An Integration of ANN Wind Power Estimation Into Unit Commitment Considering the Forecasting Uncertainty

Methaprayoon, K.; Yingvivatanapong, C.; Lee, Wei-Jen; Liao, James

doi:10.1109/tia.2007.908203

Cited by 216 publications

(101 citation statements)

References 4 publications

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“…Methaprayoon et al [349] proposed a method to integrate artificial neural network (ANN)-based wind power forecasting into unit commitment by considering the forecasting uncertainty. A concept called WindGen at Risk, which is similar to Value at Risk in financial risk management, is used to describe the wind forecasting errors.…”

Section: Bouffard and Galianamentioning

confidence: 99%

Wind power forecasting : state-of-the-art 2009.

Monteiro¹,

Bessa²,

Miranda³

et al. 2009

302

264

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Section: Bouffard and Galianamentioning

confidence: 99%

Wind power forecasting : state-of-the-art 2009.

Monteiro¹,

Bessa²,

Miranda³

et al. 2009

302

264

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“…Renewable energy generation Stochastic optimization Handling date uncertainties of renewable energy [10][11][12] Robust optimization [14][15][16][17] Wind power forecasting Linear methods Increasing the accuracy of prediction model [19,20] Nonlinear methods [24][25][26][27] Microgrid management Ordinary decision theory Optimizing energy-scheduling strategies [28][29][30] Noncooperative games [33][34][35][36] Cooperative games [37][38][39][40] and robust optimization [9]. On the one hand, stochastic optimization provides an effective framework to optimize statistical objective functions while the uncertain numerical data are assumed to follow a proverbial probability distribution.…”

Section: Application Scenarios Solution Methods Optimization Goals LImentioning

confidence: 99%

“…Nonlinear methods such as artificial neural networks (ANNs) [21], support vector machines (SVM) [22,23], etc., are demonstrated to outperform linear methods in nonlinear models. ANN, which is a simplified model of human brain neural processing, has the advantage of fast self-learning capability, easy implementation, and high prediction accuracy [24]. SVM is a machine-learning model of ANNs to analyze data which is used for classification and regression analysis [25].…”

Section: Application Scenarios Solution Methods Optimization Goals LImentioning

confidence: 99%

Energy Management in Microgrids: A Combination of Game Theory and Big Data‐Based Wind Power Forecasting

Zhou¹,

Xiong²,

Xu³

et al. 2017

Development and Integration of Microgrids

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Energy internet provides an open framework for integrating every piece of equipment involved in energy generation, transmission, transformation, distribution, and consumption with novel information and communication technologies. In this chapter, the authors adopt a combination of game theory and big data to address the coordinated management of renewable and traditional energy, which is a typical issue on energy interconnections. The authors formulate the energy management problem as a three-stage Stackelberg game and employ the backward induction method to derive the closed-form expressions of the optimal strategies. Next, we study the big data-based power generation forecasting techniques and introduce a scheme of the wind power forecasting, which can assist the microgrid to make strategies. Simulation results show that more accurate prediction results of wind power are conducive to better energy management.

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“…The error of wind generation forecast is referred to asẽ. The forecasting error is estimated with a level of confidence α% [29], which means the probability of forecasting error being greater or equal toẽ is less than (100 − α)%. The wind generation capacity counted in the optimal schedule is calculated by:…”

Section: Case Studymentioning

confidence: 99%

Electric Vehicle Charging and Discharging Coordination on Distribution Network Using Multi-Objective Particle Swarm Optimization and Fuzzy Decision Making

2016

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This paper proposed a optimal strategy for coordinated operation of electric vehicles (EVs) charging and discharging with wind-thermal system. By aggregating a large number of EVs, the huge total battery capacity is sufficient to stabilize the disturbance of the transmission grid. Hence, a dynamic environmental dispatch model which coordinates a cluster of charging and discharging controllable EV units with wind farms and thermal plants is proposed. A multi-objective particle swarm optimization (MOPSO) algorithm and a fuzzy decision maker are put forward for the simultaneous optimization of grid operating cost, CO 2 emissions, wind curtailment, and EV users' cost. Simulations are done in a 30 node system containing three traditional thermal plants, two carbon capture and storage (CCS) thermal plants, two wind farms, and six EV aggregations. Contrast of strategies under different EV charging/discharging price is also discussed. The results are presented to prove the effectiveness of the proposed strategy.

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An Integration of ANN Wind Power Estimation Into Unit Commitment Considering the Forecasting Uncertainty

Cited by 216 publications

References 4 publications

Wind power forecasting : state-of-the-art 2009.

Wind power forecasting : state-of-the-art 2009.

Energy Management in Microgrids: A Combination of Game Theory and Big Data‐Based Wind Power Forecasting

Electric Vehicle Charging and Discharging Coordination on Distribution Network Using Multi-Objective Particle Swarm Optimization and Fuzzy Decision Making

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