A Combined Three-Port LLC Structure for Adaptive Power Flow Adjustment of PV Systems

2020 ◽  
Vol 35 (10) ◽  
pp. 10413-10422
Author(s):  
Ting Qian ◽  
Kai Guo ◽  
Chenghui Qian
2014 ◽  
Vol 134 (2) ◽  
pp. 145-152
Author(s):  
Ryuichi Ogahara ◽  
Yuki Kawaura ◽  
Shinichi Iwamoto ◽  
Naohiro Kamikawa ◽  
Masayuki Namba

2021 ◽  
Author(s):  
Tianjiao Pu ◽  
Fei Jiao ◽  
Yifan Cao ◽  
Zhicheng Liu ◽  
Chao Qiu ◽  
...  

Abstract As one of the core components that improve transportation, generation, delivery, and electricity consumption in terms of protection and reliability, smart grid can provide full visibility and universal control of power assets and services, provide resilience to system anomalies and enable new ways to supply and trade resources in a coordinated manner. In current power grids, a large number of power supply and demand components, sensing and control devices generate lots of requirements, e.g., data perception, information transmission, business processing and real-time control, while existing centralized cloud computing paradigm is hard to address issues and challenges such as rapid response and local autonomy. Specifically, the trend of micro grid computing is one of the key challenges in smart grid, because a lot of in the power grid, diverse, adjustable supply components and more complex, optimization of difficulty is also relatively large, whereas traditional, manual, centralized methods are often dependent on expert experience, and requires a lot of manpower. Furthermore, the application of edge intelligence to power flow adjustment in smart grid is still in its infancy. In order to meet this challenge, we propose a power control framework combining edge computing and machine learning, which makes full use of edge nodes to sense network state and power control, so as to achieve the goal of fast response and local autonomy. Furthermore, we design and implement parameters such as state, action and reward by using deep reinforcement learning to make intelligent control decisions, aiming at the problem that flow calculation often does not converge. The simulation results demonstrate the effectiveness of our method with successful dynamic power flow calculating and stable operation under various power conditions.


2020 ◽  
Vol 12 (15) ◽  
pp. 6154 ◽  
Author(s):  
Hui Wang ◽  
Jun Wang ◽  
Zailin Piao ◽  
Xiaofang Meng ◽  
Chao Sun ◽  
...  

High-penetration grid-connected photovoltaic (PV) systems can lead to reverse power flow, which can cause adverse effects, such as voltage over-limits and increased power loss, and affect the safety, reliability and economic operations of the distribution network. Reasonable energy storage optimization allocation and operation can effectively mitigate these disadvantages. In this paper, the optimal location, capacity and charge/discharge strategy of the energy storage system were simultaneously performed based on two objective functions that include voltage deviations and active power loss. The membership function and weighting method were used to combine the two objectives into a single objective. An energy storage optimization model for a distribution network considering PV and load power temporal changes was thus established, and the improved particle swarm optimization algorithm was utilized to solve the problem. Taking the Institute of Electrical and Electronic Engineers (IEEE)-33 bus system as an example, the optimal allocation and operation of the energy storage system was realized for the access of high penetration single-point and multi-point PV systems in the distribution network. The results of the power flow optimization in different scenarios were compared. The results show that using the proposed approach can improve the voltage quality, reduce the power loss, and reduce and smooth the transmission power of the upper-level grid.


2015 ◽  
Vol 192 (2) ◽  
pp. 12-21 ◽  
Author(s):  
Ryuichi Ogahara ◽  
Yuki Kawaura ◽  
Shinichi Iwamoto ◽  
Naohiro Kamikawa ◽  
Masayuki Namba

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