Abstract
In order to balance the power flow, voltage and current between renewable energy power generation and thermal power generation, this paper takes renewable energy as the research object, analyzes the power flow and voltage after grid connection, and proposes an improved genetic algorithm for research. Firstly, the log function is used to analyze the power flow and voltage data before and after grid connection, reduce the complexity of the data, and calculate the proportion of photovoltaic energy. Then, the load analysis of the power system was carried out by genetic iteration to identify the overloaded nodes, and the identification standard was greater than 80% of the rated load. Finally, a load set is formed, and information such as changes, power flow, and voltage of different power generation nodes is recorded. The results show that the genetic algorithm can maximize the proportion of renewable energy to 50% and reduce the power flow change rate of the power system to 10%. At the same time, improve the load efficiency of the power system, so that the node load is between 75~80%, average the load of each node, especially the photovoltaic matrix and fan, and shorten the load deployment time, so that it is 10s smaller. Compared with the previous algorithms, the genetic algorithm proposed in this paper is better, which can meet the load distribution requirements of the power system and expand the energy proportion of renewable energy.
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