Special Issue "Renewable Energy Resource Assessment and Forecasting"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Sustainable Energy".

Deadline for manuscript submissions: closed (31 May 2020).

Special Issue Editor

Prof. Dr. George Galanis
Website
Guest Editor
Mathematical Modeling and Applications Laboratory, Hellenic Naval Academy, Piraeus, Greece
Interests: mathematical modeling; information geometry; wave modeling and marine meteorology; renewable energy prediction and applications

Special Issue Information

Dear Colleagues,

The exploitation of renewable energy resources is nowadays a key issue worldwide and is on the top of the priorities list for governmental and private-sector organizations under increasing energy needs and warnings from the scientific community concerning environmental threads like global warming and climate change.

In recent decades, several projects have been launched that concern the development and use of new methodologies in order to assess, monitor, and support clean forms of energy. In this framework, the accurate estimation of the available energy potential is of primary importance but is not always easy to achieve due to the variable form and the complexity of the environmental conditions that are directly or indirectly involved.

The forthcoming Special Issue of Energies on Renewable Energy Resource Assessment and Forecasting aims to provide a holistic approach to the above issues by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs.

In particular, research papers, reviews, and case studies on the following subjects are invited:

- Wind, wave, and solar energy resource assessment, monitoring, and forecasting

- Resource assessment of combined renewable energy forms

- Remote sensing for renewable energy assessment

- Numerical models for renewable energy forecasting

- Resource analysis tools and statistical models

- Renewable energy transformation and integration technologies

- Risk analysis and quantification of the uncertainty of renewables

- Extreme value analysis and forecasting for renewable energy resources

Prof. Dr. George Galanis
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wind, wave, solar, and combined energy resource assessment
  • numerical modeling
  • statistical modeling
  • remote-sensing tools
  • extreme values analysis
  • risk analysis and uncertainty quantification

Published Papers (13 papers)

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Research

Open AccessArticle
Forecasting the Structure of Energy Production from Renewable Energy Sources and Biofuels in Poland
Energies 2020, 13(10), 2539; https://doi.org/10.3390/en13102539 - 17 May 2020
Abstract
The world’s economic development depends on access to cheap energy sources. So far, energy has been obtained mainly from conventional sources like coal, gas and oil. Negative climate changes related to the high emissions of the economy based on the combustion of hydrocarbons [...] Read more.
The world’s economic development depends on access to cheap energy sources. So far, energy has been obtained mainly from conventional sources like coal, gas and oil. Negative climate changes related to the high emissions of the economy based on the combustion of hydrocarbons and the growing public awareness have made it necessary to look for new ecological energy sources. This condition can be met by renewable energy sources. Both social pressure and international activities force changes in the structure of sources from which energy is produced. This also applies to the European Union countries, including Poland. There are no scientific studies in the area of forecasting energy production from renewable energy sources for Poland. Therefore, it is reasonable to investigate this subject since such a forecast can have a significant impact on investment decisions in the energy sector. At the same time, it must be as reliable as possible. That is why a modern method was used for this purpose, which undoubtedly involves artificial neural networks. The following article presents the results of the analysis of energy production from renewable energy sources in Poland and the forecasts for this production until 2025. Artificial neural networks were used to make the forecast. The analysis covered eight main sources from which this energy is produced in Poland. Based on the production volume since 1990, predicted volumes of renewable energy sources until 2025 were determined. These forecasts were prepared for all studied renewable energy sources. Renewable energy production plans and their share in total energy consumption in Poland were also examined and included in climate plans. The research was carried out using artificial neural networks. The results should be an important source of information on the effects of implementing climate policies in Poland. They should also be utilized to develop action plans to achieve the objectives of the European Green Deal strategy. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment and Forecasting)
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Open AccessArticle
Energy Calculator for Solar Processing of Biomass with Application to Uganda
Energies 2020, 13(6), 1485; https://doi.org/10.3390/en13061485 - 21 Mar 2020
Abstract
Rural areas of developing countries often have poor energy infrastructure and so rely on a very local supply. A local energy supply in rural Uganda frequently has problems such as limited accessibility, unreliability, a high expense, harmful to health and deforestation. By carbonizing [...] Read more.
Rural areas of developing countries often have poor energy infrastructure and so rely on a very local supply. A local energy supply in rural Uganda frequently has problems such as limited accessibility, unreliability, a high expense, harmful to health and deforestation. By carbonizing waste biomass streams, available to those in rural areas of developing countries through a solar resource, it would be possible to create stable, reliable fuels with more consistent calorific values. An energy demand calculator is reported to assess the different energy demands of various thermochemical processes that can be used to create biofuel. The energy demand calculator then relates the energy required to the area of solar collector required for an integrated system. Pyrolysis was shown to require the least amount of energy to process 1 kg of biomass when compared to steam treatment and hydrothermal carbonization (HTC). This was due to the large amount of water required for steam treatment and HTC. A resource assessment of Uganda is reported, to which the energy demand calculator has been applied. Quantitative data are presented for agricultural residues, forestry residues, animal manure and aquatic weeds found within Uganda. In application to rural areas of Uganda, a linear Fresnel HTC integration shows to be the most practical fit. Integration with a low temperature steam treatment would require more solar input for less carbonization due to the energy required to vaporize liquid water. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment and Forecasting)
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Open AccessArticle
An Integrated Energy Simulation Model for Buildings
Energies 2020, 13(5), 1170; https://doi.org/10.3390/en13051170 - 04 Mar 2020
Abstract
The operation of buildings is linked to approximately 36% of the global energy consumption, 40% of greenhouse gas emissions, and climate change. Assessing the energy consumption and efficiency of buildings is a complex task addressed by a variety of methods. Building energy modeling [...] Read more.
The operation of buildings is linked to approximately 36% of the global energy consumption, 40% of greenhouse gas emissions, and climate change. Assessing the energy consumption and efficiency of buildings is a complex task addressed by a variety of methods. Building energy modeling is among the dominant methodologies in evaluating the energy efficiency of buildings commonly applied for evaluating design and renovation energy efficiency measures. Although building energy modeling is a valuable tool, it is rarely the case that simulation results are assessed against the building’s actual energy performance. In this context, the simulation results of the HVAC energy consumption in the case of a smart industrial near-zero energy building are used to explore areas of uncertainty and deviation of the building energy model against measured data. Initial model results are improved based on a trial and error approach to minimize deviation based on key identified parameters. In addition, a novel approach based on functional shape modeling and Kalman filtering is developed and applied to further minimize systematic discrepancies. Results indicate a significant initial performance gap between the initial model and the actual energy consumption. The efficiency and the effectiveness of the developed integrated model is highlighted. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment and Forecasting)
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Open AccessArticle
Granger Causality Network Methods for Analyzing Cross-Border Electricity Trading between Greece, Italy, and Bulgaria
Energies 2020, 13(4), 900; https://doi.org/10.3390/en13040900 - 18 Feb 2020
Abstract
Italy, Greece, and, to a lesser degree, Bulgaria have experienced fast growth in their renewable generation capacity (RESc) over the last several years. The consequences of this fact include a decrease in spot wholesale prices in electricity markets and a significant effect on [...] Read more.
Italy, Greece, and, to a lesser degree, Bulgaria have experienced fast growth in their renewable generation capacity (RESc) over the last several years. The consequences of this fact include a decrease in spot wholesale prices in electricity markets and a significant effect on cross border trading (CBT) among neighboring interconnected countries. In this work, we empirically analyzed historical data on fundamental market variables (i.e., spot prices, load, RES generation) as well as CBT data (imports, exports, commercial schedules, net transfer capacities, etc.) on the Greek, Italian, and Bulgarian electricity markets by applying the Granger causality connectivity analysis (GCCA) approach. The aim of this analysis was to detect all possible interactions among the abovementioned variables, focusing in particular on the effects of growing shares of RES generation on the commercial electricity trading among the abovementioned countries for the period 2015–2018. The key findings of this paper are summarized as the following: The RES generation in Italy, for the period examined, drives the spot prices in Greece via commercial schedules. In addition, on average, spot price fluctuations do not affect the commercial schedules of energy trading between Greece and Bulgaria. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment and Forecasting)
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Open AccessArticle
Energy Saving Potential of Industrial Solar Collectors in Southern Regions of Russia: The Case of Krasnodar Region
Energies 2020, 13(4), 885; https://doi.org/10.3390/en13040885 - 17 Feb 2020
Abstract
Industrial low-temperature processes are a promising sector for the introduction of solar collectors, which can partially, and in some cases, completely, replace traditional heat supply technologies. In Krasnodar Region (Russia), it is shown that the energy-saving potential when introducing industrial solar collectors only [...] Read more.
Industrial low-temperature processes are a promising sector for the introduction of solar collectors, which can partially, and in some cases, completely, replace traditional heat supply technologies. In Krasnodar Region (Russia), it is shown that the energy-saving potential when introducing industrial solar collectors only at food industry enterprises can make up 16%–17% of the total amount of thermal energy produced in the region annually. The global market of industrial solar collectors is currently developing almost without any government incentives, only due to market mechanisms, which indicates the commercial attractiveness of the technology. According to the predicted estimates, levelized cost of energy produced by industrial solar collectors in the southern regions of Russia may amount to 3.8–6.6 rubles per kWh. Even though the forecast estimates are higher than current tariffs, the economic feasibility of using solar collectors in the industry increases significantly if it is not possible to connect to centralized heating networks, as well as in the case of the seasonal load of industrial facilities. As a measure of state incentives for the development of industrial solar collectors in Russia, we offer state co-financing of demonstration projects of Russian manufacturers. This will increase the level of awareness of the population and businesses about the capabilities of this technology. Also, it will increase the technical competencies and innovative potential of companies involved in the production and installation of solar collectors. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment and Forecasting)
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Open AccessArticle
Assessment and Day-Ahead Forecasting of Hourly Solar Radiation in Medellín, Colombia
Energies 2019, 12(22), 4402; https://doi.org/10.3390/en12224402 - 19 Nov 2019
Abstract
The description and forecasting of hourly solar resource is fundamental for the operation of solar energy systems in the electric grid. In this work, we provide insights regarding the hourly variation of the global horizontal irradiance in Medellín, Colombia, a large urban area [...] Read more.
The description and forecasting of hourly solar resource is fundamental for the operation of solar energy systems in the electric grid. In this work, we provide insights regarding the hourly variation of the global horizontal irradiance in Medellín, Colombia, a large urban area within the tropical Andes. We propose a model based on Markov chains for forecasting the hourly solar irradiance for one day ahead. The Markov model was compared against estimates produced by different configurations of the weather research forecasting model (WRF). Our assessment showed that for the period considered, the average availability of the solar resource was of 5 PSH (peak sun hours), corresponding to an average daily radiation of ~5 kWh/m2. This shows that Medellín, Colombia, has a substantial availability of the solar resource that can be a complementary source of energy during the dry season periods. In the case of the Markov model, the estimates exhibited typical root mean squared errors between ~80 W/m2 and ~170 W/m2 (~50%–~110%) under overcast conditions, and ~57 W/m2 to ~171 W/m2 (~16%–~38%) for clear sky conditions. In general, the proposed model had a performance comparable with the WRF model, while presenting a computationally inexpensive alternative to forecast hourly solar radiation one day in advance. The Markov model is presented as an alternative to estimate time series that can be used in energy markets by agents and power-system operators to deal with the uncertainty of solar power plants. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment and Forecasting)
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Open AccessArticle
Incorporation of a Non-Constant Thrust Force Coefficient to Assess Tidal-Stream Energy
Energies 2019, 12(21), 4151; https://doi.org/10.3390/en12214151 - 31 Oct 2019
Abstract
A novel method for modelling tidal-stream energy capture at the regional scale is used to evaluate the performance of two marine turbine arrays configured as a fence and a partial fence. These configurations were used to study bounded and unbounded flow scenarios, respectively. [...] Read more.
A novel method for modelling tidal-stream energy capture at the regional scale is used to evaluate the performance of two marine turbine arrays configured as a fence and a partial fence. These configurations were used to study bounded and unbounded flow scenarios, respectively. The method implemented uses turbine operating conditions (TOC) and the parametrisation of changes produced by power extraction within the turbine near-field to compute a non-constant thrust coefficient, and it is referred to as a momentum sink TOC. Additionally, the effects of using a shock-capture capability to evaluate the resource are studied by comparing the performance of a gradually varying flow (GVF) and a rapidly varying flow (RVF) solver. Tidal-stream energy assessment of bounded flow scenarios through a full fence configuration is better performed using a GVF solver, because the head drop is more accurately simulated; however, the solver underestimates velocity reductions due to power extraction. On the other hand, assessment of unbounded flow scenarios through a partial fence was better performed by the RVF solver. This scheme approximated the head drop and velocity reduction more accurately, thus suggesting that resource assessment with realistic turbine configurations requires the correct solution of the discontinuities produced in the tidal-stream by power extraction. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment and Forecasting)
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Open AccessArticle
Biofuel Application as a Factor of Sustainable Development Ensuring: The Case of Russia
Energies 2019, 12(20), 3948; https://doi.org/10.3390/en12203948 - 17 Oct 2019
Abstract
Diffusion of the biofuels (BF) using is justified by opening up the opportunities for obtaining fuel and energy from previously inaccessible sources and by the existence of energy-deficient regions, in particular in Russia. Works of different scientists on the problems of creating and [...] Read more.
Diffusion of the biofuels (BF) using is justified by opening up the opportunities for obtaining fuel and energy from previously inaccessible sources and by the existence of energy-deficient regions, in particular in Russia. Works of different scientists on the problems of creating and using BF were the methodological basis of this study. Information on the state and prospects of the development of renewable energy sources in Russian regions was collected from regulatory documents and was obtained by employing a questionnaire survey. For the study of the collected materials, the different methods of comparative analysis, and the methods of expert assessments were used. The results of the Status-Quo analysis of BF production in Russia have shown that the creation of BF performed relatively successfully. However, there are many more perspectives, connected with expanding the utilization of the different raw materials. Also, the analysis of organizational and economic mechanisms applied for production of BF and the obtained data on several organizations-producers allowed for proposing six indexes for the assessment of the BF production effectiveness. It is suggested that BF production in Russia will contribute to the sustainable development of a number of the country’s regions in the near future. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment and Forecasting)
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Open AccessArticle
Evaluation of Photovoltaic Power Generation by Using Deep Learning in Solar Panels Installed in Buildings
Energies 2019, 12(18), 3564; https://doi.org/10.3390/en12183564 - 17 Sep 2019
Cited by 1
Abstract
Southern Taiwan has excellent solar energy resources that remain largely unused. This study incorporated a measure that aids in providing simple and effective power generation efficiency assessments of solar panel brands in the planning stage of installing these panels on roofs. The proposed [...] Read more.
Southern Taiwan has excellent solar energy resources that remain largely unused. This study incorporated a measure that aids in providing simple and effective power generation efficiency assessments of solar panel brands in the planning stage of installing these panels on roofs. The proposed methodology can be applied to evaluate photovoltaic (PV) power generation panels installed on building rooftops in Southern Taiwan. In the first phase, this study selected panels of the BP3 series, including BP350, BP365, BP380, and BP3125, to assess their PV output efficiency. BP Solar is a manufacturer and installer of photovoltaic solar cells. This study first derived ideal PV power generation and then determined the suitable tilt angle for the PV panels leading to direct sunlight that could be acquired to increase power output by panels installed on building rooftops. The potential annual power outputs for these solar panels were calculated. Climate data of 2016 were used to estimate the annual solar power output of the BP3 series per unit area. The results indicated that BP380 was the most efficient model for power generation (183.5 KWh/m2-y), followed by BP3125 (182.2 KWh/m2-y); by contrast, BP350 was the least efficient (164.2 KWh/m2-y). In the second phase, to simulate meteorological uncertainty during hourly PV power generation, a surface solar radiation prediction model was developed. This study used a deep learning–based deep neural network (DNN) for predicting hourly irradiation. The simulation results of the DNN were compared with those of a backpropagation neural network (BPN) and a linear regression (LR) model. In the final phase, the panel of module BP3125 was used as an example and demonstrated the hourly PV power output prediction at different lead times on a solar panel. The results demonstrated that the proposed method is useful for evaluating the power generation efficiency of the solar panels. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment and Forecasting)
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Open AccessArticle
Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter
Energies 2019, 12(16), 3050; https://doi.org/10.3390/en12163050 - 08 Aug 2019
Cited by 1
Abstract
Regional microscale meteorological models have become a critical tool for wind farm production forecasting due to their capacity for resolving local flow dynamics. The high demand for reliable forecasting tools in the energy industry is the motivation for the development of an integrated [...] Read more.
Regional microscale meteorological models have become a critical tool for wind farm production forecasting due to their capacity for resolving local flow dynamics. The high demand for reliable forecasting tools in the energy industry is the motivation for the development of an integrated system that combines the Weather Research and Forecasting (WRF) atmospheric model with an optimization obtained by the conjunction of a Kalman filter and a Bayesian model. This study focuses on the development and validation of this combined system in a very dense wind farm cluster located in Galicia (Northwest of Spain). A period of one year is simulated at 333 m horizontal resolution, with a daily operational forecasting set-up. The Kalman-Bayesian filter was tested both directly on wind speed and on the U-V (zonal and meridional) components for nowcasting periods from 10 min to 6 h periods, all of them with important applications in the wind industry. The results are quite promising, as the main statistical error indices are significantly improved in a 6 h forecasting horizon and even more in shorter horizon cases. The Mean Annual Error (MAE) for 1 h nowcasting horizon is 1.03 m/s for wind speed and 12.16 ° for wind direction. Moreover, the successful utilization of the integrated system in test cases with different characteristics demonstrates the potential utility that this tool may have for a variety of applications in wind farm operations and energy markets. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment and Forecasting)
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Open AccessArticle
Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy
Energies 2019, 12(14), 2730; https://doi.org/10.3390/en12142730 - 17 Jul 2019
Cited by 6
Abstract
The estimation of solar radiation for planning current and future periods in different fields, such as renewable energy generation, is very important for decision makers. The current study presents a hybrid model structure based on a multi-objective shark algorithm and fuzzy method for [...] Read more.
The estimation of solar radiation for planning current and future periods in different fields, such as renewable energy generation, is very important for decision makers. The current study presents a hybrid model structure based on a multi-objective shark algorithm and fuzzy method for forecasting and generating a zone map for solar radiation as an alternative solution for future renewable energy production. The multi-objective shark algorithm attempts to select the best input combination for solar radiation (SR) estimation and the optimal value of the adaptive neuro-fuzzy inference system (ANFIS) parameter, and the power parameter of the inverse distance weight (IDW) is computed. Three provinces in Iran with different climates and air quality index conditions have been considered as case studies for this research. In addition, comparative analysis has been carried out with other models, including multi-objective genetic algorithm-ANFIS and multi-objective particle swarm optimization-ANFIS. The Taguchi model is used to obtain the best value of random parameters for multi-objective algorithms. The comparison of the results shows that the relative deviation index (RDI) of the distributed solutions in the Pareto front based multi-objective shark algorithm has the lowest value in the spread index, spacing metric index, favorable distribution, and good diversity. The generated Pareto solutions based on the multi-objective shark algorithm are compared to those based on the genetic algorithm and particle swarm algorithm and found to be the optimal and near ideal solutions. In addition, the determination of the best solution based on a multi-criteria decision model enables the best input to the model to be selected based on different effective parameters. Three different performance indices have been used in this study, including the root mean square error, Nash–Sutcliffe efficiency, and mean absolute error. The generated zone map based on the multi-objective shark algorithm-ANFIS highly matches with the observed data in all zones in all case studies. Additionally, the analysis shows that the air quality index (AQI) should be considered as effective input for SR estimation. Finally, the measurement and analysis of the uncertainty based on the multi-objective shark algorithm-ANFIS were carried out. As a result, the proposed new hybrid model is highly suitable for the generation of accurate zone mapping for different renewable energy generation fields. In addition, the proposed hybrid model showed outstanding performance for the development of a forecasting model for the solar radiation value, which is essential for the decision-makers to draw a future plan for generating renewable energy based solar radiation. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment and Forecasting)
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Open AccessArticle
Solar Irradiance Forecasts by Mesoscale Numerical Weather Prediction Models with Different Horizontal Resolutions
Energies 2019, 12(7), 1374; https://doi.org/10.3390/en12071374 - 09 Apr 2019
Abstract
This study examines the performance of radiation processes (shortwave and longwave radiations) using numerical weather prediction models (NWPs). NWP were calculated using four different horizontal resolutions (5, 2 and 1 km, and 500 m). Validation results on solar irradiance simulations with a horizontal [...] Read more.
This study examines the performance of radiation processes (shortwave and longwave radiations) using numerical weather prediction models (NWPs). NWP were calculated using four different horizontal resolutions (5, 2 and 1 km, and 500 m). Validation results on solar irradiance simulations with a horizontal resolution of 500 m indicated positive biases for direct normal irradiance dominate for the period from 09 JST (Japan Standard Time) to 15 JST. On the other hand, after 15 JST, negative biases were found. For diffused irradiance, weak negative biases were found. Validation results on upward longwave radiation found systematic negative biases of surface temperature (corresponding to approximately −2 K for summer and approximately −1 K for winter). Downward longwave radiation tended to be weak negative biases during both summer and winter. Frequency of solar irradiance suggested that the frequency of rapid variations of solar irradiance (ramp rates) from the NWP were less than those observed. Generally, GHI distributions between the four different horizontal resolutions resembled each other, although horizontal resolutions also became finer. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment and Forecasting)
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Open AccessArticle
Short-Term Forecasts of DNI from an Integrated Forecasting System (ECMWF) for Optimized Operational Strategies of a Central Receiver System
Energies 2019, 12(7), 1368; https://doi.org/10.3390/en12071368 - 09 Apr 2019
Cited by 3
Abstract
Short-term forecasts of direct normal irradiance (DNI) from the Integrated Forecasting System (IFS) and the global numerical weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF) were used in the simulation of a solar power tower, through the System Advisor [...] Read more.
Short-term forecasts of direct normal irradiance (DNI) from the Integrated Forecasting System (IFS) and the global numerical weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF) were used in the simulation of a solar power tower, through the System Advisor Model (SAM). Recent results demonstrated that DNI forecasts have been enhanced, having the potential to be a suitable tool for plant operators that allows achieving higher energy efficiency in the management of concentrating solar power (CSP) plants, particularly during periods of direct solar radiation intermittency. The main objective of this work was to assert the predictive value of the IFS forecasts, regarding operation outputs from a simulated central receiver system. Considering a 365-day period, the present results showed an hourly correlation of ≈0.78 between the electric energy injected into the grid based on forecasted and measured data, while a higher correlation was found for the daily values (≈0.89). Operational strategies based on the forecasted results were proposed for plant operators regarding the three different weather scenarios. Although there were still deviations due to the cloud and aerosol representation, the IFS forecasts showed a high potential to be used for supporting informed energy dispatch decisions in the operation of central receiver units. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment and Forecasting)
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