Probabilistic Wind Generation Forecast Based on Sparse Bayesian Classification and Dempster–Shafer Theory

Yang, Ming; Lin, You; Han, Xueshan

doi:10.1109/tia.2016.2518995

Cited by 64 publications

(12 citation statements)

References 24 publications

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“…In power systems, ML methods are used for many years to predict time series or contingency cases. The prediction of load [6] and generation [7] time series is based on historical measurements and weather data. These methods focus solely on the time series, without taking into account the power system data, e.g.…”

Section: State Of the Artmentioning

confidence: 99%

Evaluating Machine Learning Models for the Fast Identification of Contingency Cases

Schaefer¹,

Menke²,

Braun³

2020

Preprint

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Fast approximations of power flow results are beneficial in power system planning and live operation. In planning, millions of power flow calculations are necessary if multiple years, different control strategies or contingency policies are to be considered. In live operation, grid operators must assess if grid states comply with contingency requirements in a short time. In this paper, we compare regression and classification methods to either predict multi-variable results, e.g. bus voltage magnitudes and line loadings, or binary classifications of time steps to identify critical loading situations. We test the methods on three realistic power systems based on time series in 15 min and 5 min resolution of one year. We compare different machine learning models, such as multilayer perceptrons (MLPs), decision trees, k-nearest neighbours, gradient boosting, and evaluate the required training time and prediction times as well as the prediction errors. We additionally determine the amount of training data needed for each method and show results, including the approximation of untrained curtailment of generation. Regarding the compared methods, we identified the MLPs as most suitable for the task. The MLP-based models can predict critical situations with an accuracy of 97-98 % and a very low number of false negative predictions of 0.0 -0.64 %.

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Section: State Of the Artmentioning

confidence: 99%

Evaluating Machine Learning Models for the Fast Identification of Contingency Cases

Schaefer¹,

Menke²,

Braun³

2020

Preprint

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show abstract

“…For a given point forecast of renewable energy, probability distribution functions are necessary to gain the future realization of renewables [30]. Currently, much previous research concerns the probability distribution functions, involving Gaussian distribution function [31], Beta distribution function [32,33] and truncated normal distribution function [34]. In this paper, network constraints are not involved in the proposed model.…”

Section: Tes Dis Tes Htf Tes Dis C T C T Cmentioning

confidence: 99%

Incorporating Concentrating Solar Power into High Renewables Penetrated Power System: A Chance-Constrained Stochastic Unit Commitment Analysis

2019

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High renewables penetrated power systems would be greatly influenced by the uncertainty of variable renewable energy such as wind power and photovoltaic power. Unlike wind and photovoltaic plant, concentrating solar power with thermal energy storage has similar dispatchable characteristics as conventional thermal unit. Besides, thermal energy storage could support the coordinated operation of concentrating solar power with an electrical heater, which can be employed to convert surplus electricity in the grid into thermal power stored in thermal energy storage for further utilization. In this paper, concentrating solar power is incorporated into a chance-constrained two-stage stochastic unit commitment model. The model considers the energy and reserve services of concentrating solar power and the uncertainty of renewables. The proposed method is employed to assess the role of a concentrating solar power station with thermal energy storage and an electrical heater to provide grid flexibility in high renewables penetrated power systems. Numerical studies are performed on a modified IEEE 24-bus system to validate the viability of the proposed method for the day-ahead stochastic scheduling. The results demonstrate the economic and reliable value of concentrating solar power station to the improvement of unit commitment schedule, to the mitigation of wind uncertainty and photovoltaic uncertainty, and to the reduction of traditional unit reserve requirement. It is concluded that concentrating solar power with thermal energy storage and an electrical heater is effective in promoting the further penetration of renewables.

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“…Dempster–Shafer evidence theory [5,6], also called Dempster–Shafer (D-S) theory or evidence theory, is a popular theory to deal with uncertain information [49,50,51,52,53,54,55,56]. Having a similar effect of aggregating operators [57,58,59,60] and as the counterpart of the probability-based approaches [61,62,63], such as composite hypothesis testing, Bayesian approaches and the maximum entropy approach, D-S theory is another widely-used framework for multi-sensor information fusion [64,65,66,67,68,69,70].…”

Section: Preliminariesmentioning

confidence: 99%

Zero-Sum Matrix Game with Payoffs of Dempster-Shafer Belief Structures and Its Applications on Sensors

Deng

Jiang

Zhang

2017

Sensors

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The zero-sum matrix game is one of the most classic game models, and it is widely used in many scientific and engineering fields. In the real world, due to the complexity of the decision-making environment, sometimes the payoffs received by players may be inexact or uncertain, which requires that the model of matrix games has the ability to represent and deal with imprecise payoffs. To meet such a requirement, this paper develops a zero-sum matrix game model with Dempster–Shafer belief structure payoffs, which effectively represents the ambiguity involved in payoffs of a game. Then, a decomposition method is proposed to calculate the value of such a game, which is also expressed with belief structures. Moreover, for the possible computation-intensive issue in the proposed decomposition method, as an alternative solution, a Monte Carlo simulation approach is presented, as well. Finally, the proposed zero-sum matrix games with payoffs of Dempster–Shafer belief structures is illustratively applied to the sensor selection and intrusion detection of sensor networks, which shows its effectiveness and application process.

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Probabilistic Wind Generation Forecast Based on Sparse Bayesian Classification and Dempster–Shafer Theory

Cited by 64 publications

References 24 publications

Evaluating Machine Learning Models for the Fast Identification of Contingency Cases

Evaluating Machine Learning Models for the Fast Identification of Contingency Cases

Incorporating Concentrating Solar Power into High Renewables Penetrated Power System: A Chance-Constrained Stochastic Unit Commitment Analysis

Zero-Sum Matrix Game with Payoffs of Dempster-Shafer Belief Structures and Its Applications on Sensors

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