Forecasting of PV power application to PV power penetration in a microgrid
04 May 2016-pp 468-473
TL;DR: In this article, the NARX model is used to forecast the power output of a photovoltaic (PV) power plant in order to predict the penetration of PV into a microgrid.
Abstract: This paper treats the subject of Photovoltaic (PV) power forecasting. The power generated by a PV system is always nonlinear because of the variability of weather parameters such as solar irradiance and temperature. This variability present a major challenge in term of penetration of power into a microgrid plus the difficulty in control and management of grid. Moreover, to respond to all these equivocal, we use the forecasting applied to PV power which will injected into a grid. Many Approaches and strategies are found in the literature review, but the difficulty is how to find the convenient forecasting method to predict the power output of any PV plant. In this study we present the NARX model destined to predict the PV power.
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01 Mar 2017
TL;DR: In this paper, the authors proposed a day-ahead prediction method of PV output, which estimates the power generated by solar panels with and without prior knowledge of solar irradiance, based on the random forest using bagging algorithm.
Abstract: With the great recent moves towards green energy exploitation worldwide, the solar photovoltaic (PV) power has gained much attention. Thanks to PV panels' cost drop and recent improvements in energy conversion systems, the PV installations are getting more and more integrated into power plants. Because of high correlation with weather conditions, accurate short-term PV output forecast is highly recommended. An accurate prediction is needed to assess the effective contribution of solar energy in the grid, and to overcome the problems of intermittence. This paper proposes a day-ahead prediction method of PV output, which estimates the power generated by solar panels with and without prior knowledge of solar irradiance. The proposed model is the random forest using bagging algorithm, characterized by built-in cross validation and immunity to irrelevant inputs. A special attention is paid to the choice of most influential weather conditions on future power. The proposed approach is validated through tests on real data from PV sites in Australia.
21 citations
Cites methods from "Forecasting of PV power application..."
...Time series predictors are the simplest models, such as nonlinear autoregressive model with exogenous inputs [5], or autoregressive integrated moving average [6]....
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TL;DR: An extensive literature review on nowcasting technologies along with their current and future possible applications in the control of MGs finds Ramp rates control and scheduling of spinning reserves are found to be the most recognized applications of nowcasting in MGs.
Abstract: The integration of solar photovoltaic (PV) into electricity networks introduces technical challenges due to varying PV output. Rapid ramp events due to cloud movements are of particular concern for the operation of remote islanded microgrids (MGs) with high penetration of solar PV generation. PV plants and optionally controllable distributed energy resources (DERs) in MGs can be operated in an optimized way based on nowcasting, which is also called very short-term solar irradiance forecasting up to 60 min ahead. This study presents an extensive literature review on nowcasting technologies along with their current and future possible applications in the control of MGs. Ramp rates control and scheduling of spinning reserves are found to be the most recognized applications of nowcasting in MGs. An online survey has been conducted to identify the limitations, benefits and challenges of deploying nowcasting in MGs. The survey outcomes show that the incorporation of nowcasting tools in MG operations is still limited, though the possibility of increasing solar PV penetration levels in MGs if nowcasting tools are incorporated is acknowledged. Additionally, recent nowcasting tools, such as sky camera-based tools, require further validation under various conditions for more widespread adaptation by power system operators.
20 citations
TL;DR: A methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented, addressing the specific criticalities of this environment.
Abstract: Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG L a b 2 ) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications.
19 citations
Cites background from "Forecasting of PV power application..."
...Load and RES forecasting in microgrids opens new challenges [17,18]....
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TL;DR: The aim of this review paper is providing the necessary data about the basic principles and standards of photovoltaic (PV) power forecasting by stating numerous research studies carried out on the PV power forecasting topic specifically in the short-term time horizon which is advantageous for the EMS and grid operator.
Abstract: The management of clean energy is usually the key for environmental, economic, and sustainable developments. In the meantime, the energy management system (EMS) ensures the clean energy which includes many sources grouped in a small power plant such as microgrid (MG). In this case, the forecasting methods are used for helping the EMS and allow the high efficiency to the clean energy. The aim of this review paper is providing the necessary data about the basic principles and standards of photovoltaic (PV) power forecasting by stating numerous research studies carried out on the PV power forecasting topic specifically in the short-term time horizon which is advantageous for the EMS and grid operator. At the same time, this contribution can offer a state of the art in different methods and approaches used for PV power forecasting along with a careful study of different time and spatial horizons. Furthermore, this current review paper can support the tenders in the PV power forecasting.
19 citations
Cites background from "Forecasting of PV power application..."
...[87] Nonlinear stationary models including nonlinear-AR exogenous (NARX)....
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TL;DR: The comparative study between the benchmarking model and the forecasting methods showed that the forecasting techniques used in this study outperform the smart persistence model not only in terms of normalized root mean square error (nRMSE) and normalized mean absolute error ( nMAE) but also in termsof the skill score technique applied to assess the short-term PV power forecasting models.
Abstract: The current research paper deals with the worldwide problem of photovoltaic (PV) power forecasting by this innovative contribution in short-term PV power forecasting time horizon based on classification methods and nonlinear autoregressive with exogenous input (NARX) neural network model. In the meantime, the weather data and PV installation parameters are collected through the data acquisition systems installed beside the three PV systems. At the same time, the PV systems are located in Morocco country, respectively, the 2 kWp PV installation placed at the Higher Normal School of Technical Education (ENSET) in Rabat city, the 3 kWp PV system set at Nouasseur Casablanca city, and the 60 kWp PV installation also based in Rabat city. The multisite modelling approach, meanwhile, is deployed for establishing the flawless short-term PV power forecasting models. As a result, the implementation of different models highlights their achievements in short-term PV power forecasting modelling. Consequently, the comparative study between the benchmarking model and the forecasting methods showed that the forecasting techniques used in this study outperform the smart persistence model not only in terms of normalized root mean square error (nRMSE) and normalized mean absolute error (nMAE) but also in terms of the skill score technique applied to assess the short-term PV power forecasting models.
8 citations
Cites background from "Forecasting of PV power application..."
...,e closed loop also called parallel architecture, meanwhile, is convenient for multistep PV power forecasting [18]....
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References
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TL;DR: The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in photovoltaic systems application, mainly because of their symbolic reasoning, flexibility and explanation capabilities.
733 citations
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28 May 2007TL;DR: In this article, the authors present a list of principal symbols and abbreviations for parameterization schemes and their application in the terrestrial environment, including land surface-atmosphere parameterizations, water-surface-layer and turbulence parameterizations.
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452 citations
TL;DR: A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output is presented and achieves better prediction accuracy than the simple SVR and traditional ANN methods.
Abstract: To improve real-time control performance and reduce possible negative impacts of photovoltaic (PV) systems, an accurate forecasting of PV output is required, which is an important function in the operation of an energy management system (EMS) for distributed energy resources. In this paper, a weather-based hybrid method for 1-day ahead hourly forecasting of PV power output is presented. The proposed approach comprises classification, training, and forecasting stages. In the classification stage, the self-organizing map (SOM) and learning vector quantization (LVQ) networks are used to classify the collected historical data of PV power output. The training stage employs the support vector regression (SVR) to train the input/output data sets for temperature, probability of precipitation, and solar irradiance of defined similar hours. In the forecasting stage, the fuzzy inference method is used to select an adequate trained model for accurate forecast, according to the weather information collected from Taiwan Central Weather Bureau (TCWB). The proposed approach is applied to a practical PV power generation system. Numerical results show that the proposed approach achieves better prediction accuracy than the simple SVR and traditional ANN methods.
390 citations
"Forecasting of PV power application..." refers background in this paper
...The Day-ahead energy markets conducted on fixed schedules and those schedules again define the time horizons for day-ahead PV forecasts [1] [5] [6]....
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TL;DR: In this paper, state-of-the-art on wind speed/power forecasting and solar irradiance forecasting with ensemble methods are reviewed and compared based on reported results and comparisons based on simulations conducted by us.
Abstract: This paper reviews state-of-the-art on wind speed/power forecasting and solar irradiance forecasting with ensemble methods. The ensemble forecasting methods are grouped into two main categories: competitive ensemble forecasting and cooperative ensemble forecasting. The competitive ensemble forecasting is further categorized based on data diversity and parameter diversity. The cooperative ensemble forecasting is divided according to pre-processing and post-processing. Typical articles are discussed according to each category and their characteristics are highlighted. We also conduct comparisons based on reported results and comparisons based on simulations conducted by us. Suggestions for future research include ensemble of different paradigms and inter-category ensemble methods among others.
312 citations