Application of probabilistic wind power forecasting in electricity markets

Zhou, Zhi; Botterud, Audun; Wang, J; Bessa, Ricardo J.; Keko, Hrvoje; Sumaili, Jean; Miranda, Vladimiro

doi:10.1002/we.1496

Cited by 49 publications

(35 citation statements)

References 44 publications

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“…Similarly, Zhou et al (2012) extended a deterministic UC formulation to a stochastic version in order to study the impact of wind forecasts on the determination of optimal reserves. In both cases, the solution framework clearly depends on the e cient solution of a UC problem.…”

Section: Introductionmentioning

confidence: 99%

Symmetry breaking in MILP formulations for Unit Commitment problems

Lima

Novais

2016

Computers & Chemical Engineering

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Please cite this article as: Ricardo M. Lima, Augusto Q. Novais, Symmetry Breaking in MILP Formulations for Unit Commitment Problems, Computers and Chemical Engineering (2015), http://dx.doi.org/10. 1016/j.compchemeng.2015.11.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. • Symmetry issues are discussed in the context of UC formulations.• Three sets of SBC are proposed and analyzed.• Three well-known UC MILP models are used to implement the SBC.• The results presented are completely disruptive compared with the results presented in the literature.• The SBC derived have a clear advantage over general methods available in two MILP solvers. *HighlightsPage 2 of 43 show that problems that could not be solved to optimality within hours can be solved with a relatively small computational burden if the symmetry breaking constraints are assumed.The proposed symmetry breaking constraints are also compared with the symmetry breaking methods included in two MILP solvers, and the symmetry breaking constraints derived in this work have a distinct advantage over the methods in the MILP solvers.

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Section: Introductionmentioning

confidence: 99%

Symmetry breaking in MILP formulations for Unit Commitment problems

Lima

Novais

2016

Computers & Chemical Engineering

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“…Commonly adopted approaches to address these challenges include advanced forecasting techniques [1][2][3] and improved system operational uncertainty modeling [4][5][6][7][8][9][10][11][12][13][14][15][16]. Traditionally, the system operators make the commitment and dispatch decisions based on a deterministic renewable point forecast.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, many researchers have applied different operational schemes to incorporate stochastic wind power generation [4][5][6][7][8][9][10][11][12][13][14][15][16]. These approaches mainly includes deterministic unit commitment (DUC) with dynamic reserves [4][5][6], scenario-based stochastic UC (SUC) [4][5][6][7], intervalbased UC (IUC) [8][9][10], robust UC (RUC) [11][12], chanceconstrained UC [13], as well as hybrid and unified approaches [14] [15].…”

Section: Introductionmentioning

confidence: 99%

“…These approaches mainly includes deterministic unit commitment (DUC) with dynamic reserves [4][5][6], scenario-based stochastic UC (SUC) [4][5][6][7], intervalbased UC (IUC) [8][9][10], robust UC (RUC) [11][12], chanceconstrained UC [13], as well as hybrid and unified approaches [14] [15]. The traditional DUC typically keep contingency reserves to address the possibility for a generation or line outage.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating stochastic methods in power system operations with wind power

Wang

Zhou

Liu

et al. 2016

2016 IEEE International Energy Conference (ENERGYCON)

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Abstract-Wind power is playing an increasingly important role in electricity markets. However, it's inherent variability and uncertainty cause operational challenges and costs as more operating reserves are needed to maintain system reliability. Several operational strategies have been proposed to address these challenges, including advanced probabilistic wind forecasting techniques, dynamic operating reserves, and various unit commitment (UC) and economic dispatch (ED) strategies under uncertainty. This paper presents a consistent framework to evaluate different operational strategies in power system operations with renewable energy. We use conditional Kernel Density Estimation (KDE) for probabilistic wind power forecasting. Forecast scenarios are generated considering spatiotemporal correlations, and further reduced to lower the computational burden. Scenario-based stochastic programming with different decomposition techniques and interval optimization are tested to examine economic, reliability, and computational performance compared to deterministic UC/ED benchmarks. We present numerical results for a modified IEEE-118 bus system with realistic system load and wind data.

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“…The prediction of wind power has become an important issue on power system planning and many researchers have established mathematical models with which to predict in recent years [5][6][7][8]. But the most models to study wind power are based on linear theory.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Wind Power by Chaos and BP Artificial Neural Networks Approach Based on Genetic Algorithm

Huang¹,

Gong²,

Gong³

2015

Journal of Electrical Engineering and Technology

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-It is very important to make accurate forecast of wind power because of its indispensable requirement for power system stable operation. The research is to predict wind power by chaos and BP artificial neural networks (CBPANNs) method based on genetic algorithm, and to evaluate feasibility of the method of predicting wind power. A description of the method is performed. Firstly, a calculation of the largest Lyapunov exponent of the time series of wind power and a judgment of whether wind power has chaotic behavior are made. Secondly, phase space of the time series is reconstructed. Finally, the prediction model is constructed based on the best embedding dimension and best delay time to approximate the uncertain function by which the wind power is forecasted. And then an optimization of the weights and thresholds of the model is conducted by genetic algorithm (GA). And a simulation of the method and an evaluation of its effectiveness are performed. The results show that the proposed method has more accuracy than that of BP artificial neural networks (BP-ANNs).

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Application of probabilistic wind power forecasting in electricity markets

Cited by 49 publications

References 44 publications

Symmetry breaking in MILP formulations for Unit Commitment problems

Symmetry breaking in MILP formulations for Unit Commitment problems

Evaluating stochastic methods in power system operations with wind power

Prediction of Wind Power by Chaos and BP Artificial Neural Networks Approach Based on Genetic Algorithm

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