Power output fluctuations in large scale PV plants: one year observations with one second resolution and a derived analytic model

The variable nature of the irradiance can produce significant fluctuations in the power generated by large grid-connected photovoltaic (PV) plants. Experimental 1 s data were collected throughout a year from six PV plants, 18 MWp in total. Then, the dependence of short (below 10 min) power fluctuation on PV plant size has been investigated. The analysis focuses on the study of fluctuation frequency as well as the maximum fluctuation value registered. An analytic model able to describe the frequency of a given fluctuation for a certain day is proposed

STC power for 15MW of PV comparing nameplate, initial power and power after 4 years

To date, the majority of quality controls performed at PV plants are based on the measurement of a small sample of individual modules. Consequently, there is very little representative data on the real Standard Test Conditions (STC) power output values for PV generators. This paper presents the power output values for more than 1300 PV generators having a total installed power capacity of almost 15.3 MW. The values were obtained by the INGEPER-UPNA group, in collaboration with the IES-UPM, through a study to monitor the power output of a number of PV plants from 2006 to 2009. This work has made it possible to determine, amongst other things, the power dispersion that can be expected amongst generators made by different manufacturers, amongst generators made by the same manufacturer but comprising modules of different nameplate ratings and also amongst generators formed by modules with the same characteristics. The work also analyses the STC power output evolution over time in the course of this 4-year study. The values presented here could be considered to be representative of generators with fault-free modules.

Statistical models for short-term wind power ramp forecasting

La predicción de energía eólica ha desempeñado en la última década un papel fundamental en el aprovechamiento de este recurso renovable, ya que permite reducir el impacto que tiene la naturaleza fluctuante del viento en la actividad de diversos agentes implicados en su integración, tales como el operador del sistema o los agentes del mercado eléctrico. Los altos niveles de penetración eólica alcanzados recientemente por algunos países han puesto de manifiesto la necesidad de mejorar las predicciones durante eventos en los que se experimenta una variación importante de la potencia generada por un parque o un conjunto de ellos en un tiempo relativamente corto (del orden de unas pocas horas). Estos eventos, conocidos como rampas, no tienen una única causa, ya que pueden estar motivados por procesos meteorológicos que se dan en muy diferentes escalas espacio-temporales, desde el paso de grandes frentes en la macroescala a procesos convectivos locales como tormentas. Además, el propio proceso de conversión del viento en energía eléctrica juega un papel relevante en la ocurrencia de rampas debido, entre otros factores, a la relación no lineal que impone la curva de potencia del aerogenerador, la desalineación de la máquina con respecto al viento y la interacción aerodinámica entre aerogeneradores. En este trabajo se aborda la aplicación de modelos estadísticos a la predicción de rampas a muy corto plazo. Además, se investiga la relación de este tipo de eventos con procesos atmosféricos en la macroescala. Los modelos se emplean para generar predicciones de punto a partir del modelado estocástico de una serie temporal de potencia generada por un parque eólico. Los horizontes de predicción considerados van de una a seis horas. Como primer paso, se ha elaborado una metodología para caracterizar rampas en series temporales. La denominada función-rampa está basada en la transformada wavelet y proporciona un índice en cada paso temporal. Este índice caracteriza la intensidad de rampa en base a los gradientes de potencia experimentados en un rango determinado de escalas temporales. Se han implementado tres tipos de modelos predictivos de cara a evaluar el papel que juega la complejidad de un modelo en su desempeño: modelos lineales autorregresivos (AR), modelos de coeficientes variables (VCMs) y modelos basado en redes neuronales (ANNs). Los modelos se han entrenado en base a la minimización del error cuadrático medio y la configuración de cada uno de ellos se ha determinado mediante validación cruzada. De cara a analizar la contribución del estado macroescalar de la atmósfera en la predicción de rampas, se ha propuesto una metodología que permite extraer, a partir de las salidas de modelos meteorológicos, información relevante para explicar la ocurrencia de estos eventos. La metodología se basa en el análisis de componentes principales (PCA) para la síntesis de la datos de la atmósfera y en el uso de la información mutua (MI) para estimar la dependencia no lineal entre dos señales. Esta metodología se ha aplicado a datos de reanálisis generados con un modelo de circulación general (GCM) de cara a generar variables exógenas que posteriormente se han introducido en los modelos predictivos. Los casos de estudio considerados corresponden a dos parques eólicos ubicados en España. Los resultados muestran que el modelado de la serie de potencias permitió una mejora notable con respecto al modelo predictivo de referencia (la persistencia) y que al añadir información de la macroescala se obtuvieron mejoras adicionales del mismo orden. Estas mejoras resultaron mayores para el caso de rampas de bajada. Los resultados también indican distintos grados de conexión entre la macroescala y la ocurrencia de rampas en los dos parques considerados. Abstract One of the main drawbacks of wind energy is that it exhibits intermittent generation greatly depending on environmental conditions. Wind power forecasting has proven to be an effective tool for facilitating wind power integration from both the technical and the economical perspective. Indeed, system operators and energy traders benefit from the use of forecasting techniques, because the reduction of the inherent uncertainty of wind power allows them the adoption of optimal decisions. Wind power integration imposes new challenges as higher wind penetration levels are attained. Wind power ramp forecasting is an example of such a recent topic of interest. The term ramp makes reference to a large and rapid variation (1-4 hours) observed in the wind power output of a wind farm or portfolio. Ramp events can be motivated by a broad number of meteorological processes that occur at different time/spatial scales, from the passage of large-scale frontal systems to local processes such as thunderstorms and thermally-driven flows. Ramp events may also be conditioned by features related to the wind-to-power conversion process, such as yaw misalignment, the wind turbine shut-down and the aerodynamic interaction between wind turbines of a wind farm (wake effect). This work is devoted to wind power ramp forecasting, with special focus on the connection between the global scale and ramp events observed at the wind farm level. The framework of this study is the point-forecasting approach. Time series based models were implemented for very short-term prediction, this being characterised by prediction horizons up to six hours ahead. As a first step, a methodology to characterise ramps within a wind power time series was proposed. The so-called ramp function is based on the wavelet transform and it provides a continuous index related to the ramp intensity at each time step. The underlying idea is that ramps are characterised by high power output gradients evaluated under different time scales. A number of state-of-the-art time series based models were considered, namely linear autoregressive (AR) models, varying-coefficient models (VCMs) and artificial neural networks (ANNs). This allowed us to gain insights into how the complexity of the model contributes to the accuracy of the wind power time series modelling. The models were trained in base of a mean squared error criterion and the final set-up of each model was determined through cross-validation techniques. In order to investigate the contribution of the global scale into wind power ramp forecasting, a methodological proposal to identify features in atmospheric raw data that are relevant for explaining wind power ramp events was presented. The proposed methodology is based on two techniques: principal component analysis (PCA) for atmospheric data compression and mutual information (MI) for assessing non-linear dependence between variables. The methodology was applied to reanalysis data generated with a general circulation model (GCM). This allowed for the elaboration of explanatory variables meaningful for ramp forecasting that were utilized as exogenous variables by the forecasting models. The study covered two wind farms located in Spain. All the models outperformed the reference model (the persistence) during both ramp and non-ramp situations. Adding atmospheric information had a noticeable impact on the forecasting performance, specially during ramp-down events. Results also suggested different levels of connection between the ramp occurrence at the wind farm level and the global scale.

Dry cooling with night cool storage to enhance solar power plants performance in extreme conditions areas

Solar thermal power plants are usually installed in locations with high yearly average solar radiation, often deserts. In such conditions, cooling water required for thermodynamic cycles is rarely available. Moreover, when solar radiation is high, ambient temperature is very high as well; this leads to excessive condensation temperature, especially when air-condensers are used, and decreases the plant efficiency. However, temperature variation in deserts is often very high, which drives to relatively low temperatures during the night. This fact can be exploited with the use of a closed cooling system, so that the coolant (water) is chilled during the night and store. Chilled water is then used during peak temperature hours to cool the condenser (dry cooling), thus enhancing power output and efficiency. The present work analyzes the performance improvement achieved by night thermal cool storage, compared to its equivalent air cooled power plant. Dry cooling is proved to be energy-effective for moderately high day–night temperature differences (20 °C), often found in desert locations. The storage volume requirement for different power plant efficiencies has also been studied, resulting on an asymptotic tendency.

Wind intermittency and CO2 reductions: the case of the spanish power system

Renewable energy sources are believed to reduce drastically greenhouse gas emissions that would otherwise be generated from fossil fuels used to generate electricity. This implies that a unit of renewable energy will replace a unit of fossil-fuel, with its CO2 emissions, on an equivalent basis (with no other effects on the grid). But, the fuel economy and emissions in the existing power systems are not proportional with the electricity production of intermittent sources due to cycling of the fossil fuel plants that make up the balance of the grid (i.e. changing the power output makes thermal units to operate less efficiently). This study focuses in the interactions between wind generation and thermal plants cycling, by establishing the levels of extra fuel use caused by decreased efficiencies of fossil back-up for wind electricity in Spain. We analyze the production of all thermal plants in 2011, studying different scenarios where wind penetration causes major deviations in programming, while we define a procedure for quantifying the carbon reductions by using emission factors and efficiency curves from the existing installations. The objectives are to discuss the real contributions of renewable energies to the environmental targets as well as suggest alternatives that would improve the reliability of future power systems.

Gasification technology assessment of sterile coal to clean electrical power generation.

Sterile coal is a low-value residue associated to the coal extraction and mining activity. According to the type and origin of the coal bed configuration, sterile coal production can mainly vary on quantity, calorific value and presence of sulphur compounds. In addition, the potential availability of sterile coal within Spain is apparently high and its contribution to the local power generation would be of interest playing a significant role. The proposed study evaluates the availability and deployment of gasification technologies to drive clean electricity generation from waste coal and sterile rock coal, incorporating greenhouse gas emission mitigation systems, like CO2, H2S and NOx removal systems. It establishes the target facility and its conceptual basic design proposal. The syngas obtained after the gasification of sterile coal is processed through specific conditioning units before entering into the combustion chamber of a gas turbine. Flue gas leaving the gas turbine is ducted to a heat recovery steam generation boiler; the steam produced within the boilerdrives a steam turbine. The target facility resembles a singular Integrated Gasification in Combined Cycle (IGCC) power station. The evaluation of the conceptual basic design according to the power output set for a maximum sterile contribution, established that rates over 95% H2S and 90% CO2 removal can be achieved. Noticeable decrease of NOx compounds can be also achieved by the use of commercial technology. A techno-economic approach of the conceptual basic design is made evaluating the integration of potential unitsand their implementation within the target facility aiming toachieve clean power generation. The criterion to be compliant with the most restrictive regulation regarding environmental emissions is setting to carry out this analysis.

Supercritical Steam power cycle for Line-Focus Solar Power Plants

The supercritical Rankine power cycle offers a net improvement in plant efficiency compared with a subcritical Rankine cycle. For fossil power plants the minimum supercritical steam turbine size is about 450MW. A recent study between Sandia National Laboratories and Siemens Energy, Inc., published on March 2013, confirmed the feasibility of adapting the Siemens turbine SST-900 for supercritical steam in concentrated solar power plants, with a live steam conditions 230-260 bar and output range between 140-200 MWe. In this context, this analysis is focused on integrating a line-focus solar field with a supercritical Rankine power cycle. For this purpose two heat transfer fluids were assessed: direct steam generation and molten salt Hitec XL. To isolate solar field from high pressure supercritical water power cycle, an intermediate heat exchanger was installed between linear solar collectors and balance of plant. Due to receiver selective coating temperature limitations, turbine inlet temperature was fixed 550ºC. The design-point conditions were 550ºC and 260 bar at turbine inlet, and 165 MWe Gross power output. Plant performance was assessed at design-point in the supercritical power plant (between 43-45% net plant efficiency depending on balance of plantconfiguration), and in the subcritical plant configuration (~40% net plant efficiency). Regarding the balance of plant configuration, direct reheating was adopted as the optimum solution to avoid any intermediate heat exchanger. One direct reheating stage between high pressure turbine and intermediate pressure turbine is the common practice; however, General Electric ultrasupercritical(350 bar) fossil power plants also considered doubled-reheat applications. In this study were analyzed heat balances with single-reheat, double-reheat and even three reheating stages. In all cases were adopted the proper reheating solar field configurations to limit solar collectors pressure drops. As main conclusion, it was confirmed net plant efficiency improvements in supercritical Rankine line-focus (parabolic or linear Fresnel) solar plant configurations are mainly due to the following two reasons: higher number of feed-water preheaters (up to seven)delivering hotter water at solar field inlet, and two or even three direct reheating stages (550ºC reheating temperature) in high or intermediate pressure turbines. However, the turbine manufacturer should confirm the equipment constrains regarding reheating stages and number of steam extractions to feed-water heaters.

PV power forecast using a nonparametric PV model

Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quantile Regression Forests as machine learning tool to forecast AC power with a confidence interval. Real data from five PV plants was used to validate the methodology, and results show that daily production is predicted with an absolute cvMBE lower than 1.3%.

Detecting and characterising ramp events in wind power time series

In order to implement accurate models for wind power ramp forecasting, ramps need to be previously characterised. This issue has been typically addressed by performing binary ramp/non-ramp classifications based on ad-hoc assessed thresholds. However, recent works question this approach. This paper presents the ramp function, an innovative wavelet- based tool which detects and characterises ramp events in wind power time series. The underlying idea is to assess a continuous index related to the ramp intensity at each time step, which is obtained by considering large power output gradients evaluated under different time scales (up to typical ramp durations). The ramp function overcomes some of the drawbacks shown by the aforementioned binary classification and permits forecasters to easily reveal specific features of the ramp behaviour observed at a wind farm. As an example, the daily profile of the ramp-up and ramp-down intensities are obtained for the case of a wind farm located in Spain

SOPHIA CPV module round robin: power rating at CSOC

Within the European funded project SOPHIA, a Round Robin measurement on CPV module has been initiated. Seven different test laboratories located in Europe between 48°N and 37°N perform measurements of four SOITEC CPV modules. The modules are electrically characterized with different measurement equipment under various climatic conditions. One pyrheliometer and one spectral sensor based on component cells are shipped together with the modules. This ensures that the irradiance and spectrum, two factors with high impact on CPV module performance, are measured with the identical equipment at each site. The round robin activity is performed in closeco-operation with the IEC TC82 WG7 power rating team in order to support the work on the CPV module power rating draft standard 62670-3. The resultingrated module power outputs at CSOC (Concentrator Standard Operating Conditions) are compared amongst the power rating methods and amongst the test labs. In this manner, a deviation in rated power output between different test labs and power rating methods is determined.

3D simulation of complex shading affecting PV systems taking benefit from the power of graphics cards developed for the video game industry

Shading reduces the power output of a photovoltaic (PV) system. The design engineering of PV systems requires modeling and evaluating shading losses. Some PV systems are affected by complex shading scenes whose resulting PV energy losses are very difficult to evaluate with current modeling tools. Several specialized PV design and simulation software include the possibility to evaluate shading losses. They generally possess a Graphical User Interface (GUI) through which the user can draw a 3D shading scene, and then evaluate its corresponding PV energy losses. The complexity of the objects that these tools can handle is relatively limited. We have created a software solution, 3DPV, which allows evaluating the energy losses induced by complex 3D scenes on PV generators. The 3D objects can be imported from specialized 3D modeling software or from a 3D object library. The shadows cast by this 3D scene on the PV generator are then directly evaluated from the Graphics Processing Unit (GPU). Thanks to the recent development of GPUs for the video game industry, the shadows can be evaluated with a very high spatial resolution that reaches well beyond the PV cell level, in very short calculation times. A PV simulation model then translates the geometrical shading into PV energy output losses. 3DPV has been implemented using WebGL, which allows it to run directly from a Web browser, without requiring any local installation from the user. This also allows taken full benefits from the information already available from Internet, such as the 3D object libraries. This contribution describes, step by step, the method that allows 3DPV to evaluate the PV energy losses caused by complex shading. We then illustrate the results of this methodology to several application cases that are encountered in the world of PV systems design. Keywords: 3D, modeling, simulation, GPU, shading, losses, shadow mapping, solar, photovoltaic, PV, WebGL

Life cycle assessment of concentrated solar power (CSP) and the influence of hybridising with natural gas

Purpose Concentrating Solar Power (CSP) plants based on parabolic troughs utilize auxiliary fuels (usually natural gas) to facilitate start-up operations, avoid freezing of HTF and increase power output. This practice has a significant effect on the environmental performance of the technology. The aim of this paper is to quantify the sustainability of CSP and to analyse how this is affected by hybridisation with different natural gas (NG) inputs. Methods A complete Life Cycle (LC) inventory was gathered for a commercial wet-cooled 50 MWe CSP plant based on parabolic troughs. A sensitivity analysis was conducted to evaluate the environmental performance of the plant operating with different NG inputs (between 0 and 35% of gross electricity generation). ReCiPe Europe (H) was used as LCA methodology. CML 2 baseline 2000 World and ReCiPe Europe E were used for comparative purposes. Cumulative Energy Demands (CED) and Energy Payback Times (EPT) were also determined for each scenario. Results and discussion Operation of CSP using solar energy only produced the following environmental profile: climate change 26.6 kg CO2 eq/KWh, human toxicity 13.1 kg 1,4-DB eq/KWh, marine ecotoxicity 276 g 1,4-DB eq/KWh, natural land transformation 0.005 m2/KWh, eutrophication 10.1 g P eq/KWh, acidification 166 g SO2 eq/KWh. Most of these impacts are associated with extraction of raw materials and manufacturing of plant components. The utilization NG transformed the environmental profile of the technology, placing increasing weight on impacts related to its operation and maintenance. Significantly higher impacts were observed on categories like climate change (311 kg CO2 eq/MWh when using 35 % NG), natural land transformation, terrestrial acidification and fossil depletion. Despite its fossil nature, the use of NG had a beneficial effect on other impact categories (human and marine toxicity, freshwater eutrophication and natural land transformation) due to the higher electricity output achieved. The overall environmental performance of CSP significantly deteriorated with the use of NG (single score 3.52 pt in solar only operation compared to 36.1 pt when using 35 % NG). Other sustainability parameters like EPT and CED also increased substantially as a result of higher NG inputs. Quasilinear second-degree polynomial relationships were calculated between various environmental performance parameters and NG contributions. Conclusions Energy input from auxiliary NG determines the environmental profile of the CSP plant. Aggregated analysis shows a deleterious effect on the overall environmental performance of the technology as a result of NG utilization. This is due primarily to higher impacts on environmental categories like climate change, natural land transformation, fossil fuel depletion and terrestrial acidification. NG may be used in a more sustainable and cost-effective manner in combined cycle power plants, which achieve higher energy conversion efficiencies.

Longitudinal Changes in Response to a Cycle-Run Field Test of Young Male National "Talent identification" and Senior Elite Triathlon Squads.

This study investigated the changes in cardiorespiratory response and running performance of 9 male ?Talent Identification? (TID) and 6 male Senior Elite (SE) Spanish National Squad triathletes during a specific cycle-run test. The TID and SE triathletes (initial age 15.2±0.7 vs. 23.8±5.6 years, p=0.03; tests through the competitive period and the preparatory period, respectively, of two consecutive seasons: Test 1 was an incremental cycle test to determine the ventilatory threshold (Thvent); Test 2 (C-R) was 30 min constant load cycling at the Thvent power output followed by a 3-km time trial run; and Test 3 (R) was an isolated 3-km time trial control run, in randomized counterbalanced order. In both seasons the time required to complete the C-R 3-km run was greater than for R in TID (11:09±00:24 vs. 10:45±00:16 min:ss, pmenor que 0.01; and 10:24±00:22 vs. 10:04±00:14, p=0.006, for season 2005/06 and 2006/07, respectively) and SE (10:15±00:19 vs. 09:45±00:30, pmenor que 0.001 and 09:51±00:26 vs. 09:46±00:06, p= 0.02 for season 2005/06 and 2006/07, respectively). Compared to the first season, completion of the time trial run was faster in the second season (6.6%, pmenor que 0.01 and 6.4%, pmenor que 0.01, for C-R and R test, respectively) only in TID. Changes in post-cycling run performance were accompanied by changes in pacing strategy but only slight or non-significant changes in the cardiorespiratory response. Thus, the negative effect of cycling on performance may persist, independently of the period, over two consecutive seasons in TID and SE triathletes; however A improvements over time suggests that monitoring running pacing strategy after cycling may be a useful tool to control performance and training adaptations in TID. O2max 77.0±5.6 vs. 77.8±3.6 mL·kg-1·min-1, NS) underwent three TE D EP C C

Sound of lasers (SOL) - An audiovisual approach to semiconductor laser dynamics

We present an educational software addressed to the students of optical communication courses, for a simple visualization of the basic dynamic processes of semiconductor lasers. The graphic interface allows the user to choose the laser and the modulation parameters and it plots the laser power output and instantaneous frequency versus time. Additionally, the optical frequency variations are numerically shifted into the audible frequency range in order to produce a sound wave from the computer loudspeakers. Using the proposed software, the student can simultaneously see and hear how the laser intensity and frequency change, depending on the modulation and device parameters.

Flow and wakes in large wind farms in complex terrain and offshore

Power losses due to wind turbine wakes are of the order of 10 and 20% of total power output in large wind farms. The focus of this research carried out within the EC funded UPWIND project is wind speed and turbulence modelling for large wind farms/wind turbines in complex terrain and offshore in order to optimise wind farm layouts to reduce wake losses and loads.