A novel flexible load coordination control based on distributed generation consumption in the active distribution network

Author(s):  
Wei Zhao ◽  
Qingsheng Li ◽  
Jing Nong ◽  
Dong Liu
2014 ◽  
Vol 668-669 ◽  
pp. 749-752 ◽  
Author(s):  
Xiao Yi Zhou ◽  
Ling Yun Wang ◽  
Wen Yue Liang ◽  
Li Zhou

Distributed generation (DG) has an important influence on the voltage of active distribution networks. A unidirectional power distribution network will be transformed into a bidirectional, multiple power supply distribution network after DGs access to the distribution network and the direction of power flow is also changed. Considering the traditional forward and backward substitution algorithm can only deal with the equilibrium node and PQ nodes, so the other types of DGs should be transformed into PQ nodes, then its impact on active distribution network can be analyzed via the forward and backward substitution algorithm. In this paper, the characteristics of active distribution networks are analyzed firstly and a novel approach is proposed to convert PI nodes into PQ nodes. Finally, a novel forward and backward substitution algorithm is adopted to calculate the power flow of the active distribution network with DGs. Extensive validation of IEEE 18 and 33 nodes distribution system indicates that this method is feasible. Numerical results show that when DG is accessed to the appropriate location with proper capacity, it has a significant capability to support the voltages level of distribution system.


2021 ◽  
Vol 257 ◽  
pp. 01010
Author(s):  
Lingyan Wei ◽  
Bing Wang ◽  
Xiaoyue Wu ◽  
Fumian Wang ◽  
Peng Chen

With the increasing number of Electric Vehicle (EV) and clean energy generation year by year, EV and distributed generation (DG) have become issues that have to be considered in active distribution network planning. Firstly, considering the time series characteristics of DG, the output time series model of DG is established; Secondly, the parking demand and space-time movement model of EV is established, and the Monta Carlo method is used to simulate the space-time distribution of EV charging load in different planning areas; Finally, taking the system investment and annual operation and maintenance cost, voltage index and environmental index as the objective function, and considering the node voltage, node current and DG installation capacity as constraints. The improved particle swarm optimization algorithm is used to solve the planning model, and the access location and capacity of EV charging station and DG are obtained. Taking a distribution network as an example, the rationality and effectiveness of the proposed model and algorithm are verified.


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