The Experts below are selected from a list of 227322 Experts worldwide ranked by ideXlab platform
Guido Cervone - One of the best experts on this subject based on the ideXlab platform.
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an analog ensemble for short term probabilistic Solar Power forecast
Applied Energy, 2015Co-Authors: Stefano Alessandrini, Delle L Monache, Simone Sperati, Guido CervoneAbstract:Abstract The energy produced by photovoltaic farms has a variable nature depending on astronomical and meteorological factors. The former are the Solar elevation and the Solar azimuth, which are easily predictable without any uncertainty. The amount of liquid water met by the Solar radiation within the troposphere is the main meteorological factor influencing the Solar Power production, as a fraction of short wave Solar radiation is reflected by the water particles and cannot reach the earth surface. The total cloud cover is a meteorological variable often used to indicate the presence of liquid water in the troposphere and has a limited predictability, which is also reflected on the global horizontal irradiance and, as a consequence, on Solar photovoltaic Power prediction. This lack of predictability makes the Solar energy integration into the grid challenging. A cost-effective utilization of Solar energy over a grid strongly depends on the accuracy and reliability of the Power forecasts available to the Transmission System Operators (TSOs). Furthermore, several countries have in place legislation requiring Solar Power producers to pay penalties proportional to the errors of day-ahead energy forecasts, which makes the accuracy of such predictions a determining factor for producers to reduce their economic losses. Probabilistic predictions can provide accurate deterministic forecasts along with a quantification of their uncertainty, as well as a reliable estimate of the probability to overcome a certain production threshold. In this paper we propose the application of an analog ensemble (AnEn) method to generate probabilistic Solar Power forecasts (SPF). The AnEn is based on an historical set of deterministic numerical weather prediction (NWP) model forecasts and observations of the Solar Power. For each forecast lead time and location, the ensemble prediction of Solar Power is constituted by a set of past production data. These measurements are those concurrent to past deterministic NWP forecasts for the same lead time and location, chosen based on their similarity to the current forecast and, in the current application, are represented by the one-hour average produced Solar Power. The AnEn performance for SPF is compared to a quantile regression (QR) technique and a persistence ensemble (PeEn) over three Solar farms in Italy spanning different climatic conditions. The QR is a state-of-the-science method for probabilistic predictions that, similarly to AnEn, is based on a historical data set. The PeEn is a persistence model for probabilistic predictions, where the most recent 20 Power measurements available at the same lead-time are used to form an ensemble. The performance assessment has been carried out evaluating important attributes of a probabilistic system such as statistical consistency, reliability, resolution and skill. The AnEn performs as well as QR for common events, by providing predictions with similar reliability, resolution and sharpness, while it exhibits more skill for rare events and during hours with a low Solar elevation.
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an analog ensemble for short term probabilistic Solar Power forecast
Applied Energy, 2015Co-Authors: Stefano Alessandrini, Delle L Monache, Simone Sperati, Guido CervoneAbstract:The energy produced by photovoltaic farms has a variable nature depending on astronomical and meteorological factors. The former are the Solar elevation and the Solar azimuth, which are easily predictable without any uncertainty. The amount of liquid water met by the Solar radiation within the troposphere is the main meteorological factor influencing the Solar Power production, as a fraction of short wave Solar radiation is reflected by the water particles and cannot reach the earth surface. The total cloud cover is a meteorological variable often used to indicate the presence of liquid water in the troposphere and has a limited predictability, which is also reflected on the global horizontal irradiance and, as a consequence, on Solar photovoltaic Power prediction. This lack of predictability makes the Solar energy integration into the grid challenging. A cost-effective utilization of Solar energy over a grid strongly depends on the accuracy and reliability of the Power forecasts available to the Transmission System Operators (TSOs). Furthermore, several countries have in place legislation requiring Solar Power producers to pay penalties proportional to the errors of day-ahead energy forecasts, which makes the accuracy of such predictions a determining factor for producers to reduce their economic losses. Probabilistic predictions can provide accurate deterministic forecasts along with a quantification of their uncertainty, as well as a reliable estimate of the probability to overcome a certain production threshold. In this paper we propose the application of an analog ensemble (AnEn) method to generate probabilistic Solar Power forecasts (SPF). The AnEn is based on an historical set of deterministic numerical weather prediction (NWP) model forecasts and observations of the Solar Power. For each forecast lead time and location, the ensemble prediction of Solar Power is constituted by a set of past production data. These measurements are those concurrent to past deterministic NWP forecasts for the same lead time and location, chosen based on their similarity to the current forecast and, in the current application, are represented by the one-hour average produced Solar Power.
Vasilis Fthenakis - One of the best experts on this subject based on the ideXlab platform.
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environmental impacts from the installation and operation of large scale Solar Power plants
Renewable & Sustainable Energy Reviews, 2011Co-Authors: Damon E Turney, Vasilis FthenakisAbstract:a b s t r a c t Large-scale Solar Power plants are being developed at a rapid rate, and are setting up to use thousands or millions of acres of land globally. The environmental issues related to the installation and operation phases of such facilities have not, so far, been addressed comprehensively in the literature. Here we identify and appraise 32 impacts from these phases, under the themes of land use intensity, human health and wellbeing, plant and animal life, geohydrological resources, and climate change. Our appraisals assume that electricity generated by new Solar Power facilities will displace electricity from traditional U.S. generation technologies. Altogether we find 22 of the considered 32 impacts to be beneficial. Of the remaining 10 impacts, 4 are neutral, and 6 require further research before they can be appraised. None of the impacts are negative relative to traditional Power generation. We rank the impacts in terms of priority, and find all the high-priority impacts to be beneficial. In quantitative terms, large-scale Solar Power plants occupy the same or less land per kW h than coal Power plant life cycles. Removal of forests to make space for Solar Power causes CO2 emissions as high as 36 g CO2 kW h−1, which is a significant contribution to the life cycle CO2 emissions of Solar Power, but is still low compared to CO2 emissions from coal-based electricity that are about 1100 g CO2 kW h−1.
Stefano Alessandrini - One of the best experts on this subject based on the ideXlab platform.
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an analog ensemble for short term probabilistic Solar Power forecast
Applied Energy, 2015Co-Authors: Stefano Alessandrini, Delle L Monache, Simone Sperati, Guido CervoneAbstract:Abstract The energy produced by photovoltaic farms has a variable nature depending on astronomical and meteorological factors. The former are the Solar elevation and the Solar azimuth, which are easily predictable without any uncertainty. The amount of liquid water met by the Solar radiation within the troposphere is the main meteorological factor influencing the Solar Power production, as a fraction of short wave Solar radiation is reflected by the water particles and cannot reach the earth surface. The total cloud cover is a meteorological variable often used to indicate the presence of liquid water in the troposphere and has a limited predictability, which is also reflected on the global horizontal irradiance and, as a consequence, on Solar photovoltaic Power prediction. This lack of predictability makes the Solar energy integration into the grid challenging. A cost-effective utilization of Solar energy over a grid strongly depends on the accuracy and reliability of the Power forecasts available to the Transmission System Operators (TSOs). Furthermore, several countries have in place legislation requiring Solar Power producers to pay penalties proportional to the errors of day-ahead energy forecasts, which makes the accuracy of such predictions a determining factor for producers to reduce their economic losses. Probabilistic predictions can provide accurate deterministic forecasts along with a quantification of their uncertainty, as well as a reliable estimate of the probability to overcome a certain production threshold. In this paper we propose the application of an analog ensemble (AnEn) method to generate probabilistic Solar Power forecasts (SPF). The AnEn is based on an historical set of deterministic numerical weather prediction (NWP) model forecasts and observations of the Solar Power. For each forecast lead time and location, the ensemble prediction of Solar Power is constituted by a set of past production data. These measurements are those concurrent to past deterministic NWP forecasts for the same lead time and location, chosen based on their similarity to the current forecast and, in the current application, are represented by the one-hour average produced Solar Power. The AnEn performance for SPF is compared to a quantile regression (QR) technique and a persistence ensemble (PeEn) over three Solar farms in Italy spanning different climatic conditions. The QR is a state-of-the-science method for probabilistic predictions that, similarly to AnEn, is based on a historical data set. The PeEn is a persistence model for probabilistic predictions, where the most recent 20 Power measurements available at the same lead-time are used to form an ensemble. The performance assessment has been carried out evaluating important attributes of a probabilistic system such as statistical consistency, reliability, resolution and skill. The AnEn performs as well as QR for common events, by providing predictions with similar reliability, resolution and sharpness, while it exhibits more skill for rare events and during hours with a low Solar elevation.
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an analog ensemble for short term probabilistic Solar Power forecast
Applied Energy, 2015Co-Authors: Stefano Alessandrini, Delle L Monache, Simone Sperati, Guido CervoneAbstract:The energy produced by photovoltaic farms has a variable nature depending on astronomical and meteorological factors. The former are the Solar elevation and the Solar azimuth, which are easily predictable without any uncertainty. The amount of liquid water met by the Solar radiation within the troposphere is the main meteorological factor influencing the Solar Power production, as a fraction of short wave Solar radiation is reflected by the water particles and cannot reach the earth surface. The total cloud cover is a meteorological variable often used to indicate the presence of liquid water in the troposphere and has a limited predictability, which is also reflected on the global horizontal irradiance and, as a consequence, on Solar photovoltaic Power prediction. This lack of predictability makes the Solar energy integration into the grid challenging. A cost-effective utilization of Solar energy over a grid strongly depends on the accuracy and reliability of the Power forecasts available to the Transmission System Operators (TSOs). Furthermore, several countries have in place legislation requiring Solar Power producers to pay penalties proportional to the errors of day-ahead energy forecasts, which makes the accuracy of such predictions a determining factor for producers to reduce their economic losses. Probabilistic predictions can provide accurate deterministic forecasts along with a quantification of their uncertainty, as well as a reliable estimate of the probability to overcome a certain production threshold. In this paper we propose the application of an analog ensemble (AnEn) method to generate probabilistic Solar Power forecasts (SPF). The AnEn is based on an historical set of deterministic numerical weather prediction (NWP) model forecasts and observations of the Solar Power. For each forecast lead time and location, the ensemble prediction of Solar Power is constituted by a set of past production data. These measurements are those concurrent to past deterministic NWP forecasts for the same lead time and location, chosen based on their similarity to the current forecast and, in the current application, are represented by the one-hour average produced Solar Power.
D H Wood - One of the best experts on this subject based on the ideXlab platform.
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impacts of large scale wind and Solar Power integration on california s net electrical load
Renewable & Sustainable Energy Reviews, 2016Co-Authors: Hamid Shaker, Hamidreza Zareipour, D H WoodAbstract:Abstract Integration of wind- and Solar-based generation into the electric grid has significantly grown over the past decade and is expected to grow to unprecedented levels in coming years. Several jurisdictions have set high targets for renewable energy integration. While electric grid operators have managed the variable and non-dispatchable nature of wind and Solar Power at current levels, large-scale integration of these resources would pose new challenges. In particular, the variable nature of wind and Solar may lead to new electric grid operation and planning procedures. Net load in electric grids is defined as the conventional load minus the non-dispatchable generation. Net load is the basis of operation planning in day-to-day delivery of electricity to the consumers. With large-scale integration of wind and Solar Power, the net load in the system would be significantly affected. In this paper, we focus on characteristics of net load in electric grids when a large amount of wind and Solar Power generation is integrated into the grid. We use the data from California׳s Power system. California intends to produce 33% of its electricity from renewable resources by 2020, 80% of which is expected to come from wind and Solar Power. We use both historical data and simulated scenarios of future wind and Solar Power generation. For future scenarios, we use the data provided by National Renewable Energy Laboratory to generate wind and Solar Power integration scenarios for years 2018 and 2023. The simulated net load data are analyzed from a variety of perspectives, such as average daily shapes, load and net load factor, duration curves, volatility, and hourly ramps. The results showed that compared to conventional load, characteristics of net load would be significantly different and need to be taken into account when designing measures and mechanisms for operating electric grids with high penetration of renewables.
Pieter Stroeve - One of the best experts on this subject based on the ideXlab platform.
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innovation in concentrated Solar Power
Solar Energy Materials and Solar Cells, 2011Co-Authors: David Barlev, Ruxandra Vidu, Pieter StroeveAbstract:Abstract This work focuses on innovation in CSP technologies over the last decade. A multitude of advancements has been developed during this period, as the topic of concentrated Solar Power is becoming more mainstream. Improvements have been made in reflector and collector design and materials, heat absorption and transport, Power production and thermal storage. Many applications that can be integrated with CSP regimes to conserve (and sometimes produce) electricity have been suggested and implemented, keeping in mind the environmental benefits granted by limited fossil fuel usage.
