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. INDEX TERMS Probabilistic power flow, hybrid AC/DC system, probabilistic uncertainty, droop control, renewable energy sources. II. STEADY-STATE MODELING OF A HYBRID AC/DC SYSTEM A. VSC STATION MODELING JUNJIE TANG (M'14) received the Ph.D. degree in electrical engineering from the E.
This paper proposes a new probabilistic power flow method for the hybrid AC/VSC-MTDC (Voltage Source Control-Multiple Terminal Direct Current) grids, which is based on the combination of ninth-order polynomial normal transformation (NPNT) and inherited Latin hypercube sampling (ILHS) techniques. NPNT is utilized to directly handle historical records of uncertain sources to build the accurate probability model of random inputs, and ILHS is adopted to propagate the randomness from inputs to target outputs. Regardless of whether the underlying probability distribution is known or unknown, the proposed method has the ability to adaptively evaluate the sample size according to a specific operational scenario of the power systems, thus achieving a good balance between computational accuracy and speed. Meanwhile, the frequency histograms, probability distributions, and some more statistics of the results can be accurately and efficiently estimated as well. The modified IEEE 118-bus system, together with the realistic data of wind speeds and diverse consumer behaviors following irregular distributions, is used to demonstrate the effectiveness and superiority of the proposed method.
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