There is a significant gap between academic research and practical application for power distribution system planning (PDSP). For most of the existing PDSP models in academic research, cost is used as the objective function, and the most common constraints are power flow equality constraints, bus voltage or voltage drop limits, substation and feeder capacity limits, etc. Although various advanced models and methods have been proposed, they are rarely used in real distribution system companies. This paper proposes a new feeder planning model for the urban distribution network considering various practical requirements. According to field investigation, "supply electricity to loads using nearby power sources" is used as the objective, and it is analytically expressed by load moment. Block loads, practical street layout, meshed network configuration, connection mode of feeders, non-crossing requirement of feeders, power supply radius, etc., are all respected and embedded in the model. The proposed model is recast as a mixed integer linear programming (MILP) problem, and it can be solved by the state-of-art commercial solver. The effectiveness and validity of the proposed model are illustrated on a 9-block test system and a real 22-block district distribution system.

In conventional contingency analysis (CA), the overloading of transmission lines is identified by the violation of current or power flow rather than the temperature rise. Thus, the transient thermal behavior of transmission lines is usually neglected, which may cause underestimation of power transfer capability and lead to misoperations. In this paper, a new CA method that considers the transient thermal behavior of overhead transmission lines is proposed, where a transient heat balance based power flow (THB-PF) model is established to estimate the temperature dynamics of the lines after contingencies. The THB-PF model is represented by algebraic differential equations, which can be solved as a sequence of extended power flow problems by discretizing the differential equations into difference equations. Moreover, a simplified solution method for the THB-PF is also developed, in which the THB difference equations are linearized and solved separately from the power flow equations. The simplified method can yield an acceptable approximate solution within a considerably shorter time. Numerical simulations verify the effectiveness and efficiency of the proposed methods.

Nowadays, an increasing number of photovoltaic (PV) plants are becoming integrated into one regional power grid. Under this circumstance, the probabilistic forecast of regional PV power generation is of significance for the regional power system operation and control. This study presents a novel probabilistic forecast method for regional PV generation that integrates the convolutional neural network (CNN) with non‐linear quantile regression (QR). In this method, the CNN structure is enhanced to extract the non‐linear features of the input data and generate the non‐linear QR function. As a result, the improved CNN can effectively process high‐dimensional and complex input data and the non‐linear QR model can provide quantile forecast information of regional PV power. The validity of the proposed method is verified by using it to forecast the regional PV generation from the clustered PV plants in the Weifang region of China.

With the rapid increase of renewable energy sources, the variability and uncertainty in power systems are significantly increased, and the power system operation is facing more and more challenges. In the traditional dynamic economic dispatch (DED) of transmission systems, the frequency and voltage regulation effects are not actively utilized. In addition, the transmission systems and distribution systems are optimized separately; the transmission–distribution coordination effect is not considered. In this article, a new DED is proposed. The optimal scheduling results are obtained by making a balance between operating cost and reliability cost. Both the automatic regulation effect and transmission–distribution coordination effect are explored and exploited in terms of the equivalent reserve, which can reduce the reserve provided by generating units, and can further enhance the security and economics of power system operation. The validity and effectiveness of the proposed model are illustrated by case studies.

The Conductor temperature is a critical state variable that should be considered in power flow calculations to improve the calculation accuracy and reveal the potential thermal loadability of transmission components. This paper proposes a calculation method for time-process power flow considering the thermal behavior (TPPFCTB) of transmission components to calculate the temperature dynamics of overhead lines, power cables, and transformers under anticipated system operation scenarios. The TPPFCTB model is represented by differential-algebraic equations, which can be solved as a sequence of extended power flow problems at discretization time intervals. Based on the Newton method, incomplete and complete decoupling methods are proposed to decouple the correction equations into several independent groups with constant coefficient matrices, thus improving computational efficiency. The work presented in this paper provides an effective method for the detection of potential power transfer capability problems associated with transmission components, thus guiding operators to make more economical decisions. Numerical simulations were conducted to verify the effectiveness of the proposed method.

For an overhead conductor, meteorological correlations exist among the meteorological elements that dominantly determine its thermal rating, and temporal correlations exist among the thermal ratings in sequential time periods. It is necessary to exploit these correlations to improve the performance of the probabilistic prediction of thermal ratings. To this end, a copula-based method of joint probability density prediction for multiperiod thermal ratings (JPDP-MPTR) is presented in this paper. In this method, the probability density functions (PDFs) of the thermal ratings for every 15 minutes over a 1-hour horizon are first predicted individually, considering the correlations among meteorological elements. Then, the joint probability density function (JPDF) of the multiperiod thermal ratings is further formulated based on copula theory. Finally, the probability distributions of the thermal ratings in the predicted time periods are estimated via joint sampling based on the JPDF. Numerical simulations based on actual meteorological data collected around an overhead conductor show that the proposed method can significantly improve prediction results through the integration of meteorological and temporal correlations into the probabilistic prediction of the thermal rating.