With the intensive integration of photovoltaic (PV) sources into the low-voltage distribution networks (LVDN), the nodal voltage limit violations and fluctuation problem cause concerns on the safety operation of a power system. The intermittent, stochastic, and fluctuating characteristics of PV output power leads to the frequent and fast fluctuation of nodal voltages. To address the voltage limit violation and fluctuation problem, this paper proposes a distributed nodal voltage regulation method based on photovoltaic reactive power and on-load tap changer transformers (OLTC). Using the local Q/V (Volt/Var) feedback controller derived from the grid sensitivity matrix, the voltage magnitude information is adopted to adjust the output of PV systems. Moreover, in order to share the burden of voltage regulation among distributed PV systems, a weighted distributed reactive power sharing algorithm is designed to achieve the voltage regulation according to the rated reactive power. Theoretical analysis is provided to show the convergence of the proposed algorithm. Additionally, the coordination strategy for distributed PV systems and OLTC is provided to reduce the reactive power outputs of PV systems. Five simulation case studies are designed to show the effectiveness of the proposed voltage regulation strategy, where the voltage regulation and proportional reactive power sharing can be achieved simultaneously.

The forecast error characteristic analysis of short-term photovoltaic power generation can provide a reliable reference for power system optimal dispatching. In this paper, the total in-day error level was stratified by fuzzy C-means algorithm. Then the historical PV output data based on the numerical characteristics of point prediction output were classified. A General Gauss Mixed Model was proposed to fit the forecast error distribution of various photovoltaic output forecast error distribution. The impact of meteorological factors together with numerical characteristics on the forecast error was taken into full consideration in this analysis method. The predicted point output with high volatility can be accurately captured, and the reliable confidence interval is given. The proposed method is independent of the point prediction algorithm and has strong applicability. The General Gauss Mixed Model can meet the peak diversity, bias, and multimodal properties of the error distribution, and the fitting effect is superior to the normal distribution, the Laplace distribution, and the t Location-Scale distribution model. The error model has a flexible shape, a concise expression, and high practical value for engineering.

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