Estimation Of Annual Energy Production Research Articles

Model-based analysis of the viability of concentrating solar power plants

This paper presents an assessment of the effect of thermal energy storage and combined heat and power production on the profitability of concentrating solar power (CSP) plants. Nowadays CSP plants produce electricity for a higher cost than commercial photovoltaic (PV) systems, therefore the mentioned opportunities enabled by the intermediate heat production have a key role in the economic viability of the technology. Models were established to describe the operation of both the parabolic trough and solar power tower systems. A Matlab simulation program was created based on the models in order to support the optimal design of these plants and perform annual energy production estimations based on meteorological databases. Thermal energy storage is a relatively cheap method even for long time storage, and it not only increases the capacity factor, but also improves the average efficiency of the plant. Furthermore a storage also improves the availability and dispatchability of the plant, which is a very important factor in an energy system with high renewable share. Cogeneration is also feasible in CSP plants, the only difficulty here is to find a heat demand coinciding with the production.

Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms

Wind farm design generally relies on the use of historical data and analytical wake models to predict farm quantities, such as annual energy production (AEP). Uncertainty in input wind data that drive these predictions can translate to significant uncertainty in output quantities. We examine two sources of uncertainty stemming from the level of description of the relevant meteorological variables and the source of the data. The former comes from a standard practice of simplifying the representation of the wind conditions in wake models, such as AEP estimates based on averaged turbulence intensity (TI), as opposed to instantaneous. Uncertainty from the data source arises from practical considerations related to the high cost of in situ measurements, especially for offshore wind farms. Instead, numerical weather prediction (NWP) modeling can be used to characterize the more exact location of the proposed site, with the trade-off of an imperfect model form. In the present work, both sources of input uncertainty are analyzed through a study of the site of the future Vineyard Wind 1 offshore wind farm. This site is analyzed using wind data from LiDAR measurements located 25 km from the farm and NWP data located within the farm. Error and uncertainty from the TI and data sources are quantified through forward analysis using an analytical wake model. We find that the impact of TI error on AEP predictions is negligible, while data source uncertainty results in 0.4%–3.7% uncertainty over feasible candidate hub heights for offshore wind farms, which can exceed interannual variability.

Design and Analysis of a Hybrid Stand-Alone Microgrid

This research article presents a comprehensive investigation into the design, optimization, and performance analysis of a hybrid stand-alone microgrid for an industrial facility in Iraq at coordinates 36.51 and 43.99. The system consists of photovoltaic (PV) modules, inverters, a battery energy storage system (BESS), a generator, and AC loads. Leveraging the capabilities of PVsyst version 7.3.1, HOMER Pro version 3.14.2 and SAM version 2022.11.21 software tools, this study assesses the feasibility and functionality of the hybrid stand-alone microgrid. In this study, PVsyst software is used for detailed designing and analysis of a PV plant, and the PVsyst design file is then used in HOMER Pro software to optimize and design the proposed hybrid stand-alone microgrid, and for detailed performance analysis SAM software is employed. This paper also investigates the impact of ground clearance and ground albedo on the annual generation of bifacial PV modules at various tilt angles. Key findings include a promising normalized production rate of 4.53 kWh/kWp/day with a performance ratio of 0.815 and annual energy production estimates of 84.31 MWh (P50), 79.57 MWh (P90), and 78.24 MWh (P95) for monofacial PV modules, highlighting the system’s potential for renewable energy generation. Notably, this research demonstrates the hybrid stand-alone microgrid’s capacity to significantly reduce CO2 emissions, saving approximately 1811.6 tons over a 30-year period, thus contributing to sustainability and environmental conservation goals. Additionally, this study reveals operational challenges during the winter months, necessitating generator support to meet load demands. The successful installation and experimental validation of the hybrid stand-alone microgrid underscore its practical viability and its role in advancing clean energy solutions. This research provides valuable insights into hybrid stand-alone microgrid design, emphasizing its importance in ensuring reliable power supply and environmental stewardship.

A Monte Carlo simulation method for probabilistic evaluation of annual energy production of wind farm considering wind flow model and wake effect

The wind energy utilization has attracted worldwide attention. The wind farm is the most important place to convert wind energy into electric energy. The accurate estimation of annual energy production and its uncertainty involves the wind resource assessment and investment risk management for the wind farm. In order to reasonably analyze the uncertainty, a Monte Carlo simulation method is proposed to obtain the annual energy production and its uncertainty for the wind farm. The method proposed in this paper not only includes the uncertainties associated with Weibull distribution parameters, measure-correlation-prediction method, power law exponent, air density and power curve in previous studies, but also incorporates the wind flow model uncertainty and wake effect. Specifically, the method proposed in this paper extrapolates the wind speed from the meteorological mast to each wind turbine in the wind farm based on the uncertainty of the wind flow model. At the same time, the wind speed reduction on the downstream wind turbines due to the wake effect will be considered, which leads to the annual energy production and uncertainty of the whole wind farm. Also, the effectiveness of the proposed method is verified based on the numerical example. The results show that the average annual energy productions of different wind turbines in the wind farm with flat terrain are close, but there are some slight differences among turbines due to the wake effect with the maximum loss of 3.5%. It is also observed that the simulated uncertainty of annual energy production of concerned wind farm is 9.0%, which is larger than 7.48% by square root of the sum of the squares method where correlations among variables are not considered.

Tidal turbine power performance assessments following IEC TS 62600-200 using measured and modelled power outputs

Demonstrating power performance in real conditions is vital for verifying tidal turbine design. The International Electrotechnical Commission’s Technical Specification IEC TS 62600-200 provides guidance for tidal developers to evaluate machine performance. This study evaluates the performance of the operational 1 MW DEEP-Gen IV commercial-scale tidal turbine deployed during the ReDAPT project at the European Marine Energy Centre’s tidal test site in Orkney, Scotland. IEC TS 62600-200 states that the power performance should be measured relative to two independently located current profilers deployed in one of two orientations; ‘in-line’ (preferred) or ‘adjacent’ (least preferred), relative to the turbine. Two measurement campaigns are used to assess the impact of instrument placement on the measured power curve and annual energy production (AEP). The ambient current flow and vertical profile form differed at each current profiler location (due to topography and bathymetry), leading to AEP variability. The results reinforce the IEC TS preferred orientation for instrument placement, with inline measurements showing smaller variation in AEP estimates compared with adjacent measurements. An additional analysis assesses the sensitivity of AEP estimates on the vertical alignment of the flow profiles with rotor positioning. For this we consider three turbine scenarios: the DEEP-Gen IV and two modelled turbine rotors occupying different regions in the water column. Results show that for regions of high vertical shear, AEP estimates can be misrepresented by up to 5.5% under an imposed vertical misalignment of 2 m. This work has quantified, at this site, the variation in AEP estimates from different flow measurement campaigns informed by IEC TS 62600-200 and has shown the need to ensure good vertical alignment between measurements and turbine rotor positioning.

A critical analysis of the uncertainty in the production estimation of wind parks in complex terrains

Assessing the wind resource in complex terrains with the level of accuracy required by industry is not a trivial task. The present study critically analyzes the different elements involved in the process and how they affect the predicted Annual Energy Production (AEP) of a wind farm. Five different sites in Italy, for which the installing company had a posteriori AEP data available, are considered. All sites are modeled with the windPRO software. To separate the effects related to the quality of experimental measurements from those due to the selected wind modeling techniques, two different approaches were followed. In the “horizontal” one, the minimum amount of wind data common to all installation sites were used, i.e., the measurements of one mast at one selected altitude for a single full year, thus highlighting the role of the terrain modelling. In the “vertical” approach, the analysis focused on the site with the most complex terrain, using wind data of different quality and duration, as well as different processing techniques, from the ubiquitous WAsP to WAsP-CFD. The results of the study confirm that, even in presence of high-quality measurements and a standard workflow, the accuracy of the farm's AEP estimation is strongly site-dependent. For mildly-complex terrains, the lack of accuracy in mast data can be compensated with a more advanced spatial scaling method, and vice versa. On the other hand, in presence of steep slopes or sneaky ridges, attention must be paid to both the experimental and modelling components. A scaling approach based on Computational Fluid Dynamics (CFD) is mandatory. In all cases, thermal effects such as atmospheric stability must be carefully evaluated and included in the assessment process.

Impact on Energy Yield of Varying Turbine Designs under Conditions of Misalignment to the Current Flow

Tidal energy resource characterisation using acoustic velocimetry sensors mounted on the seabed informs developers of the location and performance of a tidal energy converter (TEC). This work studies the consequences of miscalculating the established flow direction, i.e., the direction of assumed maximum energy yield. Considering data only above the proposed TEC cut-in velocities showed a difference in the estimated flow direction of up to 4°. Using a power weighted rotor average (PWRA) method to obtain the established flow direction resulted in a difference of less than 1° compared with the hub-height estimate. This study then analysed the impact of turbine alignment on annual energy production (AEP) estimates for a non-yawing tidal turbine. Three variants of horizontal axis tidal turbines, which operate in different locations of the water column, were examined; one using measured data, and the other two via modelled through power curves. During perfect alignment to the established flow direction, natural variations in flow meant that the estimate of AEP differed by up to 1.1% from the theoretical maximum of a fully yawed turbine. In the case of misalignment from the established flow direction, the difference in AEP increased. For a 15° misalignment, the AEP differed by up to 13%. These results quantify important uncertainties in tidal energy site design and performance assessment.

Numerical developments on the E-Motions wave energy converter: Hull design, power take-off tuning and mooring system configuration

This paper addresses a recently conducted numerical study on the E-Motions wave energy converter, aimed at improving its energy performance and efficiency. The Potential Flow Theory-based ANSYS® Aqwa™ software was used, being a robust industry-standard code that has been successfully applied to the E-Motions case study, in the past. Data from a preceding physical modelling campaign was applied towards calibration procedures for three platform variants – half-cylinder, half-sphere and trapezoidal prism, with and without the power take-off and subjected to regular waves. A non-dimensional root mean square error minimization approach was employed. Afterwards, the focus shifted towards defining and comparing the performance of sixteen E-Motions sub-variants, which included a new hull shape: enclosed frustum. Two additional stages were carried out: one oriented towards assessing ten power take-off mass-damping combinations and another to the study of four mooring system configurations. Results point towards a very significant hydrodynamic response, particularly within the resonance range and for the half-cylinder and half-sphere sub-variants, and maximum average power outputs of nearly 40 kW. For the best half-cylinder, half-sphere, trapezoidal prism and enclosed frustum sub-variants, the annual energy production estimates, per device, were 141, 110, 106 and 91 MWh/yr, respectively.

Wind resource assessment considering the influence of humidity

The current study presents a wind resource assessment (WRA) approach by combining existing approaches, including wind probability density estimation based on hourly wind speed frequency, wind power density (WPD) and wind energy density (WED), wind turbine (WT) power output and power curve modeling, and annual energy production (AEP). Wind probability density investigation employed various probability density functions (PDF), including parametric probability density functions such as Weibull, Normal, and Gamma, and non-parametric distribution, including Kernel Density Estimator (KDE). The present study also models the influence of humidity on air density for estimating WPD, WT power output, and AEP. The current study validated the proposed approach by conducting case studies for selected sites of remote Indonesian archipelago islands. AEP estimation proposed by this study can assist the site-turbine fitting design, especially for relatively moist locations.

The wide range of factors contributing to wind resource assessment accuracy in complex terrain

Abstract. Understanding the uncertainties of wind resource assessments (WRAs) is key to reducing project risks, and this is particularly challenging in mountainous terrain. In the academic literature, many complex flow sites have been investigated, but they all focus on comparing wind speeds from selected wind directions and do not focus on the overall annual energy production (AEP). In this work, the importance of converting wind speed errors into AEP errors when evaluating wind energy projects is highlighted by comparing the results of seven different WRA workflows at five complex terrain sites. Although a systematic study involving the investigation of all possible varying parameters is not within the scope of this study, the results allow some of the different factors that could lead to this discrepancy being identified. The wind speed errors are assessed by comparing simulation results to wind speed measurements at validation locations. This is then extended to AEP estimations (without wake effects), showing that wind profile prediction accuracy does not translate directly or linearly to AEP accuracy. This is due to the specific conditions at the site, to differences in workflow set-ups between the sites and to differences in workflow AEP calculation methods. The results demonstrate the complexity of the combined factors contributing to WRA errors – even without including wake effects and other losses. This means that the wind model that produces the most accurate wind predictions for a certain wind direction over a certain time period does not always result in the most suitable model for the AEP estimation of a given complex terrain site. In fact, the large number of steps within the WRA process often lead to the choice of wind model being less important for the overall WRA accuracy than would be suggested by only looking at wind speeds. It is therefore concluded that it is vitally important for researchers to consider overall AEP – and all the steps towards calculating it – when evaluating simulation accuracies of flow over complex terrain. Future work will involve a systematic study of all the factors that could contribute to this effect.

Simulation of an On-grid Wind-Hydrogen Coupling System (Case Study: Mollo Selatan Subdistrict, Indonesia)

This study proposed a wind-hydrogen coupling systems control strategy to investigate its feasibility. The control strategy regulates the electrical energy generated by a wind turbine to meet the domestic load demand of a specific site. The remaining electrical energy is regulated to supply electrolyzer for producing hydrogen as energy storage. The stored energy in the form of hydrogen can be reconverted into electricity by utilizing fuel cells to meet the domestic load demand. Selecting proper sites for evaluating the control strategy is critical. Hence this study also conducts a wind resource assessment approach that considers the humidity effect to wind power density, wind turbine power curve, and annual energy production of a wind turbine. This approach achieved a better precise annual energy production estimation than other approaches that neglect the humidity. Proper wind resource assessment resulted in a site for the case study, Mollo Selatan Subdistrict for an on-grid system. The control strategy was modeled and evaluated with MATLAB Simulink. The simulation results show the possibility of storing energy generated by the wind into hydrogen and meeting the load demand of the case study location.

A frequency-time domain method for annual energy production estimation in floating wind turbines

A new method is proposed to estimate a floating wind turbine’s annual energy production (AEP) using frequency and time-domain design techniques. The approach demonstrated herein estimates the AEP by performing a convolution between the floating platform response and the response power operators (RPOs) that map the average power produced by the turbine as a function of the amplitude and frequency of the platform motions. One advantage of this approach is that it can be performed early in the conceptual design phase to help discover design space trade-offs between the platform and rotor design. The methodology is applied to the IEA Wind 15 MW WindCrete spar-buoy model using OpenFAST. The RPOs are obtained by prescribing single-DOF platform motions to the turbine with a given amplitude and frequency. This methodology is then validated by comparing the AEP estimation from the RPOs with the AEP estimation from fully-coupled simulations. The results indicate that the method is able to estimate the value of AEP for a realistic sea-state and regular waves. However, further validation is needed as, in the first case, the turbine is moving too little and, in the second case, the contribution of the controller may be dominant.

Wind turbine ice detection using AEP loss method: A case study

This paper describes the comparison of a statistical and numerical case study of wind resource assessment and estimation of resultant Annual Energy Production due to ice of a wind park in ice prone cold region. Three years Supervisory Control and Data Acquisition data from a wind park located in arctic region is used for this study. Statistical analysis shows that the relative power loss due to icing related stops is the main issue for this wind park. While Larsen wake model is used for the CFD simulations, where results show that it is important to use the wake loss model for CFD simulations of wind resource assessment and AEP estimation of a wind park. A preliminary case study about wind park layout optimization has also been carried out which shows that AEP can be improved by optimizing the wind park layout and CFD simulations can be a good tool.

Wind power generation prediction in a complex site by comparing different numerical tools

This paper focuses on the estimation of annual energy production (AEP) by simulating the flowfield on a complex terrain located in the northeast region of Brazil using different numerical approaches: CFD RANS with k-ε and k-ω turbulence models (WindSim), simple mass-conserving (WindMap), and refined mesoscale (SiteWind). The last two are run through OpenWind software. Wind observations from five meteorological masts are used to adjust the models. Optimal layouts for a hypothetical wind farm with 50 wind turbines are obtained over each of the four wind fields to predict the power generation. As non-negligible differences are found on the spatial distribution of the winds simulated by the different models, the layouts are also substantially different. The AEP is calculated to compare scenarios varying the layouts over the wind fields. The distinct micrositing generate differences of up to 13 % on AEP prediction, which could mean the impact of an improper siting on the wind farm profitability. It is not plausible to categorically claim the superiority of accuracy of one model over the others. Nonetheless, the observed data provide an indicative that the refined mesoscale model was able to better capture the wind acceleration in the western region of the studied site.

Verification of induction zone models for wind farm annual energy production estimation

Fast engineering wake models are the backbone of wind farm annual energy production (AEP) estimators, whereas the addition of induction zone models in existing tools is a more recent response to rising concerns over wind farm blockage associated losses. Here, “blockage” describes the combined induction fields of all wind turbines inside a farm. Unlike the term might suggest, blockage not only reduces flow speeds, but also increases them; for instance along the outer edges of wind farms. Evaluating the overall impact on AEP of these gains and losses necessitates accurate wind farm induction models. Whilst engineering wake models are tuned to predict wind farm performance, existing induction zone models are all derived for accurately predicting the near field induction not the far field value—important for wind farm simulations. This paper presents the induction models implemented in the wind farm simulation tool PyWake, as well as results from novel analytical models and compares their far field predictions to RANS-AD simulations of different turbines. We demonstrate that when including blockage in AEP simulations, the downstream speed-ups need to be included to avoid an unrealistic bias toward AEP loss and that wake expansion significantly impacts induction at rated thrust levels.

Annual Energy Production Estimation for Variable-speed Wind Turbine at High-altitude Site

This letter presents a systematic approach to estimate the annual energy production (AEP) of variable-speed wind turbines erected at high-altitude sites. Compared with the existing empirical-model based approaches, the proposed approach models the influence of the air density on the power production while employing the theoretical power curve. Consequently, the proposed approach provides a precise estimation of AEP, which can serve as a foundation of the optimum turbine-site matching design at different-altitude sites.

Operational-based annual energy production uncertainty: are its components actually uncorrelated?

Abstract. Calculations of annual energy production (AEP) from a wind power plant – whether based on preconstruction or operational data – are critical for wind plant financial transactions. The uncertainty in the AEP calculation is especially important in quantifying risk and is a key factor in determining financing terms. A popular industry practice is to assume that different uncertainty components within an AEP calculation are uncorrelated and can therefore be combined as the sum of their squares. We assess the practical validity of this assumption for operational-based uncertainty by performing operational AEP estimates for more than 470 wind plants in the United States, mostly in simple terrain. We apply a Monte Carlo approach to quantify uncertainty in five categories: revenue meter data, wind speed data, regression relationship between density-corrected wind speed (from reanalysis data) and measured wind power, length of long-term-correction data set, and future interannual variability. We identify correlations between categories by comparing the results across all 470 wind plants. We observe a positive correlation between interannual variability and the linearized long-term correction; a negative correlation between wind resource interannual variability and linear regression; and a positive correlation between reference wind speed uncertainty and linear regression. Then, we contrast total operational AEP uncertainty values calculated by omitting and considering correlations between the uncertainty components. We quantify that ignoring these correlations leads to an underestimation of total AEP uncertainty of, on average, 0.1 % and as large as 0.5 % for specific sites. Although these are not large increases, these would still impact wind plant financing rates; further, we expect these values to increase for wind plants in complex terrain. Based on these results, we conclude that correlations between the identified uncertainty components should be considered when computing the total AEP uncertainty.

Wind resource assessment and wind farm modeling in Mossobo-Harena area, North Ethiopia

Although Ethiopia does not have significant fossil fuel resource, it is endowed with a huge amount of renewable energy resources such as hydro, wind, geothermal, and solar power. However, only a small portion of these resources has been utilized so far and less than 30% of the nation’s population has access to electricity. The wind energy potential of the country is estimated to be up to 10 GW. Yet less than 5% of this potential is developed so far. One of the reasons for this low utilization of wind energy in Ethiopia is the absence of a reliable and accurate wind atlas and resource maps. Development of reliable and accurate wind atlas and resource maps helps to identify candidate sites for wind energy applications and facilitates the planning and implementation of wind energy projects. The main purpose of this research is to assess the wind energy potential and model wind farm in the Mossobo-Harena site of North Ethiopia. In this research, wind data collected for 2 years from Mossobo-Harena site meteorological station were analyzed using different statistical software to evaluate the wind energy potential of the area. Average wind speed and power density, distribution of the wind, prevailing direction, turbulence intensity, and wind shear profile of the site were determined. Wind Atlas Analysis and Application Program was used to generate the generalized wind climate of the area and develop resource maps. Wind farm layout and preliminary turbine micro-sitting were done by taking various factors into consideration. The IEC wind turbine class of the site was determined and an appropriate wind turbine for the study area wind climate was selected and the net annual energy production and capacity factor of the wind farm were determined. The measured data analysis conducted indicates that the mean wind speed at 10 and 40 m above the ground level is 5.12 and 6.41 m/s, respectively, at measuring site. The measuring site’s mean power density was determined to be 138.55 and 276.52 W/m2 at 10 and 40 m above the ground level, respectively. The prevailing wind direction in the site is from east to south east where about 60% of the wind was recorded. The resource grid maps developed by Wind Atlas Analysis and Application Program on a 10 km × 10 km area at 50 m above the ground level indicate that the selected study area has a mean wind speed of 5.58 m/s and a mean power density of 146 W/m2. The average turbulence intensity of the site was found to be 0.136 at 40 m which indicates that the site has a moderate turbulence level. According to the resource assessment done, the area is classified as a wind Class IIIB site. A 2-MW rated power ENERCON E-82 E2 wind turbine which is an IEC Class IIB turbine with 82 m rotor diameter and 98 m hub height was selected for estimation of annual energy production on the proposed wind farm. 88 ENERCON E-82 E2 wind turbines were properly sited in the wind farm with recommended spacing between the turbines so as to reduce the wake loss. The rated power of the wind farm is 180.4 MW and the net annual energy production and capacity factor of the proposed wind farm were determined to be 434.315 GWh and 27.48% after considering various losses in the wind farm.

Verification of the Reliability of Offshore Wind Resource Prediction Using an Atmosphere–Ocean Coupled Model

An atmosphere–ocean coupled model is proposed as an optimal numerical prediction method for the offshore wind resource. Meteorological prediction models are mainly used for wind speed prediction, with active studies using atmospheric models. Seawater mixing occurring at sea due to solar radiation and wind intensity can significantly change the sea surface temperature (SST), an important variable for predicting wind resources and energy production, considering its wind effect, within a short time. This study used the weather research forecasting and ocean mixed layer (WRF-OML) model, an atmosphere–ocean coupled model, to reflect time-dependent SST and sea surface fluxes. Results are compared with those of the WRF model, another atmospheric model, and verified through comparison with observation data of a meteorological mast (met-mast) at sea. At a height of 94 m, the wind speed predicted had a bias and root mean square error of 1.09 m/s and 2.88 m/s for the WRF model, and −0.07 m/s and 2.45 m/s for the WRF-OML model, respectively. Thus, the WRF-OML model has a higher reliability. In comparing to the met-mast observation data, the annual energy production (AEP) estimation based on the predicted wind speed showed an overestimation of 15.3% and underestimation of 5.9% from the WRF and WRF-OML models, respectively.

Wind Turbine Wake Effects on Wind Resource Assessments – a Case Study

This paper describes a case study of wind turbine wake loss effects on wind resource assessment in cold region. One year wind park SCADA data is used. Computational Fluid Dynamics (CFD) based numerical simulations are carried out for wind resource assessment and estimation of resultant Annual Energy Production (AEP). Numerical results are compared with the field SCADA data, where a good agreement is found. To better understand the wind flow physics and effects of wind turbine turbulence wake loss effects, three different wake loss models are used for the numerical simulations, where results with wake model is found in best agreement with the AEP estimation from field SCADA data. A detailed comparison of all wind turbines is also presented with the gross AEP. A preliminary case study about wind park layout optimization has also been carried out which shows that AEP can be improved by optimizing the wind park layout and CFD simulations can be used as a tool in this regards.