K-means based multi-objective optimisation of photovoltaic carrying capacity of distribution network

Intelligent algorithms play a key role in modern power system optimisation, especially in the context of large-scale access to photovoltaic (PV) power generation, which is essential to solve the problems of voltage fluctuation, increasing network losse s and load imbalance. To this end, the study proposes a multi-objective optimisation method for PV carrying capacity based on K-means clustering algorithm. By optimising multiple objectives for PV access, the operational efficiency of the distribution network is improved. The simulation results show that the proposed model of the study outperforms the other two comparative models in all indicators, especially in reducing network losses and enhancing PV access capacity. Under the access capacity o f 6000kW, the network loss cost of the model is 1.556 million yuan, the power sales revenue is 3.412 million yuan, and the comprehensive economic cost is 4.954 million yuan, which are lower than the other two models. The results show that the proposed method can effectively enhance the access capacity of PV power generation, reduce the network loss, and optimise the comprehensive economic benefits of the system, providing an effective solution for the application of PV power generation in the distribution network.