Reserve Capacity Optimization of Power System Connected to Large-Scale Wind Farm Based on Chance Constrained Objective Programming

With the access of large-scale wind power, the new power system faces multiple uncertainties (including wind power output and load uncertainties, generator and link fault, etc.), and the total amount of newly added wind power output uncertainties is large, and the time-space correlation is strong, so the traditional reserve capacity demand criteria are no longer applicable. This paper takes the opportunity constraint goal programming as the mathematical theory and method support, and considers the situation of large-scale wind power generation integration. It models the uncertain factors such as the failure shutdown of generating units, load forecast error, and wind power generation error, and uses a genetic algorithm based on the Monte-Carlo random simulation method for solution. It takes the IEEE-9 node system as an example, and the results verify that the spare determination method proposed in this paper can ensure the system's safe and stable operation under the premise of reducing the system's overall operating cost, alleviating the pressure of providing spare capacity on the generation side, and achieving a balance between the system's peak regulation performance and economic benefits.