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001			9780429455094
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007			cr |n|||||||||
008			190225s2019 flua ob 001 0 eng d
040			_aOCoLC-P _beng _cOCoLC-P
020			_a9780429455094 _q(electronic bk.)
020			_a0429455097 _q(electronic bk.)
020			_a9780429847691 _q(electronic bk.)
020			_a0429847696 _q(electronic bk.)
020			_a9780429847684 _q(electronic bk. : Mobipocket)
020			_a0429847688 _q(electronic bk. : Mobipocket)
020			_a9780429847707 _q(electronic bk. : PDF)
020			_a042984770X _q(electronic bk. : PDF)
020			_z1138316822
020			_z9781138316829
035			_a(OCoLC)1088315474 _z(OCoLC)1089235906
035			_a(OCoLC-P)1088315474
050		4	_aTJ808
072		7	_aTEC _x009070 _2bisacsh
072		7	_aTEC _x031020 _2bisacsh
072		7	_aSCI _x024000 _2bisacsh
072		7	_aTEC _x007000 _2bisacsh
072		7	_aTJF _2bicssc
082	0	4	_a621.042 _223
245	0	0	_aAnalytics and optimization for renewable energy integration _h[electronic resource] / _cNing Zhang, Chongqing Kang, Ershun Du, Yi Wang.
260			_aBoca Raton, FL : _bCRC Press, _c2019.
300			_a1 online resource : _bcolor illustrations
490	1		_aEnergy analytics
505	0		_aCover; Half Title; Title Page; Copyright Page; Dedication; Table of Contents; Preface; List of Abbreviations; I: Mathematical Foundations; 1: Basic Stochastic Mathematics; 1.1 Random Variables, Probability Distribution, and Scenarios; 1.1.1 Random Variables; 1.1.2 Probability Distribution; 1.1.3 Scenario; 1.2 Multivariate Probabilistic Distributions; 1.2.1 Joint Distribution; 1.2.2 Marginal Distribution; 1.2.3 Conditional Distribution; 1.3 Stochastic Process; 1.4 Stochastic Differential Equation; 1.5 Stochastic Optimization; 1.5.1 Two-Stage Stochastic Programming
505	8		_a1.5.2 Chance-constrained stochastic programming1.6 Summary; 2: Copula Theory and Dependent Probabilistic Sequence Operation; 2.1 Introduction; 2.2 Dependencies and Copula Theory; 2.3 Dependent Probabilistic Sequence Operation; 2.4 High-Dimensional DPSO Computation; 2.4.1 Grouping Stage; 2.4.2 Gaussian-Distribution-Based Aggregation Stage; 2.4.3 Small-Scale Sampling Stage; 2.4.4 Recursive Sample-Guided DPSO; 2.4.5 Discussions on Computational Complexity and Error; 2.4.6 Case Study; 2.5 Summary; II: Uncertainty Modeling and Analytics; 3: Long-Term Uncertainty of Renewable Energy Generation
505	8		_a3.1 Overview3.2 Wind Power Long-Term Uncertainty Characteristics; 3.2.1 Power Generation Model of a Wind Turbine; 3.2.2 Probabilistic Distribution of Wind Power; 3.2.3 Spatio-Temporal Correlations of Wind Power Output; 3.2.4 Empirical Study; 3.3 PV Power Long-Term Uncertainty Characteristic; 3.3.1 PV Output Model; 3.3.2 Unshaded Solar Irradiation Model; 3.3.3 Uncertainty Analysis of PV Output; 3.3.4 Spatial Correlation between PV Outputs; 3.4 Summary; 4: Short-Term Renewable Energy Output Forecasting; 4.1 Overview; 4.2 Short-Term Forecasting Framework; 4.2.1 Dataset and Definitions
505	8		_a4.2.2 Proposed Methodology4.3 Improving Forecasting Using Adjustment of MWP; 4.3.1 Wind Power Forecast Engine; 4.3.2 Abnormal Detection; 4.3.3 Data Adjustment Engine; 4.4 Case Study; 4.4.1 Indices for Evaluating the Prediction Accuracy; 4.4.2 Wind Power Forecast Engine; 4.4.3 Abnormal Detection; 4.4.4 Data Adjustment Engine; 4.4.5 Results Analysis; 4.5 Summary; 5: Short-Term Uncertainty of Renewable Energy Generation; 5.1 Overview; 5.2 Wind Power Short-Term Uncertainty Modeling; 5.2.1 Modeling Conditional Error for a Single Wind Farm; 5.2.2 Modeling Conditional Errors for Multiple Wind Farms
505	8		_a5.2.3 Standard Modeling Procedure5.2.4 Discussion; 5.2.5 Empirical Analysis: The U.S. East Coast; 5.3 PV Power Short-Term Uncertainty Modeling; 5.3.1 Effect of Weather Factors on the Conditional Forecast Error of PV; 5.3.2 Standard Modeling Procedure; 5.3.3 Accuracy Analysis; 5.3.4 Empirical Analysis; 5.4 Summary; 6: Renewable Energy Output Simulation; 6.1 Overview; 6.2 Multiple Wind Farm Output Simulation; 6.2.1 Historical Wind Speed Data Processing; 6.2.2 Generating Wind Speed Time Series; 6.2.3 Calculating Wind Turbine Output; 6.2.4 Wind Turbine Reliability Model and Wake Effect
520			_aThe scope of this book covers the modeling and forecast of renewable energy and operation and planning of power system with renewable energy integration.The first part presents mathematical theories of stochastic mathematics; the second presents modeling and analytic techniques for renewable energy generation; the third provides solutions on how to handle the uncertainty of renewable energy in power system operation. It includes advanced stochastic unit commitment models to acquire the optimal generation schedule under uncertainty, efficient algorithms to calculate the probabilistic power, and an efficient operation strategy for renewable power plants participating in electricity markets.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aRenewable resource integration.
650		0	_aRenewable energy sources.
650		7	_aTECHNOLOGY & ENGINEERING / Mechanical. _2bisacsh
650		7	_aSCIENCE / Energy _2bisacsh
650		7	_aTECHNOLOGY / Electricity _2bisacsh
700	1		_aZhang, Ning, _eauthor.
700	1		_aKang, Chongqing, _eauthor.
700	1		_aDu, Ershun, _eauthor.
700	1		_aWang, Yi, _eauthor.
856	4	0	_3Read Online _uhttps://www.taylorfrancis.com/books/9780429455094
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942			_2lcc _cEBK
999			_c16631 _d16631