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
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Abstract Global radiation measured on fixed-tilt, south-facing planes (40 and vertical) and a 2-axis tracker at NRELs Solar Radiation Research Lab. in Golden, CO is compared to predictions from ten transposition models, in combination with either optimal or suboptimal input data of horizontal irradiance. Suboptimal inputs are typically used in everyday engineering calculations, for which the necessary data are usually unavailable for the site under scrutiny, and must be estimated in some way. The performance of the transposition models is first evaluated for ideal conditions when optimal data are used. In this specific case, it is found that the Gueymard and Perez models provide the best estimates of global tilted irradiance under clear skies in particular. The performance of four direct diffuse separation models is also evaluated. Their predictions of direct and diffuse radiation appear biased in most cases, with a model-dependent magnitude. Finally, the performance of the resulting combinations of separation and transposition models is analyzed in a variety of situations. When only global irradiance is known, the accuracy of the tilted irradiance predictions degrades significantly, and is mainly conditioned by the local performance of the direct diffuse separation method. For the south-facing vertical surface, inaccuracies in the ground reflection calculations becomes another key factor and significantly increase the prediction error. The Reindl transposition algorithm appears to perform best in this case. When using suboptimal input data for the prediction of plane-of-array irradiance on a moderately tilted plane (40S) or a 2-axis tracking plane, the Hay, Reindl and Skartveit mod 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. In last decade, the higher penetration of renewable energy resources (RES) in energy market was encouraged by implementing the energy polices in several developed and developing countries due to increasing environmental concerns. Among wide range of RES, Photovoltaic (PV) electricity generation get higher attention by researcher, energy policy makers and power production companies due to its economic and environmental benefits. Therefore, a large PV penetration was observed in energy market with rapid growth in the last decade. The PV output power is highly uncertain due to several meteorological factors such as temperature, wind speed, cloud cover, atmospheric aerosol levels and humidity level. The inherent variability of PV output power creates different issues directly or indirectly for power grid such as power system control and reliability, reserves cost, dispatchable and ancillary generation, grid integration and power planning. Therefore, there is need to accurately forecast the PV output over the spectrum of forecast horizon at different chronological scales. In this paper, a comprehensive and systematic review of PV output power forecast models were provided. This review covers the different factors affecting PV forecast, PV output power profile and performance matrices to evaluate the forecast model. The critical analysis regressive and artificial intelligence based forecast models are also presented. In addition, the potential benefits of hybrid techniques for PV forecast models are also thoroughly discussed.