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Zhaofang Song
State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China

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Journal article
Published: 23 March 2021 in IEEE Transactions on Applied Superconductivity
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The high-frequency PWM pulse voltage of SMES power condi-tioning system (PCS) transmitted to the HTS magnet will influ-ence the security of the magnet. Therefore, to analyze the dynam-ic response of the magnet with the interaction of the PWM pulse voltage is necessary for the SMES operation. This paper proposes a frequency-domain analysis method for the transient character-istic of the HTS magnet, which can be utilized to analyze the voltage distribution and the frequency response characteristic of the magnet. To evaluate the effectiveness of the proposed method, a sample HTS magnet is assembled to measure its voltage distri-bution characteristic. Furthermore, the results of amplitude-frequency characteristics coincide with the trend of magnet volt-age variation.

ACS Style

Jing Shi; Zitong Zhang; Meng Liao; Dengquan Lin; Shujian Li; Ying Xu; Zhaofang Song; Zexu Chen; Li Ren. Frequency-domain analysis and the effect on voltage distribution of the HTS SMES. IEEE Transactions on Applied Superconductivity 2021, PP, 1 -1.

AMA Style

Jing Shi, Zitong Zhang, Meng Liao, Dengquan Lin, Shujian Li, Ying Xu, Zhaofang Song, Zexu Chen, Li Ren. Frequency-domain analysis and the effect on voltage distribution of the HTS SMES. IEEE Transactions on Applied Superconductivity. 2021; PP (99):1-1.

Chicago/Turabian Style

Jing Shi; Zitong Zhang; Meng Liao; Dengquan Lin; Shujian Li; Ying Xu; Zhaofang Song; Zexu Chen; Li Ren. 2021. "Frequency-domain analysis and the effect on voltage distribution of the HTS SMES." IEEE Transactions on Applied Superconductivity PP, no. 99: 1-1.

Journal article
Published: 19 October 2020 in Applied Sciences
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As the electricity consumption and controllability of residential consumers are gradually increasing, demand response (DR) potentials of residential consumers are increasing among the demand side resources. Since the electricity consumption level of individual households is low, residents’ flexible load resources can participate in demand side bidding through the integration of load aggregator (LA). However, there is uncertainty in residential consumers’ participation in DR. The LA has to face the risk that residents may refuse to participate in DR. In addition, demand side competition mechanism requires the LA to formulate reasonable bidding strategies to obtain the maximum profit. Accordingly, this paper focuses on how the LA formulate the optimal bidding strategy considering the uncertainty of residents’ participation in DR. Firstly, the physical models of flexible loads are established to evaluate the ideal DR potential. On this basis, to quantify the uncertainty of the residential consumers, this paper uses a fuzzy system to construct a model to evaluate the residents’ willingness to participate in DR. Then, based on the queuing method, a bidding decision-making model considering the uncertainty is constructed to maximize the LA’s income. Finally, based on a case simulation of a residential community, the results show that compared with the conventional bidding strategy, the optimal bidding model considering the residents’ willingness can reduce the response cost of the LA and increase the LA’s income.

ACS Style

Zhaofang Song; Jing Shi; Shujian Li; Zexu Chen; Wangwang Yang; Zitong Zhang. Day Ahead Bidding of a Load Aggregator Considering Residential Consumers Demand Response Uncertainty Modeling. Applied Sciences 2020, 10, 7310 .

AMA Style

Zhaofang Song, Jing Shi, Shujian Li, Zexu Chen, Wangwang Yang, Zitong Zhang. Day Ahead Bidding of a Load Aggregator Considering Residential Consumers Demand Response Uncertainty Modeling. Applied Sciences. 2020; 10 (20):7310.

Chicago/Turabian Style

Zhaofang Song; Jing Shi; Shujian Li; Zexu Chen; Wangwang Yang; Zitong Zhang. 2020. "Day Ahead Bidding of a Load Aggregator Considering Residential Consumers Demand Response Uncertainty Modeling." Applied Sciences 10, no. 20: 7310.