基于不平衡配电节点边际电价驱动的配电网与智能充电枢纽分布式协同调度

Abstract The low-carbon energy transition introduces new challenges to the operation of power distribution networks (PDN). In particular, the growing proliferation of single-phase loads and distributed renewable energies exacerbates three-phase imbalances within the PDN. The smart charging hub, as a low-carbon unit for transportation electrification, integrating photovoltaic (PV) generation, energy storage (ES), and electric vehicle (EV) charging infrastructure, processes phase-balancing capabilities by regulating the three-phase power. However, existing distribution markets lack effective mechanisms to incentivize the participation of PV-ES-EV integrated smart charging hubs (PEV-Hubs) in mitigating imbalances. This paper proposes a three-phase distribution locational marginal price (DLMP)-driven distributed optimization framework that coordinates PDN and PEV-Hubs to actively mitigate phase imbalances. A bi-level optimization model is formulated to capture the interaction between the distribution system operator (DSO) and PEV-Hubs. At the lower level, a three-phase distribution market clearing model is established based on the branch power flow model with voltage imbalance constraints, which can generate unbalanced DLMPs. At the upper level, PEV-Hubs optimize phase-to-phase power allocation to maximize revenue by taking unbalanced DLMP from the lower level as inputs. To solve the bi-level model, the Karush-Kuhn-Tucker (KKT) conditions and strong duality theory are employed to reformulate the bi-level problem into a single-level problem. A novel distributed solution method based on dual problem decomposition is then proposed for privacy protection. Simulation results on two different-scale systems confirm that the proposed method effectively limits the voltage imbalance in the PDN and enhances economic benefits for both the DSO and PEV-Hubs. Compared to the traditional iterative algorithms, the proposed distributed solution method exhibits better convergence and solution optimality.