Anlagenscharfe Simulation der PV-Leistung basierend auf Referenzmessungen und Geodaten

Zwischen 2005 und 2016 ist die weltweit installierte Leistung von PV-Anlagen von 5.1 GWp auf 300 GWp angestiegen. Für eine sichere Netzbetriebsführung und wirtschaftliche Vermarktung ist es daher von immer größerer Bedeutung, die aktuelle sowie die in den nächsten Stunden oder Tagen erzeugte PV-Leistung abschätzen zu können. Da kontinuierliche Messdaten nur von wenigen Anlagen vorliegen, werden sogenannte Referenzanlagen in Hochrechnungsverfahren eingesetzt, um die PV-Leistung aller übrigen Zielanlagen simulieren zu können. Die Genauigkeit solcher Hochrechnungsverfahren hängt u. a. von der räumlichen Verteilung aller PV-Anlagen, der räumlichen Aggregationsebene, der zeitlichen Auflösung der Messdaten, dem Zusammenspiel aus individuellem und kollektivem Anlagenverhalten und der Modulausrichtung ab. Ein weiterer wichtiger Einflussfaktor ist die teilweise hohe Variabilität der Globalstrahlung sowie der davon abhängigen PV-Leistung an und zwischen einzelnen Standorten. Ziel der vorliegenden Arbeit ist es Methoden zu entwickeln, welche die aufgezeigten komplexen Abhängigkeiten durch einen hohen Detaillierungsgrad besser abbilden können und damit die Genauigkeit von Hochrechnungsverfahren steigern. Bei der Konzeption dieser Methoden wird auf eine hohe praktische Relevanz und Übertragbarkeit auf andere Regionen geachtet. Zu den wichtigsten methodischen Entwicklungen in dieser Arbeit zählt eine Leistungsprojektion, mit der auf Basis von Referenzanlagen die Leistung beliebiger Zielanlagen unter Berücksichtigung ihrer Modulausrichtung abgeschätzt werden kann. Für großflächige Anwendungen wird zudem ein Ansatz vorgestellt, mit dem die Modulorientierung von Referenzanlagen überprüft und von Zielanlagen abgeschätzt werden kann. Da sowohl Referenz- als auch Zielanlagen nur schwierig abbildbaren Einflüssen unterliegen, werden Ansätze zur Kalibrierung der simulierten Erzeugungsleistung erarbeitet. Messfehler bei Referenzanlagen beeinträchtigen die Genauigkeit der Hochrechnung beträchtlich und werden mithilfe einer dafür konzipierten Qualitätskontrolle zuverlässig detektiert. Durch die kombinierte Anwendung aller in dieser Arbeit vorgestellten Methoden zeigt sich ein hohes Verbesserungspotential gegenüber einem Standardverfahren. Während der RMSErel um bis zu 13 % gesenkt werden kann, steigt der Korrelationskoeffizient τ um 3.5 %. Zudem wird der MBErel um etwa 66 % reduziert und relative Kosteneinsparungen von durchschnittlich 15 % bis 25 % erreicht

A regional simulation and optimisation of renewable energy supply from wind and photovoltaics with respect to three key energy-political objectives

Currently, most photovoltaic (PV) modules are aligned in a way that maximizes the overall annual yields, which leads to significant peaks in electricity production and could threaten energy policy objectives such as security of supply as well as environmental sustainability. The exploitation of remaining PV potentials at seemingly economically sub-optimal inclinations and azimuth angles could partly counteract this trend by achieving significant temporal shifts in the electricity production. This paper addresses the potential of these counter-measures by evaluating the optimal mix of wind and PV installations with different inclination and azimuth angles in a regional context. It does so by adhering to three distinctive energy policy goals: economic efficiency, sustainability and security of supply. It is further assumed that the examined regions aim for energetic autarky. The hourly yields of wind parks and PV installations with different mounting configurations are simulated for four representative NUTS3-regions in Germany, based on assumed installed capacities and specific weather conditions. These profiles are combined with standardized regional electricity demand profiles and fed into an optimization model, which is employed to maximise each of the three energy policy goals independently. As a result the optimal installed capacity for PV for every possible configuration – determined by inclination and azimuth angles – and the optimal installed capacity of wind power are determined. The results indicate that the optimal mix differs significantly for each of the chosen goals and depends on regional conditions, but shows a high transferability in terms of general conclusions. For economic efficiency – the first of the three goals – a focus on a high share of wind power and south-oriented PV-systems is feasible for all German regions. When sustainability is chosen as the energy policy goal, results depend largely on the conventional power plant utilization and its CO2-equivalent emissions leading to a high share of PV-systems in ratio to wind power. When maximizing the third goal, the security of supply, PV plants facing east and west as well as wind turbines are preferred, since this homogenizes the daily combined PV production. The developed methodology is found to be robust with regard to the relative conclusions, whilst the absolute magnitude of the results is sensitive to the input data. Further work should focus on refining the representativeness of the four model regions and on quantifying the three considered criteria more holistically

Anlagenscharfe Simulation der PV-Leistung basierend auf Referenzmessungen und Geodaten

Für eine sichere Netzbetriebsführung und wirtschaftliche Vermarktung ist die Kenntnis der Stromerzeugung von PV-Anlagen überaus wichtig. Da kontinuierliche Messdaten nur von wenigen Anlagen vorliegen, werden diese in Hochrechnungsverfahren verwendet, um die PV-Leistung aller übrigen PV-Anlagen zu simulieren. Die Modellentwicklungen in dieser Arbeit bilden komplexe Abhängigkeiten mithilfe einer anlagenscharfen Simulation ab und zeigen signifikante Verbesserungen gegenüber Standardverfahren

Himawari-8 enabled real-time distributed PVsimulations for distribution networks

High resolution, next generation satellites such as Himawari-8 show great promise for the provision of accurate estimations of Behind-the-Meter (BtM) solar PV power production. This paper presents a methodology that produces real-time PV power estimates as derived from Himawari-8 satellite imagery, validating them against seven Australian radiation monitoring sites and 78 small-scale BtM solar PV sites in Canberra, Australia. We report an MBE of -7 W m-2 and RMSE of 55 W m-2 for global horizontal radiation values (Gh) and an MBE of 0.04 W/Wp and RMSE of 0.15 W/Wp for estimated actuals at the PV sites. As a capstone, we apply this satellite based radiation modeling tool to a distribution network level distributed PV simulation in a single case-study using 15,500 PV sites. This work was completed in collaboration with industry partner, Solcast

A tuning routine to correct systematic influences in reference PV systems' power outputs

Power output measurements from PV systems are subject to a wide variety of systematic external and internal influences, such as shading, soiling, degradation, module and inverter quality issues and other system-level losses. All of these influences upon PV power measurements make the use of PV power output datasets for higher-level analysis problematic, particularly in their use as reference PV systems for estimating the power of a regional portfolio. To address these issues, we present a three-step method. Firstly, a parameterisation and quality control of power measurements is performed, which also corrects for consistent inefficiencies by a loss factor LF. Secondly, the detection of systematic de-ratings affecting PV system power output differently for each time step of the day (predominantly due to shading) together with the implementation of a subsequent “re-rating” of the power output measurements in a process referred to as tuning. The pivotal element of this approach is a 30-day running 90th percentile of the clear-sky index for photovoltaics kpv and the computation of a daily de-rating profile. Lastly, high kpv related variance in the early morning and evening is detected and filtered. Whilst these three methods are independent of each other, we suggest applying them in combination following the same order as in our paper. Cross-validations of these methods demonstrate significant improvements to the PV power measurement profiles, particularly in their use as reference PV systems for upscaling approaches. The RMSE falls from 0.174 to 0.09 W/Wp, rRMSE from 46.5% to 21.9%, MAPE from 47.9% to 20.8% and the correlation r increases from 0.767 to 0.919. Hence, we report overall improvements to RMSE, rRMSE, MAPE and r by 48%, 53%, 57% and 20%, respectively

Towards a Tuning Method of PV Power Measurements to Balance Systematic Influences

With rapid deployment and penetration rates of residential photovoltaic (PV) systems in the distribution grid, there is growing need for accurate assessment of the real-time power generation for grid management and energy market operations. Many of these installed PV systems report their live power generation to online databases and can be used as references to estimate the power generation of neighbouring systems. Upscaling approaches have demonstrated their capability of using the data from these reference PV systems to estimate the power output of target PV systems that do not report their power generation data. However, there is an inherent issue with the representativeness of these reference PV systems power data, e.g. due to quality issues or system specific influences such as shading. Three methods were developed by the authors in earlier work: (1) a parametrisation of PV system metadata and quality control of the measured power, (2) a tuning routine that detects diurnal influences from shading and tunes the PV power in order to reach the expected generation without any shading. And (3) a method which eliminates high variances in kpv based upscaling. An extensive cross-validation with 308 systems in Canberra, Australia in this paper shows significant improvements as a direct result of the application of these three methods. Furthermore, we present the preliminary findings for developments in: the parametrisation of shaded/multi-azimuth reference PV systems, as well as a method to reduce inertia in the shade detection and tuning. Overall, we successfully improve the management of reference PV system power data for use in upscaling

Improved satellite-derived PV power nowcasting using real-time power data from reference PV systems

Rapid growth in the global penetration of solar photovoltaic (PV) systems means electricity network operators and electricity generators alike are increasingly concerned with the short-term solar forecasting (nowcasting) of solar irradiance. This paper proposes a methodology that considers a varying number of available reference PV systems for supporting satellite-derived PV power real-time nowcasting. We evaluate conventional satellite-only and upscaling-only PV fleet estimate methodologies and compare them to two newly developed correction and hybrid cases. When using only a single reference PV system to estimate the aggregated power of 48 independent target PV systems for the location of Canberra, Australia; we show that the newly proposed correction or hybrid cases improve the performance of the satellite-derived PV power estimate medians in terms of MBE, rMBE, RMSE and rRMSE from 0.031 W/Wp,7.46%, 0.079 W/Wp and 23.4%, down to 0.006 W/Wp,-0.711%, 0.068 W/Wp and 20.0%, representing relative improvements of 80.6%, 90.5%, 13.9% and 14.5%, respectively. Similarly, when using 30 reference PV systems, we report median improvements from 0.036 W/Wp,8.25%, 0.083 W/Wp and 24.8%, down to 0.01 W/Wp,1.41%, 0.049 W/Wp and 11.4%, representing relative improvements of 72.2%, 82.9%, 41.0% and 54.0%, respectively. We discuss the fundamental challenges facing the use of reference PV systems, satellite-derived power estimates, combining the two data sources, and the knowledge required to address these issues. We ultimately conclude that combining satellite-based PV power estimates with data from reference PV systems is always more beneficial than either on their own

Identification of PV system shading using a LiDAR-based solar resource assessment model: An evaluation and cross-validation

Photovoltaic (PV) systems are subject to several different systematic de-rating factors, such as soiling, degradation, inverter mismatch and shading. With increasing penetration of PV in the local grid, Distribution Network Service Providers (DNSPs) are inclined to assess such losses, in order to accurately estimate the total regional power output of distributed PV. The most influential de-rating factor is shading, which can cause ramps on the generated power output, similar to clouds. In this study we evaluate and compare two fundamentally different methods for module orientation parametrisation and shading analysis of PV systems that have been developed in previous work. In the first method, LiDAR (Light Detection and Ranging) data are used to derive the PV module orientation and shading, referred to herein as the LiDAR model. The second method, referred to as the QCPV-Tuning model, is based on reported PV power generation, which is firstly quality controlled and parameterised in order to derive module orientation and a loss factor, LF, representing systematic de-rating factors. Secondly, variations in de-ratings throughout the day, mainly due to shading, are explored in a process referred to as Tuning. For both methods, binary time series are derived expressing the presence of shading, which are used to evaluate how the methods corroborate. We evaluate four cases; (case 1) evaluates the original versions of the LiDAR and QCPV-Tuning models, while in cases 2–4 improvements to the models are introduced. A new filter for extracting representative LiDAR data points for the shading analysis was introduced for the LiDAR model (case 2). For the QCPV-Tuning model significant inaccuracies in the parametrisation of the module orientation were identified due to strong shading in either morning or evening and were thus corrected to observed parameters (case 3). For (case 4) improvements on both models were introduced. The Pearson correlation coefficients of shading events for the methods were 0.28, 0.36, 0.42 and 0.50 for cases 1–4, respectively. A mismatch in the timing of shading events motivated the comparison of the mean hourly shading, with correlation coefficients of 0.34, 0.43, 0.49 and 0.57 for cases 1–4, respectively. The results of this study show that both methods can confidently be used for solar resource assessment, given the suggested improvements

A Comparison of Two Models for the Separation of Direct and Diffuse Irradiance in Plane of Array

For advanced automated operational monitoring of Photovoltaic (PV) systems, e.g. in order to distinguish losses due to self-shading from malfunctions, direct and diffuse irradiance at the module plane (plane of array, POA) are required separately. However, in practice, usually only measured values of global POA irradiance (GPOA) are recorded on site. There is thus a need for a model to divide GPOA into its direct (IPOA) and diffuse components (DPOA). This paper compares and evaluates two models recently developed that perform such separation. The aim of this work is to show the advantages and disadvantages that these two models offer and how they differ from each other. In order to compare and evaluate them, two different POA data sets were used. The first data set is generated using SolarGIS satellite-based data for 16 different locations across Germany. The second data set contains on-site measured DPOA and GPOA data from a PV power plant located in south-west Germany. Results from both models show good matching with satellite-based data from the 16 different locations

Comparing the capability of low- and high-resolution LiDAR data with application to solar resource assessment, roof type classification and shading analysis

LiDAR (Light Detection and Ranging) data have recently gained popularity for use in solar resource assessment and solar photovoltaics (PV) suitability studies in the built environment due to robustness at identifying building orientation, roof tilt and shading. There is a disparity in the geographic coverage of low- and high-resolution LiDAR data (LL and LH, respectively) between rural and urban locations, as the cost of the latter is often not justified for rural areas where high PV penetrations often pose the greatest impact on the electricity distribution network. There is a need for a comparison of the different resolutions to assess capability of LL. In this study, we evaluated and improved upon a previously reported methodology that derives roof types from a LiDAR-derived, low-resolution Digital Surface Model (DSM) with a co-classing routine. Key improvements to the methodology include: co-classing routine adapted for raw LiDAR data, applicability to differing building type distribution in study area, building height and symmetry considerations, a vector-based shading analysis of building surfaces and the addition of solar resource assessment capability. Based on the performance of different LiDAR resolutions within the developed model, a comparison between LL (0.5–1 pts/m2) and LH (6–8 pts/m2) LiDAR data was applied; LH can confidently be used to evaluate the applicability of LL, due to its significantly higher point density and therefore accuracy. We find that the co-classing methodology works satisfactory for LL for all types of building distributions. Roof-type identification errors from incorrect co-classing were rare (<1%) with LL. Co-classing buildings using LL improves accuracy of roof-type identification in areas with homogeneous distribution of buildings, here from 78% to 86% in accuracy. Contrastingly, co-classing accuracy using LH is marginally reduced for all building distributions from 94.8% to 94.4%. We adapt the Hay and Davies solar transposition model to include shading. The shading analysis demonstrates similarity of results between LL and LH. We find that the proposed methodology can confidently be used for solar resource assessments on buildings when only LiDAR data of low-resolution (<1 pts/m2) is available