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.

Across the world, the geographical conditions are varied, and the characteristics of dust depend on the local environmental conditions. The solar power generators must incorporate the soiling losses in their estimation for power output and therefore a methodology was developed to estimate the soiling correction factor. After extensive research, a comprehensive review was presented on the effect of soiling on performance of PV plants along with case studies of soiling experiments around the world. A soiling experiment was designed to develop the soiling correction factor. A methodology to calculate the soiling correction factor, which can be implemented in any location, was developed by analyzing the data from the soiling experiment. The effect of rainfall, humidity and wind on soiling was analyzed and documented. The performance of one 20 kWp PV plant was monitored to study the effect of weather-related parameters on the performance. The soiling correction factor varied from -1.36% to 3.67% during the period between June 2018 and June 2019 in Chennai. It was observed that the average PV conversion efficiency of the 20-kW plant was 11.75% and the average PR was 75%. It was observed that the correlation between module temperature and DC power; between humidity and DC power; between GTI and DC power varied every month. The soiling factor developed could be incorporated into the short-term day ahead solar forecasting model. The developed methodology could be applied at the any large-scale solar power plant around the world for yield assessment, designing as well as operational forecasting purposes.

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.

The use of renewable sources of energy is rising in Australia, and with solar energy becoming the most dominant; the solar (PV) roof-top plant penetration in the electrical energy distribution grid is increasing. As Australia is the sixth largest country in the world consisting of a diverse range of climates, this may be a concern to Distribution Service Operators (DSOs) as the variability in PV power output in different areas, climates/weather and even time of day. This means that DSOs are required to quantify these 'uncertainties' for different zones in Australia to aid in the energy planning. This paper will examine PV variability metrics to identify suitable PV variable metric based on purpose of application and propose a method to compare PV variability of large cities in Australia based on historical roof-top PV solar data. This proposed method examined variability metrics and find out suitable variability metric based on purpose of application. The comparative study shows that the PV variability and the amount of smoothing are not equal at all the distribution area in Australia and varies with geographical climatic scenario.

Aggregation of PV power plants can mitigate electricity demand as well as reduce consumption of fossil fuels. Due to high penetration of roof-top PV power into the Australian electricity market, utility planners and grid operators have to deal with short time variability of output power which can be a potential limiting factor in deploying PV systems. In this Paper, PV variability between different large cities will be analysed due to large there size and various climatic zones within Australia. In order to examine the smoothing effect of geographically distributed roof-top plants, output electricity data of five (05) minutes resolution for 200 sites are analysed. A significant smoothing effect are observed compare to a single site but it depends on the number of aggregated roof-top PV plants and relative correlation between them. Overall PV variability and the amount of smoothing are not the same in all cities within Australia.

This paper reports results and an evaluation methodology from two new decision-aid tools that were demonstrated at a Transmission System Operator (REN, Portugal) during several months in the framework of the E.U. project Anemos.plus. The first tool is a probabilistic method intended to support the definition of the operating reserve requirements. The second is a fuzzy power flow tool that identifies possible congestion situations and voltage violations in the transmission network. Both tools use as input probabilistic wind power predictions.

In this paper, we discuss the need to predict and alarm upcoming extreme events of the wind power domain, such as a sharp increase in wind power production or safety shut downs of turbines. These predictions are needed as a complement to daily operational wind power predictions to ensure grid stability. As there is no universal definition for these kind of extreme events, we describe important parameters for their definition and factors that influence these parameters. A tool for extreme event predictions, Anemos.Rulez, will be presented, including evaluation results from an application test case.

The integration of wind energy into electricity grids will pose future challenges as the levels of production rise, power fluctuations have to be balanced and especially coastal grids are overloaded. Ways of controlling and storing wind electricity have to be developed in order to better integrate the wind resource into the electricity supply system and overcome limitations of grid development. Hydrogen storage offers some advantages towards these goals.

Accurate forecasting of wind farms power produ c- tion up to two days ahead is recognized as a major contribution for rel iable large -scale wind power integration. Especially, in a liberalized electricity market, prediction tools enhance th e pos i- tion of wind energy compared to other forms of dispatchable ge n- eration. As wind integration increases, the requirements for wind power forecasting diversify depending on the end -user and the context. In the frame of the EU project Anemos multidiscipl inary research has been carried out in wind forecasting by a number of research organizations and end -users with wide e xperience in the field. Advanced statistical, physical and combined modeling a p- proaches were developed including methods for on -line unce r- tainty and prediction risk a ssessment. An integrated software platform was developed to host the various models. It was i n- stalled by several end -users for on -line o peration and evaluation at a local, regional and national scale. This paper pr esents the re search methodology and the major results o btained.

With the increase of penetration of the utility networks by windand solar derived electricity both the the power flow in the grids and the conditions on an electricity spot market will be subject to additional stochastic disturbences. Thus for optimal control of the network an the actions on a eclectricity market there will be a need for an accurate short term forecast of the respective power flow. We present the state of the art of power predictios for wind and solar power plants.with a time horizon of several hours (solar) up to several days (wind). This includes a presentation of the methods for the derivation of power predictions based on meteorological forecasts and the discussion of the quality of the predictions. In view of the application of the forecasts, the focus will be centered only not on the predictions of the output of individual plants but of the combined output of the ensemble of all installations within specified sections of the grid or of all distributed installations operated by one electricity supplier.

The aim of the European Project ANEMOS is to develop accurate and robust models that substantially outperform current state-of-the-art methods, for onshore and offshore wind power forecasting. Advanced statistical, physical and combined modelling approaches were developed for this purpose. Priority was given to methods for on-line uncertainty and prediction risk assessment. An integrated software platform, 'ANEMOS', was developed to host the various models. This system is installed by several end-users for on-line operation and evaluation at a local, regional and national scale. Finally, the project demonstrates the value of wind forecasts for the power system management and market integration of wind power. Keywords: Wind power, short-term forecasting, numerical weather predictions, on-line software, tools for wind integration.

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. Calculations are performed with a kinematic and an eddy-viscosity model. Both procedures result in systematic deviations from the measured single turbine performance. These are believed to be due to the increase in turbulence intensity in wakes, which is not modelled adequately. Simple modifications are proposed to enhance the accuracy of the models. The analysis of the wake losses in the case of turbines with different hub heights shows that both models give results within the limit of the measurement uncertainties.

This paper presents the objectives and the research work carried out in the frame of the ANEMOS project on short-term wind power forecasting. The aim of the project is to develop accurate models that substantially outperform current state-of-the-art methods, for onshore and offshore wind power forecasting, exploiting both statistical and physical modeling approaches. The project focus on prediction horizons up to 48 hours ahead and investigates predictability of wind for higher horizons up to 7 days ahead useful i.e. for maintenance scheduling. Emphasis is given on the integration of highresolution meteorological forecasts. For the offshore case, marine meteorology is considered as well as information by satellite-radar images. An integrated software platform, ‘ANEMOS', is developed to host the various models. This system will be installed by several utilities for on-line operation at onshore and offshore wind farms for prediction at a local, regional and national scale. The applications include different terrain types and wind climates, on- and offshore cases, and interconnected or island grids. The on-line operation by the utilities will allow validation of the models and an analysis of the value of wind prediction for a competitive integration of wind energy in the developing liberalized electricity markets in the EU.

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 h 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 depends only weakly on the number of sites and is mainly determined by the size of the region, e.g for the size of a typical large utility (∼370 km in diameter) <50 sites are sufficient to have an error reduction of 63%. 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 analysed for single sites and ensembles. This knowledge on previous errors can be beneficially used to correct the actual ensemble forecast.

The wind farm program FLaP (Farm Layout Program), developed at the University of Oldenburg, has been extended to improve the description of wake development in offshore conditions, especially the low ambient turbulence and the effect of atmospheric stability. Model results have been compared with measurements from the Danish offshore wind farm Vindeby. Vertical wake profiles and mean turbulence intensities in the wake were compared for 32 scenarios of single, double and quintuple wake cases with different mean wind speed, turbulence intensity and atmospheric stability. It was found that within the measurement uncertainties the results of the wake model compares well with the measurements for the most important ambient conditions. The effect of the low turbulence intensity offshore on the wake development was modelled well. Deviations have been found when atmospheric stability deviates from near-neutral conditions. Especially for stable atmospheric conditions both the free flow model and the wake model do not give satisfying results.

Previento is an operational forecast system which provides a prediction of the expected power output for a time horizon up to 48 hours. It is based on an physical approach with input from a large scale weather prediction model like Lokalmodell of the German Weather Service. In 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.