Multi-regions Bundled Planning of Wind Farm, Thermal, Energy Storage with Renewable Energy Consumption

Ma, Su; Lu, Linfeng; Zhang, Heng; Cheng, Haozhong; Zhang, Xiaohu; Lu, Jianhua

doi:10.1109/aeees51875.2021.9403194

Cited by 1 publication

(1 citation statement)

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“…T HE injection of renewable energy power generation (REPG) into the power grid is crucial for improving energy security and achieving carbon neutrality [1], [2]. However, the volatility and randomness of wind power (WP) and photovoltaic (PV) present a significant challenge for system operators who must ensure the efficient and safe absorption of the grid-connected REPG while maintaining the economic and operational stability of the power grid [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

Situation Awareness and Sensitivity Analysis for Absorption of Grid-connected Renewable Energy Power Generation Integrating Robust Optimization and Radial Basis Function Neural Network

Zhang,

Chen,

Zhao

et al. 2023

Journal of Modern Power Systems and Clean Energy

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The significance of situation awareness (SA) in power systems has increased to enhance the utilization of gridconnected renewable energy power generation (REPG). This paper proposes a real-time calculation architecture based on the integration of robust optimization (RO) and artificial intelligence. First, the time-series simulation of the REPG consumption capacity is carried out under the current grid operating conditions. RO is employed in this simulation, given the randomness of the REPG output and the grid load. Then, the radial basis function neural network (RBFNN) is trained with the results under different parameters using the artificial fish swarm algorithm (AFSA), enabling the neural network (NN) to be the replacement for the time-series simulation model. The trained NN can quickly perceive the REPG absorption situation within the predefined grid structure and period. Moreover, the Sobol' method is adopted to conduct the global sensitivity analysis for different parameters based on the input-output samples obtained by the trained NN. Finally, the simulation experiments based on the modified IEEE 14-bus system prove the real-time performance and accuracy of the proposed SA architecture.

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