Optimization Configuration of Wind-Solar-Storage Capacity of Renewable Energy Bases with Consideration of Transmission Confidence Probability

This paper proposed an optimization model for wind-solar-storage capacity configuration in renewable energy bases with consideration of the transmission confidence probability. The K-means algorithm is used to process the load demand of the receiving power grid, generate typical scenarios, and then determine the DC transmission plan. In the capacity optimization configuration model, a transmission confidence probability index is proposed for daily transmission curve based scenarios with consideration of the impact of sending power grid support. Case study demonstrate that the transmission confidence probability not only enhances system reliability in response to unexpected events but also improves its economic efficiency. Furthermore, the proportion of wind and solar capacity significantly affects system costs. Additionally, sending power grid support mitigates the uncertainty of renewable energy generation, and at high support coefficients, the wind solar capacity tends to be equal. This indicates that in the absence of grid support, wind power generation can effectively make up for the instability of photovoltaic generation, the complementary effect of wind and solar power generation in renewable energy bases is validated.