State estimation (SE) is regarded as an essential tool for achieving the secure and efficient operation of distribution networks, and extensive research on SE has been conducted over the past three decades. Nonetheless, the high penetration of distribution generations (DGs) is accompanied by uncertainties and dynamics, and the extensive application of intelligent electronic devices (IEDs) is associated with data processing issues, all of which raise new challenges, and these issues must be taken care of for further development of SE in smart distribution networks. This paper attempts to present a comprehensive literature review of numerous works that address various issues in SE, examining key technical research issues and future perspectives. Hopefully, it will be able to meet the needs for the development of smart distribution networks.

The increasing penetration of distributed renewable energy and the emergence of large-scale, flexible loads such as data centers pose significant challenges to the economic and secure operation of distribution systems. Traditional static pricing mechanisms are often inadequate, leading to inefficient resource dispatch and curtailment of renewable generation. To address these issues, this paper proposes a hierarchical pricing and dispatch framework modeled as a tri-level Stackelberg game that coordinates interactions among an upstream grid, a distribution system operator (DSO), and multiple virtual power plants (VPPs). At the upper level, the DSO acts as the leader, formulating dynamic time-varying purchase and sale prices to maximize its revenue based on upstream grid conditions. In response, at the lower level, each VPP acts as a follower, optimally scheduling its portfolio of distributed energy resources—including microturbines, energy storage, and interruptible loads—to minimize its operating costs under the announced tariffs. A key innovation is the integration of a schedulable data center within one VPP, which responds to a specially designed wind-linked incentive tariff by shifting computational workloads to periods of high renewable availability. The resulting high-dimensional bilevel optimization problem is solved using a Kriging-based surrogate methodology to ensure computational tractability. Simulation results verify that, compared to a static-pricing baseline, the proposed strategy increases DSO revenue by 18.9% and reduces total VPP operating costs by over 28%, demonstrating a robust framework for enhancing system-wide economic and operational efficiency.

The high resistance connection fault of the stator is a common fault in doubly fed induction generators, which causes a three-phase imbalance in the stator circuit. Since the stator winding is directly connected to the power grid, interference from the asymmetric power grid must be eliminated in order to achieve the accurate diagnosis of stator resistance imbalance faults. Therefore, a new diagnosis method based on filter shunt capacitor banks is proposed in this paper. By introducing shunt capacitor banks, an artificial neutral point is constructed to replace the neutral point of the power grid. Then, the neutral point voltage of the stator winding relative to the artificial neutral point is selected as a fault characteristic signal. In this paper, the change in three-phase stator currents after a high-resistance connection fault is analyzed in detail, and by comparing the fault characteristic signal with three-phase stator currents, the fault phase location and fault severity of high-resistance connection can be accurately obtained. Finally, simulations are carried out via the field-circuit coupling method to validate the effectiveness of the proposed method.