Robust Optimal Dispatching Of The Active Distribution Network With Renewable Energy

As photovoltaics and wind power-based renewable energy increasingly penetrate into the distribution network,the uncertainty and volatility of renewable energy bring great challenges to the optimal operation of the distribution network.The traditional deterministic optimization method is no longer applicable,and the uncertainty optimization method comes into being.In order to ensure the safe and reliable operation of the distribution network and adapt to the uncertain development of renewable energy and load,this paper studies the optimal scheduling problem of active distribution network with source-load uncertainty.Based on the robust optimization theory,an adjustable two-stage robust optimization model is constructed.A set of uncertainties is established for photovoltaic,wind power and volatility loads,and uncertainty adjustment parameters are set to adjust the uncertainty set to control the conservative degree of the optimal solution.The steerable unit is considered in the first stage scheduling model.The scheduling cost and the network loss are minimized.In the second stage scheduling model,the optimization target of the abandoned light,the abandoned wind and the least load is added to promote the full consumption of the renewable energy;the model is transformed into the mixed whole by the convex optimization technique.The second-order cone optimization model is solved,and the column-constrained-generation method(C&CG)is used to solve the model quickly.Finally,the improved IEEE33 node AC-DC system is used to simulate the case analysis,and the stochastic programming and robust optimization using Benders decomposition method.The method is compared and the results verify the feasibility and effectiveness of the model and the speed of the proposed algorithm.Based on the proposed robust optimization model,this paper introduces the distributed robust optimization theory and constructs a two-stage distributionally robust optimization model.Compared with stochastic programming,this method is less lazy than the exact probability distribution data.Compared with the robust optimization method,this method can introduce some distribution information into the fuzzy set to produce a less conservative solution.Therefore,this paper establishes an ambiguity set to capture the uncertainty of photovoltaic,wind power and load,and introduces piecewise linear function and auxiliary parameters to help characterize the probability distribution of uncertain variables.The optimization goal of the model is to make the ambiguity set worst.The total expected cost under the distribution situation is minimized.The expected cost of the first stage is obtained based on the predicted value of the uncertainty variable,and the expected cost of the second-stage is based on the actual value of the uncertainty variable to solve the first-stage decision.The generalized linear decision rule approximates the two-stage optimization model,and the affine function is introduced to provide a closer approximation to the second-stage optimization model.Finally,the improved IEEE33 and IEEE 118-node systems are simulated and analyzed with deterministic methods and randomization.The planning and robust optimization methods are compared to verify the feasibility and superiority of the proposed model and algorithm.