Optimal Scheduling Strategy for Electric Vehicles Cooperating with Wind Power

Smart Grid ◽  
2018 ◽  
Vol 08 (03) ◽  
pp. 249-258
Author(s):  
荣义 牛
Keyword(s):  
Wind Power ◽  
Electric Vehicles ◽  
Optimal Scheduling ◽  
Scheduling Strategy

scholarly journals Two-Stage Optimal Scheduling Strategy for Large-Scale Electric Vehicles

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 13821-13832 ◽  
Author(s):  
Xiuyun Wang ◽  
Chao Sun ◽  
Rutian Wang ◽  
Tianyuan Wei
Keyword(s):  
Electric Vehicles ◽  
Large Scale ◽  
Optimal Scheduling ◽  
Two Stage ◽  
Scheduling Strategy

scholarly journals Day-Ahead and Intra-Day Optimal Scheduling of Integrated Energy System Considering Uncertainty of Source & Load Power Forecasting

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2539
Author(s):  
Zhengjie Li ◽  
Zhisheng Zhang
Keyword(s):  
Wind Power ◽  
Demand Response ◽  
Solar Power ◽  
Load Forecasting ◽  
Energy System ◽  
Optimal Scheduling ◽  
Scheduling Scheme ◽  
Load Power ◽  
Integrated Energy System ◽  
Operation Cost

At present, due to the errors of wind power, solar power and various types of load forecasting, the optimal scheduling results of the integrated energy system (IES) will be inaccurate, which will affect the economic and reliable operation of the integrated energy system. In order to solve this problem, a day-ahead and intra-day optimal scheduling model of integrated energy system considering forecasting uncertainty is proposed in this paper, which takes the minimum operation cost of the system as the target, and different processing strategies are adopted for the model. In the day-ahead time scale, according to day-ahead load forecasting, an integrated demand response (IDR) strategy is formulated to adjust the load curve, and an optimal scheduling scheme is obtained. In the intra-day time scale, the predicted value of wind power, solar power and load power are represented by fuzzy parameters to participate in the optimal scheduling of the system, and the output of units is adjusted based on the day-ahead scheduling scheme according to the day-ahead forecasting results. The simulation of specific examples shows that the integrated demand response can effectively adjust the load demand and improve the economy and reliability of the system operation. At the same time, the operation cost of the system is related to the reliability of the accurate prediction of wind power, solar power and load power. Through this model, the optimal scheduling scheme can be determined under an acceptable prediction accuracy and confidence level.

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