Active Power Load Dispatch Of Power System In Taizhou Area Based On Genetic Algorithm

With the rapid development of national economy, the grid needs to improve their operational efficiency, including how to optimize power allocation is particularly important. Active power optimization problem of domestic and foreign scholars through research for nearly a century, has experienced from the classic economic dispatch model to modern economic dispatch model. The actual situation of the modern economic dispatch is closer to the operation of the power grid, considering the time synchronization of the unit and the line in the operation of the power network.Classical genetic algorithm are studied in this paper, the algorithm is efficient and convenient, strong universality suitable to solve the active power optimization allocation problem, but the classical algorithm code is not unified, easy to premature convergence, to solve this problem, this paper presents improved genetic algorithm. Improved genetic algorithm increases the adaptation degree function design and species similarity judgment, which greatly increased the diversity of the population, effectively avoid the local optimal population due to the diversity of the population problem in search results. The single target decision optimization problem has been improved, compared to the traditional equal incremental rate optimization algorithm, improved genetic algorithm is better for decision-making optimization, to obtain greater benefits, effectively avoid the premature convergence problem.Finally, through active power optimization research of Taizhou area model, the improved genetic algorithm is analyzed and the model, the results show that the improved genetic algorithm can be very good to solve the problem of optimal power grid.Programming to achieve the above process in the MATLAB platform, and forecast the wind power of a wind farm in the next twenty-four hours. The numerical examples show that the proposed method has the advantages of fast calculation speed, high engineering practicability, strong applicability for all kinds of weather conditions, and can be used to realize the short-term forecasting of wind power output.