Relevant bibliographies by topics / Forecasting of PV power generation

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Academic literature on the topic 'Forecasting of PV power generation'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Forecasting of PV power generation"

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Yu, Dukhwan, Seowoo Lee, Sangwon Lee, Wonik Choi, and Ling Liu. "Forecasting Photovoltaic Power Generation Using Satellite Images." Energies 13, no. 24 (2020): 6603. http://dx.doi.org/10.3390/en13246603.

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As the relative importance of renewable energy in electric power systems increases, the prediction of photovoltaic (PV) power generation has become a crucial technology, for improving stability in the operation of next-generation power systems, such as microgrid and virtual power plants (VPP). In order to improve the accuracy of PV power generation forecasting, a fair amount of research has been applied to weather forecast data (to a learning process). Despite these efforts, the problems of forecasting PV power generation remains challenging since existing methods show limited accuracy due to inappropriate cloud amount forecast data, which are strongly correlated with PV power generation. To address this problem, we propose a PV power forecasting model, including a cloud amount forecasting network trained with satellite images. In addition, our proposed model adopts convolutional self-attention to effectively capture historical features, and thus acquire helpful information from weather forecasts. To show the efficacy of the proposed cloud amount forecast network, we conduct extensive experiments on PV power generation forecasting with and without the cloud amount forecast network. The experimental results show that the Mean Absolute Percentage Error (MAPE) of our proposed prediction model, combined with the cloud amount forecast network, are reduced by 22.5% compared to the model without the cloud amount forecast network.
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Kure, Taiki, Haruka Danil Tsuchiya, Yusuke Kameda, Hiroki Yamamoto, Daisuke Kodaira, and Junji Kondoh. "Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation." Energies 15, no. 8 (2022): 2855. http://dx.doi.org/10.3390/en15082855.

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The power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical approaches for various time ranges. A short-term (30 min ahead) forecasting method had been proposed previously for multiple PV systems using motion estimation. This method forecasts the short time ahead PV power generation by estimating the motion between two geographical images of the distributed PV power systems. In this method, the parameter λ, which relates the smoothness of the resulting motion vector field and affects the accuracy of the forecasting, is important. This study focuses on the parameter λ and evaluates the effect of changing this parameter on forecasting accuracy. In the periods with drastic power output changes, the forecasting was conducted on 101 PV systems. The results indicate that the absolute mean error of the proposed method with the best parameter is 10.3%, whereas that of the persistence forecasting method is 23.7%. Therefore, the proposed method is effective in forecasting periods when PV output changes drastically within a short time interval.
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Iheanetu, Kelachukwu J. "Solar Photovoltaic Power Forecasting: A Review." Sustainability 14, no. 24 (2022): 17005. http://dx.doi.org/10.3390/su142417005.

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The recent global warming effect has brought into focus different solutions for combating climate change. The generation of climate-friendly renewable energy alternatives has been vastly improved and commercialized for power generation. As a result of this industrial revolution, solar photovoltaic (PV) systems have drawn much attention as a power generation source for varying applications, including the main utility-grid power supply. There has been tremendous growth in both on- and off-grid solar PV installations in the last few years. This trend is expected to continue over the next few years as government legislation and awareness campaigns increase to encourage a shift toward using renewable energy alternatives. Despite the numerous advantages of solar PV power generation, the highly variable nature of the sun’s irradiance in different seasons of various geopolitical areas/regions can significantly affect the expected energy yield. This variation directly impacts the profitability or economic viability of the system, and cannot be neglected. To overcome this challenge, various procedures have been applied to forecast the generated solar PV energy. This study provides a comprehensive and systematic review of recent advances in solar PV power forecasting techniques with a focus on data-driven procedures. It critically analyzes recent studies on solar PV power forecasting to highlight the strengths and weaknesses of the techniques or models implemented. The clarity provided will form a basis for higher accuracy in future models and applications.
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Lim, Su-Chang, Jun-Ho Huh, Seok-Hoon Hong, Chul-Young Park, and Jong-Chan Kim. "Solar Power Forecasting Using CNN-LSTM Hybrid Model." Energies 15, no. 21 (2022): 8233. http://dx.doi.org/10.3390/en15218233.

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Photovoltaic (PV) technology converts solar energy into electrical energy, and the PV industry is an essential renewable energy industry. However, the amount of power generated through PV systems is closely related to unpredictable and uncontrollable environmental factors such as solar radiation, temperature, humidity, cloud cover, and wind speed. Particularly, changes in temperature and solar radiation can substantially affect power generation, causing a sudden surplus or reduction in the power output. Nevertheless, accurately predicting the energy produced by PV power generation systems is crucial. This paper proposes a hybrid model comprising a convolutional neural network (CNN) and long short-term memory (LSTM) for stable power generation forecasting. The CNN classifies weather conditions, while the LSTM learns power generation patterns based on the weather conditions. The proposed model was trained and tested using the PV power output data from a power plant in Busan, Korea. Quantitative and qualitative evaluations were performed to verify the performance of the model. The proposed model achieved a mean absolute percentage error of 4.58 on a sunny day and 7.06 on a cloudy day in the quantitative evaluation. The experimental results suggest that precise power generation forecasting is possible using the proposed model according to instantaneous changes in power generation patterns. Moreover, the proposed model can help optimize PV power plant operations.
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Chang, Wen Yeau. "Power Generation Forecasting of Solar Photovoltaic System Using Radial Basis Function Neural Network." Applied Mechanics and Materials 368-370 (August 2013): 1262–65. http://dx.doi.org/10.4028/www.scientific.net/amm.368-370.1262.

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An accurate forecasting method for power generation of the solar photovoltaic (PV) system can help the power systems operator to reduce the risk of unreliability of electricity supply. This paper proposed a radial basis function (RBF) neural network method to forecast the power generation of PV system. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of power generation of a PV system. The good agreements between the realistic values and forecasting values are obtained; the numerical results show that the proposed forecasting method is accurate and reliable.
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Anuradha, K., Deekshitha Erlapally, G. Karuna, V. Srilakshmi, and K. Adilakshmi. "Analysis Of Solar Power Generation Forecasting Using Machine Learning Techniques." E3S Web of Conferences 309 (2021): 01163. http://dx.doi.org/10.1051/e3sconf/202130901163.

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Solar power is generated using photovoltaic (PV) systems all over the world. Because the output power of PV systems is alternating and highly dependent on environmental circumstances, solar power sources are unpredictable in nature. Irradiance, humidity, PV surface temperature, and wind speed are only a few of these variables. Because of the unpredictability in photovoltaic generating, it’s crucial to plan ahead for solar power generation as in solar power forecasting is required for electric grid. Solar power generation is weather-dependent and unpredictable, this forecast is complex and difficult. The impacts of various environmental conditions on the output of a PV system are discussed. Machine Learning (ML) algorithms have shown great results in time series forecasting and so can be used to anticipate power with weather conditions as model inputs. The use of multiple machine learning, Deep learning and artificial neural network techniques to perform solar power forecasting. Here in this regression models from machine learning techniques like support vector machine regressor, random forest regressor and linear regression model from which random forest regressor beaten the other two regression models with vast accuracy.
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Kim, Taeyoung, and Jinho Kim. "A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation." Energies 14, no. 14 (2021): 4256. http://dx.doi.org/10.3390/en14144256.

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Rooftop photovoltaic (PV) systems are usually behind the meter and invisible to utilities and retailers and, thus, their power generation is not monitored. If a number of rooftop PV systems are installed, it transforms the net load pattern in power systems. Moreover, not only generation but also PV capacity information is invisible due to unauthorized PV installations, causing inaccuracies in regional PV generation forecasting. This study proposes a regional rooftop PV generation forecasting methodology by adding unauthorized PV capacity estimation. PV capacity estimation consists of two steps: detection of unauthorized PV generation and estimation capacity of detected PV. Finally, regional rooftop PV generation is predicted by considering unauthorized PV capacity through the support vector regression (SVR) and upscaling method. The results from a case study show that compared with estimation without unauthorized PV capacity, the proposed methodology reduces the normalized root mean square error (nRMSE) by 5.41% and the normalized mean absolute error (nMAE) by 2.95%, It can be concluded that regional rooftop PV generation forecasting accuracy is improved.
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Fernandez-Jimenez, L. Alfredo, Sonia Terreros-Olarte, Alberto Falces, Pedro M. Lara-Santillan, Enrique Zorzano-Alba, and Pedro J. Zorzano-Santamaria. "Probabilistic reference model for hourly PV power generation forecasting." E3S Web of Conferences 152 (2020): 01002. http://dx.doi.org/10.1051/e3sconf/202015201002.

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This paper presents a new probabilistic forecasting model of the hourly mean power production in a Photovoltaic (PV) plant. It uses the minimal information and it can provide probabilistic forecasts in the form of quantiles for the desired horizon, which ranges from the next hours to any day in the future. The proposed model only needs a time series of hourly mean power production in the PV plant, and it is intended to fill a gap in international literature where hardly any model has been proposed as a reference for comparison or benchmarking purposes with other probabilistic forecasting models. The performance of the proposed forecasting model is tested, in a case study, with the time series of hourly mean power production in a PV plant with 1.9 MW capacity. The results show an improvement with respect to the reference probabilistic PV power forecasting models reported in the literature.
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Yu, Dukhwan, Wonik Choi, Myoungsoo Kim, and Ling Liu. "Forecasting Day-Ahead Hourly Photovoltaic Power Generation Using Convolutional Self-Attention Based Long Short-Term Memory." Energies 13, no. 15 (2020): 4017. http://dx.doi.org/10.3390/en13154017.

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The problem of Photovoltaic (PV) power generation forecasting is becoming crucial as the penetration level of Distributed Energy Resources (DERs) increases in microgrids and Virtual Power Plants (VPPs). In order to improve the stability of power systems, a fair amount of research has been proposed for increasing prediction performance in practical environments through statistical, machine learning, deep learning, and hybrid approaches. Despite these efforts, the problem of forecasting PV power generation remains to be challenging in power system operations since existing methods show limited accuracy and thus are not sufficiently practical enough to be widely deployed. Many existing methods using long historical data suffer from the long-term dependency problem and are not able to produce high prediction accuracy due to their failure to fully utilize all features of long sequence inputs. To address this problem, we propose a deep learning-based PV power generation forecasting model called Convolutional Self-Attention based Long Short-Term Memory (LSTM). By using the convolutional self-attention mechanism, we can significantly improve prediction accuracy by capturing the local context of the data and generating keys and queries that fit the local context. To validate the applicability of the proposed model, we conduct extensive experiments on both PV power generation forecasting using a real world dataset and power consumption forecasting. The experimental results of power generation forecasting using the real world datasets show that the MAPEs of the proposed model are much lower, in fact by 7.7%, 6%, 3.9% compared to the Deep Neural Network (DNN), LSTM and LSTM with the canonical self-attention, respectively. As for power consumption forecasting, the proposed model exhibits 32%, 17% and 44% lower Mean Absolute Percentage Error (MAPE) than the DNN, LSTM and LSTM with the canonical self-attention, respectively.
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Wu, Yuan-Kang, Cheng-Liang Huang, Quoc-Thang Phan, and Yuan-Yao Li. "Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints." Energies 15, no. 9 (2022): 3320. http://dx.doi.org/10.3390/en15093320.

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Solar power has rapidly become an increasingly important energy source in many countries over recent years; however, the intermittent nature of photovoltaic (PV) power generation has a significant impact on existing power systems. To reduce this uncertainty and maintain system security, precise solar power forecasting methods are required. This study summarizes and compares various PV power forecasting approaches, including time-series statistical methods, physical methods, ensemble methods, and machine and deep learning methods, the last of which there is a particular focus. In addition, various optimization algorithms for model parameters are summarized, the crucial factors that influence PV power forecasts are investigated, and input selection for PV power generation forecasting models are discussed. Probabilistic forecasting is expected to play a key role in the PV power forecasting required to meet the challenges faced by modern grid systems, and so this study provides a comparative analysis of existing deterministic and probabilistic forecasting models. Additionally, the importance of data processing techniques that enhance forecasting performance are highlighted. In comparison with the extant literature, this paper addresses more of the issues concerning the application of deep and machine learning to PV power forecasting. Based on the survey results, a complete and comprehensive solar power forecasting process must include data processing and feature extraction capabilities, a powerful deep learning structure for training, and a method to evaluate the uncertainty in its predictions.
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Dissertations / Theses on the topic "Forecasting of PV power generation"

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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.<br>Ph. D.
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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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Carr, Anna J. "A detailed performance comparison of PV modules of different technologies and the implications for PV system design methods /." Access via Murdoch University Digital Theses Project, 2005. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20050830.94641.

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Ahmed-Mahmoud, Ashraf. "Power conditioning unit for small scale hybrid PV-wind generation system." Thesis, Durham University, 2011. http://etheses.dur.ac.uk/580/.

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Small-scale renewable energy systems are becoming increasingly popular due to soaring fuel prices and due to technological advancements which reduce the cost of manufacturing. Solar and wind energies, among other renewable energy sources, are the most available ones globally. The hybrid photovoltaic (PV) and wind power system has a higher capability to deliver continuous power with reduced energy storage requirements and therefore results in better utilization of power conversion and control equipment than either of the individual sources. Power conditioning units (p.c.u.) for such small-scale hybrid PV-wind generation systems have been proposed in this study. The system was connected to the grid, but it could also operate in standalone mode if the grid was unavailable. The system contains a local controller for every energy source and the grid inverter. Besides, it contains the supervisory controller. For the wind generator side, small-scale vertical axis wind turbines (VAWTs) are attractive due to their ability to capture wind from different directions without using a yaw. One difficulty with VAWTs is to prevent over-speeding and component over-loading at excessive wind velocities. The proposed local controller for the wind generator is based on the current and voltage measured on the dc side of the rectifier connected to the permanent magnet synchronous generator (PMSG). Maximum power point tracking (MPPT) control is provided in normal operation under the rated speed using a dc/dc boost converter. For high wind velocities, the suggested local controller controls the electric power in order to operate the turbine in the stall region. This high wind velocity control strategy attenuates the stress in the system while it smoothes the power generated. It is shown that the controller is able to stabilize the nonlinear system using an adaptive current feedback loop. Simulation and experimental results are presented. The PV generator side controller is designed to work in systems with multiple energy sources, such as those studied in this thesis. One of the most widely used methods to maximize the output PV power is the hill climbing technique. This study gives guidelines for designing both the perturbation magnitude and the time interval between consecutive perturbations for such a technique. These guidelines would improve the maximum power point tracking efficiency. According to these guidelines, a variable step MPPT algorithm with reduced power mode is designed and applied to the system. The algorithm is validated by simulation and experimental results. A single phase H-bridge inverter is proposed to supply the load and to connect the grid. Generally, a current controller injects active power with a controlled power factor and constant dc link voltage in the grid connected mode. However, in the standalone mode, it injects active power with constant ac output voltage and a power factor which depends on the load. The current controller for both modes is based on a newly developed peak current control (p.c.c.) with selective harmonic elimination. A design procedure has been proposed for the controller. Then, the method was demonstrated by simulation. The problem of the dc current injection to the grid has been investigated for such inverters. The causes of dc current injection are analyzed, and a measurement circuit is then proposed to control the inverter for dc current injection elimination. Characteristics of the proposed method are demonstrated, using simulation and experimental results. At the final stage of the study, a supervisory controller is demonstrated, which manages the different operating states of the system during starting, grid-connected and standalone modes. The operating states, designed for every mode, have been defined in such a hybrid model to allow stability and smooth transition between these states. The supervisory controller switches the system between the different modes and states according to the availability of the utility grid, renewable energy generators, the state of charge (SOC) of energy storage batteries, and the load. The p.c.u. including the supervisory controller has been verified in the different modes and states by simulation.
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Foreman, Mark McKinney. "Control and operation of SMES and SMES/PV systems." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-10062009-020156/.

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Agalgaonkar, Yashodhan Prakash. "Control and operation of power distribution system for optimal accommodation of PV generation." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24954.

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The renewable policies in various countries are driving significant growth of grid connected renewable generation sources such as the Photovoltaics (PVs). Typically a PV generation is integrated into power systems at the low and the medium voltage distribution level. The uptake of an intermittent power from the PVs is challenging the power system operation and control. The network voltage control is one of the major challenges during the operation of the distribution connected PVs. The active power injection from a PV plant causes variable voltage rise. This forces the existing voltage control devices such as on-load tap-changer (OLTC) and voltage regulator (VR) to operate continuously. The consequence is the reduction of the operating life of the voltage control mechanism. Also, the conventional non-coordinated reactive power control results in the operation of the VR at its control limit (VR runaway condition). This research focuses on the distribution voltage control in the presence of PV generation and helps to establish detailed insights into the various associated challenges. Firstly, the typical grid integrated PV topologies are discussed. The existing power system operational practices are presented and their limitations are identified. A voltage control methodology to tackle challenges such as over-voltage, excessive tap counts and VR runaway is presented. These challenges are alleviated through the coordinated reactive power control. The reactive power coordination is achieved through the deterministic distribution optimal power flow solved through the interior point technique. The irradiance and the load forecasting errors are another set of challenges from the distribution network operators' perspective. The stochastic optimal voltage control strategy is proposed to tackle the element of randomness associated with the forecast errors. The stochastic operational risks such as an over- voltage and a VR runaway are defined through a chance constrained optimization problem. The simulation study is performed using a realistic 95-bus UK generic distribution network model and a practically measured irradiance to demonstrate the effectiveness of the proposed control strategies. The thesis makes an effort to offer an insight into the operational challenges and propose strategies to achieve a seamless integration of the PVs into the power systems.
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Agrawal, Ashish. "Performance of PV Generation Feedback Controllers: Power Factor versus Volt-VAR Control Strategies." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/78088.

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The variable nature of photovoltaic (PV) generation can cause voltage fluctuations in power distribution systems. Feedback control can be used to minimize the voltage fluctuations. This thesis presents the results obtained from comparing the control performance of two types of PV generation feedback control, namely Volt-VAR control and constant power factor control. A three minute PV generation transient is used to evaluate controller performance, where the transient data used originated from one second measurements taken on an actual PV generator. Using the three minute transient, a set of parametric studies are performed on both feedback control strategies. The performance of the control strategies are compared as to voltage control on the distribution feeder and also to the effect that the control may have on transmission system voltage. In considering transmission system voltage, the reactive power drawn from the substation during the transient is evaluated. Simulation results suggest that the choice of control to be implemented should be based on both transmission and distribution system operational concerns.<br>Master of Science
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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.<br>Ph. D.
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Sahoo, Smrutirekha. "Impact Study: Photo-voltaic Distributed Generation on Power System." Thesis, Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-32369.

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The grid-connected photo-voltaic (PV) system is one of the most promising renewable energy solutions which offers many benefits to both the end user and the utility network and thus it has gained the popularity over the last few decades. However, due to the very nature of its invariability and weather dependencies, the large scale integration of this type of distributed generation has created challenges for the network operator while maintaining the quality of the power supply and also for reliable and safe operations of the grids. In this study, the behavioral impact of large scale PV system integration which are both steady and dynamic in nature was studied.  An aggregate PV model suited to study the impacts was built using MATLAB/Simulink.  The integration impacts of PV power to existing grids were studied with focus on the low voltage residential distribution grids of Mälarenergi Elnät AB (10/0.4 kV). The steady state impacts were related to voltage profile, network loss. It was found that the PV generation at the load end undisputedly improves the voltage profile of the grid especially for the load buses which are situated at farther end of the grid. Further, with regard to the overvoltage issue, which is generally a concern during the low load demand period it was concluded that, at a 50% PV penetration level, the voltage level for the load buses is within the limit of 103% as prescribed by the regulator excepting for few load buses. The voltage level for load buses which deviate from the regulatory requirement are located at distance of 1200 meter or further away from the substation. The dynamic impact studied were for voltage unbalancing in the grid, which was found to have greater impact at the load buses which is located farther compared to a bus located nearer to the substation. With respect to impact study related to introduction of harmonics to the grid due to PV system integration, it was found that amount of harmonic content which was measured as total harmonic distortion (THD) multiplies with integration of more number of PV system. For a 50 % penetration level of PV, the introduced harmonics into the representative network is very minimal. Also, it was observed from the simulation study that THD content are be less when the grid operates at low load condition with high solar irradiance compared to lower irradiance and high load condition.
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VARGAS, SORAIDA AGUILAR. "FORECASTING PROBABILISTIC DENSITY DISTRIBUTION OF WIND POWER GENERATION USING NON-PARAMETRIC TECHNIQUES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=26821@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Como resultado do processo de contração de novos Leilões de energia eólica e a entrada em operação de novos parques eólicos ao sistema elétrico Brasileiro, é necessário que o planejamento da operação das atividades de curto prazo como a regulação, atendimento da carga, balanceamento e programação do despacho das unidades geradoras entre outras atividades, seja efetuado de tal que os riscos técnicos e financeiros sejam minimizados. Porém esta não é uma tarefa simples, já que fornecer previsões exatas para esse processo apresenta uma série de desafios, como a incorporação da incerteza no cálculo das previsões. Daqui que a literatura técnica reporta diversas técnicas que proporcionam estimativas da densidade de probabilidade de geração de energia eólica, pois tais estimações permitem obter previsões da densidade de probabilidade para a energia eólica. Neste contexto, a previsão da velocidade do vento nos aproveitamentos eólicos passa a ser uma informação fundamental para os modelos de apoio à decisão que suportam a operação econômica e segura dos sistemas elétricos, pois a maioria dos modelos precisa da previsão da velocidade do vento para calcular a previsão da energia eólica. Este trabalho apresenta uma proposta uma estratégia de especificação não paramétrica para a previsão da geração de energia eólica, empregando a comumente conhecida densidade condicional por kernel, o qual permite calcular a função densidade de probabilidade da produção eólica para qualquer horizonte de tempo, condicionada à previsão da velocidade do vento obtida através da aplicação da metodologia de Análise Espectral Singular (SSA) para previsão. A metodologia foi validada com sucesso usando a série temporal das medias horárias da velocidade do vento e da produção eólica de um parque eólico Brasileiro. Os resultados foram comparados contra outras metodologias para a previsão da velocidade do vento, onde a abordagem não paramétrica proposta produz resultados muito proeminentes.<br>As a result of the new contracting process wind power auctions and the entrance into operation of new wind farms to the Brazilian electrical system, it is requires that the planning of the operation of short-term activities such as regulation, balancing and programming dispatch of units commitment among other activities, is made such that the technical and financial risks are minimized. But this is not a simple task, since providing accurate forecasts for this process presents several challenges, as the incorporation of uncertainty in the calculation of the forecasts. Hence the technical literature reports several techniques that provide estimates of the probability of wind power generation density, because such estimates allow to obtain forecasts of the wind power probability density function. In this context, wind speed forecasting in wind farms becomes essential information for decision support models which helps the economic and safe operation of electrical systems, due to the fact that most of the models need to the wind speed predictions for forecasting wind energy. This thesis proposes a non-parametric specification strategy for forecasting of wind power generation, using the commonly known conditional kernel density estimation, which allows the estimation of the probability density function of wind power generation for any time horizon, conditioned on wind speed forecast obtained by applying the Singular Spectrum Analysis methodology (SSA). The methodology has been successfully validated using the time series of wind speed and hourly averages of wind production of a Brazilian wind farm. The results were compared against other methodologies for wind speed prediction, and the proposed non-parametric approach produced very prominent results.
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Books on the topic "Forecasting of PV power generation"

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National Renewable Energy Laboratory (U.S.), ed. Future of grid-tied PV business models: What will happen when PV penetration on the distribution grid is significant? : preprint. National Renewable Energy Laboratory, 2008.

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(Organization), IT Power, ed. Solar photovoltaic power generation using PV technology. Asian Development Bank, 1996.

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Deambi, Suneel. Solar PV power: A global perspective. The Energy and Resources Institute, 2011.

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Goodrich, Alan C. Solar PV manufacturing cost model group: Installed solar PV system prices. National Renewable Energy Laboratory, 2011.

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Coddington, Michael H. Updating interconnection screens for PV system integration. National Renewable Energy Laboratory, 2012.

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Kempe, Michael D. Predicting the performance of edge seal materials for PV. National Renewable Energy Laboratory, 2012.

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Marion, William F. Overview of the PV module model in PVwatts. National Renewable Energy Laboratory, 2010.

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IEEE Standards Coordinating Committee 21, Photovoltaics., Institute of Electrical and Electronics Engineers., and IEEE Standards Board, eds. IEEE recommended practice for qualification of photovoltaic (PV) modules. Institute of Electrical and Electronics Engineers, 1996.

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Center, Lewis Research, ed. Design, optimization, and analysis of a self-deploying PV tent array. Lewis Research Center, 1991.

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Paulescu, Marius. Weather Modeling and Forecasting of PV Systems Operation. Springer London, 2013.

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Book chapters on the topic "Forecasting of PV power generation"

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Deng, Tian-Yang, Wen-Li Li, Feng Zhang, Qiang Hua, Chun-Ru Dong, and Boon Han Lim. "A Multiscale Global-Local Transformer for Long-Sequence PV Power Generation Forecasting." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4207-6_55.

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Yan, Huabin, Yichen zheng, Huimin Mei, Xiangou Zhu, and Honghong Pu. "Dynamic Spatial-Temporal Graph Neural Network for Ultra Short-Term PV Power Generation Forecasting." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2042-5_5.

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Yadav, Pankaj, Gulshan Sihag, Vikas Pratap Singh, and Vivek Vijay. "Solar PV Power Generation Forecasting Using Synergy of Artificial Intelligence and Metaheuristics Based Approaches." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84602-1_15.

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Pachauri, Rupendra Kumar, Ashutosh Shukla, Ahmad Faiz Minai, et al. "Comparative Study on Solar PV Module Performance with Sun Irradiance Trapping Mechanism: Power Generation Forecasting Using Machine Learning." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6749-0_12.

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Gupta, Priya, and Rhythm Singh. "Indirect Forecasting of Hourly PV Power Generation Based on a Hybrid Model Combining Data Analysis and Machine Learning Technique." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6616-5_21.

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Kassem, Youssef, Hüseyin Gökçekuş, Aliyu Babangida, Emmanuel J. Larmouth, and Lloyd Garmeriah Mafela. "Time Series Forecasting of Solar Power Generation for 5.4 kW Off-Grid PV System: A Case Study in Al Mahmra, Lebanon." In Intelligent Computing & Optimization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19958-5_58.

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Blazev, Anco S. "PV Power Generation." In Photovoltaics for Commercial and Utilities Power Generation. River Publishers, 2020. http://dx.doi.org/10.1201/9781003151630-5.

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Paulescu, Marius, Eugenia Paulescu, Paul Gravila, and Viorel Badescu. "Forecasting the Power Output of PV Systems." In Weather Modeling and Forecasting of PV Systems Operation. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4649-0_10.

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Blazev, Anco S. "Large-scale PV Projects." In Photovoltaics for Commercial and Utilities Power Generation. River Publishers, 2020. http://dx.doi.org/10.1201/9781003151630-6.

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Syu, Jia-Hao, Chi-Fang Chao, and Mu-En Wu. "Forecasting System for Solar-Power Generation." In Recent Challenges in Intelligent Information and Database Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1685-3_6.

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Conference papers on the topic "Forecasting of PV power generation"

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Jittratorn, Nuttapat, Chen-Shuo Liu, Chao-Ming Huang, and Hong-Tzer Yang. "PV Power Forecasting for Operation of BESS Integrated with a PV Generation Plant." In 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2024. http://dx.doi.org/10.1109/iciea61579.2024.10664871.

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Velani, Janki, Abhishek Tiwari, and Naran M. Pindoriya. "Short-Term Solar PV Generation Forecasting Using Bi-GRU Neural Network." In 2024 23rd National Power Systems Conference (NPSC). IEEE, 2024. https://doi.org/10.1109/npsc61626.2024.10986970.

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Joshi, Siddharth, and Amee Daiya. "Energy Generation Forecasting for Solar PV and Hybrid Solar PV & BESS Using System Advisor Model." In 2024 7th International Conference on Electric Power and Energy Conversion Systems (EPECS). IEEE, 2024. https://doi.org/10.1109/epecs62845.2024.10805502.

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Deanseekeaw, Anurak, Khin Phyu Ye Myint, Nonthawat Khortsriwong, Promphak Boonraksa, Terapong Boonraksa, and Boonruang Marungsri. "Application of Deep Reinforcement Learning Techniques for Power Generation Forecasting of a PV Power Plant." In 2024 International Conference on Power, Energy and Innovations (ICPEI). IEEE, 2024. http://dx.doi.org/10.1109/icpei61831.2024.10749009.

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Amjad, Furqan, Tarmo Korotko, and Argo Rosin. "Forecasting PV Energy Generation Using Transformer-Based Architectures: A Comparative Study of Lag-Llama, TFT, and DeepAR." In 2024 IEEE 65th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). IEEE, 2024. https://doi.org/10.1109/rtucon62997.2024.10830763.

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Nguyen, Binh Nam, Emanuele Ogliari, Emilio Pafumi, Davide Alberti, Sonia Leva, and Minh Quan Duong. "Forecasting Generating Power of Sun Tracking PV Plant using Long-Short Term Memory Neural Network Model: A case study in Ninh Thuan - Vietnam." In 2024 Tenth International Conference on Communications and Electronics (ICCE). IEEE, 2024. http://dx.doi.org/10.1109/icce62051.2024.10634629.

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Jin, Xinyi, Ruyue Han, and Yuechao Ma. "PV Power Forecasting Based on VMD-RIME-LSTM." In 2024 IEEE China International Youth Conference on Electrical Engineering (CIYCEE). IEEE, 2024. https://doi.org/10.1109/ciycee63099.2024.10846005.

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Yang, Yanru, Yu Liu, and Shaolong Shu. "Temporal Hierarchy Reconciliation Network for PV Power Forecasting." In 2025 15th International Conference on Power, Energy, and Electrical Engineering (CPEEE). IEEE, 2025. https://doi.org/10.1109/cpeee64598.2025.10987302.

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Wang, Jie, Wenping Qin, Ruipeng Lu, Wenbo Zhang, Haixiao Zhu, and Anting Zhao. "Short-term PV power forecasting system Based on GraphCast Weather Forecasting Model." In 2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2024. https://doi.org/10.1109/ei264398.2024.10991445.

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Aboelmagd, Mohamed R., Ali Selim, and Mamdouh Abdel-Akher. "PV Grid Forecasting Analysis using Artificial Neural Network." In 2024 25th International Middle East Power System Conference (MEPCON). IEEE, 2024. https://doi.org/10.1109/mepcon63025.2024.10850132.

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Reports on the topic "Forecasting of PV power generation"

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Broderick, Robert Joseph, Jimmy Edward Quiroz, Abraham Ellis, Matthew J. Reno, Jeff Smith, and Roger Dugan. Time series power flow analysis for distribution connected PV generation. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1088099.

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Bryce, Richard, Grant Buster, Kate Doubleday, et al. Solar PV, Wind Generation, and Load Forecasting Dataset for ERCOT 2018: Performance-Based Energy Resource Feedback, Optimization, and Risk Management (P.E.R.F.O.R.M.). Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1972698.

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Seema, Seema, Andreas Theocharis, and Andreas Kassler. Evaluate Temporal and Spatio-Temporal Correlations for Different Prosumers Using Solar Power Generation Time Series Dataset. Karlstad University, 2024. http://dx.doi.org/10.59217/yjll7238.

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This study investigates the temporal and spatio-temporal correlations of solar power generation among different prosumers of Uppsala and Halmstad, Sweden. Using solar power generation data from seven prosumer in Uppsala and five in Halmstad, we evaluate the correlation of solar power production generation at specific locations correlates with itself over different time lags (autocorrelation). In addition, we examine the spatiotemporal correlations of solar power production at various locations over a range of lags using time shifted cross correlation. These spatio-temporal correlations can facilitate the development of synchronized demand response strategies and dynamic energy pricing. Moreover, the timeshifted cross-correlation analysis assists in improving forecasting models for solar power generation. By identifying significant correlations between solar generation data from different locations and applying time shifts to account for variations in weather and sunlight exposure, operators can enhance the accuracy of their predictions. This methodology enables them to fill in missing data points by leveraging correlated information from neighboring regions. Consequently, more robust forecasting models can be developed, leading to better resource allocation, improved energy management, and reduced operational uncertainties in the grid. This research highlights the evaluation and potential of utilizing spatio-temporal and temporal correlations in solar power data to enhance energy management and planning.
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Baumgartner, Franz, Cyril Allenspach, Ebrar Özkalay, et al. Performance of Partially Shaded PV Generators Operated by Optimized Power Electronics 2024. Edited by Ulrike Jahn. International Energy Agency Photovoltaic Power Systems Programme, 2024. https://doi.org/10.69766/leof5152.

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Inhomogeneous shading on the PV generator leads to disproportionately high losses. As the potential of PV generation on roofs or façades is to be increasingly utilised in the coming decades, these cases will occur more frequently. The aim here is to provide an overview of the challenges and state-of-the-art technical solutions for partial shading. Current developments in PV engineering show that maximum performance lies in the combination between optimised module placement, the use of modules that are tolerant of shading and optimised power electronics. Shortly after the discovery of the solar cell, blocking or bypass diodes were used to solve the inhomogeneous currents of groups of solar cells arranged in series or parallel wiring. Even today, they are still the most efficient and robust solution for the majority of common shading PV applications. Due to the very high rated outputs of the solar modules and the presence of only three bypass diodes, high temperatures can occur on a locally shaded solar cell. This forces heat outputs of up to 200W or 100W in the butterfly module connection through the associated activated bypass diode, which must be dissipated by the most shaded cell. If additional small-area defects occur in this affected solar cell, hotspot peak temperatures can occur, which can lead to permanent damage to the module or the risk of fire. However, in order to prevent a third of the module output being lost in this case, four or more bypass diodes are now used in so-called shadow-tolerant PV modules. With a higher number of bypass diodes per module area, it is also possible to selectively bypass smaller, less efficient areas of the module, which leads to an increase in the module yield. The hotspot effects can also be comprehensively and robustly prevented by the small number of solar cells per bypass diode, provided the bypass diode is properly designed. The first manufacturers are beginning to place these shade-tolerant PV modules on the markets. Today, planners can also select different power electronics systems for the next step in system integration towards grid feed-in, i.e. the connection of the individual modules in the string. This is the classic series connection of all modules in the string to the input of the DC/AC string inverter (SINV), which leads to the highest yields for weak and medium shading. This applies, for example, to light shading with a chimney or a ventilation pipe, where no more than one tenth of the modules in the string are reached by the shade at the same time during the six hours around midday, even when using standard modules with only three bypass diodes. (see Table 1) With medium to heavy shading, the widely used DC/DC converters directly on the PV module (MLPE), often also called power optimisers, can be used profitably. However, the combination of shade-tolerant PV modules with conventional SINVs can often deliver comparable annual yields. However, if the optimisers are also used behind each module even with weak shading (allMLPE), they deliver less yield in total than the simple SINV, as their own DC/DC losses then have a negative impact compared to simple connectors. This only becomes apparent if the MLPE manufacturers' data sheet claims of 99% efficiency are not viable. The published measurements carried out in independent laboratories over the last four years are listed in this report, which suggest that losses are around 2% higher. As the differences in yield between the power electronics variants SINV and MLPE are usually less than four per cent in annual yield for light to medium shading, the above-mentioned real MLPE efficiency at the specific operating points plays the decisive role in planning the most efficient system. However, as the commercial PV software planning tools currently use these MLPE manufacturer specifications which are over estimated, no meaningful system comparison can be expected for these shading categories. In this report the results of annual simulations performed by some sophisticated simulation tools that take these real MLPE losses into account are discussed.
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Elshurafa, Amro, Fakhri Hasanov, and Lester C. Hunt. Macroeconomic, Energy and Emission Effects of Solar PV Deployment at Utility and Distributed Scales in Saudi Arabia. King Abdullah Petroleum Studies and Research Center, 2023. http://dx.doi.org/10.30573/ks--2023-dp10.

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This study assesses the macroeconomic, energy and emissions impacts of solar photovoltaic (PV) deployment in the Kingdom of Saudi Arabia for the period 2021–2030. This is accomplished by linking an energy and environmental sector augmented macroeconometric model with a power model and a distributed generation model. Furthermore, this study distinguishes between the macroeconomic, energy and emissions impacts of PV deployment at the utility and distributed generation scales. To the best of our knowledge, these two aspects make this work novel. We analyze three scenarios: (i) fully government-funded utility-scale PV deployment, (ii) half-government-funded utility-scale PV deployment and (iii) household-funded distributed-generation-scale PV deployment, with some government support alongside a business-as-usual (BaU) scenario.
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Backstrom, Robert, and David Dini. Firefighter Safety and Photovoltaic Systems Summary. UL Firefighter Safety Research Institute, 2011. http://dx.doi.org/10.54206/102376/kylj9621.

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Under the United States Department of Homeland Security (DHS) Assistance to Firefighter Grant Fire Prevention and Safety Research Program, Underwriters Laboratories examined fire service concerns of photovoltaic (PV) systems. These concerns include firefighter vulnerability to electrical and casualty hazards when mitigating a fire involving photovoltaic (PV) modules systems. The need for this project is significant acknowledging the increasing use of photovoltaic systems, growing at a rate of 30% annually. As a result of greater utilization, traditional firefighter tactics for suppression, ventilation and overhaul have been complicated, leaving firefighters vulnerable to potentially unrecognized exposure. Though the electrical and fire hazards associated with electrical generation and distribution systems is well known, PV systems present unique safety considerations. A very limited body of knowledge and insufficient data exists to understand the risks to the extent that the fire service has been unable to develop safety solutions and respond in a safe manner. This fire research project developed the empirical data that is needed to quantify the hazards associated with PV installations. This data provides the foundation to modify current or develop new firefighting practices to reduce firefighter death and injury. A functioning PV array was constructed at Underwriters Laboratories in Northbrook, IL to serve as a test fixture. The main test array consisted of 26 PV framed modules rated 230 W each (5980 W total rated power). Multiple experiments were conducted to investigate the efficacy of power isolation techniques and the potential hazard from contact of typical firefighter tools with live electrical PV components. Existing fire test fixtures located at the Delaware County Emergency Services Training Center were modified to construct full scale representations of roof mounted PV systems. PV arrays were mounted above Class A roofs supported by wood trusses. Two series of experiments were conducted. The first series represented a room of content fire, extending into the attic space, breaching the roof and resulting in structural collapse. Three PV technologies were subjected to this fire condition – rack mounted metal framed, glass on polymer modules, building integrated PV shingles, and a flexible laminate attached to a standing metal seam roof. A second series of experiments was conducted on the metal frame technology. These experiments represented two fire scenarios, a room of content fire venting from a window and the ignition of debris accumulation under the array. The results of these experiments provide a technical basis for the fire service to examine their equipment, tactics, standard operating procedures and training content. Several tactical considerations were developed utilizing the data from the experiments to provide specific examples of potential electrical shock hazard from PV installations during and after a fire event.
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Backstrom, Robert, and David Backstrom. Firefighter Safety and Photovoltaic Installations Research Project. UL Firefighter Safety Research Institute, 2011. http://dx.doi.org/10.54206/102376/viyv4379.

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Under the United States Department of Homeland Security (DHS) Assistance to Firefighter Grant Fire Prevention and Safety Research Program, Underwriters Laboratories examined fire service concerns of photovoltaic (PV) systems. These concerns include firefighter vulnerability to electrical and casualty hazards when mitigating a fire involving photovoltaic (PV) modules systems. The need for this project is significant acknowledging the increasing use of photovoltaic systems, growing at a rate of 30% annually. As a result of greater utilization, traditional firefighter tactics for suppression, ventilation and overhaul have been complicated, leaving firefighters vulnerable to potentially unrecognized exposure. Though the electrical and fire hazards associated with electrical generation and distribution systems is well known, PV systems present unique safety considerations. A very limited body of knowledge and insufficient data exists to understand the risks to the extent that the fire service has been unable to develop safety solutions and respond in a safe manner. This fire research project developed the empirical data that is needed to quantify the hazards associated with PV installations. This data provides the foundation to modify current or develop new firefighting practices to reduce firefighter death and injury. A functioning PV array was constructed at Underwriters Laboratories in Northbrook, IL to serve as a test fixture. The main test array consisted of 26 PV framed modules rated 230 W each (5980 W total rated power). Multiple experiments were conducted to investigate the efficacy of power isolation techniques and the potential hazard from contact of typical firefighter tools with live electrical PV components. Existing fire test fixtures located at the Delaware County Emergency Services Training Center were modified to construct full scale representations of roof mounted PV systems. PV arrays were mounted above Class A roofs supported by wood trusses. Two series of experiments were conducted. The first series represented a room of content fire, extending into the attic space, breaching the roof and resulting in structural collapse. Three PV technologies were subjected to this fire condition – rack mounted metal framed, glass on polymer modules, building integrated PV shingles, and a flexible laminate attached to a standing metal seam roof. A second series of experiments was conducted on the metal frame technology. These experiments represented two fire scenarios, a room of content fire venting from a window and the ignition of debris accumulation under the array. The results of these experiments provide a technical basis for the fire service to examine their equipment, tactics, standard operating procedures and training content. Several tactical considerations were developed utilizing the data from the experiments to provide specific examples of potential electrical shock hazard from PV installations during and after a fire event.
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Ayele, Seife, Wei Shen, Tadesse Kuma Worako, Lucy H. Baker, and Samson Hadush. Renewable Energy Procurement in Ethiopia: Overcoming Obstacles in Procurement from Independent Power Producers. Institute of Development Studies (IDS), 2021. http://dx.doi.org/10.19088/ids.2021.064.

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Developing countries are increasingly using auctions for the procurement of utility-scale renewable electricity, due to the potential for attracting private investment. However, auction design and implementation can face serious obstacles due to complex context-specific factors. In 2017, Ethiopia launched its Public–Private Partnership (PPP) policy and procurement framework to promote infrastructure development, including electricity generation. Since 2018, it has organised renewable energy auctions to procure new capacity from independent power producers (IPPs). However, the new framework faces numerous challenges. Using a literature review and primary data from more than 70 interviews and from stakeholder consultations, this study explores the political economy challenges and opportunities facing IPP project preparation, decision-making, coordination and implementation, and risks to investors. To date, Ethiopia has held two rounds of tenders to procure 1,000 megawatts (MW) of electricity from eight projects; the first tender for two solar photovoltaic (PV) projects led to the signing of Power Purchase Agreements (PPAs) and was hailed as one of the cheapest tariff rates in sub-Saharan Africa, at US$2.526 cents/kilowatt hour (kWh) over 25 years. However, none of the projects have yet become operational. This study also finds fault lines impeding the implementation of IPP projects, including the risk of foreign currency availability and convertibility of Ethiopian birr to expatriate profits. It proposes measures to overcome these obstacles and mitigate risks, to put Ethiopia on course to achieve universal access to electricity by 2030.
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