1,439 research outputs found

    Generation and Evaluation of Space-Time Trajectories of Photovoltaic Power

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    In the probabilistic energy forecasting literature, emphasis is mainly placed on deriving marginal predictive densities for which each random variable is dealt with individually. Such marginals description is sufficient for power systems related operational problems if and only if optimal decisions are to be made for each lead-time and each location independently of each other. However, many of these operational processes are temporally and spatially coupled, while uncertainty in photovoltaic (PV) generation is strongly dependent in time and in space. This issue is addressed here by analysing and capturing spatio-temporal dependencies in PV generation. Multivariate predictive distributions are modelled and space-time trajectories describing the potential evolution of forecast errors through successive lead-times and locations are generated. Discrimination ability of the relevant scoring rules on performance assessment of space-time trajectories of PV generation is also studied. Finally, the advantage of taking into account space-time correlations over probabilistic and point forecasts is investigated. The empirical investigation is based on the solar PV dataset of the Global Energy Forecasting Competition (GEFCom) 2014.Comment: 33 pages, 11 Figure

    Spatio-temporal solar forecasting

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    Current and future photovoltaic (PV) deployment levels require accurate forecasting to ensure grid stability. Spatio-temporal solar forecasting is a recent solar forecasting approach that explores spatially distributed solar data sets, either irradiance or photovoltaic power output, modeling cloud advection patterns to improve forecasting accuracy. This thesis contributes to further understanding of the potential and limitations of this approach, for different spatial and temporal scales, using different data sources; and its sensitivity to prevailing local weather patterns. Three irradiance data sets with different spatial coverages (from meters to hundreds of kilometers) and time resolutions (from seconds to days) were investigated using linear autoregressive models with external inputs (ARX). Adding neighboring data led to accuracy gains up to 20-40 % for all datasets. Spatial patterns matching the local prevailing winds could be identified in the model coefficients and the achieved forecast skill whenever the forecast horizon was of the order of scale of the distance between sensors divided by cloud speed. For one of the sets, it was shown that the ARX model underperformed for non-prevailing winds. Thus, a regime-based approach driven by wind information is proposed, where specialized models are trained for different ranges of wind speed and wind direction. Although forecast skill improves by up to 55.2 % for individual regimes, the overall improvement is only of 4.3 %, as those winds have a low representation in the data. By converting the highest resolution irradiance data set to PV power, it was also shown that forecast accuracy is sensitive to module tilt and orientation. Results are shown to be correlated with the difference in tilt and orientation between systems, indicating that clear-sky normalization is not totally effective in removing the geometry dependence of solar irradiance. Thus, non-linear approaches, such as machine learning algorithms, should be tested for modelling the non-linearity introduced by the mounting diversity from neighboring systems in spatio-temporal forecasting

    Sensitive parameter analysis for solar irradiance short-term forecasting: application to LoRa-based monitoring technology

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    Due to the relevant penetration of solar PV power plants, an accurate power generation forecasting of these installations is crucial to provide both reliability and stability of current grids. At the same time, PV monitoring requirements are more and more demanded by different agents to provide reliable information regarding performances, efficiencies, and possible predictive maintenance tasks. Under this framework, this paper proposes a methodology to evaluate different LoRa-based PV monitoring architectures and node layouts in terms of short-term solar power generation forecasting. A random forest model is proposed as forecasting method, simplifying the forecasting problem especially when the time series exhibits heteroscedasticity, nonstationarity, and multiple seasonal cycles. This approach provides a sensitive analysis of LoRa parameters in terms of node layout, loss of data, spreading factor and short time intervals to evaluate their influence on PV forecasting accuracy. A case example located in the southeast of Spain is included in the paper to evaluate the proposed analysis. This methodology is applicable to other locations, as well as different LoRa configurations, parameters, and networks structures; providing detailed analysis regarding PV monitoring performances and short-term PV generation forecasting discrepancies.This research was funded by the Fondo Europeo de Desarrollo Regional/Ministerio de Ciencia e Innovación–Agencia Estatal de Investigación (FEDER/MICINN-AEI), project RTI2018–099139–B–C21

    A review on the complementarity of renewable energy sources: concept, metrics, application and future research directions

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    It is expected, and regionally observed, that energy demand will soon be covered by a widespread deployment of renewable energy sources. However, the weather and climate driven energy sources are characterized by a significant spatial and temporal variability. One of the commonly mentioned solutions to overcome the mismatch between demand and supply provided by renewable generation is a hybridization of two or more energy sources in a single power station (like wind-solar, solar-hydro or solar-wind-hydro). The operation of hybrid energy sources is based on the complementary nature of renewable sources. Considering the growing importance of such systems and increasing number of research activities in this area this paper presents a comprehensive review of studies which investigated, analyzed, quantified and utilized the effect of temporal, spatial and spatio-temporal complementarity between renewable energy sources. The review starts with a brief overview of available research papers, formulates detailed definition of major concepts, summarizes current research directions and ends with prospective future research activities. The review provides a chronological and spatial information with regard to the studies on the complementarity concept.Comment: 34 pages 7 figures 3 table

    Short-term power prediction for renewable energy using hybrid graph convolutional network and long short-term memory approach

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    Accurate short-term solar and wind power predictions play an important role in the planning and operation of power systems. However, the short-term power prediction of renewable energy has always been considered a complex regression problem, owing to the fluctuation and intermittence of output powers and the law of dynamic change with time due to local weather conditions, i.e. spatio-temporal correlation. To capture the spatio-temporal features simultaneously, this paper proposes a new graph neural network-based short-term power forecasting approach, which combines the graph convolutional network (GCN) and long short-term memory (LSTM). Specifically, the GCN is employed to learn complex spatial correlations between adjacent renewable energies, and the LSTM is used to learn dynamic changes of power generation curves. The simulation results show that the proposed hybrid approach can model the spatio-temporal correlation of renewable energies, and its performance outperforms popular baselines on real-world datasets.Comment: This paper was accepted the 22nd Power Systems Computation Conference (PSCC 2022
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