Energy Resource Estimates Research Articles

Predictive control technique for solar photovoltaic power forecasting

An accurate estimation of photovoltaic (PV) power production is crucial for organising and regulating solar PV power plants. The suitable prediction is often affected by the variable nature of solar resources, system location and some internal/external disturbances, such as system effectiveness, climatic factors, etc. This paper develops a novel strategy for applying a predictive control technique to PV power forecasting applications in a smart grid environment. The strategy develops the model predictive control (MPC) under demand response (DR) and some data-driven methods. It has been found that it is challenging to model an MPC for solar power forecasting regardless of its robustness and ability to handle constraints and disturbance. Thus, an optimal quadratic performance index-based MPC scheme is formulated to model a forecasting method for a PV power prediction. This strategy is then compared with some machine learning approaches. The developed strategies solve the problem of accurately estimating the direct current (DC) power yielded from the PV plant in a real-world implementation. The study also considers external disturbances to evaluate the significance of the developed methods for a suitable forecast. Therefore, this study optimally demonstrates that an accurate solar PV DC power prediction can relatively be estimated with an appropriate strategy, such as MPC and MLs, considering the system disturbances. This study also offers promising results for intelligent and real-time energy resource estimation that assist in developing the solar power sector.

Exploitation of an operative wave forecast system for energy resource assessment in the Mediterranean Sea

Ocean Energy is now emerging as a viable long-term form of renewable energy, which might contribute around 10% of EU power demand by 2050, if sufficient support is guaranteed along its road to full commercialization, allowing to further demonstrate the reliability, robustness and overall economic competitiveness of technologies. Although wave energy is still less developed than other marine renewables, its high density, great potential and minimal environmental impact have renewed the interest of developers, investors and governments globally, also in view of the increasing awareness of climate change and of the necessity to reduce carbon emissions. In parallel with technological development, the reliable characterization of wave climate and of the associated energy resource is crucial to the design of efficient Wave Energy Converters and to an effective site-technology matching, especially in low-energy seas. The preliminary scrutiny of suitable technologies and the identification of promising sites for their deployment often rely on wave climatological atlases, yet a more detailed characterization of the local resource is needed to account for high-frequency spatial and temporal variability that significantly impact power generation and the economic viability of WEC farms. We present a high-resolution assessment of the wave energy resource at specific locations in the Mediterranean Sea, based on a 7-years dataset derived from the operative wave forecast system that has been developed at ENEA and has been running since 2013. The selected areas correspond to the target regions of the Blue Deal project, where energy resource estimates were combined with technical and environmental considerations, so as to identify optimal sites for Blue Energy exploitation, from a Maritime Spatial Planning perspective. The available resource at selected sites is analysed together with site theoretical productivity for three state-of-the art WECs, showing interesting potential for future deployment.

Limitations of reanalysis data for wind power applications

AbstractWind energy resource estimates commonly depend on simulated wind speed profiles generated by reanalysis or weather models due to the lack of long time series measurements with sufficient coverage at relevant heights (roughly 90 m above ground). However, modeled data, including reanalyses, can be noisy and display a wide range of biases and errors, variously attributed to terrain effects, poor coverage of assimilated inputs, and model resolution. Wind generation records, if available at high temporal and geographical resolution, can provide a proxy for wind measurements and allow for evaluation of reanalyses and weather model wind time series. We use a 7‐year‐long data set of hourly, plant‐level generation records from over 100 wind plants across Texas to evaluate two commonly used reanalysis data sets (MERRA2 and ERA5). Additionally, we use 1‐year of records (2019) to evaluate an operational, high‐resolution regional weather modeling product (HRRR v3). We find that across the region, and across all modeling products, the modeled representation of wind generation (i.e., wind speeds at hub heights passed through a power curve) has relatively small mean errors when aggregated daily, but that accuracy and hourly correlation have a strong diurnal sensitivity. Accuracy and correlation systematically decline through the evening and markedly improve after sunrise. These diurnal patterns persist even in the highest resolution model tested (HRRR v3). We hypothesize the nighttime decline in accuracy is mostly due to poorly represented boundary layer conditions, perhaps related to model representation of stability, while other uncertainties (such as wake effects) play a secondary role.

SARAL-AltiKa Wind and Significant Wave Height for Offshore Wind Energy Applications in the New England Region

The SARAL-AltiKa dataset was evaluated for refined offshore wind energy resources assessment and potential metocean monitoring capability in the Southern New England region. Surface wind speed and Significant Wave Height (Hs) products were assessed with corresponding variables from buoy observations for 2014–2019. To increase the sample size, this study analyzed and applied an approach to collect data around the reference buoys beyond the satellite footprint at the expense of a bias increment. The study corroborated the accuracy of the SARAL-AltiKa measurements for the offshore area of interest and added details for stations closer to the coast compared with past studies. A proportional bias with underestimation of high values of Hs was found in coastal sites. Wind speed estimates on the other hand appear to be less sensitive to the closeness to the coast. The empirical relationship between wind strength and Hs in the buoy observations is reproduced to a large extent by the AltiKa measurements in locations where land contamination is minimal. The histograms of surface wind and Hs are well described by the Weibull distribution and the shape and scale parameters closely resemble those of the histograms of the collocated in situ observations. We use these results to extrapolate the winds to a target domain with no in situ observations for wind energy resource estimation.

Assessment of Landsat 8 TIRS data capability for the preliminary study of geothermal energy resources in West Sumatra

West Sumatra is one of has big geothermal energy resources potential. Remote sensing technology can have a role in geothermal exploration activity to measure the distribution of land surface temperatures (LST) and predict the geothermal potential area. Main study to obtain the assessment of Landsat 8 TIRS (Landsat`s Thermal Infrared Sensor) data capability for geothermal energy resources estimation. Mono-window algorithms were used to generate the LST maps. Data set was combined with a digital elevation model (DEM) to identify the potential geothermal energy based on the variation in surface temperature. The result that were derived from LST map of West Sumatra shows that ranged from -8.6 C 0 to 32.59 C 0 and the different temperatures are represented by a graduated pink to brown shading. A calculated result clearly identifies the hot areas in the dataset, which are brown in colour images. Lima Puluh Kota, Tanah Datar, Solok, and South Solok areas showed the high-temperature value (Brown) in the range of 28.1 C 0 to 32.59 C 0 color in images which means that they possess high potential for generating thermal energy. In contrast, the temperatures were lower (Pink) in the north-eastern areas and the range distribution was from-8.5 C 0 to 5 C 0 .

A high-resolution, long-term wave resource assessment of Japan with wave–current effects

The largest source of uncertainty in estimating annual energy production from a wave energy converter is natural variability. Standards recommend the collection of long-term on-site records, but with a concurrent requirement of dense data coverage, it becomes intractable to assess resources even at the feasibility development stage. Here, we provide uncertainty estimates of wave energy resources along the coast of Japan at 1 km resolution based on 21-year long numerical simulation, assuring a mean absolute percentage error of 4%. The uncertainties of the annual, seasonal and monthly climatologies are provided, and the epistemic uncertainty due to the effects of ocean current and finite depth on wave power estimation are quantified. Additionally, a significant wave height threshold is applied to exclude severe storm periods when devices are not in operation. The estimated total available wave power in Japan is 115.1 GW, whereas with a 3 m significant wave height threshold, it reduces to about 70 GW. Wave modeling under strong influence of ocean current is yet another source of epistemic uncertainty and the local and remote effects of strong current field are discussed. Results from the wave resource assessment are accessible to the public via a user-friendly web-based Geographical Information System, which was developed to help foster the use of marine renewable energy.

Обзор и классификация методов оценки и прогнозирования солнечной радиации

Various methods used to estimate and predict solar radiation are reviewed. Ground-based solar radiation intensity measurements are not carried out everywhere, despite the fact that the need to estimate the gross (theoretical) potential of solar energy in a region arises quite often. In such cases, approximate estimates of solar energy resources are carried out using various models, the main ones of which are considered in this paper. The availability of an adequately estimated local solar energy potential is important not only for rational distribution of funds in designing, but also for predicting the output of a power plant that uses solar energy. A general classification of methods for estimating and predicting solar radiation by the type of used parameters and estimated quantities (physical and statistical models) is presented. Statistical methods include conventional empirical models, in which the monthly average daily sums of solar radiation on a horizontal surface are estimated with dividing them into 4 categories depending on the input parameters involved, and models that evaluate the sums of the solar radiation diffuse component on horizontal (decomposition models and parametric models) and inclined (isotropic and anisotropic models) receiving areas. Models based on data mining methods are also referred to statistical methods for estimating and predicting solar radiation. The suggested statistical models are based on various parameters such as sunshine duration, maximum ambient temperature, relative humidity, pressure, latitude and longitude of the locality, the day of year ordinal number, cloudiness index, average ambient temperature, altitude, average wind velocity, precipitation, etc. The results of modeling the monthly average daily amounts of total solar radiation on a horizontal surface according to the universal empirical models are evaluated and compared with the long-term average data of ground-based measurements on the territory of Kyrgyzstan and with the data from the NASA POWER database.

Parameterizing the seasonal–diurnal wind climate of Rome: Fiumicino and Ciampino

AbstractThis study uses the Offset Elliptical Normal (OEN) mixture model to parameterize the seasonal–diurnal wind climate of Rome, Italy, for engineering design purposes, and also to reveal the physical meteorology of this “mixed” wind climate. The joint probability density function (PDF) of the wind vectors in the zonal–meridional plane are represented by a mixture of disjoint bivariate‐Normal ellipses. Each OEN ellipse is tentatively attributed to a physical wind mechanism: sea/land breezes; transient coastal jets; katabatic flows; the migrating atmospheric tide; and (non‐diurnal) geostrophic winds. The model enables a fuzzy probability of each wind mechanism to be assigned to each observation, allowing the progression of the various diurnal cycles to be tracked through time. This seasonal–diurnal parametric model is directly applicable to wind energy resource estimation, pollution studies, pedestrian comfort, and heating and ventilation design. The model is also an essential starting point in methods to simulate realistic time series of wind speed and direction.

The threat of energy diversification to a bioregion: a landscape-level analysis of current and future impacts on the US Chihuahuan Desert

Landscapes are being impacted by rapid change, especially by diversifying energy industries. As this process occurs, habitats with little development and fragmentation are now facing increasing anthropogenic change. We studied current patterns of land use and ecosystem services costs of energy development and predicted future impacts in the US Chihuahuan Desert. We measured land developed and modified by oil and gas, wind, and solar industries and mapped levels of development and fragmentation across the US Chihuahuan Desert, followed by an estimation of annual energy-related ecosystem services costs. Based on energy resource estimates, we then projected future risk of development in the bioregion. The oil and gas industry has developed and fragmented about 27% (58,000 km2) of the US Chihuahuan Desert. Wind and solar comprise small amounts of development and fragmentation. We estimated annual ecosystem services costs of all energy industries at 180 million USD, concentrated in climate regulation, raw materials, and cultural services. Two-thirds of the desert remains relatively unfragmented in a contiguous corridor along the western portion of the bioregion. However, this corridor is threatened by energy expansion, especially wind and solar energy. With this expansion, the bioregion could become highly fragmented and continuity of habitats compromised. We suggest conservation efforts focus on the remaining corridor, while future energy development should occur in areas already highly modified by energy infrastructure. With continuing expansion and diversification of global energy sources, our findings in the Chihuahuan Desert could represent threats to other ecologically valuable bioregions that have historically experienced little industrial development.

Hydrokinetic energy resource estimates of River ERO at Lafiagi, Kwara State, North-Central Nigeria

Exploiting the kinetic energy of water currents is considered a smart and cost-effective renewable energy solution without requiring the construction of a dam or of large civil works. Field studies were performed at a selected site along the Ero River, one of the tributaries of River Niger, North-central Nigeria to assess the hydrokinetic energy conversion potentials of the river reach. Relevant data and tools dealing with the hydrology, topography, bathymetry and digital elevation models were used together with year round on-site measurements for the determination of the hydraulic and hydrological parameters. These were used to obtain the instantaneous stream velocities and to compute the power estimates. Preliminary results show that monthly averaged hydrokinetic power of the range 0.034kW-2.3MW can be obtained with the highest values being generated between July and November, with stream velocities above 0.7ms -1 . The highest energy value obtainable was 14.76MW while the lowest was 0.11715W. The values also compare well with estimates gleaned from a previous study. Keywords: Hydrokinetic Power, Energy Resource, River Ero, Water Resources

Optimal day-ahead operational planning of microgrids

Providing a cost-efficient, eco-friendly and sustainable energy is one of the main issues in modern societies. In response to this demand, new features of microgrid technology have provided huge potentials while distributing electricity more effectively, economically and securely. Accordingly, this paper presents a new multi-objective generation management model for optimal day-ahead operational planning of medium voltage microgrids. The proposed model optimizes both pollutant emission and operating cost of a microgrid by using multi-objective optimization. Besides, a seamlessness-selective algorithm is integrated into the model, which can be adopted to achieve the desired self-sufficiency level for microgrids along a specified planning horizon. Furthermore, the model is characterized by a reserve-assessment strategy developed to handle the load and renewable energy resources estimation errors. The introduced model is solved using a combination of numerical and evolutionary methods of species-based quantum particle swarm optimization to find the optimal scheduling scheme and minos-based optimal power flow to optimize the operating cost and emission. In addition, the suggested solution approach also incorporates an efficient mechanism for considering energy storage systems and coding the candidate solutions in the evolutionary algorithm. The proposed model is implemented on a test microgrid and is investigated through simulations to study the different aspects of the problem. The results show significant improvements and benefits which are obtained by applying the proposed intelligent management.

Implications of applying solar industry best practice resource estimation on project financing

Solar resource estimation risk is one of the main solar PV project risks that influences lender’s decision in providing financing and in determining the cost of capital. More recently, a number of measures have emerged to mitigate this risk. The study focuses on solar industry’s best practice energy resource estimation and assesses its financing implications to the 27MWp solar PV project study in Brunei Darussalam. The best practice in resource estimation uses multiple data sources through the measure-correlate-predict (MCP) technique as compared with the standard practice that rely solely on modelled data source. The best practice case generates resource data with lower uncertainty and yields superior high-confidence energy production estimate than the standard practice case. Using project financial parameters in Brunei Darussalam for project financing and adopting the international debt-service coverage ratio (DSCR) benchmark rates, the best practice case yields DSCRs that surpass the target rates while those of standard practice case stay below the reference rates. The best practice case could also accommodate higher debt share and have lower levelized cost of electricity (LCOE) while the standard practice case would require a lower debt share but having a higher LCOE.

Mapping Offshore Winds Around Iceland Using Satellite Synthetic Aperture Radar and Mesoscale Model Simulations

The offshore wind climate in Iceland is examined based on satellite synthetic aperture radar (SAR), coastal meteorological station measurements, and results from two atmospheric model data sets, HARMONIE and NORA10. The offshore winds in Iceland are highly influenced by the rugged coastline. Lee effects, gap flow, coastal barrier jets, and atmospheric gravity waves are not only observed in SAR, but are also modeled well from HARMONIE. Offshore meteorological observations are not available, but wind speed and wind direction measurements from coastal meteorological masts are found to compare well to nearby offshore locations observed by SAR. More than 2500 SAR scenes from the Envisat ASAR wide swath mode are used for wind energy resource estimation. The wind energy potential observed from satellite SAR shows high values above ${1000}\;{Wm}^{ - {2}}$ in coastal regions in the south, east, and west, with lower values in the north. The most promising region for wind energy production is the southwestern coastal region.

THE EFFECTIVENESS OF THE SOLAR ENERGY USE FOR POWER SUPPLY IN THE CLIMATIC CONDITIONS OF KYRGYZSTAN

The paper discusses the possibility of solar energy using in the Republic of Kyrgyzstan (RK) for the purpose of electricity distribution. The data sources analysis for the calculation of solar resources was conducted. Due to the scarcity of solar radiation stations on the territory of the RK, international thematic databases containing sufficient information for resource assessment and design were considered. These databases contain the results of long-term ground-based measurements, mathematical modeling and satellite observations. The paper justifies the compliance of a global database with public access NASA SSE to the problem of solar energy resources estimation, and estimates the gross capacity for the territory of the Kyrgyzstan. The authors propose their method of initial evaluation of performance, capital cost and footprint for network photovoltaic plant. The calculations are performed based on monthly averages of daily insolation and the average temperature. To account for the effect of ambient temperature on the output of PV modules, the paper considers two scenarios: the maximum and minimum daily mean ambient temperatures in each month. Evaluate the performance and the cost of electricity are made for plants with a capacity of 5 MW in the case of placing it near the cities of Bishkek and Osh (northern and central part of the RK). At the same time the authors carry out a comparative analysis of the results obtained for several types of silicon photovoltaic modules on the market today: monocrystalline, multicrystalline and the thin film. It is shown that the maximum performance of the network station is provided at an inclination of modules close to the latitude. The temperature dependence of the module characteristics makes the largest contribution to the change in productivity in the summer. The calculations also show that the multicrystalline photovoltaic modules are optimal in terms of price and quality for selected conditions.

Meta-analysis of non-renewable energy resource estimates

This paper offers a review of estimates of ultimately recoverable resources (URR) of non-renewable energy sources: coal, conventional and unconventional oil, conventional and unconventional gas, and uranium for nuclear fission. There is a large range in the estimates of many of the energy sources, even those that have been utilized for a long time and, as such, should be well understood. If it is assumed that the estimates for each resource are normally distributed, then the total value of ultimately recoverable fossil and fissile energy resources is 70,592EJ. If, on the other hand, the best fitting distribution from each of the resource estimate populations is used, a the total value is 50,702EJ, a factor of around 30% smaller.

The effect of missing data on wind resource estimation

Investment in renewable energy sources requires reliable data. However, meteorological datasets are often plagued by missing data, which can bias energy resource estimates if the missingness is systematic. We address this issue by considering the influence of missing data due to icing of equipment during the winter on the wind resource estimation for a potential wind turbine site in Norway. Using a mean-reverting jump-diffusion (MRJD) process to model electricity prices, we also account for the impact on the expected revenue from a wind turbine constructed at the site. While missing data due to icing significantly bias the wind resource estimate downwards, their impact on revenue estimates is dampened because of volatile electricity spot prices. By contrast, with low-volatility electricity prices, the effect of missing data on revenue estimates is highly significant. The seasonality-based method we develop removes most of the bias in wind resource and revenue estimation caused by missing data.

Analysis of vertical wind shear in the Southern Great Plains and potential impacts on estimation of wind energy production

This paper describes the analysis of near-surface wind speeds in Western Oklahoma. The goals of this research are to characterise the nocturnal low-level jet (LLJ) in the region, and to assess the impact wind variability, vertical shear and the LLJ on wind energy calculations. Results show the seasonal variability in wind characteristics as well as in the vertical wind shear, and also illustrate the errors associated with traditional estimates of wind power density in locations with a decoupled wind field. A comparison of projected energy output from three standard commercial turbines shows that traditional density estimations significantly underestimated power yield. The mean wind power density estimated via traditional means is significantly less than a more complete determination incorporating the LLJ. This difference illustrates the need to acquire accurate information regarding 50-150 m wind variability to produce improved estimates of wind energy resources.

Evaluating the impact of wind induced roughness change and tidal range on extrapolation of offshore vertical wind speed profiles

AbstractWind energy resource estimation frequently requires extrapolation of wind speeds from typical measurement heights to turbine hub‐heights. However, this extrapolation is uncertain, and this uncertainty is exacerbated in the offshore environment by the effect of the dynamic surface (i.e. surface roughness and height respond to wind speed or vary over time). This paper examines the impact of roughness variations and small tidal ranges on mean predicted wind speeds in near‐neutral conditions. Roughness variations offshore are in the range 0.002 and 0.00002 m. This range of roughnesses gives a difference in predicted wind speed extrapolated from 10 to 50 m of less than 8%. For a more typical range of 0.0005 tp 0.00005 m, the difference will be smaller (∼3%). With a tidal range of 4 m the difference in mean wind speed extrapolated from 10 to 50 m height is about 1%. Copyright © 2001 John Wiley & Sons, Ltd.

On the Use of Wind-Wave Spectral Formulas to Estimate Wave Energy

It is common practice by wave energy conversion technologists to estimate long-term wave energy potentials at both offshore and coastal sites by using established wind-wave spectral formulas. It is shown that the use of these formulas can lead to both incorrect wave energy resource estimates and improperly designed conversion systems. The formulas are shown to poorly predict modal and peak spectral periods for long-term seas.

Gas hydrate and free gas volumes in marine sediments: Example from the Niger Delta front

Bottom simulating reflectors (BSRs) detected on reflection seismic records from various deep-water locations world-wide, are known to occur as a response to the formation of gas hydrates above the reflector, and accumulation of small amounts of free gas below the reflector. Although estimates of enormous potential energy resources, and dramatic climate change scenarios have been discussed on account of the BSRs, our conclusions with respect to the commercially recoverable amounts of energy from gas hydrates and free gas associated with BSRs on the Niger Delta front, are disappointingly negative. We base our estimates on recent results from BSR-penetrating scientific drilling, a review of natural gas hydrate observations, and on theoretical considerations of gas hydrate formation and host sediment conditions. The main conclusions are as follows: 1. 1. Natural gas hydrates and BSRs most probably occur as a direct consequence of focused and diffusive vertical fluid escape. 2. 2. The mean maximum amount of gas hydrates residing in sediments above the BSR is most probably only 3% by volume. 3. 3. The mean maximum amount of free gas charge in the sediments below even well developed (strong) BSRs is only 5% by volume. 4. 4. Currently there is no commercial potential for recovering and exploiting ‘trapped’ energy sources in the form of BSR-associated gas hydrates and free gas on the Niger Delta front, mainly because they occur as dispersed, rather than concentrated deposits.