1,439 research outputs found
Generation and Evaluation of Space-Time Trajectories of Photovoltaic Power
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
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Robust cloud motion estimation by spatio-temporal correlation analysis of irradiance data
Spatio-temporal solar forecasting
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
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
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Corrective receding horizon EV charge scheduling using short-term solar forecasting
Forecast errors can cause sub-optimal solutions in resource planning optimization, yet they are usually modeled simplistically by statistical models, causing unrealistic impacts on the optimal solutions. In this paper, realistic forecast errors are prescribed, and a corrective approach is proposed to mitigate the negative effects of day-ahead persistence forecast error by short-term forecasts from a state-of-the-art sky imager system. These forecasts preserve the spatiotemporal dependence structure of forecast errors avoiding statistical approximations. The performance of the proposed algorithm is tested on a receding horizon quadratic program developed for valley filling the midday net load depression through electric vehicle charging. Throughout one month of simulations the ability to flatten net load is assessed under practical forecast accuracy levels achievable from persistence, sky imager and perfect forecasts. Compared to using day-ahead persistence solar forecasts, the proposed corrective approach using sky imager forecasts delivers a 25% reduction in the standard deviation of the daily net load. It is demonstrated that correcting day-ahead forecasts in real time with more accurate short-term forecasts benefits the valley filling solution
A review on the complementarity of renewable energy sources: concept, metrics, application and future research directions
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
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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