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2 Archives of Electrical Engineering

2 2021

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Dynamic interactions stability analysis of hybrid renewable energy system with SSSC

He Ping, Qi Pan, Ji Yuqi, Li Zhao

Archives of Electrical Engineering

2021

Vol. 70, nr 2

445--462

The static series synchronous compensator (SSSC) has demonstrated its capability in providing voltage support and improving power system stability. The objective of this paper is to analyze the dynamic interaction stability mechanism of a hybrid renewable energy system connected with doubly-fed induction generators (DFIGs) and solid oxide fuel cell (SOFC) energy with the SSSC. For this purpose, a linearized mathematical model of this modified hybrid single-machine infinite-bus (SMIB) power system is developed to analyze the physical mechanism of the SSSC in suppressing oscillations and the influence on the dynamic stability characteristics of synchronization. Typical impacting factors such as the series compensation level, the SOFC penetration and tie-line power are considered in the SMIB and two-area systems. The impact of dynamic interactions on enhancing damping characteristics and improving transient performance of the studied systems is demonstrated using eigenvalue analysis and dynamic time-domain simulations, which validates the validity of the proposed physical mechanism simultaneously.

A novel combinational evaluation method of voltage and reactive power in regional power grid containing renewable energy

Ji Yuqi, Chen Xuehan, Xiao Han, Shi Shaoyu, Kang Jing, Wang Jialin, Zhang Shaofeng

Archives of Electrical Engineering

2021

Vol. 70, nr 4

925--942

The output of renewable energy is strongly uncertain and random, and the distribution of voltage and reactive power in regional power grids is changed with the access to large-scale renewable energy. In order to quantitatively evaluate the influence of renewable energy access on voltage and reactive power operation, a novel combinational evaluation method of voltage and reactive power in regional power grids containing renewable energy is proposed. Firstly, the actual operation data of renewable energy and load demand are clustered based on the K-means algorithm, and several typical scenarios are divided. Then, the entropy weight method (EWM) and the analytic hierarchy process (AHP) are combined to evaluate the voltage qualified rate, voltage fluctuation, power factor qualified rate and reactive power reserve in typical scenarios. Besides, the evaluation results are used as the training samples for back-propagation (BP) neural networks. The proposed combinational evaluation method can calculate the weight coefficient of the indexes adaptively with the change of samples, which simplifies the calculation process of the indexes’ weight. At last, the case simulation of an actual regional power grid is provided, and the historical data of one year is taken as the sample for training, evaluating and analyzing. And finally, the effectiveness of the proposed method is verified based on the comparison with the existing method. The evaluated results could provide reference and guidance to the operation analysis and planning of renewable energy.