecast in India.…”

Section: B Literature Reviewmentioning

confidence: 99%

Open-Access Data and Toolbox for Tracking COVID-19 Impact on Power Systems

Ruan¹,

Yu²,

Pu³

et al. 2021

Preprint

Self Cite

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Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation. Analyzing these pandemic-induced patterns is imperative to identify the potential risks and impacts of this extreme event. With this purpose, we developed an open-access data hub (COVID-EMDA+), an open-source toolbox (CoVEMDA), and a few evaluation methods to explore what the U.S. power systems are experiencing during COVID-19. These resources could be broadly used for research, policy making, or educational purposes. Technically, our data hub harmonizes a variety of raw data such as generation mix, demand profiles, electricity price, weather observations, mobility, confirmed cases and deaths. Several support methods and metrics are then implemented in our toolbox, including baseline estimation, regression analysis, and scientific visualization. Based on these, we conduct three empirical studies on the U.S. power systems and markets to introduce some new solutions and unexpected findings. This conveys a more complete picture of the pandemic's impacts, and also opens up several attractive topics for future work. Python, Matlab source codes, and user manuals are all publicly shared on a Github repository.

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Review of Learning-Assisted Power System Optimization

Cited by 15 publications

References 64 publications

A Framework for Analytical Power Flow Solution Using Gaussian Process Learning

A Framework for Analytical Power Flow Solution Using Gaussian Process Learning

An Extreme Learning Machine-Based System Frequency Nadir Constraint Linearization Method

Open-Access Data and Toolbox for Tracking COVID-19 Impact on Power Systems

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