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Design of Short-Term Forecasting Model of Distributed Generation Power for Solar Power Generation
摘要： Background/Objectives: For efficient PhotoVoltaic (PV) power generation, computing and information technologies are increasingly used in irradiance forecasting and correction. Today the majority of PV modules are used for grid-connected power generation, so solar generation forecasting that predicts available PV output ahead is essential for integrating PV resources into electricity grids. This paper proposes a short-term solar power forecasting system that employs Neural Network (NN) models to forecast irradiance and PV power. Methods/Statistical Analysis: The proposed system uses the weather observations of a ground weather station, the medium-term weather forecasts of a physical model, and the short-term weather forecasts of the Weather Research and Forecasting (WRF) model as input. To increase prediction accuracy, the proposed system performs forecast corrections and determines the correction coefficients based on the characteristics and temperature of PV modules. The proposed system also analyzes the inclination angle of PV modules to predict PV power outputs. Results: In the correlation analysis of the forecasted and measured irradiance, R2 was over 0.85 for all look-ahead periods, indicating a high correlation between the two data. Conclusion/Application: In the future, the proposed forecasting system for solar power generation resources will be further refined and run in real environments.
关键词： Photovoltaic Power Forecasts,Wind Power,Forecasting System,Solar Energy,Distributed Power Generation
更新于2025-09-23 15:22:29
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Adaptive Voltage Regulation for Solar Power Inverters on Distribution Systems
摘要： By means of time-series power flow, this paper compares the performance of several reactive power control modes allowed in the new IEEE 1547. It is shown that voltage-reactive power mode (sometimes called volt-var) should be considered for a universal default setting, with no deadband, the highest allowed gain of 22 for Category B inverters, and a gain of 12.5 for Category A inverters. The reference voltage should adapt to the system voltage, with a 300s time constant.
关键词： Photovoltaic systems,smart grids,inverters,automatic voltage control,distributed power generation
更新于2025-09-23 15:21:01
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A Hybrid Intelligent Approach for Solar Photovoltaic Power Forecasting: Impact of Aerosol Data
摘要： The penetration of solar photovoltaic (PV) power in distributed generating system is increasing rapidly. The increased level of PV penetration causes various issues like grid stability, reliable power generation and power quality; therefore, it becomes utmost important to forecast the PV power using the meteorological parameters. The proposed model is developed on the basis of meteorological data as input parameters, and the impacts of these parameters have been analyzed with respect to forecasted PV power. The main focus of this research is to explore the performance of optimization-based PV power forecasting models with varying aerosol particles and other meteorological parameters. A newly developed intelligent approach based on grey wolf optimization (GWO) using multilayer perceptron (MLP) has been used to forecast the PV power. The performance of the GWO-based MLP model is evaluated on the basis of statistical indicators such as normalized mean bias error (NMBE), normalized mean absolute error (NMAE), normalized root-mean-square error (NRMSE) and training error. The results of the developed model show the values of NMBE, NMAE and NRMSE as 2.267%, 4.681% and 6.67% respectively. To validate the results, a comparison has been made with particle swarm optimization, Levenberg–Marquardt algorithm and adaptive neuro-fuzzy approach. The performance of the model is found better as compared to other intelligent techniques. The obtained results may be used for demand response applications in smart grid environment.
关键词： Solar power forecasting,Artificial neural network,Distributed power generation,Grey wolf optimization,Solar PV
更新于2025-09-16 10:30:52