Overview of Solar-Forecasting Methods and a Metric for Accuracy Evaluation (original) (raw)
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Intra-Hour Forecasts of Solar Power and Ramp Events †
2018
In this study an adjusting post-processing approach is implemented for improving intra-hourly forecasts of solar power and ramp events of PV solar power systems at different locations in the United States. This study also serves as an out-of-sample test to evaluate the performance of the adjusting approach with different locations and timescales. Thus, various individual intra-hourly forecasts of solar power are combined and adjusted by applying the adjusting approach. Both point and probabilistic forecasts of solar power are included. After that, solar power ramp event forecasting by the adjusting approach is carried out.
The value of day-ahead solar power forecasting improvement
Solar Energy, 2016
The value of day-ahead solar power forecasting improvements was analyzed by simulating the operation of the Independent System Operator-New England (ISO-NE) power system under a range of scenarios with varying solar power penetrations and solar power forecasting improvements. The results showed how the integration of solar power decreased operational electricity generation costs, by decreasing fuel and variable operation and maintenance costs, while decreasing start and shutdown costs of fossil fueled conventional generators. Solar power forecasting improvements changed the impacts that the uncertainty of solar power has on bulk power system operations; electricity generation from the fast start and lower efficiency power plants, ramping of all generators, start and shutdown costs, and solar power curtailment were all reduced. These impacts led to a reduction in overall operational electricity generation costs in the system that translates into an annual economic value for improving solar power forecasting. I.
SSRN Electronic Journal, 2000
Forecasts of power production are necessary for the electricity market participation of Concentrating Solar Power (CSP) plants. Deviations from the production schedule may lead to penalty charges. the mitigation impact on deviation penalties of an electricity production forecasting tool for Therefore, the accuracy of direct normal irradiance (DNI) forecasts is an important issue. This paper elaborates the 50 MW el parabolic trough plant Andasol 3 in Spain. A commercial DNI model output statistics (MOS) forecast for the period July 2007 to December 2009 is assessed and compared to the two-day persistence approach, which assumes yesterday's weather conditions and electricity generation also for the following day. Forecasts are analyzed both with meteorological forecast verification methods and from the perspective of a power plant operator. Using MOS, penalty charges in the study period are reduced by 47.6% compared to the persistence case. Finally, typical error patterns of DNI forecasts and their financial impact are discussed.
Solar Energy, 2018
Solar power forecasting plays a critical role in power-system management, scheduling, and dispatch operations. Accurate forecasts of direct normal irradiance (DNI) are essential for an optimized operation strategy of concentrating solar thermal (CST) systems, particularly during partly cloudy days, due to solar intermittency. In this work, short-term forecasts from the radiative scheme McRad (Cycle 41R2) included in the Integrated Forecasting System (IFS), the global numerical weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF), together with in-situ ground-based measurements, are used in a simulated linear focus parabolic-trough power system through the System Advisor Model (SAM). Results are part of a preliminary analysis concerning the value of DNI predictions from the IFS for operation improvement of a CST system with similar configurations as the Andasol 3 CST power plant. For a 365-day period, the present results show high correlations between predictions of energy to grid based on measurements and IFS forecasts mainly for daily values (≈0.94), while lower correlations are obtained for hourly values (≈0.88), due to cloud representation of the IFS during overcast periods, leading to small deviations with respect to those from measurements. Moreover, to measure the forecasting skill of the IFS, daily and hourly skill scores based on local measurements and a persistence model are obtained (≈0.66 and ≈0.51, respectively), demonstrating that the IFS has a good overall performance. These aspects show the value that forecasted DNI has in the operation management of CST power systems, and, consequently, in the electricity market.
Impact of onsite solar generation on system load demand forecast
Energy Conversion and Management, 2013
Net energy metering tariffs have encouraged the growth of solar PV in the distribution grid. The additional variability associated with weather-dependent renewable energy creates new challenges for power system operators that must maintain and operate ancillary services to balance the grid. To deal with these issues power operators mostly rely on demand load forecasts. Electric load forecast has been used in power industry for a long time and there are several well established load forecasting models. But the performance of these models for future scenario of high renewable energy penetration is unclear. In this work, the impact of onsite solar power generation on the demand load forecast is analyzed for a community that meets between 10% and 15% of its annual power demand and 3-54% of its daily power demand from a solar power plant. Short-Term Load Forecasts (STLF) using persistence, machine learning and regression-based forecasting models are presented for two cases: (1) high solar penetration and (2) no penetration. Results show that for 1-h and 15-min forecasts the accuracy of the models drops by 9% and 3% with high solar penetration. Statistical analysis of the forecast errors demonstrate that the error distribution is best characterized as a t-distribution for the high penetration scenario. Analysis of the error distribution as a function of daily solar penetration for different levels of variability revealed that the solar power variability drives the forecast error magnitude whereas increasing penetration level has a much smaller contribution. This work concludes that the demand forecast error distribution for a community with an onsite solar generation can be directly characterized based on the local solar irradiance variability.
Evaluation of PV Generation Capicity Credit Forecast on Day-Ahead Utility Markets
Following a successful preliminary evaluation of the NDFD-based solar radiation forecasts for several climatically distinct locations, the evaluation is now continued by testing the forecasts' end-use operational accuracy, focusing on their ability to accurately predict the effective capacity of grid-connected PV power plants. The predicted and actual utility peak load reduction performance of PV power plants are compared for two case studies:
Solar irradiance forecasting can reduce the uncertainty of solar power plant output caused by solar irradiance intermittency. Concentrated solar thermal (CST) plants generate electricity from the direct normal irradiance (DNI) component of solar irradiance. Different forecasting methods have been recommended for a range of forecast horizons relevant to electricity generation. High DNI forecast accuracy is important for achieving accurate forecasts of CST plant output which are shown to increase CST plant profitability. This paper reviews the DNI forecast accuracy of numerical weather prediction models, time series analysis methods, cloud motion vectors, and hybrid methods. The results of the reviewed papers are summarised to identify the best DNI forecast accuracy for particular forecast horizons. The application of DNI forecasts to operate CST plants is also briefly reviewed.
Building the Sun4Cast System: Improvements in Solar Power Forecasting
Bulletin of the American Meteorological Society
As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynam...
Factoring Behind-the-Meter Solar into Load Forecasting: Case Studies under Extreme Weather
2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2020
Distributed energy resources (DERs), especially distributed photovoltaics (PV), have been rising dramatically over the past years. However, behind-the-meter (BTM) PV devices are not monitored, and thus are invisible to utilities and system operators. In addition, electricity demand is likely to increase as a result of extreme hot/cold weather conditions, which stretches the grid to its limits, and thus triggers high electricity price. High electricity prices and extreme temperatures also stimulate the adoption of solar panels, which in turn add difficulties to load forecasting. This paper proposes a data-driven feeder-level load forecasting method by taking account of BTM PV under extreme weather conditions. The BTM PV penetration is first estimated, and in this study the PV penetration is defined as the ratio of total BTM PV capacity to peak load of the feeder. A machine learning model is adopted to quantify the relationship between measured PV power generation and corresponding solar irradiance. The BTM PV generation within the entire feeder can be estimated through the PV penetration and forecasted PV irradiance, which is then integrated in load forecasting. Numerical results of case studies at three distribution feeders show that the performance of load forecasting under extreme weather conditions is significantly enhanced by considering the contribution of BTM PV. Index Terms-load forecasting, behind-the-meter solar forecasting, extreme weather.