Research On Robust Probabilistic Energy Flow Optimization Method With Large-scale Wind Power

With the advent of the era for energy Internet,wind energy will become the main source of power generation in the future energy network.For the power system and integrated energy system,as the physical carrier of energy Internet,how to absorb wind energy with uncertainty of power generation on a large scale has become the main challenge it faces.As an effective method to deal with wind power uncertainty,robust optimization has been widely used in the economic scheduling problems of power system and integrated energy system.However,the existing robust economic schedualing model often take the energy supply cost for ground state as the objective function,which is difficult to reflect the optimization of actual energy supply cost.In order to solve the above problems,this paper takes into account the probability distribution characteristics of large-scale wind power,fuses the concept of probabilistic optimal energy flow into the robust economic dispatching model,and studies the robust probabilistic energy flow optimization method that reflects the actual energy supply cost.Based on the above background,the main research contents and achievements of this paper are as follows:1)Firstly,this paper analyzes the principe of robust economic schedualing model,and introduces two kinds of robust optimization methods,including adaptive robust optimization method and affine adjustable robust optimization method,which are applied to power system economic scheduling problems with wind power uncertainty,and then a robust economic schedualing model is described to minimize the ground state power generation cost and maximize the wind power utilization.Subsequently,this paper analyzes the principle of probabilistic power flow calculation,and introduces the application characteristics of monte carlo simulation and point estimation in probabilistic power flow calculation.2)Based on the robust economic dispatching model of maximizing wind power utilization,this paper introduces a three-point estimation method based on Nataf inverse transform,and proposes a robust probabilistic power flow optimization method that synchronously optimizes the allowable interval of wind power and the unit’s expected generation cost,this method maximiazes the allowable interval of wind power,and takes the expected generation cost tracking wind power fluctuation as economic objective to reflect the minimization of actual generation cost.Finally,the effectiveness of the proposed method is verified by monte carlo simulation on an IEEE 118-node power system using the calibration model.3)Applying the above robust probabilistic power flow optimization method to the scenario of integrated electric and gas system,this paper further proposes a robust probabilistic energy flow optimization method considering the constraints of gas network operation,this method takes into account the power regulation of gas-electric coupling gas generating units and P2 G devices to track wind power fluctuations,and converts the upper and lower limits of allowable output range of gas generating units and P2 G devices into limit scenarios to further ensure the operation feasibility of gas grid.Finally,the effectiveness of the method is verified by monte carlo simulation on the integrated electric and gas system composed of IEEE 39-node power grid and Belgium 20-node gas network using the calibration model.