Multiobjective Reactive Power Optimization Planning for Medium Voltage Distribution Networks Based on Improved Genetic Algorithm

The medium voltage distribution network is a key bridge between the power sector and electricity users. In the process of increasing user demand for electricity, the medium voltage distribution network system has encountered problems such as insufficient reactive power, unreasonable distribution, and insufficient voltage at the end nodes of the line, which have affected the power supply quality and stability of the power system. Therefore, a multiobjective reactive power optimization planning method for medium voltage distribution networks based on an improved genetic algorithm is studied. Establish a mathematical model for medium voltage distribution network planning based on the multiobjective functions of active power loss, total voltage deviation of system nodes, and minimum total compensation amount of system compensation devices. The balance equation between active and reactive power of power nodes and power absorption losses is taken as the equality constraint, and the maximum and minimum constraints of variables such as voltage at the generator end and tap position of the on-load tap changer are taken as the constraints of the model. By combining the advantages of the standard genetic algorithm and simulated annealing algorithm, an improved genetic algorithm is formed to effectively solve the constructed mathematical model. After countless iterations, the effective solution of the model is obtained to achieve multiobjective reactive power optimization planning for medium voltage distribution networks. The experimental results show that this method can achieve multiobjective reactive power optimization in medium voltage distribution networks and improve the stability of the power system.