This paper presents a set of methods for estimating the renewable energy generation downstream of a measurement device using real-world measurements. First, we present a generation disaggregation scheme where the only information available for estimation is the micro-synchrophasor measurements obtained at the substation or feeder head. We then propose two strategies in which we use measurements from the substation as well as a proxy solar irradiance measurement. Using these two measurement points, we first propose a multiple linear regression strategy, in which we estimate a relationship between the measured reactive power and the load active power consumption, which are then used in disaggregation. Finally, we expand this strategy to strategically manage the reconstruction errors in the estimators. We simultaneously disaggregate the solar generation and load. We show that it is possible to disaggragate the generation of a 7.5 megawatt photovoltaic site with a root-mean-squared error of approximately 450 kilowatts.
A common method for predicting the output of PV systems is a model-based approach @cite_3 @cite_10 , which uses a model, composed of the design characteristics of the PV array and the irradiance measurements, to estimate PV generation. In order to understand this approach, and how the proposed methodology in this paper offers a significant improvement for the purpose of real-time estimation of PV generation, we briefly discuss its limiting factors. The critical input to these models is the plane-of-array (POA) irradiance. The POA irradiance on a tilted plane, @math , whose tilt is @math degrees from the horizontal plane can be calculated using Equation @cite_14
@cite_14 @cite_10 @cite_3
2259944928 2028782916 2054032642 2057325636
We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statistics such as mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative MBE (rMBE), mean percentage error (MPE) and relative RMSE (rRMSE). This work provides findings on how forecasts from individual inverters will improve the total solar power generation forecast of the PV system. An important issue for the growth and management of grid-connected photovoltaic (PV) systems is the possibility to forecast the power output over different horizons. In this work, statistical methods based on multiregression analysis and the Elmann artificial neural network (ANN) have been developed in order to predict power production of a 960 kWP grid-connected PV plant installed in Italy. Different combinations of the time series of produced PV power and measured meteorological variables were used as inputs of the ANN. Several statistical error measures are evaluated to estimate the accuracy of the forecasting methods. A decomposition of the standard deviation error has been carried out to identify the amplitude and phase error. The skewness and kurtosis parameters allow a detailed analysis of the distribution error. This paper proposes a method for selecting explanatory variables for global solar radiation forecasting. The weather conditions affect generation output of renewable energy significantly. As a result, smart grids increase the uncertainties caused by the power injections of photovoltaic (PV) and or wind power generation. To smooth smart grid operation, it is necessary to select the predicted input variables for the forecasting model. In this paper, how to select explanatory variables in the forecasting model of PV generation is discussed because Japan gives much higher priority to PV generation in the framework of future energy policy. The global solar radiation is one of the most important variables in dealing with PV generation output forecasting. This paper focuses on the relationship between the global solar radiation and its explanatory variables. The proposed method makes use of the CART (Classification and Regression Trees) algorithm of data mining method to select the explanatory or input variables in the forecasting model of global solar radiation. CART has the function to give priority to explanatory variables through an index called Variable Importance. The proposed method was applied to real data of global solar radiation in Tokyo, Japan. In this paper, a method for estimating power injected into an electrical distribution system from spatially distributed residential PV systems using data from ground-based weather stations is described. This method was developed as part of the High Penetration PV Deployment Project at the Arizona Public Service (APS). Verification of this predictive method is also described in this paper. Finally, correlation statistics and power production ramp rates are calculated for irradiance based on measured power within the APS study area using two different sets of weather station measurements. The method has potential applications to real-time or forecasted power estimation on distribution circuits in addition to the development of broader guidelines on the optimal number and location of irradiance sensors for power estimation in distribution circuits with high penetrations of PV.