Demand Response of Electric Vehicles for Building Integrated Energy System

2018 ◽  
Author(s):  
Jingqi Huang ◽  
Zheng Yang ◽  
Xiao Zeng ◽  
Hongliang Zou ◽  
Yiqin Tang ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Demand Response ◽  
Energy System ◽  
Integrated Energy System

scholarly journals Medium- and Long-Term Integrated Demand Response of Integrated Energy System Based on System Dynamics

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 710 ◽  
Author(s):  
Shuhui Ren ◽  
Xun Dou ◽  
Zhen Wang ◽  
Jun Wang ◽  
Xiangyan Wang
Keyword(s):  
Energy Storage ◽  
System Dynamics ◽  
Electric Vehicles ◽  
Demand Response ◽  
Energy System ◽  
Energy Utilization ◽  
Response Model ◽  
Integrated Energy System ◽  
Flexible Load

For the integrated energy system of coupling electrical, cool and heat energy and gas and other forms of energy, the medium- and long-term integrated demand response of flexible load, energy storage and electric vehicles and other demand side resources is studied. It is helpful to mine the potentials of demand response of various energy sources in the medium- and long-term, stimulate the flexibility of integrated energy system, and improve the efficiency of energy utilization. Firstly, based on system dynamics, the response mode of demand response resources is analyzed from different time dimensions, and the long-term, medium-term and short-term behaviors of users participating in integrated demand response are considered comprehensively. An integrated demand response model based on medium-and long-term time dimension is established. Then the integrated demand response model of integrated energy system scheduling and flexible load, energy storage and electric vehicles as the main participants is established to simulate the response income of users participating in the integrated demand response project, and to provide data sources for the medium- and long-term integrated demand response system dynamics model. Finally, an example is given to analyze the differences in response behaviors of flexible load, energy storage and electric vehicle users in different time dimensions under the conditions of policy subsidy, regional location and user energy preferences in different stages of the integrated energy system.

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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