Research on the Capacity of In-Depth Peak Regulation of Large-Scale Heat Supply Unit Based on Characteristics of Thermal Storage of Heat Supply Network and Buildings

2014 ◽  
Vol 521 ◽  
pp. 187-195
Author(s):  
Qing Sheng Bi ◽  
Xiang Yu Lv ◽  
Tian Dong ◽  
De Xin Li ◽  
Xiao Juan Han

Against the backdrop that wind power is always abandoned in heat supply period in winter after wind power is taken into power grid in northern areas of China and there exists a big proportion of large-scale heat supply units in power grid, a method for in-depth peak regulation of large-scale heat supply unit based on characteristics of thermal storage of heat supply network and buildings is put forward in this paper, and a mathematical model for in-depth peak regulation is established. The calculation in a case indicates that the change of heat production of large-scale heat supply unit will not affect the quality of heat supply to a certain extent. During heat supply period, thermal load of heat supply network shall be moderately lowered to coordinate with the in-depth peak regulation when power grid is at its low ebb, and heat supply shall be moderately decreased based on characteristics of thermal storage of heat supply network and buildings when the peak regulation capacity of power grid is very limited, which indicates that it is feasible and practical to help power grid go through its low ebb with the peak regulation capacity gained here. Through the mathematical model established in this paper, time of thermal storage and thermal release as well as such units capacity of in-depth peak regulation can be calculated, which provides a scientific basis for dispatcher of power grid to conduct in-depth peak regulation of heat supply unit.

2012 ◽  
Vol 45 (21) ◽  
pp. 236-241 ◽  
Author(s):  
Boming Zhang ◽  
Jianhua Chen ◽  
Wenchuan Wu ◽  
Taiyi Zheng ◽  
Hongbin Sun ◽  
...  

2021 ◽  
Vol 261 ◽  
pp. 01037
Author(s):  
Ruomeng Jiang

This paper expounds the influence of decentralized wind power on the characteristics of distribution network. Through analysis, it can be concluded that after installing an appropriate amount of decentralized wind power, the voltage level of load bus can be improved. The power flow distribution will be changed, and the network loss of the power grid will be reduced. The decentralized wind power has also brought about negative impacts, such as voltage flicker and harmonics, the impact on the scope and direction of protection of relay protection, and greater uncertainty in the planning and operation of regional power grid. The analysis above provides some theoretical guidance for the large-scale development of decentralized wind power in the future.


2019 ◽  
Vol 79 ◽  
pp. 03017
Author(s):  
Mingyu Dong ◽  
Dezhi Li ◽  
Fenkai Chen ◽  
Meiyan Wang ◽  
Rongjun Chen ◽  
...  

With the development of smart power distribution technology in the future, a large range of power supply load (such as distributed wind power generation) will appear on the power receiving end. When distributed wind power is connected to the power grid on a large scale, it will have a certain impact on the safe and stable operation of the power grid. However, if the wind power output characteristics can be analyzed and the wind power output is properly regulated, the one-way flow of power from the distribution network to the user side will be broken, so that the future "network-load" has dual interaction characteristics based on response and substantial power exchange.


2014 ◽  
Vol 1008-1009 ◽  
pp. 137-143
Author(s):  
Ying Jie Qin ◽  
Shan Song ◽  
Bin Shi ◽  
Zhen Jian Xie ◽  
Li Wei Qiao

For power grid with large-scale wind energy, the short-term wind power prediction is important to the grid’s scheduling and stable operating. The overall short-term forecast for wind power connected to the grid relies on the wind velocity and historical power data. Firstly, K-means clustering is introduced to model the power grid, so that the relationship between wind velocity and power can be perfectly described. Considering that there are multiple factors contributing to the prediction of wind velocity and power, we use real data of 15 wind generating set to obtain dependable weight factors of all those dimensions. With the support of mass data, the prediction of power is proved by several measurements (ME, MRE, MAE, RMSE) to be accurate.


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