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- 저자 : Mingshi Cui (검색결과 2 건)
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Zhaoyang Qu,Jiajun Song,Yuqing Liu,Hongbo Lv,Kewei Hu,Jian Sun,Miao Li,Wei Liu,Mingshi Cui,Wanxin Wang 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.6
The problem of load fuctuation in the distribution network and increasing power grid cost input caused by the unpredictable behavior of electric vehicle (EV) users in response to electricity price is investigated in this paper. An optimization model method for the charging and discharging price of electric vehicles is proposed, considering the vehicle owner response and power grid cost. The rule of EV user travel is frst analyzed, and the travel and battery state constraints are defned. Under the constraints of user charging and discharging behavior and battery characteristics, a user transfer rate and unit energy cost function is designed to construct a multi-objective model of charging and discharging price that minimizes electricity expenditure and avoids an increase in power grid investment. Finally, an improved multi-target fsh swarm algorithm is presented to solve the model optimization problem. The example analysis shows that the proposed method can reduce the peak-valley load diference of the system and cost input of the power grid, as well as provide users with regulation ability to access the power grid at diferent time periods
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Probability Prediction Method of Short‑Term Electricity Price Based on Quantile Neural Network Model
Zhaoyang Qu,Manyang Gao,Yuqing Liu,Hongbo Lv,Jian Sun,Miao Li,Wei Liu,Mingshi Cui 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.2
Aiming at the inaccuracy of short-term electricity price forecasting in competitive power markets, a probabilistic short-term electricity price forecasting method based on the quantile neural network model is proposed. First, a method for selecting electricity price similarity based on comprehensive infuencing factors is designed to select the forecast data set with similar characteristics to the forecast date. The similar daily quantile regression algorithm is then combined with the generalized dynamic fuzzy neural network to construct a quantile neural network electricity price model for obtaining the predicted daily electricity price condition quantile. Finally, the kernel density function is used to convert the predicted daily electricity price condition quantile into the predicted probability density curve to realize short-term electricity price probability prediction. The data of the electricity market of the city of Dayton, Ohio in the United States is used as an example. The experimental results demonstrate that the proposed method can efectively improve the accuracy of short-term electricity price forecasting
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