Research on Location and Capacity Planning of Renewable Energy Sources Based on Node Correlation Line Loss Sensitivity Factor and Short-Circuit Ratio

The integration of renewable energy stations, dominated by wind power and photovoltaic power, into the receiving-end power grid tends to induce system stability issues. To enhance the accommodation capacity of the receiving-end grid for renewable energy, it is necessary to investigate the location and capacity of renewable energy access points. Given the random fluctuations in renewable energy output and the heterogeneity in the carrying capacity of power grid nodes, this paper proposes a renewable energy site selection and capacity planning method based on the node correlation line loss sensitivity factor (NSF) and the short-circuit ratio. First, the calculation method of the node correlation line loss sensitivity factors is derived based on the existing calculation method. The principle of minimizing the nodal correlation line loss sensitivity factor of the system is adopted to optimally select candidate connection points for renewable energy. Subsequently, a renewable energy capacity planning model is developed considering the enhancement of the system voltage support capability. Finally, an improved butterfly optimization algorithm is employed to solve the planning model and determine the optimal installed capacity of renewable energy. Validation conducted on a standard test case of a modern power system demonstrates that, compared with traditional multi-objective optimization algorithms, the improved butterfly optimization algorithm can yield a broader and more uniformly distributed Pareto frontier. In addition, the proposed method can effectively improve the voltage support capability of the system after renewable energy integration while maintaining cost-effectiveness.