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Swanepoel, Paul. "A forecasting model for photovoltaic module energy production". Thesis, Nelson Mandela Metropolitan University, 2011. http://hdl.handle.net/10948/1420.
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Energy is of concern for governments and economies all over the world. As conventional methods of energy production are facing the prospect of depleting fossil fuel reserves, economies are facing energy risks. With this tension, various threats arise in terms of energy supply security. A shift from intensive fossil fuel consumption to alternative energy consumption combined with the calculated use of fossil fuels needs to be implemented. Using the energy radiated from the sun and converted to electricity through photovoltaic energy conversion is one of the alternative and renewable sources to address the limited fossil fuel dilemma. South Africa receives an abundance of sunlight irradiance, but limited knowledge of the implementation and possible energy yield of photovoltaic energy production in South Africa is available. Photovoltaic energy yield knowledge is vital in applications for farms, rural areas and remote transmitting devices where the construction of electricity grids are not cost effective. In this study various meteorological and energy parameters about photovoltaics were captured in Port Elizabeth (South Africa) and analyzed, with data being recorded every few seconds. A model for mean daily photovoltaic power output was developed and the relationships between the independent variables analyzed. A model was developed that can forecast mean daily photovoltaic power output using only temperature derived variables and time. The mean daily photovoltaic power model can then easily be used to forecast daily photovoltaic energy output using the number of sunlight seconds in a given day.
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Cormode, Daniel. "Large and Small Photovoltaic Powerplants". Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/556469.
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The installed base of photovoltaic power plants in the United States has roughly doubled every 1 to 2 years between 2008 and 2015. The primary economic drivers of this are government mandates for renewable power, falling prices for all PV system components, 3rd party ownership models, and a generous tariff scheme known as net-metering. Other drivers include a desire for decreasing the environmental impact of electricity generation and a desire for some degree of independence from the local electric utility. The result is that in coming years, PV power will move from being a minor niche to a mainstream source of energy. As additional PV power comes online this will create challenges for the electric grid operators. We examine some problems related to large scale adoption of PV power in the United States. We do this by first discussing questions of reliability and efficiency at the PV system level. We measure the output of a fleet of small PV systems installed at Tucson Electric Power, and we characterize the degradation of those PV systems over several years. We develop methods to predict energy output from PV systems and quantify the impact of negatives such as partial shading, inverter inefficiency and malfunction of bypass diodes. Later we characterize the variability from large PV systems, including fleets of geographically diverse utility scale power plants. We also consider the power and energy requirements needed to smooth those systems, both from the perspective of an individual system and as a fleet. Finally we report on experiments from a utility scale PV plus battery hybrid system deployed near Tucson, Arizona where we characterize the ability of this system to produce smoothly ramping power as well as production of ancillary energy services such as frequency response.
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Chowdhury, Badrul Hasan. "Irradiance forecasting and dispatching central station photovoltaic power plants". Diss., Virginia Polytechnic Institute and State University, 1987. http://hdl.handle.net/10919/82903.
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This dissertation introduces a new operational tool for integrating a photovoltaic (PV) system into the utility's generation mix. It is recognized at the outset, that much of the existing research concentrated on the central PV system and its operations have concluded that technical problems in PV operation will override any value or credit that can be earned by a PV system, and that penetration of a PV plant in the utility will be severely limited. These are real problems and their solutions are sought in this dissertation. Judging from the drawbacks of the static approach, it is felt that a new approach or methodology needs to be developed which would give a central station PV plant its due share of credit. This dissertation deals mainly with the development and implementation of this new approach -- a dynamic rule-based dispatch algorithm which takes into account the problems faced by the dispatch operator during a dispatch interval and channels those into a knowledge base.
The new dynamic dispatch requires forecasts of photovoltaic generations at the beginning of each dispatch interval. A Box-Jenkins time-series method is used to model the sub-hourly solar irradiance. The irradiance data at any specific site is stripped of its periodicities using a pre-whitening process which involves parameterization of certain known atmospheric phenomena. The pre-whitened data series is considered stationary, although some non-stationarity might be introduced by the discontinuities in the data collection during night hours. This model is extended to yield forecast equations which are then used to predict the photovoltaic output expected to occur at certain lead times coinciding with the economic dispatch intervals.
A rule-based (RB) dispatch algorithm is developed in this dissertation. The RB is introduced to operate as a substitute for the dispatch operator. Some of the dispatcher's functions are routine jobs, while some require specialized knowledge or experience. The RB is given these two qualities through a number of rules. This algorithm works in tandem with a conventional economic dispatch algorithm. The functions of the two are coordinated by another algorithm which oversees the now of information and records them.
The RB gives one of 16 possible solutions as and when required. These solutions are written as rules which manipulate the non-committable generation to achieve an optimal solution. The RB system during its operation supervises the fact that the PV generation are kept at the maximum level possible under all constraints. The case study revealed that the thermal generating units which are scheduled by the unit commitment are able to absorb most of the small to medium variations present in the PV generations. In cases of large variations during a single interval, the thermal generators reach their response limits before they can reach their maximum or minimum generation, thus causing mismatches in the load and generation. The mismatches are then picked up by the non-committable sources of generation, comprised of pumped storage units, hydro generation plant, or by interconnection tie-lines. If none of these are sufficient, changes are made in the PV generation schedule.
It is concluded that results depend on the time of the year and the specific utility. The time of the year information is reflected in the load demand profile. Most utilities in the U.S. have single peaks in summer and double peaks in winter. Also, the time of the peak load occurrence varies with season. The utility generating capacity mix influences the results greatly.Ph. D.
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Carriere, Thomas. "Towards seamless value-oriented forecasting and data-driven market valorisation of photovoltaic production". Thesis, Université Paris sciences et lettres, 2020. http://www.theses.fr/2020UPSLM019.
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La décarbonation de la production d’électricité à échelle mondiale est un élément de réponse clé face aux pressions exercées par les différents enjeux environnementaux. Par ailleurs, la baisse des coûts de la filière photovoltaïque (PV) ouvre la voie à une augmentation significative de la production PV dans le monde. L’objectif principal de cette thèse est alors de maximiser le revenu d’un producteur d’énergie PV sous incertitude des prix de marché et de la production. Pour cela, un modèle de prévision probabiliste de la production PV à court (5 minutes) et moyen (24 heures) terme est proposé. Ce modèle est couplé à une méthode de participation au marché maximisant l’espérance du revenu. Dans un second temps, le couplage entre une centrale PV et une batterie est étudié, et une analyse de sensibilité des résultats est réalisée pour étudier la rentabilité et le dimensionnement de tels systèmes. Une méthode de participation alternative est proposée, pour lequel un réseau de neurones artificiel apprend à participer avec ou sans batterie au marché de l’électricité, ce qui permet de simplifier le processus de valorisation de l'énergie PV en diminuant le nombre de modèles requisThe decarbonation of electricity production on a global scale is a key element in responding to the pressures of different environmental issues. In addition, the decrease in the costs of the photovoltaic (PV) sector is paving the way for a significant increase in PV production worldwide. The main objective of this thesis is then to maximize the income of a PV energy producer under uncertainty of market prices and production. For this purpose, a probabilistic forecast model of short (5 minutes) and medium (24 hours) term PV production is proposed. This model is coupled with a market participation method that maximizes income expectation. In a second step, the coupling between a PV plant and a battery is studied, and a sensitivity analysis of the results is carried out to study the profitability and sizing of such systems. An alternative participation method is proposed, for which an artificial neural network learns to participate with or without batteries in the electricity market, thus simplifying the process of PV energy valuation by reducing the number of models required
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Rudd, Timothy Robert. "BENEFITS OF NEAR-TERM CLOUD LOCATION FORECASTING FOR LARGE SOLAR PV". DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/597.
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As the ‘green’ energy movement continues to gain momentum, photovoltaic generation is becoming an increasingly popular source for new power generation. The primary focus of this paper is to demonstrate the benefits of close-to real-time cloud sensing for Photovoltaic generation. In order to benefit from this close-to real-time data, a source of cloud cover information is necessary. This paper looks into the potential of point insolation sensors to determine overhead cloud coverage. A look into design considerations and economic challenges of implementing such a monitoring system is included. The benefits of cloud location sensing are examined using computer simulations to target important time-scales and options available to plant operators. Finally, the economics of advanced forecasting options will be examined in order to determine the benefit to plant operators.
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Nobre, André Maia. "Short-term solar irradiance forecasting and photovoltaic systems performance in a tropical climate in Singapore". reponame:Repositório Institucional da UFSC, 2015. https://repositorio.ufsc.br/xmlui/handle/123456789/162682.
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Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Civil, Florianópolis, 2015.Made available in DSpace on 2016-05-24T17:37:07Z (GMT). No. of bitstreams: 1 338190.pdf: 9968372 bytes, checksum: e1c28dfcf84e191f0457a82aa5715399 (MD5) Previous issue date: 2015
A humanidade usou e continua consumindo em grande quantidade os recursos não-renováveis do planeta como petróleo, gás natural e carvão mineral para suprir suas necessidades energéticas. Somente nas últimas duas décadas que outras fontes de energia renováveis, como a solar fotovoltaica e a eólica, passaram a se tornar relevantes na geração de energia elétrica em nÃvel mundial. Instalações de sistemas fotovoltaicos ao redor do mundo atingiram crescimento da ordem de 40% durante os últimos quinze anos. Entretanto, a grande maioria destes sistemas, (acima de 90%), estão localizados em regiões onde o recurso solar não é tão abundante, ou seja, fora da região dos trópicos do planeta. Devido a este fato, ao tentar incorporar a energia solar fotovoltaica à s redes elétricas, uma pergunta que sempre surge está relacionada a variação desta forma de geração de energia elétrica com a produção alternante durante o dia devido ao movimento das nuvens e total ausência no perÃodo noturno. Mesmo assim, em alguns paÃses, já se atinge percentuais em torno de 5 a 10% de contribuição da energia elétrica proveniente de energia solar fotovoltaica. Passa a ser desafiador a inserção dessa fonte de energia à rede, de maneira intensiva, em paralelo com os recursos já existentes (em sua maioria ainda de origem fóssil). Nesta tese, foi avaliada a previsão do recurso solar em curtÃssimo prazo (como 15-min, 30-min e uma hora) para uma região tropical do planeta, neste caso em Cingapura, ilha que se localiza próxima à linha do equador, no Sudeste Asiático. Esta tese foca em métodos existentes de previsão de irradiância, mas também explora uma nova proposta hÃbrida, adaptada a uma localidade tropical. Além das previsões de irradiação solar, simulações de sistemas fotovoltaicos e o cálculo de seu desempenho foram estudados e avaliados de modo a se prever quanto de energia elétrica é produzida com a mesma antecedência dada nos produtos de previsão do recurso solar. A influência da gaze de queimada foi um fenômeno particular, comum na Cingapura de hoje, que afeta o desempenho de sistemas fotovoltaicos e que foi investigado em detalhe. Todo o trabalho foi validado por redes detalhadas de estações meteorológicas em solo e também através de monitoramento de sistemas fotovoltaicos por toda Cingapura.
Abstract : Humanity has used and continues to consume in great proportion non-renewable energy resources of the planet such as oil, natural gas and coal in order to fulfil its energy needs. It was only during the past two decades that other sources of renewable energy such as solar photovoltaics (PV) and wind energy became somewhat relevant towards electricity generation in the world. PV installations worldwide have reached a compound annual growth rate of ~40% for the last fifteen years. However, the great majority of these systems (over 90% of them) are located where the solar energy resource is not the most abundant - outside of the tropical regions of the planet. While trying to incorporate solar energy PV into electrical power grids, one common question which arises is related to the variable aspect of this form of energy generation - with alternating production during the day due to cloud motion, and total absence during night time. Nonetheless, in some countries, contribution ratios of 5 to 10% of electrical energy from solar PV have been achieved. It becomes then challenging to integrate this source of energy into grids in a professional way, in parallel with existing resources (mostly still fossil-fuel-based). In this thesis, short-term forecasting (for time horizons such as 15-min, 30-min and 1-hour) of the solar resource was investigated in a tropical region of the world - in Singapore, 1° North of the Equator, in Southeast Asia. This thesis focuses on existing methods for irradiance forecasting, but also explores a novel Hybrid proposal, tailored to the tropical environment at hand. Beyond the forecast of the solar energy irradiance ahead of time, PV system simulation and performance assessment were studied and evaluated with the goal of predicting how much electricity is produced in the same time frame given by the solar irradiance forecasting products. The influence of haze was a particular phenomenon, common in today?s Singapore, which affects PV system performance and which was investigated in detail. All work has been validated by a comprehensive network of ground-based meteorological stations, as well as by various PV system monitoring sites throughout Singapore.
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Nobre, André Maia. "Short-term solar irradiance forecasting and photovoltaic systems performance in a tropical climate in Singapore". reponame:Repositório Institucional da UFSC, 2015. https://repositorio.ufsc.br/xmlui/handle/123456789/169480.
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Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Civil, Florianópolis, 2015.Made available in DSpace on 2016-10-19T12:59:30Z (GMT). No. of bitstreams: 1 338190.pdf: 9968372 bytes, checksum: e1c28dfcf84e191f0457a82aa5715399 (MD5) Previous issue date: 2015
A humanidade usou e continua consumindo em grande quantidade os recursos não-renováveis do planeta como petróleo, gás natural e carvão mineral para suprir suas necessidades energéticas. Somente nas últimas duas décadas que outras fontes de energia renováveis, como a solar fotovoltaica e a eólica, passaram a se tornar relevantes na geração de energia elétrica em nível mundial. Instalações de sistemas fotovoltaicos ao redor do mundo atingiram crescimento da ordem de 40% durante os últimos quinze anos. Entretanto, a grande maioria destes sistemas, (acima de 90%), estão localizados em regiões onde o recurso solar não é tão abundante, ou seja, fora da região dos trópicos do planeta. Devido a este fato, ao tentar incorporar a energia solar fotovoltaica às redes elétricas, uma pergunta que sempre surge está relacionada a variação desta forma de geração de energia elétrica com a produção alternante durante o dia devido ao movimento das nuvens e total ausência no período noturno. Mesmo assim, em alguns países, já se atinge percentuais em torno de 5 a 10% de contribuição da energia elétrica proveniente de energia solar fotovoltaica. Passa a ser desafiador a inserção dessa fonte de energia à rede, de maneira intensiva, em paralelo com os recursos já existentes (em sua maioria ainda de origem fóssil). Nesta tese, foi avaliada a previsão do recurso solar em curtíssimo prazo (como 15-min, 30-min e uma hora) para uma região tropical do planeta, neste caso em Cingapura, ilha que se localiza próxima à linha do equador, no Sudeste Asiático. Esta tese foca em métodos existentes de previsão de irradiância, mas também explora uma nova proposta híbrida, adaptada a uma localidade tropical. Além das previsões de irradiação solar, simulações de sistemas fotovoltaicos e o cálculo de seu desempenho foram estudados e avaliados de modo a se prever quanto de energia elétrica é produzida com a mesma antecedência dada nos produtos de previsão do recurso solar. A influência da gaze de queimada foi um fenômeno particular, comum na Cingapura de hoje, que afeta o desempenho de sistemas fotovoltaicos e que foi investigado em detalhe. Todo o trabalho foi validado por redes detalhadas de estações meteorológicas em solo e também através de monitoramento de sistemas fotovoltaicos por toda Cingapura.
Abstract : Humanity has used and continues to consume in great proportion non-renewable energy resources of the planet such as oil, natural gas and coal in order to fulfil its energy needs. It was only during the past two decades that other sources of renewable energy such as solar photovoltaics (PV) and wind energy became somewhat relevant towards electricity generation in the world. PV installations worldwide have reached a compound annual growth rate of ~40% for the last fifteen years. However, the great majority of these systems (over 90% of them) are located where the solar energy resource is not the most abundant - outside of the tropical regions of the planet. While trying to incorporate solar energy PV into electrical power grids, one common question which arises is related to the variable aspect of this form of energy generation - with alternating production during the day due to cloud motion, and total absence during night time. Nonetheless, in some countries, contribution ratios of 5 to 10% of electrical energy from solar PV have been achieved. It becomes then challenging to integrate this source of energy into grids in a professional way, in parallel with existing resources (mostly still fossil-fuel-based). In this thesis, short-term forecasting (for time horizons such as 15-min, 30-min and 1-hour) of the solar resource was investigated in a tropical region of the world - in Singapore, 1° North of the Equator, in Southeast Asia. This thesis focuses on existing methods for irradiance forecasting, but also explores a novel Hybrid proposal, tailored to the tropical environment at hand. Beyond the forecast of the solar energy irradiance ahead of time, PV system simulation and performance assessment were studied and evaluated with the goal of predicting how much electricity is produced in the same time frame given by the solar irradiance forecasting products. The influence of haze was a particular phenomenon, common in today?s Singapore, which affects PV system performance and which was investigated in detail. All work has been validated by a comprehensive network of ground-based meteorological stations, as well as by various PV system monitoring sites throughout Singapore.
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Mayol, Cotapos Carolina de los Ángeles. "Mitigation control against partial shading effects in large-scale photovoltaic power plants using an improved forecasting technique". Tesis, Universidad de Chile, 2017. http://repositorio.uchile.cl/handle/2250/144113.
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Magíster en Ciencias de la Ingeniería, Mención EléctricaEn un trabajo previo se propuso un control de mitigación de efecto nube que permitía disminuir los efectos nocivos de la nubosidad parcial sobre parques fotovoltaicos en la frecuencia de sistemas eléctricos de potencia. Esto último sin la necesidad del uso de acumuladores de energía. La estrategia se basa en la operación sub-óptima de los parques (operación en deload) con tal de disponer de reservas de potencia. A pesar que la implementación del sistema nombrado mejoró la frecuencia del sistema de forma significativa en comparación al caso base (sin el sistema de control), la operación en deload de los parques implica una gran cantidad de energía que no se está aprovechando, lo que no se consideró en la metodología. Con tal de mejorar esto, el siguiente trabajo propone un control de mitigación de efecto nube en parques fotovoltaicos de gran escala basado en una herramienta de pronóstico de radiación. Esto último permite disminuir las pérdidas de energía junto con mitigar los efectos de la nubosidad parcial, mediante la determinación de un nivel de deload en los parques fotovoltaicos usando dicho pronóstico. En primer lugar, esta tesis presenta una revisión bibliográfica y discusión del estado del arte de las técnicas de pronóstico en parques fotovoltaicos. Se muestra que la selección de la técnica de pronóstico depende en la información disponible y la ventana de tiempo del pronóstico, es decir, dependerá del caso de estudio. Dicho esto, se propone el uso de una técnica de pronóstico basada en redes neuronales en el Sistema Interconectado del Norte Grande (SING) de Chile. El pronóstico sirve para determinar el nivel de deload en el parque fotovoltaico para los siguientes 10 minutos, en función de una rampa de radiación. Los resultados muestran que la implementación de la técnica de pronóstico no solo mejora la respuesta en frecuencia del sistema, sino que también disminuye las pérdidas energéticas de forma significativa.
Este trabajo fue parcialmente financiado por el Proyecto CONICYT/FONDAP/15110019 "Solar Energy Research Center" SERC-Chile y el Instituto de Sistemas Complejos de Ingeniería (ISCI)
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D, Pepe. "New techniques for solar power forecasting and building energy management". Doctoral thesis, Università di Siena, 2019. http://hdl.handle.net/11365/1072873.
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The electrical grid can no longer be considered a unidirectional means of distributing energy from conventional plants to the final users, but a Smart Grid, where strong interaction between producers and users takes place. In this context, the importance of independent renewable generation is constantly increasing, and new tools are needed in order to reliably manage conventional power plant operation, grid balancing, real-time unit dispatching, demand constraints and energy market requirements. This dissertation is focused on two aspects of this general problem: cost-optimal management of smart buildings in a Demand-Response framework, and estimation of photovoltaic generation forecasting models.
In the first part of this thesis a novel Model Predictive Control approach for integrated management of HVAC, electrical and thermal storage, and photovoltaic generation in building is presented. The proposed methodology also considers participation of the building in a Demand-Response program, which allows the consumer to become an active player in the electricity system. The related optimization problems turn out to be computationally appealing, even uncertainty sources is also addressed by means of a two-step procedure.
The second part deals with the problem of estimating photovoltaic generation forecasting models in scenarios where measurements of meteorological variables (i.e., solar irradiance and temperature) at the plant site are not available. This scenario is relevant to electricity network operation, when a large number of photovoltaic plants are deployed in the grid. In particular, two methods have been developed. The first approach makes use of raw cloud cover data provided by a weather service combined with power generation measurements to estimate the parameters of a novel class of models. The second approach is based on a set of tests performed on the generated power time series aimed at detecting data portions that were generated under clear sky conditions. These data are then used for fit the parameters of the PVUSA
model to the theoretical clear sky irradiance. All the methods covered in this thesis have been extensively validated either using industry-standard simulation frameworks or via experiments performed on real data.
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Almquist, Isabelle, Ellen Lindblom i Alfred Birging. "Workplace Electric Vehicle Solar Smart Charging based on Solar Irradiance Forecasting". Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-323319.
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The purpose of this bachelor thesis is to investigate different outcomes of the usage of photovoltaic (PV) power for electric vehicle (EV) charging adjacent to workplaces. In the investigated case, EV charging stations are assumed to be connected to photovoltaic systems as well as the electricity grid. The model used to simulate different scenarios is based on a goal of achieving constant power exchange with the grid by adjusting EV charging to a solar irradiance forecast. The model is implemented in MATLAB. This enables multiple simulations for varying input parameters. Data on solar irradiance are used to simulate the expected PV power generation. Data on driving distances are used to simulate hourly electricity demands of the EVs at the charging stations. A sensitivity analysis, based on PV irradiance that deviates from the forecast, is carried out. The results show what power the grid needs to have installed capacity for if no PV power system is installed. Furthermore, appropriate PV power installation sizes are suggested. The suggestions depend on whether the aim is to achieve 100 percent self-consumption of PV generated power or full PV power coverage of charging demands. For different scenarios, PV power installations appropriate for reducing peak powers on the grid are suggested. The sensitivity analysis highlights deviations caused by interference in solar irradiance.
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Ghosh, Shibani. "A Real-time Management of Distribution Voltage Fluctuations due to High Solar Photovoltaic (PV) Penetrations". Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/74424.
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Due to the rapid growth of grid-tied solar photovoltaic (PV) systems in the generation mix, the distribution grid will face complex operational challenges. High PV penetration can create overvoltages and voltage fluctuations in the network, which are major concerns for the grid operator. Traditional voltage control devices like switched capacitor banks or line voltage regulators can alleviate slow-moving fluctuations, but these devices need to operate more frequently than usual when PV generation fluctuates due to fast cloud movements. Such frequent operations will impact the life expectancy of these voltage control devices.
Advanced PV inverter functionalities enable solar PV systems to provide reliable grid support through controlled real injection and/or reactive power compensation. This dissertation proposes a voltage regulation technique to mitigate probable impacts of high PV penetrations on the distribution voltage profile using smart inverter functionalities. A droop-based reactive power compensation method with active power curtailment is proposed, which uses the local voltage regulation at the inverter end. This technique is further augmented with very short-term PV generation forecasts. A hybrid forecasting algorithm is proposed here which is based on measurement-dependent dynamic modeling of PV systems using the Kalman Filter theory. Physical modeling of the PV system is utilized by this forecasting algorithm. Because of the rise in distributed PV systems, modeling of geographic dispersion is also addressed under PV system modeling.
The proposed voltage regulation method is coordinated with existing voltage regulator operations to reduce required number of tap-change operations. Control settings of the voltage regulators are adjusted to achieve minimal number of tap-change operations within a predefined time window. Finally, integration of energy storage is studied to highlight the value of the proposed voltage regulation technique vis-à-vis increased solar energy use.Ph. D.
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Karimi, Ahmad Maroof. "DATA SCIENCE AND MACHINE LEARNING TO PREDICT DEGRADATION AND POWER OF PHOTOVOLTAIC SYSTEMS: CONVOLUTIONAL AND SPATIOTEMPORAL GRAPH NEURAL NETWORK". Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1601082841477951.
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Roy, Joseph Claude Eric. "Design and installation of a Sky-camera network and data acquisition system for intra-hour solar irradiance and photovoltaic system output forecasting". Thesis, Roy, Joseph Claude Eric (2016) Design and installation of a Sky-camera network and data acquisition system for intra-hour solar irradiance and photovoltaic system output forecasting. Honours thesis, Murdoch University, 2016. https://researchrepository.murdoch.edu.au/id/eprint/36738/.
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Murdoch University is enabling research in the area of intra-hour solar irradiance and photovoltaic (PV) system output forecasting by installing a stereo vision capable Sky-camera network and system. This research is being led by Dr Martina Calais and is in collaboration with the University of Oldenburg Solar Energy Meteorology Group.
This document describes the process of designing and installation of Sky-camera equipment, and along with its network and data acquisition system. Two Sky-camera locations had already been identified on campus as suitable, by having few occluding objects in the Sky-camera’s field-of-view, a power and network connection, as well as being near a data acquisition system that can obtain PV power output and meteorological data.
The first location, on the top rail of a PV array on the roof of the Engineering and Energy Building, was found to be marginally suitable due to tall trees and an elevator shaft. However, a different location on this roof was chosen even though it required a greater installation effort. The second Sky-camera location in the Renewable Energy Outdoor Test Area, offered a much simpler installation process but lacked of an Ethernet access point. This required a wireless bridge to be installed and configured.
The design of all custom made hardware for this project was accomplished using the Autodesk Inventor® software suite, and then fabricated with the help of Murdoch University technical staff and using in-house facilities. Networking all the Sky-camera equipment, the recreation of Python codes into LabVIEW codes and changing a Linux Server to a Windows Server caused the largest deviation from the original project plan. However, this led to the development of a different data acquisition (DAQ) system program architecture that is anticipated to provide a more favorable data availability rate. Other additional works that were outside the immediate scope includes; the creation of a custom made Sky-camera image editing software for creating binary mask images and the assembly of a Solys2 solar taker.
The overall Sky-camera network installation was successful and is now in a state that allows research to begin. It is envisaged that the knowledge obtained through this project and following projects will lead to the implementation of short term solar forecasting systems in remote diesel networks.
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Thorey, Jean. "Prévision d’ensemble par agrégation séquentielle appliquée à la prévision de production d’énergie photovoltaïque". Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066526/document.
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Notre principal objectif est d'améliorer la qualité des prévisions de production d'énergie photovoltaïque (PV). Ces prévisions sont imparfaites à cause des incertitudes météorologiques et de l'imprécision des modèles statistiques convertissant les prévisions météorologiques en prévisions de production d'énergie. Grâce à une ou plusieurs prévisions météorologiques, nous générons de multiples prévisions de production PV et nous construisons une combinaison linéaire de ces prévisions de production. La minimisation du Continuous Ranked Probability Score (CRPS) permet de calibrer statistiquement la combinaison de ces prévisions, et délivre une prévision probabiliste sous la forme d'une fonction de répartition empirique pondérée.Dans ce contexte, nous proposons une étude du biais du CRPS et une étude des propriétés des scores propres pouvant se décomposer en somme de scores pondérés par seuil ou en somme de scores pondérés par quantile. Des techniques d'apprentissage séquentiel sont mises en oeuvre pour réaliser cette minimisation. Ces techniques fournissent des garanties théoriques de robustesse en termes de qualité de prévision, sous des hypothèses minimes. Ces méthodes sont appliquées à la prévision d'ensoleillement et à la prévision de production PV, fondée sur des prévisions météorologiques à haute résolution et sur des ensembles de prévisions classiquesOur main objective is to improve the quality of photovoltaic power forecasts deriving from weather forecasts. Such forecasts are imperfect due to meteorological uncertainties and statistical modeling inaccuracies in the conversion of weather forecasts to power forecasts. First we gather several weather forecasts, secondly we generate multiple photovoltaic power forecasts, and finally we build linear combinations of the power forecasts. The minimization of the Continuous Ranked Probability Score (CRPS) allows to statistically calibrate the combination of these forecasts, and provides probabilistic forecasts under the form of a weighted empirical distribution function. We investigate the CRPS bias in this context and several properties of scoring rules which can be seen as a sum of quantile-weighted losses or a sum of threshold-weighted losses. The minimization procedure is achieved with online learning techniques. Such techniques come with theoretical guarantees of robustness on the predictive power of the combination of the forecasts. Essentially no assumptions are needed for the theoretical guarantees to hold. The proposed methods are applied to the forecast of solar radiation using satellite data, and the forecast of photovoltaic power based on high-resolution weather forecasts and standard ensembles of forecasts
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Vallance, Loïc. "Synergie des mesures pyranométriques et des images hémisphériques in-situ avec des images satellites météorologiques pour la prévision photovoltaïque". Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEM064/document.
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L’exploitation de l’énergie solaire soulève des défis liés à la nature variable de la res- source concernée : le rayonnement solaire. Son comportement intermittent est un problème pour la gestion des centrales photovoltaïques et du réseau électrique. L'une des solutions largement envisagées est la prévision de la production photovoltaïque à différents horizons.L'objectif de cette thèse est d'explorer de nouvelles voies pour améliorer les prévisions existantes du rayonnement solaire, pour des horizons allant de quelques minutes à quelques heures, en exploitant les synergies possibles entre les mesures pyranométriques, les images hémisphériques du ciel prises depuis le sol et les images acquises par les satellites météorologiques géostationnaires. Ces deux types d’images ont des couvertures spatiales, des résolutions spatio-temporelles et des points de vue très différents.L’approche proposée dans cette thèse exploite cette différence de points de vue afin d’affiner la géolocalisation des nuages en 3D par stéréoscopie, dont l’évolution des ombres peut alors être estimée et prévue. Un simulateur géométrique de la méthode a été développé et permet d’en identifier certains avantages et limitations. La géolocalisation des nuages appliquée à des données réelles a permis d’élaborer des schémas d’estimations et de prévisions prometteurs du rayonnement solaire incident. Enfin, pour compléter l’analyse usuelle de ces performances de prévision, deux nouvelles métriques ont été proposées dans l’optique de quantifier deux notions essentielles : le respect du suivi des rampes et l’alignement temporel de la prévision par rapport à la mesureThe exploitation of solar energy raises challenges related to the variable nature of the resources involved: the incident solar irradiance. Its intermittent behavior is an is- sue for photovoltaic power plants and grid management. One of the solutions that have been widely considered is the forecast of photovoltaic production at different time horizon.The aim of this thesis is to explore new ways for improving the existing solar irradiance forecasts, for horizons ranging from the present moment to few hours, by exploiting possible synergies between pyranometric measurements, hemispherical images of the sky taken from the ground and images acquired by geostationary meteorological satellites. These two types of images have completely different spatial coverage, spatio-temporal resolutions and are taken from two different locations.The proposed approach in this thesis exploits this difference in points of view in order to geolocate the clouds in 3D by stereoscopy, which shadows’ location and motion can then be estimated and forecasted. A geometric simulator of the method has been developed to identify some of the advantages and limitations of this approach. The geolocation of clouds applied to real data made it possible to develop promising estimates and forecasts of incident solar irradiance. Finally, to complete the usual analysis of forecasting performances, two new metrics have been proposed in order to quantify two essential notions: the ability to monitor the ramps and the temporal alignment of the forecast with the measurements
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Mathieu, Valentin. "Solutions avancées de gestion pour les micro-réseaux à fort taux de pénétration des sources renouvelables sous l’incertitude". Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALT058.
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Dans un contexte d'évolution du système électrique, une attention particulière est portée sur l'intégration des énergies renouvelables dans les réseaux. L'objectif principal du projet de thèse est de développer des solutions pour le pilotage des micro-réseaux à forte pénétration d'énergie renouvelable. Ce projet de recherche explore comment planifier et anticiper le fonctionnement des entités d'un micro-réseau et en particulier son système de stockage, en intégrant les incertitudes liées à la production photovoltaïque. Pour cela, des modèles stochastiques sont proposés pour optimiser la gestion de ces réseaux, améliorer la fiabilité et la qualité de l'énergie, tout en réduisant les coûts opérationnels à partir de prévisions probabilistes.Les travaux présentent des méthodes pour modéliser l'incertitude dans la production photovoltaïque et démontrent l'efficacité des approches stochastiques. Ils montrent notamment comment ces méthodes peuvent réduire les risques économiques associés au soutirage depuis le réseau principal et offrir un service système précieux en diminuant l'amplitude journalière de puissance soutirée. La thèse propose également une méthode de génération d'ensemble de scénarios réduits pour la planification stochastique, contribuant ainsi à une meilleure opération des micro-réseaux. Cette approche, basée sur la modélisation de la distribution et la dépendance entre les variables étudiées, permet également d'améliorer les prévisions en assimilant des données observéesIn the context of the evolving electrical system, particular attention is given to the integration of renewable energy into the grids. The main objective of the thesis project is to develop solutions for the management of microgrids with a high penetration of renewable energy. This research project explores how to plan and anticipate the operation of the entities within a microgrid, particularly its storage system, by incorporating the uncertainties associated with photovoltaic production. To achieve this, stochastic models are proposed to optimize the management of these networks, enhance the reliability and quality of energy, and reduce operational costs using probabilistic forecasts.The work presents methods to model the uncertainty in photovoltaic production and demonstrates the effectiveness of stochastic approaches. It notably shows how these methods can reduce the economic risks associated with drawing power from the main grid and provide a valuable system service by decreasing the daily amplitude of drawn power. The thesis also proposes a method for generating a reduced set of scenarios for stochastic planning, thus contributing to better microgrid operation. This approach, based on modeling the distribution and dependence between the studied variables, also improves forecasts by assimilating observed data
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Shepero, Mahmoud. "Modeling and forecasting the load in the future electricity grid : Spatial electric vehicle load modeling and residential load forecasting". Licentiate thesis, Uppsala universitet, Fasta tillståndets fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-359432.
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The energy system is being transitioned to increase sustainability. This transition has been accelerated by the increased awareness about the adverse effects of the greenhouse gas (GHG) emissions into the atmosphere. The transition includes switching to electricity as the energy carrier in some sectors, e.g., transportation, increasing the contribution of renewable energy sources (RES) to the grid, and digitalizing the grid services. Electric vehicles (EVs) are promoted and subsidized in many countries among the sustainability initiatives. Consequently, the global sales of EVs rapidly increased in the recent years. Many EV owners might charge their EVs only at home, thereby increasing the residential load. The residential load might further increase due to the initiatives to electrify the heating/cooling sector. This thesis contributes to the knowledge about the operation of the future energy system by modeling the spatial charging load of private EVs in cities, and by proposing a forecasting model to predict the residential load. Both models can be used to evaluate the impacts of both technologies on the local electricity grid. In addition, demand response (DR) schemes can be proposed to reduce the adverse effects of both the charging load of EVs and the residential load. A case study of the EV model on the Herrljunga city grid showed that 100% EV penetration with 3.7 kW (charging rate of 14.8 km/h) chargers will not cause voltage violations in the grid. Winter load is responsible for 5% voltage drop at the weakest bus, and EVs add only 1% to this drop. In a Swedish city, charging EVs will require adding extra 1.43 kW/car to the grid capacity—assuming 22 kW (charging rate of 88 km/h) residential chargers. If the EV charging is not restricted to residential locations, an increase of 1.23 kW/car is expected. The proposed forecasting model is comparable in accuracy to previously developed models. As an advantage, the model produces a probability density function (PDF) describing the model’s certainty in the forecast. In contrast, many previous contributions provided only point forecasts.
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Ouedraogo, Sarah. "Développement de Stratégies Optimisées de Gestion de l’Energie Intermittente dans un Micro Réseau Photovoltaïque avec Stockage". Electronic Thesis or Diss., Corte, 2023. http://www.theses.fr/2023CORT0008.
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Les micro-réseaux sont considérés comme l'avenir de la production d'énergie et de la distribution dans les réseaux électriques. Beaucoup d’entre eux comportent une production photovoltaïque et un stockage, le plus souvent sous forme de batteries, pour alimenter différentes charges. Cette thèse a pour objectif principal de proposer des stratégies de gestion de l'énergie visant à optimiser les coûts de fonctionnement d'un micro-réseau photovoltaïque avec batterie, tout en respectant des contraintes spécifiques. Ce micro-réseau alimente des logements ainsi que des véhicules électriques.Pour ce faire, cinq stratégies de gestion de l'énergie basées sur des règles, avec une complexité croissante, ont été développées. Ces stratégies ont été comparées à une stratégie d'optimisation par programmation linéaire en termes de performances énergétiques et économiques. Les résultats obtenus indiquent que la stratégie la plus optimale atteint un niveau de performance proche de la stratégie par programmation linéaire, considérée comme « optimale ». Cependant, certaines limitations ont été observées pour les premières stratégies avec notamment la présence de coupures d'électricité dont nous ne pouvons pas nous satisfaire. Pour améliorer ces stratégies, l'effet saisonnier, particulièrement au niveau de la production solaire, a été pris en compte éliminant ainsi les coupures d'électricité. Selon la stratégie choisie, nous avons également observé que les batteries sont plus au moins sollicitées, il convenait donc de considérer ce vieillissement plus au moins important des batteries au niveau des performances. Des modèles de vieillissement adaptés ont ainsi été mis en œuvre. Les résultats ont montré que la rentabilité des batteries dépend du coût d'installation et qu’elles restent économiquement intéressantes pour des coûts inférieurs à environ 175 €/kWh. La stratégie de contrôle basée sur les règles la plus performante intègre la variation du coût du coût de l'électricité, la prévision de la production photovoltaïque, la variation saisonnière de la production PV et la dégradation de la batterie dans son processus de décision. Cette stratégie améliore le gain financier d'environ 68 % par rapport à la stratégie basée sur les règles la plus simple, proche d’une stratégie d’autoconsommationUne analyse de l'influence dans les simulations de différents paramètres tels que le tarif d’achat de l'électricité, la capacité de la batterie, les puissances échangées avec le réseau principal et le profil de consommation a été réalisée. Il a été constaté que le modèle de tarification de l'électricité a un effet important sur la répartition des flux d'énergie ainsi que le gain financier. L'influence de la taille de la batterie, de la limitation de la puissance échangeable avec le réseau principal et du profil de consommation dépend fortement de la stratégie utilisée mais aussi du modèle de tarification de l'électricité.Ce travail met en évidence l'importance d'intégrer les caractéristiques de l'énergie photovoltaïque dans les stratégies de gestion de l'énergie en utilisant différents outils tels que la prévision de la production photovoltaïque. Ces informations sont précieuses pour les décisions d'investissement et d'exploitationMicrogrids are considered as the future of energy production and distribution in electrical grid. Many of them incorporate photovoltaic generation and storage, mostly in the form of batteries, to power various loads. The main objective of this thesis is to propose energy management strategies designed to optimize the operating costs of a photovoltaic microgrid with battery while respecting specific constraints. This microgrid powers residential buildings and electric vehicles.To achieve this, five energy management strategies based on rules, with increasing complexity, were developed. These strategies were compared to an optimization using linear programming in terms of energy and economic performance. The results indicate that the most optimal strategy achieved a performance level close to the linear programming, which is considered "optimal." However, some limitations were observed for the initial strategies, including power cuts, which are not acceptable. To improve these strategies, the seasonal effect, particularly in photovoltaic production, was taken into account, eliminating power cuts. Depending on the chosen strategy, the batteries are more or less stressed, so it was necessary to consider the varying battery aging and its impact on performance. Suitable battery aging models were thus implemented. The results showed that the profitability of batteries depends on their installation cost and they remain economically viable for costs below approximately 175 €/kWh. The most effective rule-based control strategy considers variations in electricity costs, photovoltaic production forecasting, seasonal variation in PV production, and battery degradation in its decision-making process. This strategy improves financial gain by approximately 68 % compared to the simplest rule-based strategy, which is similar to a self-consumption strategy.An analysis of the influence of different parameters, such as electricity purchase tariffs, battery capacity, power exchanged with the main grid and consumption profiles was conducted through simulations. It was found that the electricity pricing model has a significant effect on energy distribution and financial gain. The influence of battery size, limitation of power exchange with the main grid, and consumption profile strongly depends on the strategy used, as well as the electricity pricing model.This work highlights the importance of integrating the characteristics of photovoltaic energy into energy management strategies through the use of various tools such as photovoltaic production forecasting. This information is valuable for investment and operational decision-making
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Agoua, Xwégnon. "Développement de méthodes spatio-temporelles pour la prévision à court terme de la production photovoltaïque". Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEM066/document.
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L’évolution du contexte énergétique mondial et la lutte contre le changement climatique ont conduit à l’accroissement des capacités de production d’énergie renouvelable. Les énergies renouvelables sont caractérisées par une forte variabilité due à leur dépendance aux conditions météorologiques. La maîtrise de cette variabilité constitue un enjeu important pour les opérateurs du système électrique, mais aussi pour l’atteinte des objectifs européens de réduction des émissions de gaz à effet de serre, d’amélioration de l’efficacité énergétique et de l’augmentation de la part des énergies renouvelables. Dans le cas du photovoltaïque(PV), la maîtrise de la variabilité de la production passe par la mise en place d’outils qui permettent de prévoir la production future des centrales. Ces prévisions contribuent entre autres à l’augmentation du niveau de pénétration du PV,à l’intégration optimale dans le réseau électrique, à l’amélioration de la gestion des centrales PV et à la participation aux marchés de l’électricité. L’objectif de cette thèse est de contribuer à l’amélioration de la prédictibilité à court-terme (moins de 6 heures) de la production PV. Dans un premier temps, nous analysons la variabilité spatio-temporelle de la production PV et proposons une méthode de réduction de la non-stationnarité des séries de production. Nous proposons ensuite un modèle spatio-temporel de prévision déterministe qui exploite les corrélations spatio-temporelles entre les centrales réparties sur une région. Les centrales sont utilisées comme un réseau de capteurs qui permettent d’anticiper les sources de variabilité. Nous proposons aussi une méthode automatique de sélection des variables qui permet de résoudre les problèmes de dimension et de parcimonie du modèle spatio-temporel. Un modèle spatio-temporel probabiliste a aussi été développé aux fins de produire des prévisions performantes non seulement du niveau moyen de la production future mais de toute sa distribution. Enfin nous proposons, un modèle qui exploite les observations d’images satellites pour améliorer la prévision court-terme de la production et une comparaison de l’apport de différentes sources de données sur les performances de prévisionThe evolution of the global energy context and the challenges of climate change have led to anincrease in the production capacity of renewable energy. Renewable energies are characterized byhigh variability due to their dependence on meteorological conditions. Controlling this variabilityis an important challenge for the operators of the electricity systems, but also for achieving the Europeanobjectives of reducing greenhouse gas emissions, improving energy efficiency and increasing the share of renewable energies in EU energy consumption. In the case of photovoltaics (PV), the control of the variability of the production requires to predict with minimum errors the future production of the power stations. These forecasts contribute to increasing the level of PV penetration and optimal integration in the power grid, improving PV plant management and participating in electricity markets. The objective of this thesis is to contribute to the improvement of the short-term predictability (less than 6 hours) of PV production. First, we analyze the spatio-temporal variability of PV production and propose a method to reduce the nonstationarity of the production series. We then propose a deterministic prediction model that exploits the spatio-temporal correlations between the power plants of a spatial grid. The power stationsare used as a network of sensors to anticipate sources of variability. We also propose an automaticmethod for selecting variables to solve the dimensionality and sparsity problems of the space-time model. A probabilistic spatio-temporal model has also been developed to produce efficient forecasts not only of the average level of future production but of its entire distribution. Finally, we propose a model that exploits observations of satellite images to improve short-term forecasting of PV production
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Vaz, André Gabriel Casaca de Rocha. "Photovoltaic forecasting with artificil neural networks". Master's thesis, 2014. http://hdl.handle.net/10451/11405.
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Tese de mestrado em Engenharia da Energia e do Ambiente, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2014São necessários esforços adicionais para promover a utilização de sistemas de produção de energia fotovoltaica conectados à rede como uma fonte fundamental de sistemas de energia elétrica, em níveis de penetrações mais elevados. Nesta tese é abordada a variabilidade da geração elétrica por sistemas fotovoltaicos e é desenvolvida com base na premissa de que o desempenho e a gestão de pequenas redes elétricas podem ser melhorados quando são utilizadas as informações de previsão de energia solar. É implementado um sistema de arquitetura de rede neuronal para o modelo auto-regressivo não-linear com variáveis exógenas (NARX) utilizando, não só, dados meteorológicos locais, mas também medições de sistemas fotovoltaicos circunjacentes. Diferentes configurações de entrada são otimizadas e comparadas para avaliar os efeitos no desempenho do modelo para previsão. A precisão das previsões revelou melhoria quando lhe são adicionadas informações de sistemas fotovoltaicos circunjacentes. Após ser selecionada a configuração de entrada da rede com o melhor desempenho, são testadas previsões com várias horas de antecedência e comparadas com o modelo da persistência, para verificar a precisão do modelo na previsão de diferentes horizontes temporais de curto prazo. O modelo NARX superou, claramente, o modelo de persistência, resultando num RMSE de 3,7% e de 4,5% aquando da antecipação das previsões de 5min e 2h30min, respetivamente.
Additional efforts are required to promote the use of grid-connected photovoltaic (PV) systems as a fundamental source in electric power systems at the higher penetration levels. This thesis addresses the variability of PV electric generation and is built based on the premise that the performance and management of small electric networks can be improved when solar power forecast information is used. A neural network architecture system for the Nonlinear Autoregressive with eXogenous inputs (NARX) model is implemented using not only local meteorological data but also measurements of neighbouring PV systems. Input configurations are optimized and compared to assess the effects in the model forecasting performance. The added value of the information of the neighbouring PV systems has demonstrated to further improve the prediction accuracy. After selecting the input configuration with the best network performance, forecasts up to several hours in advance are tested to verify the model forecasting accuracy for different short-term time horizons and compared with the persistence model. The NARX model clearly outperformed the persistence model and yielded a 3.7% and a 4.5% RMSE for the anticipation of the 5min and 2h30 forecasts, respectively.
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Serra, Pedro Henrique Cardeal. "Short-Term Forecasting of Photovoltaic Power Plants". Master's thesis, 2014. https://repositorio-aberto.up.pt/handle/10216/84962.
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Serra, Pedro Henrique Cardeal. "Short-Term Forecasting of Photovoltaic Power Plants". Dissertação, 2014. https://repositorio-aberto.up.pt/handle/10216/84962.
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Yeh, Chih-Ming, i 葉志明. "Research on the Short-Term Photovoltaic Power Forecasting". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/xg65cb.
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碩士國立臺北科技大學
電機工程系研究所
99
Since the the signing of the Kyoto Protocol and the global efforts in reducing carbon emission, the green energy industry has been developing with great vitality in recent years. Taiwan in particular boasts a well-established solar energy industry. Characterized by advantages like easy installation and integration into buildings, low pollution, and the capability of lowering fossil fuel consumption, Solar energy relies on capturing and converting solar radiation into electricity. However, subject to the changes in season, time, weather, cloud amount and other external factors, solar radiation is marked with uncertainty as it is difficult to predict the energy output in the even the next hour. This inherent instability renders the prediction of energy output an especially crucial issue in the effective operation of solar power systems. This paper uses prediction methods including Time series analysis aims at measuring the correlation between data and identifying the special features of data to facilitate prediction. Back-propagation neural network is capable of performing effective prediction by analyzing nonlinear statistical data; The main essence of the Adaptive Neuro-Fuzzy Inference Systems solution is the use of fuzzy theory and neural network learning characteristics and thus enhance the prediction accuracy. The forecast data are historical data in Taichung, Penghu and Malaysia, and the solar energy capacities are respectively 72kW, 70kW and 45.36kW. The predicted results show that Adaptive Neuro-Fuzzy Inference Systems prediction error and low frequency high, because it can effectively be done for each input variable fuzzy classification, and learning by neural networks, fuzzy features that not only has the characteristics of neural networks, and strengthen the overall predicted structure. This will increase the forecast accuracy is relatively many, with the predicted structure, when the capacity increases to more accurately predict when the document generation. Simulation results show that in these five cases, ANFIS is more accurate the prediction error is about 3.8% accurate forecasts for the industry not only provides reference for the development towards a greater capacity, can also provide this information as an economic Taipower scheduling.
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Kartini, Unit Three, i Unit Three Kartini. "Short Term Global Solar Irradiance Forecasting of Photovoltaic Plants". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/8awkbt.
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博士國立臺北科技大學
電機工程研究所
105
Forecasting global solar irradiance is an essential task to perform, particularly related to the rise of photovoltaic solar energy as a source of power. Forecasting global solar irradiance can be executed in different terms: long-term, medium-term, and short-term. The performance of photovoltaic systems (PV) is heavily influenced by some meteorological conditions, consisting of a temperature, global irradiation, humidity, wind speed and wind direction. The first part proposes a novel methodology for very short term forecasting of hourly global solar irradiance (GSI). The proposed methodology is based on meteorology data, especially for optimizing the operation of power generating electricity from photovoltaic (PV) energy. This methodology is a combination of k-nearest neighbor (kNN) algorithm modelling and artificial neural network (ANN) model. The kNN-ANN method is designed to forecast GSI for 60 min ahead based on meteorology data for the target PV station which position is surrounded by eight other adjacent PV stations. The novelty of this method is taking into account the meteorology data. A set of GSI measurement samples was available from the PV station in Taiwan which is used as test data. The first method implements kNN as a preprocessing technique prior to ANN method. The error statistical indicators of kNN-ANN model the mean absolute bias error (MABE) is 42 W/m2 and the root-mean-square error (RMSE) is 242 W/m2. The models forecasts are then compared to measured data and simulation results indicate that the kNN-ANN-based model presented in this research can calculate hourly GSI with satisfactory accuracy. The second part proposes based on meteorology data, especially for optimizing the operation of power generating electricity from photovoltaic energy. This methodology is a combination of k-nearest neighbor algorithm (kNN) modelling and multilayer backpropagation artificial neural network (BPANN) model. The kNN- BPANN model is designed to forecast GSI for 1 hours or 60 minutes ahead based on meteorology data for the target PV station which position is surrounded by eight other adjacent PV stations. The forecasting for global solar irradiance using kNN- BPANN modelling is a very powerful technique to determine the behaviour of time series data. The second method implements kNN as a preprocessing technique prior to backpropagation learning method. The error statistical indicators of kNN- BPANN models used momentum (mc) = 0.8 and RMSE is 176.5 W/m2. The models forecasts are then compared to measured data and validation results indicate that the kNN-BPANN based method presented in this study can estimate hourly GSI with satisfactory accuracy. The third part proposes a novel methodology for forecasting of one hourly global solar irradiance (GSI). This methodology is a combination of kNN decompotition method and artificial neural network (ANN) algorithm modelling. The kNN Decomposition-ANN method is designed to forecast GSI for 60 min ahead based on meteorology data for the target PV station which position is surrounded by eight other adjacent PV stations. A set of GSI measurement samples was available from the PV station in Taiwan which is used as test data. The third method implements kNN Decomposition as a preprocessing technique prior to ANN method. The error statistical indicators of kNN Decomposition- ANN model and the root-mean-square error (RMSE) is 20 W/m2. The models forecasts are then compared to measured data and simulation results indicate that the kNN Decomposition-ANN based model presented in this research can calculate hourly GSI with satisfactory accuracy.
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Lin, Yi-Song, i 林逸松. "Forecasting Short-Term Power Output of Photovoltaic Systems Based on Support Vector Regression". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/5wrty5.
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碩士國立臺灣大學
電機工程學研究所
107
Photovolatic will be the major power supply in the future, but it is not stable due to the weather condtion. Forecasting power output of PV system and optimal power dispatach can solve this problem, but how to accurate prediction? This thesis tries to predition based on support vector regression(SVR) and improve this method. The power data is collected from Taiwan Power Company and the weather data is collected from Taiwan Central Weather Bureau(TCWB). We use those historical data to forecast the PV output. Finally, we propose algorithm that is combined by K-Means Algorithm and SVR.It’s mean relative error is reduced to 7 %. This algorithm has better prediction accuracy than regression tree, K nearest neighbors regression and neural network. We extend this method to the online forecast. It still works, but needs to improve. Use the interpolation and weather forecast to predict the receant PV power output. Beacause of the inaccurate weather forecast, but its accuracy is limited by weather forecast.
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Huang, Jiang-Jun, i 黃江竣. "Apply Feature Selection and Neural Network for Forecasting the Ultra-Short-Term Photovoltaic Generation". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/wx84ke.
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碩士國立臺灣科技大學
電機工程系
107
According to Bureau of Energy, MOEA, R.O.C, penetration of renewable energies is expected to reach 27% by 2025. Due to the intermittent and low controllability of renewable energy generation, behind the higher proportion of renewable energy, there is a concern about the safe operation of the grid. In order to reduce the derivation of unstable characteristics of renewable energy generation, power generation prediction technology is an important research to stabilize grid power supply security. In foreign countries, solar energy accounts for the highest proportion of solar energy, so this article will focus on the solar power system's power generation forecast. In this paper, the forward selection method (FS) is used to characterize the input variables, and then the correlation between each feature and the solar photovoltaic power is judged. From the results of the forward selection method (FS), the coupling relationship between the input variables and the output (power generation) can be observed. Different neural network models are established based on the results of feature selection. By evaluating the prediction errors of these models, the best model for each season is obtained. Observing the problem that the non-correlated features affect the accuracy of the prediction model, and finally predicting the power generation with the best model of each season. The experimental results show that for the Back Propagation Neural Networks (BPNN), when the non-correlated features are excluded from modeling, the best prediction models for each season are generated; For Radial Basis Function Neural Networks (RBFNN), the best prediction model for each season is produced when modeling only with the most relevant features.
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"Photovoltaic Systems: Forecasting for Demand Response Management and Environmental Modelling to Design Accelerated Aging Tests". Master's thesis, 2017. http://hdl.handle.net/2286/R.I.44105.
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abstract: Distributed Renewable energy generators are now contributing a significant amount of energy into the energy grid. Consequently, reliability adequacy of such energy generators will depend on making accurate forecasts of energy produced by them. Power outputs of Solar PV systems depend on the stochastic variation of environmental factors (solar irradiance, ambient temperature & wind speed) and random mechanical failures/repairs. Monte Carlo Simulation which is typically used to model such problems becomes too computationally intensive leading to simplifying state-space assumptions. Multi-state models for power system reliability offer a higher flexibility in providing a description of system state evolution and an accurate representation of probability. In this study, Universal Generating Functions (UGF) were used to solve such combinatorial problems. 8 grid connected Solar PV systems were analyzed with a combined capacity of about 5MW located in a hot-dry climate (Arizona) and accuracy of 98% was achieved when validated with real-time data. An analytics framework is provided to grid operators and utilities to effectively forecast energy produced by distributed energy assets and in turn, develop strategies for effective Demand Response in times of increased share of renewable distributed energy assets in the grid. Second part of this thesis extends the environmental modelling approach to develop an aging test to be run in conjunction with an accelerated test of Solar PV modules. Accelerated Lifetime Testing procedures in the industry are used to determine the dominant failure modes which the product undergoes in the field, as well as predict the lifetime of the product. UV stressor is one of the ten stressors which a PV module undergoes in the field. UV exposure causes browning of modules leading to drop in Short Circuit Current. This thesis presents an environmental modelling approach for the hot-dry climate and extends it to develop an aging test methodology. This along with the accelerated tests would help achieve the goal of correlating field failures with accelerated tests and obtain acceleration factor. This knowledge would help predict PV module degradation in the field within 30% of the actual value and help in knowing the PV module lifetime accurately.Dissertation/Thesis
Masters Thesis Industrial Engineering 2017
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Alluhaidah, Bader. "MOST INFLUENTIAL VARIABLES FOR SOLAR RADIATION FORECASTING USING ARTIFICIAL NEURAL NETWORKS". 2014. http://hdl.handle.net/10222/50646.
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Decaying fossil fuel resources, international relation complexities, and the risks associated with nuclear power have led to an increased demand for alternative energy sources. Renewable energy sources offer adequate solutions to these challenges.
Forecasting of solar energy has also increased over the past decade due to its use in photovoltaic (PV) system design, load balance in hybrid systems, and projected potential future PV system feasibility. Artificial neural networks (ANN) have been used successfully for solar energy forecasting. In this work, several meteorological variables from Saudi Arabia as a case study will be used to determine the most effective variables on Global Solar Radiation (GSR) prediction. Those variables will be used as inputs for a proposed GSR prediction model. This model will be applicable in different locations and conditions. This model has a simple structure and offers better results in terms of error between actual and predicted solar radiation values.
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Mutwali, Bandar. "An Economic Analysis of Grid-tie Residential Photovoltaic System and ?Oil Barrel Price Forecasting: A Case Study of Saudi Arabia". 2013. http://hdl.handle.net/10222/15897.
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The demand for electricity is increasing daily due to technological advancement, and ?luxurious lifestyles. Increasing utilization of electricity means the depletion of fossil fuel ?reserves. Thus, governments around the world are seeking alternative and sustainable ?sources of energy such as the solar powered system. The main purpose of this research is ?to develop a knowledge base on residential electric generation from the grid and solar ?energy. This paper examined the economic feasibility of using grid-tied residential ?photovoltaic (GRPV) system in Saudi Arabia with the HOMER software. Models ?forecasting the price of oil barrels through artificial neural networks (ANN) were also ?employed in the analysis. The study shows that an oil-rich country like Saudi Arabia has ?potential to utilize the GRPV system as an alternative source of energy.This paper examined the economic feasibility of using grid-tied residential photovoltaic ??(GRPV) system in Saudi Arabia with the HOMER software. Models forecasting the ?price of oil barrels through artificial neural networks (ANN) were also employed in the ?analysis. The study shows that an oil-rich country like Saudi Arabia has potential to ?utilize the GRPV system as an alternative source of energy. This study provides a ?discussion of the potential for applying solar-powered and an assessment of the ?performance of existing systems based on collecting output data.?
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Poshtkouhi, Shahab. "Analysis and Implementation of Fine-grained Distributed Maximum Power Point Tracking in Photovoltaic Systems". Thesis, 2011. http://hdl.handle.net/1807/31391.
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This thesis deals with quantifying the merits of Distributed Maximum Power Point Tracking (DMPPT), as well as providing solutions to achieve DMPPT in PV systems. A
general method based on 3D modeling is developed to determine the energy yield of PV
installations exploiting different levels of DMPPT granularity. Sub-string-level DMPPT
is shown to have up to 30% more annual energy yield than panel-level DMPPT. A
Multi-Input-Single-Output (MISO) dc-dc converter is proposed to achieve DMPPT in
parallel-connected applications. A digital current-mode controller is used to operate the MISO converter in pseudo-CCM mode. For series-connected applications, the virtualparallel concept is introduced to utilize the robustness of the parallel connection. This concept is demonstrated on a three-phase boost converter. The topology offers reduced output voltage ripple under shading which increases the life-time of the output capacitor.
The prototypes yield output power benefits of up to 46% and 20% for the tested shading
conditions.