Short-Term Photovoltaic Generation Forecasting Using Multiple Heterogenous Sources of Data

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

Western China has good conditions for constructing large-scale photovoltaic (PV) power stations; however, such power plants with large fluctuations and strong randomness suffer from the long-distance power transmission problem, which needs to be solved. For large-scale PV power stations that do not have the conditions for simultaneous hydropower and PV power, this study examined long-distance delivery mode and energy storage optimization. The objective was to realize the long-distance transmission of electrical energy and maximize the economic value of the energy storage and PV power storage. For a large-scale PV power station, the energy storage optimization was modelled under a given long-distance delivery mode, and the economic evaluation system quantified using the net present value (NPV) of the battery was based on the energy dispatch optimization model. By contrast, a lithium battery performance model was developed. Therefore, further analysis of the economics of the energy storage and obtaining the best capacity of the energy storage battery and corresponding replacement cycle considered battery degradation. The case study of Qinghai Gonghe 100 MWp demonstration base PV power station showed that the optimal energy storage capacity was 5 MWh, and the optimal replacement period was 2 years. Therefore, the annual abandoned electricity was reduced by 3.051 × 10 4 MWh compared with no energy storage. The utilization rate of both the PV power station and quality of the delivered electricity were modelled to realize a long-distance transmission to the grid net. This will have an important guiding significance to develop and construct large-scale single PV power stations.

Short-term photovoltaic power forecasting method based on irradiance correction and error forecasting

Considering the recent drop (up to 86%) in photovoltaic (PV) module prices from 2010 to 2017, many countries have shown interest in investing in PV plants to meet their energy demand. In this study, a detailed design methodology is presented to achieve high benefits with low installation, maintenance and operation costs of PV plants. This procedure includes in detail the semi-hourly average time meteorological data from the location to maximise the accuracy and detailed characteristics of different PV modules and inverters. The minimum levelised cost of energy (LCOE) and maximum annual energy are the objective functions in this proposed procedure, whereas the design variables are the number of series and parallel PV modules, the number of PV module lines per row, tilt angle and orientation, inter-row space, PV module type, and inverter structure. The design problem was solved using a recent hybrid algorithm, namely, the grey wolf optimiser-sine cosine algorithm. The high performance for LCOE-based design optimisation in economic terms with lower installation, maintenance and operation costs than that resulting from the use of maximum annual energy objective function by 12%. Moreover, sensitivity analysis showed that the PV plant performance can be improved by decreasing the PV module annual reduction coefficient.

Performance study on a grid connected 20 kWp solar photovoltaic installation in an industry in Tiruchirappalli (India)

This study developed a rational approach to the consideration of solar photovoltaic (PV) power plants applied to electric utility systems, then performed a requirements assessment and preliminary impact and penetration analyses by studying PV generation in three actual utility systems. Conventional utility loss-of-load probability and production simulation methods were used, along with a PV plant performance model developed for the study.Evaluations were based on comparison of total utility generation system costs with and without PV plants. The study is reported under the following sections: 1) geographic areas for photovoltaic power plants; 2) siting of photovoltaic power plants; 3) electric utility characteristics; 4) photovoltaic power plant characteristics; 5) methods of study; 6) study inputs; 7) results; 8) cost and performance goals for viability; 9) reference PV selection; 10) reference PV plant definitions; 11) comparison with conventional generation; 12) requirements for PV applications; 13) preliminary impact analysis; and 14) preliminary penetration analysis. (WHK)

Photovoltaic (PV) technology is one of the most promising forms of renewable energy. However, power generation from PV technologies is highly dependent on variable weather conditions, which are neither constant nor controllable, which can affect grid stability. Accurate forecasting of PV power production is essential to ensure reliable operation within the power system. The primary challenge of this study is to accurately predict photovoltaic energy production, considering that weather conditions, such as irradiance, temperature, and wind speed, are random variables. The key contribution of this article is developing a machine learning model to predict the energy production of a real PV power plant in Algeria. Using real measurements sourced from the Center of Renewable Energy Development (CDER) in Adrar, Algeria, in 2021. The data are from two PV power plants located in harsh desert climate conditions. The results presented in this study offer a comparison of several predictive methods applied to real-world data from a PV power plant situated in the Saharan Region. Our findings reveal that the artificial neural network (ANN) model yields the most accurate predictions of 94.96%, with the smallest prediction error: root mean square (RMSE) and mean absolute error (MAE) are 7.78% and 3.80%, respectively.

Photovoltaic (PV) technology, as an efficient solution for mitigating impacts of climate change, has been increasingly used across the world to replace fossil-fuel power to minimize greenhouse gas emissions. With the world's highest cumulative and fastest built PV capacity, China needs to assess the environmental and social impacts of these established photovoltaic (PV) power plants. However, a comprehensive map regarding the locations and extent of the PV power plants remains to be scarce at the country scale. This study developed a workflow combining machine learning and visual interpretation methods with big satellite data to map the PV power plants in China. We applied a pixel-based Random Forest (RF) model to classify the PV power plants from composite images in 2020 with 30-meter spatial resolution on Google Earth Engine (GEE). The result classification map was further improved by a visual interpretation approach. Eventually, we established a map of PV power plants in China by 2020, covering a total area of 2917 km2. Based on the derived national PV map, we found that most PV power plants were sited on cropland, followed by barren land and grassland. In addition, the installation of PV power plants has generally decreased the vegetation cover. This new dataset is expected to be conducive to policy management, environmental assessment, and further classification of PV power plants.

This paper presents distributed and asynchronous active fault management (DA-AFM) to manage renewable energy upon faults. Addressed here are two challenges in fault management for photovoltaic (PV) farms and wind farms. The first one is the activation of crowbars in doubly-fed induction generator (DFIG) wind turbine systems during fault ride-though. The activation undesirably makes DFIG-based wind farms lose control and absorb reactive power. The second challenge is implementation of distributed fault management for distinct PV farms with different objectives and constraints. Coordination for large number of PV farms facilitates integration of themselves and other renewable energy. To prevent crowbars from being activated, DA-AFM controls nearby PV farms' interface converters to smooth voltage drops so that DFIGs experience voltages with a lower dropping speed. To enable distributed computation of DA-AFM's optimization formulation, a distributed and asynchronous surrogate Lagrangian relaxation (DA-SLR) method is devised to coordinate a cluster of PV farms. Simulation results have demonstrated DA-AFM 's effectiveness in preventing crowbars' activation in wind farms and in coordinating diverse PV farms.

Operational performance of megawatt-scale grid integrated rooftop solar PV system in tropical wet and dry climates of India

The paper proposed control strategies for a large-scale photovoltaic power plant in different operating modes in order to improve the utilization of solar energy and power quality of the integration. Take one 3MW photovoltaic power plant as an example, the energy storage device was used as the node to provide frequency and voltage support during the switching period. Then the photovoltaic power generation simulation model was built based on the MW level photovoltaic power plant’s information and data on the PSCAD/EMTDC software platform. This model achieved a coordinated control between photovoltaic power generation and the grid under different operating modes. The simulation result also proved the effectiveness of the control strategies and the significance in ensuring the reliability of power supply and grid stability.

A generic methodology to efficiently integrate weather information in short-term Photovoltaic generation forecasting models

This paper presents an algorithm for the economical design of a utility-scale photovoltaic (PV) power plant via compromising between the cost of energy and the availability of the plant. The algorithm inputs are the plant peak power and the price of inverters with respect to their power ratings. The outputs are the optimum inverter ratings and the interconnection topology of PV panels. This paper introduces the effective levelized cost of energy (LCOE) (ELCOE) index as the core of the proposed design algorithm. ELCOE is an improved index based on the conventional LCOE that includes the availability of a power plant in economical assessments. The conventional LCOE index determines centralized topology (e.g., 1-MW inverter for a 1-MW PV power plant) for minimizing the energy generation cost, whereas based on ELCOE, a multistring topology (e.g., a 1-MW PV plant consists of fifty 20-kW inverters) despite of higher investment cost becomes the economically winning topology. Given the price of commercially available PV inverters at present, the case studies in this paper show that, for 0.1-100-MW PV power plants, the economical ratings of inverters range from 8 to 100 kW. The recently installed PV power plants confirm the feasibility of the calculations based on the suggested algorithm.

Photovoltaic (PV) technology is becoming more popular due to climate change because it allows for replacing fossil-fuel power generation to reduce greenhouse gas emissions. Consequently, many countries have been attempting to generate electricity through PV power plants over the last decade. Monitoring PV power plants through satellite imagery, machine learning models, and cloud-based computing systems that may ensure rapid and precise locating with current status on a regional basis are crucial for environmental impact assessment and policy formulation. The effect of fusion of the spectral, textural with different neighbor sizes, and topographic features that may improve machine learning accuracy has not been evaluated yet in PV power plants’ mapping. This study mapped PV power plants using a random forest (RF) model on the Google Earth Engine (GEE) platform. We combined textural features calculated from the Grey Level Co-occurrence Matrix (GLCM), reflectance, thermal spectral features, and Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) from Landsat-8 imagery and elevation, slope, and aspect from Shuttle Radar Topography Mission (SRTM) as input variables. We found that the textural features from GLCM prominent enhance the accuracy of the random forest model in identifying PV power plants where a neighbor size of 30 pixels showed the best model performance. The addition of texture features can improve model accuracy from a Kappa statistic of 0.904 ± 0.05 to 0.938 ± 0.04 and overall accuracy of 97.45 ± 0.14% to 98.32 ± 0.11%. The topographic and thermal features contribute a slight improvement in modeling. This study extends the knowledge of the effect of various variables in identifying PV power plants from remote sensing data. The texture characteristics of PV power plants at different spatial resolutions deserve attention. The findings of our study have great significance for collecting the geographic information of PV power plants and evaluating their environmental impact.

Models for the calculation of the production of large photovoltaic (PV) power plants with different types of modules (monofacial or bifacial) have been developed in detail in the literature. In addition, detailed models have been developed for PV power plants that have fixed modules as well as PV power plants with tracking the azimuth angle of the Sun and/or the tilt angle of the PV module. On the other hand, due to the increasing installation of PV power plant, their construction is often imposed on the field with complex geometry, so it is interesting to develop models that can optimize the spatial layout and geometric elements of panels on sloping surface in relation on horizontal. Namely, during various analyses in the literature, production models for PV power plants that are planned on sloping terrains have not been developed. Accordingly, in this paper, a procedure was developed for the calculation of the production of PV power plants that are placed on terrain that is not flat, but oriented towards the arbitrary side of the World and with an arbitrary tilt angle.