Short-term Photovoltaic Research Articles

A Real-Time EV Charging Scheduling for Parking Lots With PV System and Energy Store System

The problem of electric vehicle (EV) charging scheduling in commercial parking lots has become a meaningful study in recent years, especially for the parking lots near the workplace that serve fixed users. This paper focuses on the optimization of the EV charging in the parking lot integrating energy storage system (ESS) and photovoltaic (PV) system. A smart charging management system is first established. The charging optimization problem is formulated as a cost minimization problem. Then, grey wolf optimizer (GWO) is introduced as a method to find the optimal solution. Considering the constraint conditions in the optimization problem, an improved binary grey wolf optimizer (IBGWO) is proposed, which can improve the convergence speed and optimization accuracy. Finally, a real-time EV charging scheduling strategy based on short-term PV power prediction and IBGWO is proposed. Several cases are simulated to analyze the performance of the proposed strategy. The experimental results show that the proposed IBGWO is superior in solving the proposed charging scheduling problem compared with other meta-heuristic algorithms. Moreover, the proposed strategy can effectively improve the utilization rate of the PV power and reduce the electricity cost of operators.

Short-term power prediction for photovoltaic power plants using a hybrid improved Kmeans-GRA-Elman model based on multivariate meteorological factors and historical power datasets

With the continuous consumption of fossil fuels such as coal, oil and natural gas, the environmental energy problem has become the focus of attention in the world. The utilization of clean and non-polluting solar energy for photovoltaic (PV) power generation can effectively utilize renewable energy. However, the instability of weather condition makes the output of PV power have strong randomness, fluctuations and intermittence. Therefore, reliable PV power prediction method can reduce the disadvantages of PV power generation, which is of great significance to maintenance and repair of power plants. In the study, a novel hybrid prediction model combining improved K-means clustering, grey relational analysis (GRA) and Elman neural network (Hybrid improved Kmeans-GRA-Elman, HKGE) for short-term PV power prediction is proposed. The proposed model is established by using multivariate meteorological factors and historical power datasets for two years. The improved K-means approach is applied to cluster the historical power datasets, and combining the GRA method to determine the similarity days and the optimal similarity day of the forecasting day. The Elman neural network model is employed to better develop the nonlinear relationship between multivariate meteorological factors and power data. Compared with the other eight prediction methods, the results show that the proposed method has an outstanding performance on improving the prediction accuracy.

Hybrid method for short‐term photovoltaic power forecasting based on deep convolutional neural network

Photovoltaic (PV) electric power has been widely employed to satisfy rising energy demands because inexhaustible renewable energy is environmentally friendly. In order to mitigate the impact caused by the uncertainty of solar radiation in grid-connected PV systems, a hybrid method based on a deep convolutional neural network (CNN) is introduced for short-term PV power forecasting. In the proposed method, different frequency components are first decomposed from the historical time series of PV power through variational mode decomposition (VMD). Then, they are constructed into a two-dimensional data form with correlations in both daily and hourly timescales that can be extracted by convolution kernels. Moreover, the time series of residue from VMD is refined into advanced features by a CNN, which could reduce the data size and be easier for further model training along with meteorological elements. The hybrid model has been verified by forecasting the output power of PV arrays with diverse capacities in various hourly timescales, which demonstrates its superiority over commonly used methods.

Prediction of short-term PV power output and uncertainty analysis

Due to the intermittency and uncertainty in photovoltaic (PV) power outputs, not only deterministic point predictions (DPPs), but also associated prediction Intervals (PIs) are important information for promoting the application of PV in practice, especially when grid connection continues to grow. While there are few studies focused on quantifying the uncertainty of forecasting PV power outputs, this paper developed a novel two-stage model to quantify the PIs of PV power outputs. In the first stage, three different neural networks, namely Generalized Regression Neural Network (GRNN), Extreme Learning Machine Neural Network (ELMNN) and Elman Neural Network (ElmanNN), were integrated using the Genetic Algorithms optimized Back Propagation (GA-BP) method to develop a Weight-Varying Combination Forecast Mode (WVCFM) model. The WVCFM model was applied to generate DPPs. In the second stage, a nonparametric kernel density estimation (NKDE) method was adopted to estimate the PIs regarding the statistical distribution of the errors of the DPPs derived in the first stage. The proposed method was tested using four types of PV output and weather data measured from a 15 kW grid-connected PV system. The cover percentage of prediction intervals (PICPs) were computed under the confidence level of 95%, 90%, 85% and 80%, respectively. The results imply that the two-stage model proposed in the paper outperforms conventional forecast methods in terms of prediction of short-term PV power outputs and associated uncertainties.

Short-term solar power forecasting considering cloud coverage and ambient temperature variation effects

Abstract The use of solar photovoltaic (PV) systems as green renewable sources for electricity generation in modern power networks is steadily increasing. One of the problems with using PV units in developing countries and small companies is access to a simple model to assess the short-time output of a solar cell. For example, a robust model that is capable of analyzing cloud variations and ambient temperature (important factors impacting the PV output) to assess short-term PV output would be very helpful. This article proposes a new methodology to assess the impacts of these factors on the hourly output power of a PV system. Our results showed that the proposed model has the ability to assess PV output by using the hourly data of cloud and ambient temperature change. To validate the model, the output was compared to results of measurements from PV systems installed in Sanandaj and Rasht cities located in North-West (latitude 35.31 and longitude 47.00) and North (latitude 37.28 and longitude 49.58) Iran, respectively. Also, the standardized root-mean-square error method (nRMSE) was used to validate the high accuracy of the proposed method.

A PSO-ANFIS based Hybrid Approach for Short Term PV Power Prediction in Microgrids

This paper proposes a hybrid approach based on a combination of particle swarm optimization (PSO) and adaptive neuro-fuzzy inference systems (ANFIS) for one-day-ahead hourly photovoltaic (PV) power generation prediction in microgrids. The increasing penetration of solar PV energy into electric power generation systems imposes important issues to address resulting from its intermittent and uncertain nature. These challenges necessitate an accurate PV power generation forecasting tool for planning efficient operation of power systems and to ensure reliability of supply. In this paper, a combination of PSO and ANFIS is used to develop a PV power prediction model. To demonstrate the effectiveness of the proposed method, it is tested based on practical information of PV power generation data of a real case study microgrid in Beijing. The proposed approach is compared with two other prediction methods. Evaluation of forecasting performance is made with the persistence forecasting method as a reference model, and results are compared with actual scenario. The proposed approach outperformed back propagation neural network and persistence based forecasting methods, demonstrating its favorable accuracy and reliability.

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

High quality photovoltaic (PV) power prediction intervals (PIs) are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.

A Solar Battery Dc Simplified Model Based Short-term Prediction Method for Urban Distributed PV Generation

It is the heart of the matter for development and utilization of photovoltaic (PV) station connecting grid to raise the level of short-term PV generation prediction. In addition, it is important to improve the solar photovoltaic power development and utilization and ensure the safety of the grid. This paper presents solar battery dc simplified model based short-term prediction method for urban distributed PV generation. To put into operation for the PV station recently, the proposed method owns higher accuracy. Finally, by comparing to the actual measured data with the predicted data, the experimental results have verified the validity and correctness of the proposed method.

A Short-Term Photovoltaic Power Prediction Model Based on an FOS-ELM Algorithm

With the increasing proportion of photovoltaic (PV) power in power systems, the problem of its fluctuation and intermittency has become more prominent. To reduce the negative influence of the use of PV power, we propose a short-term PV power prediction model based on the online sequential extreme learning machine with forgetting mechanism (FOS-ELM), which can constantly replace outdated data with new data. We use historical weather data and historical PV power data to predict the PV power in the next period of time. The simulation result shows that this model has the advantages of a short training time and high accuracy. This model can help the power dispatch department schedule generation plans as well as support spatial and temporal compensation and coordinated power control, which is important for the security and stability as well as the optimal operation of power systems.

Distribution Voltage Regulation Through Active Power Curtailment With PV Inverters and Solar Generation Forecasts

Distribution voltage profiles are subjected to overvoltage limit violations from high penetration of grid-connected photovoltaic (PV) systems. Such voltage rises seen at the point of PV interconnection can be mitigated by adaptively changing the active and/or reactive power injection from the PV inverter. This work proposes a local voltage regulation technique that utilizes very short-term (15 s) PV power forecasts to circumvent imminent upper voltage limit violation or an overvoltage scenario. To provide these PV generation forecasts, a hybrid forecasting method is formulated based on Kalman filter theory, which applies physical PV generation modeling using high-resolution (15 s) data from on-site measurements. The proposed algorithm employs an active power curtailment based on these PV power forecasts, when the reactive power estimate given by a droop-based method cannot provide the desired voltage regulation within predefined power factor limits. The curtailment threshold values are calculated in such a way that this voltage regulation technique can reduce possible voltage limit violations. The effectiveness of the proposed method is demonstrated with case studies developed on a standard test feeder with realistic load and PV generation profiles.

Ultra short‐term PV power forecasting based on ELM segmentation model

With the rapid increase of the installed capacity of photovoltaic (PV) power plants, it is important to forecast PV power output in order to meet the requirements of power systems. Based on extreme learning machine (ELM), this study proposes a methodology of ultra short-term PV power forecasting by using historical power output data, which can predict power output for a prediction horizon of 30 min with 10 min time step. Compared with auto-regressive and moving average (ARMA) time series model and BPAQ3 neural network, ELM provides better prediction accuracy in each time horizons. To further enhance the model performance, the authors analyse the forecasting error distribution in different time intervals during each day, and according to the error expectation, divide the data set into two subsets, then train each subset separately to establish segmentation model. The result shows that ELM segmentation model based on error distribution provides a better performance than using ELM alone.

Study of Short-Term Photovoltaic Power Forecast Based on Error Calibration under Typical Climate Categories

With the increasing permeability of photovoltaic (PV) power production, the uncertainties and randomness of PV power have played a critical role in the operation and dispatch of the power grid and amplified the abandon rate of PV power. Consequently, the accuracy of PV power forecast urgently needs to be improved. Based on the amplitude and fluctuation characteristics of the PV power forecast error, a short-term PV output forecast method that considers the error calibration is proposed. Firstly, typical climate categories are defined to classify the historical PV power data. On the one hand, due to the non-negligible diversity of error amplitudes in different categories, the probability density distributions of relative error (RE) are generated for each category. Distribution fitting is performed to simulate probability density function (PDF) curves, and the RE samples are drawn from the fitted curves to obtain the sampling values of the RE. On the other hand, based on the fluctuation characteristic of RE, the recent RE data are utilized to analyze the error fluctuation conditions of the forecast points so as to obtain the compensation values of the RE. The compensation values are adopted to sequence the sampling values by choosing the sampling values closest to the compensation ones to be the fitted values of the RE. On this basis, the fitted values of the RE are employed to correct the forecast values of PV power and improve the forecast accuracy.

Local short-term variability in solar irradiance

Abstract. Characterizing spatiotemporal irradiance variability is important for the successful grid integration of increasing numbers of photovoltaic (PV) power systems. Using 1 Hz data recorded by as many as 99 pyranometers during the HD(CP)2 Observational Prototype Experiment (HOPE), we analyze field variability of clear-sky index k* (i.e., irradiance normalized to clear-sky conditions) and sub-minute k* increments (i.e., changes over specified intervals of time) for distances between tens of meters and about 10 km. By means of a simple classification scheme based on k* statistics, we identify overcast, clear, and mixed sky conditions, and demonstrate that the last of these is the most potentially problematic in terms of short-term PV power fluctuations. Under mixed conditions, the probability of relatively strong k* increments of ±0.5 is approximately twice as high compared to increment statistics computed without conditioning by sky type. Additionally, spatial autocorrelation structures of k* increment fields differ considerably between sky types. While the profiles for overcast and clear skies mostly resemble the predictions of a simple model published by Hoff and Perez (2012), this is not the case for mixed conditions. As a proxy for the smoothing effects of distributed PV, we finally show that spatial averaging mitigates variability in k* less effectively than variability in k* increments, for a spatial sensor density of 2 km−2.

An Empirical Model for Rack-Mounted PV Module Temperatures for Southeast Asian Locations Evaluated for Minute Time Scales

Short-term power output forecasting of photovoltaic (PV) systems has become increasingly important to balance the supply and demand of grid electricity in an efficient manner. It is well known that the operating temperature has a strong influence on the power output of photovoltaic devices; therefore, improvements in simulating the short-term PV module temperature add to the accuracy of PV power output forecasting. In this study, a new empirical model is proposed, which is suitable for transient weather conditions. This model uses four variables to predict the temperature of PV modules, namely ambient temperature, irradiance, wind speed, and relative humidity. The model is applied and evaluated for various silicon-based PV technologies at two Southeast Asian locations (Singapore and Jayapura) on a 1-min basis. Results show root-mean-squared errors (RMSE) of 1.5–3.2 K, which marks an average improvement in RMSE of 46%, compared with similar existing empirical models.

Comparison of Adaptive Neuro-Fuzzy Inference Systems and Echo State Networks for PV Power Prediction

Use of photo-voltaic (PV) power as a source of electricity has been rapidly growing. Integration of large PV power into the grid operation introduces several challenges. Uncertainty of PV power generation causes frequency fluctuations and power system instabilities. Due to this, short term PV power prediction has become an important area of study. Short term PV power prediction supports proper decision-making in control centers. Power generation output of a PV plant is highly dependent on different weather conditions such as solar irradiance, temperature and cloud covers. Weather data analysis and prediction can be considered as big data due to its complexity and dynamically changing characteristics. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is developed and compared with an echo state network (ESN) for short term PV power prediction. The ANFIS approach consists of three ANFIS modules for predicting solar irradiance and temperature, and then estimating the PV power, respectively. The ESN on the other hand predicts the PV power based on current weather parameters. A weather database containing data sampled every second is used in developing the ANFIS and ESN based PV power prediction systems. Results are compared under different Clemson, SC weather conditions with the two approaches.

Multitime-Scale Data-Driven Spatio-Temporal Forecast of Photovoltaic Generation

The increasing penetration of stochastic photovoltaic (PV) generation in electric power systems poses significant challenges to system operators. To ensure reliable operation of power systems, accurate forecasting of PV power production is essential. In this paper, we propose a novel multitime-scale data-driven forecast model to improve the accuracy of short-term PV power production. This model leverages both spatial and temporal correlations among neighboring solar sites, and is shown to have improved performance compared to the conventional persistence (PSS) model. The tradeoff between computation cost and improved forecast quality is studied using real datasets from PV sites in California and Colorado.

A Novel Hybrid Model for Short-Term Forecasting in PV Power Generation

The increasing use of solar power as a source of electricity has led to increased interest in forecasting its power output over short-time horizons. Short-term forecasts are needed for operational planning, switching sources, programming backup, reserve usage, and peak load matching. However, the output of a photovoltaic (PV) system is influenced by irradiation, cloud cover, and other weather conditions. These factors make it difficult to conduct short-term PV output forecasting. In this paper, an experimental database of solar power output, solar irradiance, air, and module temperature data has been utilized. It includes data from the Green Energy Office Building in Malaysia, the Taichung Thermal Plant of Taipower, and National Penghu University. Based on the historical PV power and weather data provided in the experiment, all factors that influence photovoltaic-generated energy are discussed. Moreover, five types of forecasting modules were developed and utilized to predict the one-hour-ahead PV output. They include the ARIMA, SVM, ANN, ANFIS, and the combination models using GA algorithm. Forecasting results show the high precision and efficiency of this combination model. Therefore, the proposed model is suitable for ensuring the stable operation of a photovoltaic generation system.

Storage requirements for PV power ramp-rate control

Short-term variability in the power generated by large grid-connected photovoltaic (PV) plants can negatively affect power quality and the network reliability. New grid-codes require combining the PV generator with some form of energy storage technology in order to reduce short-term PV power fluctuation. This paper proposes an effective method in order to calculate, for any PV plant size and maximum allowable ramp-rate, the maximum power and the minimum energy storage requirements alike. The general validity of this method is corroborated with extensive simulation exercises performed with real 5-s one year data of 500kW inverters at the 38.5MW Amaraleja (Portugal) PV plant and two other PV plants located in Navarra (Spain), at a distance of more than 660km from Amaraleja.

06/00822 A simple photo-voltaic tracking system: Karimov, K. S. et al. Solar Energy Materials and Solar Cells, 2005, 87, (1–4), 49–59.

Use of photo-voltaic (PV) power as a source of electricity has been rapidly growing. Integration of large PV power into the grid operation introduces several challenges. Uncertainty of PV power generation causes frequency fluctuations and power system instabilities. Due to this, short term PV power prediction has become an important area of study. Short term PV power prediction supports proper decision-making in control centers. Power generation output of a PV plant is highly dependent on different weather conditions such as solar irradiance, temperature and cloud covers. Weather data analysis and prediction can be considered as big data due to its complexity and dynamically changing characteristics. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is developed and compared with an echo state network (ESN) for short term PV power prediction. The ANFIS approach consists of three ANFIS modules for predicting solar irradiance and temperature, and then estimating the PV power, respectively. The ESN on the other hand predicts the PV power based on current weather parameters. A weather database containing data sampled every second is used in developing the ANFIS and ESN based PV power prediction systems. Results are compared under different Clemson, SC weather conditions with the two approaches.