Online Optimization in Power Systems With High Penetration of Renewable Generation: Advances and Prospects

Wang, Zhaojian; Wei, Wei; Pang, John Z. F.; Liu, Feng; Yang, Bo; Guan, Xinchun; Mei, Shengwei

doi:10.1109/jas.2023.123126

Cited by 13 publications

(3 citation statements)

References 145 publications

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“…• Convergence is earlier [21] reviewed the several online optimization strategies MAE, RMSE, R 2, and more Faster, less computation complexity,…”

Section: Related Workmentioning

confidence: 99%

“…Usually, the power system with offline optimization strategies is a more reliable energy source to reduce the power domain issues. Therefore, Zhaojian Wang et al [21] have reviewed several online optimization strategies and their application power system. Moreover, the power system domain has many issues like voltage regulation, unwanted power flow, etc Huang et al [22] developed the QoSbased prediction strategy, which is adapted as a deep learning technique.…”

Section: Related Workmentioning

confidence: 99%

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AI-based efficiency analysis technique for photovoltaic renewable energy system

Alam,

Alshahrani,

Khan

et al. 2023

Phys. Scr.

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Artificial intelligence (AI) in renewable energy technologies plays a crucial part due to its modeling and performance forecasting. Therefore, a novel AI-based evolving generative adversarial Fuzzy network (EGAFN) has been presented in this paper as a forecasting tool for the efficiency analysis of renewable solar energy for four distinct regions. The power ratings from environmental parameters and solar panels were monitored and recorded for a year. The data pre-processing is primarily applied to improve the system's function using a data filter. Furthermore, the data's energy estimation accuracy is enhanced using feature extraction and selection by a multi-objective lionized manta-ray foraging optimizer (MLMRFO). Finally, the hyperparameters of the EGAFN method are optimized by multi-objective optimization. The proposed technique aims to enhance the energy efficiency of PV systems for solar power production forecasting using an optimized multi-objective algorithm. The findings show that the suggested technique's prediction performance is better than earlier methods. Thus, the proposed methodology can assist in increasing energy efficiency and making better use of renewable energy sources.

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“…• Convergence is earlier [21] reviewed the several online optimization strategies MAE, RMSE, R 2, and more Faster, less computation complexity,…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

AI-based efficiency analysis technique for photovoltaic renewable energy system

Alam,

Alshahrani,

Khan

et al. 2023

Phys. Scr.

View full text Add to dashboard Cite

show abstract

“…For a few decades, power systems have been undergoing dramatic changes due to the penetration of renewable power generations, the widespread use of demand-response devices as well as access to dynamic loads [1], [2]. In order to monitor power systems under complex working conditions, a new generation of infrastructure, namely, wide area monitoring systems (WAMS), has been increasingly deployed in modern power systems [3].…”

Section: Introductionmentioning

confidence: 99%

Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

Qu,

Wang,

Shen

et al. 2024

IEEE/CAA J. Autom. Sinica

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This paper investigates the anomaly-resistant decentralized state estimation (SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements (i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points (MEEF) criterion. Specifically, i) an augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach; ii) using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement; and iii) the local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme. Index Terms-Wide-area power systems, decentralized state estimation, minimum error entropy, unscented Kalman filter, measurements with anomalies.

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