Development of a day-ahead solar power forecasting model chain for a 250 MW PV park in India

Due to the steep rise in grid-connected solar Photovoltaic (PV) capacity and the intermittent nature of solar generation, accurate forecasts are becoming ever more essential for the secure and economic day-ahead scheduling of PV systems. The inherent uncertainty in Numerical Weather Prediction (NWP) forecasts and the limited availability of measured datasets for PV system modeling impacts the achievable day-ahead solar PV power forecast accuracy in regions like India. In this study, an operational day-ahead PV power forecast model chain is developed for a 250 MWp solar PV park located in Southern India using NWP-predicted Global Horizontal Irradiance (GHI) from the European Centre of Medium Range Weather Forecasts (ECMWF) and National Centre for Medium Range Weather Forecasting (NCMRWF) models. The performance of the Lorenz polynomial and a Neural Network (NN)-based bias correction method are benchmarked on a sliding window basis against ground-measured GHI for ten months. The usefulness of GHI transposition, even with uncertain monthly tilt values, is analyzed by comparing the Global Tilted Irradiance (GTI) and GHI forecasts with measured GTI for four months. A simple technique for back-calculating the virtual DC power is developed using the available aggregated AC power measurements and the inverter efficiency curve from a nearby plant with a similar rated inverter capacity. The AC power forecasts are validated against aggregated AC power measurements for six months. The ECMWF derived forecast outperforms the reference convex combination of climatology and persistence. The linear combination of ECMWF and NCMRWF derived AC forecasts showed the best result

Modelling Tools for Wind Farm Upgrading

Planning of modifications of existing wind farms by adding or replacing turbines makes new demands on wind farm modelling. Emphasis shifts from the calculation of the mean efficiency of the farm to that of individual turbines. Additionally, modelling has to deal with different turbine types and hub heights combined in one farm. Full scale measurements at two wind farms in Northern Germany provide test cases for investigating the capability of wind farm models to predict the power output of individual turbines

Reduction Of Wind Power Prediction Error By Spatial Smoothing Effects

this paper we focus on the reduction of the forecast error for the aggregated power output of wind farms in a spatially extended region. Due to spatial smoothing effects the error decreases considerably compared to a single site. This reduction strongly depends on the size of the region rather than on the number of wind farms it contain

PREVIENTO REGIONAL WIND POWER PREDICTION WITH RISK CONTROL

In recent years much research has been done in the field of wind power forecasting. Developed wind power prediction systems like Previento are able to calculate power forecasts for single wind farms quite well with advanced methods. Not much effort has been done to produce regional forecasts with an ensemble of wind farms. In this paper we calculate the number of representatives in a region which are nessessary for a best possible regional forecast. Besides an regional wind power forecast itself the uncertainty of the forecast as a measure of the risk is calculated. For this purpose the prevailling weather situations, the behaviour of the power curve and the regional distribution of wind farms is considerd. 1. Overview of Previento Previento is a system for wind power prediction which has been developed at the University of Oldenburg and is in operationall use for more than two year. The system follows the physical approach of modelling the relevant physical phenomena of the boundary layer whic

Wind assessment in complex terrain with the numeric model Aiolos --

Introduction It's general practice to use mass consistent models for simulation of stationary three dimensional wind fields in complex terrain. The advantage of a mass consistent model compared with primitive equation models is relativly short computing time. Therefore it is not possible to look at complex phenomenons as turbulence or heat flux. The initialisation of Aiolos, which is developed from the well known model NOABL, is changed within this framework. The influence of roughness changes on the terrain surface and thermal stratification of the atmosphere are taken into account. Due to good results of the EWA in flat terrain it servers the basis for this model. 2 Initialisation To simulate the air-flow, the boundary layer is dived into a grid of x # y # z boxes. with equidistant horizontal box distances. The vertical coordinate is transformed in a terrain following #-coordinate. The space between these #-levels decreases to get a higher resolution near the surface. For each b

Previento - A Wind Power Prediction System

this paper we focus on the forecast of power output of regional distributed wind farms. Due to spatial smoothing effects the fluctuations of the combined power output of distributed wind farms are damped, which results in decrease of fluctuations of the regional power output compared to the forecast for single sites. These effects are already covered with the forecast of a small numbers of turbines. Therefore a detailed forecast for each turbine is not necessary and a linear upscaling from a small number of turbines is possible. As an example we make a forecast for whole Germany and show how this method works practicaly and which data is needed. Keywords: Forecastin Methods, Utility Integration, Dispersed Turbine Systems, Uncertainty Analysis

Reducing Operational Costs of Offshore HVDC Energy Export Systems Through Optimized Maintenance

For the grid connection of offshore wind farms today, in many cases a high-voltage direct current (HVDC) connection to the shore is implemented. The scheduled maintenance of the offshore and onshore HVDC stations makes up a significant part of the operational costs of the connected wind farms. The main factor for the maintenance cost is the lost income from the missing energy yield (indirect maintenance costs). In this study, we show an in-depth analysis of the used components, maintenance cycles, maintenance work for the on- and offshore station, and the risks assigned in prolonging the maintenance cycle of the modular multilevel converter (MMC). In addition, we investigate the potential to shift the start date of the maintenance work, based on a forecast of the energy generation. Our findings indicate that an optimized maintenance design with respect to the maintenance behavior of an HVDC energy export system can decrease the maintenance-related energy losses (indirect maintenance costs) for an offshore wind farm to almost one half. It was also shown that direct maintenance costs for the MMC (staff costs) have small effect on the total maintenance costs. This can be explained by the fact that the additional costs for maintenance staff are two orders of magnitude lower than the revenue losses during maintenance

A Statistical Analysis of the Reduction of the Wind

We discuss the accuracy of the prediction of the aggregated power output of wind farms distributed over given regions. Our forecasting procedure provides the expected power output for a time horizon up to 48 hours ahead. It is based on the large scale wind field prediction which is generated operationally by the German weather service. Our investigation focuses on the statistical analysis of the power prediction error of an ensemble of wind farms compared to single sites. Due to spatial smoothing effects the relative prediction error decreases considerably. Using measurements of the power output of 30 wind farms in Germany we find that this reduction depends on the size of the region. To generalize these findings an analytical model based on the spatial correlation function of the prediction error is derived to describe the statistical characteristics of arbitrary configurations of wind farms. This analysis shows that the magnitude of the error reduction only weakly depends on the number of sites and is mainly determined by the size of the region. Towards a correction of systematic prediction errors an analysis of the temporal structure of the forecast error is performed. For this purpose the correlation of the errors for consecutive forecasts is analyzed for single sites and ensembles

Initial presenting manifestations in 16,486 patients with inborn errors of immunity include infections and noninfectious manifestations

Background: Inborn errors of immunity (IEI) are rare diseases, which makes diagnosis a challenge. A better description of the initial presenting manifestations should improve awareness and avoid diagnostic delay. Although increased infection susceptibility is a well-known initial IEI manifestation, less is known about the frequency of other presenting manifestations. Objective: We sought to analyze age-related initial presenting manifestations of IEI including different IEI disease cohorts. Methods: We analyzed data on 16,486 patients of the European Society for Immunodeficiencies Registry. Patients with autoinflammatory diseases were excluded because of the limited number registered. Results: Overall, 68% of patients initially presented with infections only, 9% with immune dysregulation only, and 9% with a combination of both. Syndromic features were the presenting feature in 12%, 4% had laboratory abnormalities only, 1.5% were diagnosed because of family history only, and 0.8% presented with malignancy. Two-third of patients with IEI presented before the age of 6 years, but a quarter of patients developed initial symptoms only as adults. Immune dysregulation was most frequently recognized as an initial IEI manifestation between age 6 and 25 years, with male predominance until age 10 years, shifting to female predominance after age 40 years. Infections were most prevalent as a first manifestation in patients presenting after age 30 years. Conclusions: An exclusive focus on infection-centered warning signs would have missed around 25% of patients with IEI who initially present with other manifestations. (J Allergy Clin Immunol 2021;148:1332-41.