http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Analytics and optimization for renewable energy integration
1 online resource (xxii, 371 pages) : illustrations (chiefly color)
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.
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
I: Mathematical Foundations: Basic Stochastic Mathematics; Copula Theory and Dependent Probabilistic Sequence Operation -- II: Uncertainty Modeling and Analytics: Long-Term Uncertainty of Renewable Energy Generation; Short-Term Renewable Energy Output Forecasting; Short-Term Uncertainty of Renewable Energy Generation; Renewable Energy Output Simulation; Finding Representative Renewable Energy Scenarios -- III: Short-Term Operation Optimization: Probabilistic Load Flow under Uncertainty; Risk-Based Stochastic Unit Commitment; Managing Renewable Energy Uncertainty in Electricity Market; Tie-Line Scheduling for Interconnected Power Systems -- IV: Long-Term Planning Optimization: Power System Operation Simulation; Capacity Credit of Renewable Energy; Sequential Renewable Energy Planning; Generation Expansion Planning; Transmission Expansion Planning.
Ning Zhang, Chongqing Kang, Ershun Du, Yi Wang.
Includes bibliographical references and index.
Ning Zhang got B. Sc. degree of electrical engineering and Excellent Graduate Student Award from Tsinghua University, Beijing, China in 2007. He got his Ph. D of electrical engineering, Excellent Doctoral Thesis Award and Excellent Graduate Student Award from Tsinghua University in 2012. He completed his two-year research as a post doctor and was assigned to work in Tsinghua University in 2014. He was a research associate in The University of Manchester from Oct. 2010 to Jul. 2011 and a research assistant in Harvard University from Dec. 2013 to Mar 2014. His research interests include power system planning, multiple energy system integration, wind power photovoltaic, concentrated solar power. He has led more than 10 scientific research projects including National Natural Science Foundation of China, National Key Research and Development Program of China (sub-task), The State Key Laboratories Development Program of China, and several projects from industry. He was awarded Young Elite Scientists Sponsorship Program by Chinese Association of Science and Technology in 2016. He was awarded The Twelfth Tsinghua University-Yokoyama Ryoji Outstanding Paper Award, one hundred most influential papers and top articles in outstanding S & T journal of China in 2012. He serves as the editor of International Transactions on Electrical Energy Systems (ITEES), CSEE Journal of Power and Energy Systems (CSEE JPES) and the Editorial Board Member of Protection and Control of Modern Power Systems (PCMP). He also severs as guest editor of IEEE Transactions on power system and Proceedings of CSEE. He is the peer reviewer of more than 10 international journals including IEEE TPWRS, IEEE TSTE, IEEE TSG, IEEE TEC, IEEE PESL, IET RPG, IET GTD. He is also the peer reviewer of Proceedings of the CSEE, Automation of Electric Power Systems, Power System Technology, Electric Power Construction and Southern Power System Technology.