Ultra-Short-Term Wind Generation Forecast Based on Multivariate Empirical Dynamic Modeling

Ma, Jiayi; Yang, Ming; Han, Xueshan; Li, Zhi

doi:10.1109/tia.2017.2782207

Cited by 45 publications

(6 citation statements)

References 39 publications

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“…For future research, we suggest running the model over a longer time window in order to help make better decisions such as analysis of the optimal ESU sizing and the analysis of the performances of some components of microgrid . We also suggest integrating innovative renewable power generation forecasting models such as the multivariate empirical dynamic model discussed in [23]. Additionally, future research may capture the correlation between power load, energy market prices, and weather forecasts.…”

Section: Resultsmentioning

confidence: 99%

Dynamic optimal management of a hybrid microgrid based on weather forecasts

Bouaicha¹,

Craparo²,

Dallagi³

et al. 2020

Turk J Elec Eng & Comp Sci

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Hybrid microgrids containing both renewable and conventional power sources are becoming increasingly attractive for a variety of reasons. However, intermittency of renewable power production and uncertainty in future load prediction increase risks of electric grid instability and, by consequence, restrict the portion of renewable power production in microgrids. In order, to prefigure the upcoming renewable power production, particularly, wind power and photovoltaic power, we suggest using weather forecasts. In addition to illustrating short term renewable power prediction based on ensemble weather forecasts, this paper focuses on optimizing the management of distributed power generation, power storage, and power exchange with the commercial electric grid. Establishing an optimal operating plan for the grid makes the integration of the predicted renewable production more efficient. The optimization problem is formulated as an integer linear program. The operating plan is initially optimized over the upcoming day and then regularly updated and incremented to cover a longer time horizon. After analyzing the obtained plans, we test and evaluate them relative to the observed weather conditions. Finally, we investigate some practical problems that had arisen and we apply a time cascade optimization technique to mitigate the effects of initial conditions and end-of-horizon effects, as well as to take advantage of updated forecasts.

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Section: Resultsmentioning

confidence: 99%

Dynamic optimal management of a hybrid microgrid based on weather forecasts

Bouaicha¹,

Craparo²,

Dallagi³

et al. 2020

Turk J Elec Eng & Comp Sci

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“…Common analogue forecasting operators are presented in Platzer et al (2021) and their respective properties and performances are analysed from a theoretical point of view, connecting analogue forecasting error to local approximations of the system's dynamics. Empirical dynamical modelling, locating analogues in the shadow attractor space or in one of its sub-spaces, is still used to perform model-free predictions (Ma et al, 2017) or to give insight on predictability (Trevisan, 1995;Ramesh and Cane, 2019).…”

Section: Possibility Theory and Epssmentioning

confidence: 99%

Beyond probabilities: A possibilistic framework to interpret ensemble predictions and fuse imperfect sources of information

Carrer

Ferson

2021

Quart J Royal Meteoro Soc

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Ensemble forecasting is widely used in medium‐range weather predictions to account for the uncertainty that is inherent in the numerical prediction of high‐dimensional, nonlinear systems with high sensitivity to initial conditions. Ensemble forecasting allows one to sample possible future scenarios in a Monte‐Carlo‐like approximation through small strategical perturbations of the initial conditions, and in some cases stochastic parametrization schemes of the atmosphere–ocean dynamical equations. Results are generally interpreted in a probabilistic manner by turning the ensemble into a predictive probability distribution. Yet, due to model bias and dispersion errors, this interpretation is often not reliable and statistical postprocessing is needed to reach probabilistic calibration. This is all the more true for extreme events which, for dynamical reasons, cannot generally be associated with a significant density of ensemble members. In this work we propose a novel approach: a possibilistic interpretation of ensemble predictions, taking inspiration from possibility theory. This framework allows us to integrate in a consistent manner other imperfect sources of information, such as the insight about the system dynamics provided by the analogue method. We thereby show that probability distributions may not be the best way to extract the valuable information contained in ensemble prediction systems, especially for large lead times. Indeed, shifting to possibility theory provides more meaningful results without the need to resort to additional calibration, while maintaining or improving skills. Our approach is tested on an imperfect version of the Lorenz '96 model, and results for extreme event prediction are compared against those given by a standard probabilistic ensemble dressing.

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“…In the operation of an integrated energy system, two multi-energy loads and renewable energy generation predictions with different time scales are generally required, which are short-term prediction and ultra-short-term prediction. Among them, the short-term prediction generally refers to the day-ahead or a few days ago prediction; the time scale is carried out by days and is generally used for day-ahead dispatching, while the ultra-short-term prediction is generally executed in units of minutes and used for predicting the multi-energy loads and the renewable energy power generation situation at the next moment, which are used for guiding the dispatch system and dispatch personnel to perform related dispatch operations [35][36][37].…”

Section: Determination Of Energy Supply Incrementmentioning

confidence: 99%

Research on Real-Time Optimized Operation and Dispatching Strategy for Integrated Energy System Based on Error Correction

et al. 2020

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A real-time error correction operation model for an integrated energy system is proposed in this paper, based on the analysis of the real-time optimized operation structure of an integrated energy system and the characteristics of the system. The model makes real-time corrections to the day-ahead operation strategy of the integrated energy system, to offset forecast errors from the renewable power generation system and multi-energy load system. When unbalanced power occurs in the system due to prediction errors, the model comprehensively considers the total capacity of each energy supply and energy storage equipment, adjustable margin, power climbing speed and adjustment cost, to formulate the droop rate which determines the unbalanced power that each device will undertake at the next time interval, while taking the day-ahead dispatching goals of the system into consideration. The case study shows that the dispatching strategy obtained by the real-time error correction operation model makes the power output change trend of the energy supply equipment consistent with the day-ahead dispatching plan at the next time interval, which ensures the safety, stability and economy of the real-time operation of the integrated energy system.

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Ultra-Short-Term Wind Generation Forecast Based on Multivariate Empirical Dynamic Modeling

Cited by 45 publications

References 39 publications

Dynamic optimal management of a hybrid microgrid based on weather forecasts

Dynamic optimal management of a hybrid microgrid based on weather forecasts

Beyond probabilities: A possibilistic framework to interpret ensemble predictions and fuse imperfect sources of information

Research on Real-Time Optimized Operation and Dispatching Strategy for Integrated Energy System Based on Error Correction

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