Distributionally robust chance-constrained optimization for soft open points operation in active distribution networks

The increasing installations of distributed generation (DG) in active distribution networks (ADNs) reduce the consumption of fossil fuels but exacerbate the demand/generation imbalance among different regions. Moreover, due to the intermittent and stochastic characteristics, DG also introduces uncertain forecasting errors, which further increases the difficulties for power dispatch. To overcome these challenges, an emerging flexible interconnection device, soft open point (SOP), is introduced. A distributionally robust chance-constrained optimization (DR-CCO) model is also proposed to effectively exploit the benefits of SOPs in ADNs under uncertainties. Compared with conventional robust, stochastic and chance-constrained models, the DRCCO model can better balance reliability and economic profits without the exact distribution of uncertainties. Moreover, unlike most published works that employ two individual chance constraints to approximate the upper and lower bound constraints (e.g. bus voltage and branch current limitations), joint two-sided chance constraints are introduced and exactly reformulated into conic forms to avoid redundant conservativeness. Based on numerical experiments, we validate that SOPs' employment can significantly enhance the energy efficiency of ADNs by alleviating DG curtailment and load shedding problems. Simulation results also confirm that the proposed joint two-sided DRCCO method can achieve a good balance between economic efficiency and reliability as well as decrease the conservativeness of conventional DRCCO methods.