Bi-level decomposition algorithm of real-time AGC command for large-scale electric vehicles in frequency regulation

Feng, Donghan; Zhao, Yi; Su, Hao; Li, Hengjie; Zhou, Yun

doi:10.1016/j.est.2023.106852

Cited by 2 publications

(1 citation statement)

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“…Due to the implementation of the "two regulations" in each regional grid of the power system, the demands on thermal power units have become increasingly stringent. Consequently, a more detailed and comprehensive evaluation index for AGC variable operating conditions in thermal power units has been developed [39]. A specific evaluation index for the "two regulations" is proposed [40], and its calculation method is outlined in Equations ( 18)- (20).…”

Section: Agc Variable Operating Condition Evaluation Indexmentioning

confidence: 99%

Optimization of the Load Command for a Coal-Fired Power Unit via Particle Swarm Optimization–Long Short-Term Memory Model

Hao,

Yang,

Chen

et al. 2024

Energies

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This study addresses the challenges faced by coal-fired power plants in adapting to energy fluctuations following the integration of renewable energy sources into the power grid. The flexible operation of thermal power plants has become a focal point in academic research. A numerical model of a coal-fired power plant was developed in this study using the Long Short-Term Memory (LSTM) algorithm and the Particle Swarm Optimization (PSO) algorithm based on actual operation data analysis. The combined PSO-LSTM approach improved the accuracy of the model by optimizing parameters. Validation of the model was performed using a Dymola physical simulation model, demonstrating that the PSO-LSTM coupled numerical model accurately simulates coal-fired power plant operations with a goodness of fit reaching 0.998. Overall system performance for comprehensively evaluating the rate and accuracy of unit operation is proposed. Furthermore, the model’s capability to simulate the load variation process of automatic generation control (AGC) under different load command groups was assessed, aiding in optimizing the best load command group. Optimization experiments show that the performance index of output power is optimal within the experimental range when the set load starts and stops are the same and the power of load command γ = 1.8. Specifically, the 50–75% Turbine Heat Acceptance (THA) load rise process enhanced the overall system performance index by 55.1%, while the 75–50% THA load fall process improved the overall system performance index by 54.2%. These findings highlight the effectiveness of the PSO-LSTM approach in optimizing thermal power plant operations and enhancing system performance under varying load conditions.

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Section: Agc Variable Operating Condition Evaluation Indexmentioning

confidence: 99%