This paper proposes a coordinated topology reconfiguration method for power distribution networks (PDNs) and urban drainage networks (UDNs) to facilitate the load restoration and flood prevention with diversified mobile emergency resources (MERs) under rainstorm disasters. The proposed method synergistically controls the pumps-sluice clusters and drainage rescue vehicles (DRVs) to reconfigure the optimal draining route and flow direction of UDNs for resilience enhancement of PDNs with localized damages. Mobile emergency generators (MEGs) and renewable distributed generations (DGs) are optimally dispatched for supporting PDN reconfiguration to guarantee a reliable electricity supply for UDN drainage facilities under rainstorm-induced transportation interruptions and power outages. Then, a multi-network coupling model with hydrodynamic partial differential equations (PDEs) of pressure and free-surface flows is developed to describe the failure propagation across PDNs, UDNs and urban transportation net-works (UTNs). Furthermore, an equivalent transfer matrix of dynamic UDN flows is derived based on Laplace transforms to cope with the PDE-constrained optimization problem for computational burden alleviation. Comparative results have illustrated the superior performance of the proposed method in reducing the critical load curtailment of PDNs and inundation depth of UTN roads.

Inexpensive, nonintrusive, and easy-installation current sensors are of great help in the economical and reliable operation of power systems. In this article, a novel contactless current sensor with small size, ultramicro power consumption, and self-power capability is proposed based on tunnel magnetoresistance (TMR). For measuring arbitrary line currents under an interference magnetic field, the single-axis TMR chips array and anti-interference method are introduced. Then, the designs of software and hardware are given for ultralow-power consumption and self-power. The experimental results showed that the measuring range of the current sensor is from 0 to 1500 A, and the error is less than 0.6% when the measured current is over 150 A.

Tunnel magnetoresistance (TMR) sensors, known for their high sensitivity, efficiency, and compact size, are ideal for detecting weak currents, particularly leakage currents in smart grids. However, temperature variations can negatively impact their accuracy. This work investigates the effects of temperature variations on measurement accuracy. We analyzed the operating principles and temperature characteristics of TMR sensors and proposed a high-precision, software-based temperature compensation method using cubic spline interpolation combined with polynomial regression and zero-point self-calibration. Additionally, a field-programmable gate array (FPGA)-based temperature compensation circuit was designed and implemented. An experimental platform was established to comprehensively evaluate the sensor’s performance under various temperature conditions. Experimental results demonstrate that this method significantly enhances the sensor’s temperature stability, reduces the sensitivity temperature drift coefficient, and improves zero-point drift stability, outperforming other compensation methods. After compensation, the sensor’s measurement accuracy in complex temperature environments is substantially improved, enabling effective weak current detection in smart grids across diverse environments.

This article proposes a novel N–1 interval optimal power flow (NIOPF) scheme that considers N–1 contingencies and interval uncertainties in renewable generation. Based on the scenario-equivalent decoupling method (SEDM), the NIOPF addresses the problems associated with existing methods, including constraint violations and low solution efficiency. First, the NIOPF model is formulated by integrating security constraints involving interval variables under both N–0 normal and N–1 contingency scenarios, along with scenario transition constraints. Second, a credible domain reduction method (CDRM) based on the Minkowski difference is proposed to handle interval constraints, converting them into tractable deterministic constraints that guarantee the system operates within safe limits across all uncertain scenarios. Then, the SEDM represents numerous N–1 scenarios equivalently as two positive and negative deviation scenarios, effectively reducing the dimensionality of N–1 scenarios. Next, a two-stage optimization model that integrates the N–0 normal operating scenario with the two equivalent deviation scenarios is constructed and solved iteratively to improve computational efficiency. Finally, case studies on the modified IEEE 30-bus system verify that the proposed SEDM ensures constraint satisfaction under uncertainties while improving computational efficiency. Further tests on the modified IEEE 118-bus and PEGASE 1354-bus systems confirm its engineering applicability.

Low-voltage distribution area is the “last kilometer” connecting the distribution network and users, and the traditional distribution system is difficult to digitally manage in the low-voltage area, resulting in untimely and imprecise handling of voltage overruns, short-circuit outages, and other abnormal problems. With the deployment of smart meters, new sensors, smart gateways, and other devices in distribution areas, digital intelligent monitoring and management based on digital twins in LV distribution areas has gradually become the focus of distribution network research. In view of the profound changes that are taking place in the low-voltage distribution area, this paper first summarizes the characteristics and shortcomings of the existing digital twin research in the low-voltage distribution area, then puts forward the ‘6C’ development goals for the digital transformation of the low-voltage distribution area, introduces the practice work of Guangxi Power Grid Corporation around the ‘6C’ development goals in the low-voltage distribution area. Finally, the future research work of the ‘6C’ development goals for the digital transformation of the low-voltage distribution area is promising.

The construction of an energy distribution network can improve the system’s ability to absorb new energy, and its stable and efficient operation has become more and more important. The security and stability analysis of a distribution network needs accurate distribution network model parameters as support. At present, the installation of PMU equipment in China’s distribution network synchronous phase angle measurement unit is limited, which brings challenges to the parameter correction of the distribution network. In this paper, an automatic correction algorithm of distribution network parameters based on the CIM model and measurement data is proposed for the distribution network system without PMU. Firstly, the distribution network topology construction technology based on XML files and key fields of the distribution network is proposed, and the non/small impedance devices (such as switches) are merged and reduced. The breadth-first traversal algorithm is used to verify the connectivity of the constructed topology. Then, based on the topology construction and the least squares method, an iterative parameter correction technique is constructed. Finally, the accuracy and effectiveness of the proposed algorithm are verified in a standard IEEE 33-bus system distribution network and an example of the China Southern Power Grid. The topology connections constructed based on the CIM model have significantly enhanced the efficiency of parameter correction.