Distributed reactive power optimization of flexible distribution network based on probability scenario-driven

To address the uncertainties introduced into reactive power optimization by the hierarchical and distributed access of massive distributed resources to the flexible distribution network, this paper proposes a distributed reactive power optimization method for the flexible distribution network driven by probabilistic scenarios. Firstly, considering various reactive power regulation measures in the distribution network, a reactive power optimization model for the flexible distribution network is established with the objective of minimizing system losses. Then, to handle the uncertainties of distributed photovoltaic (PV) output and loads during the reactive power optimization process, taking into account the confidence constraints of the 1-norm and infinity- norm comprehensively, the reactive power optimization model of the flexible distribution network is transformed into a distributionally robust reactive power optimization model of the flexible distribution network based on the probabilistic scenario fuzzy set. On this basis, to improve the solution efficiency of the distributionally robust reactive power optimization model of the flexible distribution network, taking the distributed optimization model as the external framework, the consensus accelerated gradient alternating direction method of multipliers (ADMM) is used for global coordination and update iteration solution. For each sub-area's distributionally robust optimization model, taking it as the internal framework, the column and constraint generation (CCG) algorithm is used for solution. Thus, a distributed solution method for the distributionally robust reactive power optimization of the flexible distribution network driven by probabilistic scenarios is proposed. The simulation results of the improved IEEE-33 bus system show that the proposed distributed reactive power optimization method for the flexible distribution network has good convergence and strikes a balance between economy and robustness.