The medium voltage (MV) and low voltage (LV) distribution systems are usually built in a mixed three-phase three-wire and three-phase four-wire pattern, in which the conventional measurements are obtained through the two-wattmeter method (TWM) and three-wattmeter method (HWM). The existing three-phase state estimation (SE) models only involve the measurement characteristics of the HWM. Instead, a set of new and improved measurement equations are formulated in this paper for such buses measured by TWM, and the measurements include line-to-line complex power and virtual zero-sequence current. Along with the increasing underground cable, the neutral point of MV feeder head is normally grounded through a small resistance, and its voltage magnitude is not equal to zero. Accordingly, an accurate model for the slack bus together with its measurement equations is proposed herein, based on the conventional measurements and neutral point virtual measurement. Moreover, this work presents a three-phase SE model for MV and LV distribution systems that incorporates the constraints of zero-sequence current and zero injection, in combination with the exponentially weighted least squares method. The test results of the IEEE 34 bus system and IEEE 123 bus system verify that the proposed formulation takes full advantage of the measurements and structural characteristics of practical distribution networks, and simultaneously boosts the estimation accuracy.

Ensuring the resilience of power system is the prerequisite for maintaining its normal operation after the occurrence of natural disasters. This paper proposes a data-driven transmission defense planning (DTDP) model to address the power system resilience issue against extreme weather events. DTDP enables the determination of the optimal resilience enhancement portfolio, including line hardening and construction. The target events of DTDP are selected according to survivability based severity indices. The component survivability and post-disaster system failure state are treated as random variables in two separate levels, and the selected historical data are used to construct the confidence sets of their ambiguous probability distribution. Thus, the man-made uncertainty budget can be avoided and the expected economic loss under the worst-case distribution is minimized. Moreover, a sparsification method of system failure states is designed to boost computational efficiency. The effectiveness of DTDP and its solution method are verified on a six-bus test system and RTS-79 system. The results demonstrate that the DTDP can obtain a better defense plan compared to other optimization techniques.

External weather conditions have significant effects on the operation of composite power systems. Stochastic renewable generations, random loads, dynamic thermal ratings and failure probabilities of outdoor power devices are all influenced by diverse meteorological factors. To integrate the probabilistic impacts of multiple meteorological factors and their resulting uncertainties, a new nested sampling framework for probabilistic operational reliability evaluation of composite power systems is proposed. As one critical step of the framework, a general modeling method to fit the probability distribution of each meteorological factor at each time point by historical weather records is presented in details, and several specific distributions are introduced for the first time to refine the accuracy of probabilistic weather representation. In subsequent tests, the actual failure records and corresponding meteorological data are used to validate the performance of the proposed framework. Several existing methods, considering but not fully considering meteorological effects, are adopted for comparisons to verify the effectiveness of the proposed method in the test case. As one benefit of the proposed framework, uncertainty importance measures of all involved random variables are available and discussed as well.

Some innovative works have been done to probabilistic power flow (PPF) analysis for hybrid HVAC and LCC-VSC HVDC system in this paper. Firstly, a unified method considering precise model of converters is proposed to solve a general deterministic power flow (DPF) calculation including hybrid LCC (Line Current Converter) and VSC (Voltage Source Converter), the pure VSC-MTDC (Voltage Source Converter-Multiple Terminal Direct Current) and pure LCC system. Meanwhile, with a large amount of renewable energy sources integrated to the main grid through DC grids, it will impose a stochastic impact on the secure operation of such hybrid AC/DC grids. Therefore, it becomes necessary to model the probabilistic uncertainties and analyze their effects on the operation of hybrid AC/DC systems under different control modes. Nevertheless, most power flow analysis methods for hybrid AC/DC system are still deterministic in nature. Therefore, a probabilistic method based on the combination of Nataf transformation and Latin hypercube sampling (LHS) is developed and proposed to solve this complex PPF problem in an efficient manner, which considers correlated various probabilistic uncertainties, e.g. wind speeds, solar radiations and loads following different types of probability distribution. Finally, the effectiveness of the unified DPF method is validated in a modified IEEE 14-bus system, while the proposed PPF is verified in a modified IEEE 118-bus system and the effects of uncertainties on the diverse operation modes of hybrid AC/DC grids are discussed as well.