The aim of this work is to develop an algorithm that can utilize historical PV power measurements to establish the parameters of a physical model for power production. The chosen approach consists in evaluating the parameters of a PV model that maximize the likelihood that simulations match with power measurements. The proposed method offers advantages beyond the standard approaches used for the simulation or prediction of PV power production, as it makes maxinnun use of the information typically available on a PV plant (plant description and measurement history). Furthermore, an interpretation and control of the algorithm output is made possible. The performance of the proposed approach has been evaluated and analyzed using measurements from two PV plants, It is shown that the proposed approach may identify the orientation angles of a PV module to within an accuracy of less than 2 degrees in optimal cases, Situations were also found with a difference between the estimated and actual angles of 5 degrees, for which the estimated parameters lead to better simulation/forecast accuracy than the actual ones as they balance the systematic error of the chosen PV-model. (C) 2015 Elsevier Ltd. All rights reserved.

The presented study is a detailed assessment of the value of Photovoltaic (PV) energy within the German energy supply structure under consideration of the special attributes of PV: its correlation with actual consumption and local power generation. Contrary to previous statistical approaches a new dynamic approach was developed in this study. The dynamic behaviour of the PV power generation is modelled as a one-year time series. A comparison with the time series of the power demand allows assessing the value of PV energy. The value of PV energy mainly results from its ability to substitute conventional power generation and the benefit of this kind of decentralized power generation for network stability and quality. An evaluation of these aspects is carried out for the year 2005 and a scenario in 2015.

Small grid-connected photovoltaic systems up to 5 kW(p) are often not monitored because advanced surveillance systems are not economical. Hence, some system failures which lead to partial energy losses stay unnoticed for a long time. Even a failure that results in a larger energy deficit can be difficult to detect by PV laymen due to the fluctuating energy yields.Within the EU project PVSAT-2, a fully automated performance check has been developed to assure maximum energy yields and to optimize system maintenance for small grid-connected PV systems. The aim is the early detection of system malfunctions and changing operating conditions to prevent energy and subsequent financial losses for the operator. The developed procedure is based on satellite-derived solar irradiance information that replaces on-site measurements. In conjunction with a simulation model the expected energy yield of a PV system is calculated. In case of the occurrence of a defined difference between the simulated and actual energy yield, an automated failure detection routine searches for the most probable failure sources and notifies the operator.This paper describes the individual components of the developed procedure-the satellite-derived irradiance, the used PV simulation model, and the principles of the automated failure detection routine. Moreover, it presents results of an 8-months test phase with 100 PV systems in three European countries. (c) 2006 Elsevier Ltd. All rights reserved.

Within the Earth Observation Market Development (EOMD) program of the European Space Agency (ESA) the ENVISOLAR project aims at an intensified usage of earth observation based information products in the solar energy industries. Existing services for investment decision, plant management, load forecasting, and science and consulting rely on high quality surface solar irradiance measurements and reliable processing chains to deliver such information regularly. Requirements for earth observation data as well as blockages preventing their use have been identified. In consequence, existing data processing chains are analyzed as to their conformity with the needs of the solar industry.The paper focuses on how earth observation itself can contribute to the market development of solar energy technologies. Issues like quality of irradiance data for planning and managing solar energy systems, reliability and availability of earth observation information, requirements as to temporal, spatial and spectral resolution of earth observation data, and the cost-effectiveness of satellite based information compared to maintenance costs for a large set of on-site measurement devices are addressed.

The contribution of power production by PV systems to the electricity supply is constantly increasing. An efficient use of the fluctuating solar power production will highly benefit from forecast information on the expected power production. This forecast information is necessary for the management of the electricity grids and for solar energy trading. This paper will present and evaluate an approach to forecast regional PV power production. The forecast quality was investigated for single systems and for ensembles of distributed PV systems. Due to spatial averaging effects the forecast for an ensemble of distributed systems shows higher quality than the forecast for single systems. Forecast errors are reduced to an RMSE of 0.05 Wh/Wp for an ensemble of the size of Germany compared to a RMSE of 0.13 Wh/Wp for single PV systems. Besides the forecast accuracy, also the specification of the forecast uncertainty is an important issue for an effective application. An approach to derive weather specific confidence intervals is presented that describe the maximum expected uncertainty of the forecast.

Failure-free operation of grid-connected photovoltaic systems is important for the economic success of a system. Within the EU-funded project PVSAT-2, a service, based on satellite-derived irradiance data that detects automatically occurred system malfunctions, has been developed to ensure reliable operation of small systems up to 5 kWp. The detection and identification of a failure is strongly influenced by the accuracy of the satellite- derived irradiance data. This accuracy changes with the predominant weather situation, e.g. under clear sky conditions the errors are low, while they increase under cloudy skies. This determines the quality of the PV simulation and finally the period of time that is needed to detect a failure. Under clear-sky conditions and high power production also a small failure (~15% energy loss) can be detected and identified within a few days. During winter time the energy loss has to be larger than 30% to be detected.

This paper presents a detailed assessment of the value of photovoltaic energy within the German energy supply structure, taking into account the correlation between actual consumption and local power generation. Contrary to previous statistical approaches, this paper takes a new dynamic approach, modelling the dynamic behaviour of the PV power generation as a one-year time series. A comparison with the time series of the power demand allows assessment of the value of PV energy. The value of PV energy mainly results from its ability to substitute conventional power generation and the benefit of this kind of decentralized power generation for network stability and quality. An evaluation of these aspects is carried out for the year 2005 and a likely scenario in 2015.

This paper looks at identification and application of an amorphous model current / voltage characteristic of silicon from solar modules

To assure the maximal energy yield of grid connected PV systems, system faults have to be identified as quickly as possible. For this task several procedures that offer an almost continuous performance check are in development or application. One of them is the EU-funded project PVSat2. In the framework of this project, a model, able to reflect the efficiency characteristics η(G,T) at MPP of both the classic crystalline silicon and the various thin film technologies is analysed. The applicability of this model for the application to grid connected PV systems using cSi, aSi and CIS modules is demonstrated.

In order to manage the remarkable share of wind power as present in several utilities forecast information is needed. In the last years several respective procedures have been developed and put into operation. Up to now the outcome of the forecast tools is mainly restricted to the forecasted value itself and general information of the overall error of the procedure as e.g. the standard deviation. However, for the handling of these forecast information in the framework of e.g. power station dispatch schemes, more detailed information of the structure of the expected errors - beyond the expected standard deviation - seem to be desirable. Due to the fact that the output of wind turbine systems is limited between zero and the maximum power, the error statistics cannot follow a normal distribution. Thus, for the assessment of the probability of occurrence of a certain forecast error a model for the distribution function of the errors has to be set up. We have analysed the errors of the forecast model PREVIENTO as applied for an ensemble of installations representing the lumped power output of the turbines within a given region. Given bias free forecasts, the applicability of various models for the distribution function of the set of errors has been tested. It turned out, that the use of a beta function is justifiable for this task with respect to chi -squared tests. Together with an empirically derived parametric model for the expected standard deviation of the ensemble forecast, the knowledge of the respective distribution function allows for the assignment of risk figures to any decision taken based on the wind power forecast.

Due to the rapidly growing photovoltaic (PV) market in Germany, solar electricity fed in the grid amounts already to a share of approx. 2% of the total grid load during midday summertime. This is already a significant figure for grid operators. For several years a wind power prediction for wind energy is already available. But until now, there has been no operational solar electricity forecast. Therefore, the paper is aimed at providing an approach for a solar electricity forecast system based on the forecasts of the European center of midrange weather forecast ECMWF locally refined by model output statistics MOS with local weather stations. To evaluate the results, they have been compared with measured values of several thousand PV plants in Germany. The results show for daily values an RMSE of 24.5 % for the months July and August 2006, which is of course a satisfying result. Keywords: Forecasting, Utilities, Grid-connected 1 MOTIVATION Germany's conventional power plants are traditionally cooled by means of river water. The maximum allowable water temperature is limited by law. Due to the hot summer 2003 and this year, a significant part of conventional base load power plants have to reduce their output power significantly. For instance, nuclear power plants Krümmel und Brunsbüttel had to reduce their output up to 25% and, Isar 1 had to limit its output to 20% (18). Considering this and the fact that these power plants cover base load, it becomes obvious that during warm summers another technical solution has to be found to substitute traditional base load and, if possible, to reduce the needed peak load, too. In order to investigate, if PV electricity may help to cover the shortage, the hourly grid load of three typical summer days during the last three years and the measured power of about 4000 PV plants have been taken as basis for this evaluation. The data for grid load are available from the "Union for the Co-ordination of Transmission of Electricity" (UCTE) under (20). (Please refer to section 5.1 for more information about the data basis of measured PV power). The characteristics of the grid load of three summer days are very similar to each other. Now we compared these to the feed-in PV power normalized to the total installed PV capacity. The feed-in electricity we took from a typical summer day, the 17th of July 2006. The results are shown