Analytics and optimization for renewable energy integration [electronic resource] / Ning Zhang, Chongqing Kang, Ershun Du, Yi Wang.

Cover; 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

1.5.2 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

3.1 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

4.2.2 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

5.2.3 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

The 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.

OCLC-licensed vendor bibliographic record.