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E-raamat: Analytics and Optimization for Renewable Energy Integration

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(Tsinghua University, Beijing, China), (Tsinghua University, Beijing, China), (Tsinghua University, Beijing, China), (Tsinghua University, Beijing, China)
  • Formaat: 394 pages
  • Sari: Energy Analytics
  • Ilmumisaeg: 21-Feb-2019
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9780429847691
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Analytics and Optimization for Renewable Energy IntegrationSuurem pilt
  • Formaat: 394 pages
  • Sari: Energy Analytics
  • Ilmumisaeg: 21-Feb-2019
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9780429847691
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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.

Preface xv
List of Abbreviations
xxi
I Mathematical Foundations
1(32)
1 Basic Stochastic Mathematics
3(8)
1.1 Random Variables, Probability Distribution, and Scenarios
3(1)
1.1.1 Random Variables
3(1)
1.1.2 Probability Distribution
4(1)
1.1.3 Scenario
4(1)
1.2 Multivariate Probabilistic Distributions
4(2)
1.2.1 Joint Distribution
5(1)
1.2.2 Marginal Distribution
5(1)
1.2.3 Conditional Distribution
6(1)
1.3 Stochastic Process
6(1)
1.4 Stochastic Differential Equation
7(2)
1.5 Stochastic Optimization
9(1)
1.5.1 Two-Stage Stochastic Programming
9(1)
1.5.2 Chance-constrained stochastic programming
10(1)
1.6 Summary
10(1)
2 Copula Theory and Dependent Probabilistic Sequence Operation
11(22)
2.1 Introduction
11(1)
2.2 Dependencies and Copula Theory
12(3)
2.3 Dependent Probabilistic Sequence Operation
15(4)
2.4 High-Dimensional DPSO Computation
19(11)
2.4.1 Grouping Stage
21(1)
2.4.2 Gaussian-Distribution-Based Aggregation Stage
22(1)
2.4.3 Small-Scale Sampling Stage
23(1)
2.4.4 Recursive Sample-Guided DPSO
23(3)
2.4.5 Discussions on Computational Complexity and Error
26(1)
2.4.6 Case Study
26(4)
2.5 Summary
30(3)
II Uncertainty Modeling and Analytics
33(126)
3 Long-Term Uncertainty of Renewable Energy Generation
35(30)
3.1 Overview
35(2)
3.2 Wind Power Long-Term Uncertainty Characteristics
37(9)
3.2.1 Power Generation Model of a Wind Turbine
37(1)
3.2.2 Probabilistic Distribution of Wind Power
37(2)
3.2.3 Spatio-Temporal Correlations of Wind Power Output
39(1)
3.2.4 Empirical Study
40(6)
3.3 PV Power Long-Term Uncertainty Characteristic
46(14)
3.3.1 PV Output Model
46(1)
3.3.2 Unshaded Solar Irradiation Model
47(5)
3.3.3 Uncertainty Analysis of PV Output
52(4)
3.3.4 Spatial Correlation between PV Outputs
56(4)
3.4 Summary
60(5)
4 Short-Term Renewable Energy Output Forecasting
65(20)
4.1 Overview
65(2)
4.2 Short-Term Forecasting Framework
67(1)
4.2.1 Dataset and Definitions
67(1)
4.2.2 Proposed Methodology
67(1)
4.3 Improving Forecasting Using Adjustment of MWP
68(8)
4.3.1 Wind Power Forecast Engine
68(2)
4.3.2 Abnormal Detection
70(4)
4.3.3 Data Adjustment Engine
74(2)
4.4 Case Study
76(5)
4.4.1 Indices for Evaluating the Prediction Accuracy
77(1)
4.4.2 Wind Power Forecast Engine
77(1)
4.4.3 Abnormal Detection
77(1)
4.4.4 Data Adjustment Engine
78(1)
4.4.5 Results Analysis
79(2)
4.5 Summary
81(4)
5 Short-Term Uncertainty of Renewable Energy Generation
85(26)
5.1 Overview
85(2)
5.2 Wind Power Short-Term Uncertainty Modeling
87(9)
5.2.1 Modeling Conditional Error for a Single Wind Farm
87(1)
5.2.2 Modeling Conditional Errors for Multiple Wind Farms
87(1)
5.2.3 Standard Modeling Procedure
88(1)
5.2.4 Discussion
89(1)
5.2.5 Empirical Analysis: The U.S. East Coast
90(6)
5.3 PV Power Short-Term Uncertainty Modeling
96(10)
5.3.1 Effect of Weather Factors on the Conditional Forecast Error of PV
96(3)
5.3.2 Standard Modeling Procedure
99(1)
5.3.3 Accuracy Analysis
99(3)
5.3.4 Empirical Analysis
102(4)
5.4 Summary
106(5)
6 Renewable Energy Output Simulation
111(28)
6.1 Overview
111(2)
6.2 Multiple Wind Farm Output Simulation
113(4)
6.2.1 Historical Wind Speed Data Processing
113(1)
6.2.2 Generating Wind Speed Time Series
114(1)
6.2.3 Calculating Wind Turbine Output
115(1)
6.2.4 Wind Turbine Reliability Model and Wake Effect
115(2)
6.3 Multiple PV Power Station Output Simulation
117(9)
6.3.1 PV Output Model
117(1)
6.3.2 PV Output Simulation Framework
117(3)
6.3.3 Calculation Model for Unshaded Irradiance at Ground Level ht
120(1)
6.3.4 Calculation Model for PV Array Radiation Ratio rt for Different Tracking Types
120(2)
6.3.5 Solar Radiation Probability Density Model
122(4)
6.4 Case Study
126(10)
6.4.1 Wind Power Output Simulation
126(7)
6.4.2 PV Power Output Simulation
133(3)
6.5 Summary
136(3)
7 Finding Representative Renewable Energy Scenarios
139(20)
7.1 Overview
139(2)
7.2 Framework of Modeling Wind Power Uncertainty
141(1)
7.3 Wind Power Scenario Reduction Techniques
141(2)
7.3.1 Scenario Clustering Methods
142(1)
7.3.2 Scenario Selection Criteria
143(1)
7.4 The Statistical Quality of Reduced Scenarios
143(2)
7.4.1 Measurement of Losses on Output Uncertainty
144(1)
7.4.2 Measurement of Losses on Ramp Diversity
144(1)
7.4.3 Quality of Reduced Scenarios
145(1)
7.5 The Economic Value of Reduced Scenarios
145(6)
7.5.1 Notations
145(2)
7.5.2 Formulation of SUC
147(2)
7.5.3 The Economic Value of Reduced Scenarios
149(2)
7.6 Data and Results
151(4)
7.6.1 Probabilistic Forecast and Scenario Generation
151(1)
7.6.2 Reduced Scenario Sets from Different Methods
151(2)
7.6.3 Comparison of the Quality of Reduced Scenarios
153(1)
7.6.4 Comparison on the Value of Reduced Scenarios
154(1)
7.6.5 Discussions
155(1)
7.7 Summary
155(4)
III Short-Term Operation Optimization
159(88)
8 Probabilistic Load Flow under Uncertainty
161(18)
8.1 Introduction
161(2)
8.2 PLF Formulation for ADS
163(4)
8.2.1 Uncertainty of Loads and Wind Power
164(1)
8.2.2 Copula-Based Uncertainty Correlation Modeling
165(1)
8.2.3 Linearized Power Flow Considering Nodal Voltage
166(1)
8.3 DPSO-Based Algorithm for PLF
167(3)
8.3.1 DPSO-Based PLF Calculation
168(1)
8.3.2 Dimension Reduction
168(2)
8.3.3 Procedures of DPSO-Based PLF for the ADS
170(1)
8.4 Numerical Examples
170(6)
8.4.1 Description of Basic Data
170(2)
8.4.2 Accuracy of the Proposed Linearized Power Flow
172(1)
8.4.3 Comparative Studies
172(2)
8.4.4 Scalability Tests
174(2)
8.5 Summary
176(3)
9 Risk-Based Stochastic Unit Commitment
179(24)
9.1 Overview
179(2)
9.2 Modeling Risks of Renewable Energy Integration
181(4)
9.2.1 Model Assumptions and Notations
181(1)
9.2.2 Notations
182(1)
9.2.3 Modeling Risks
183(1)
9.2.4 Risk-Based Stochastic Unit Commitment
184(1)
9.3 Solving Method of Risk-Based Unit Commitment
185(5)
9.3.1 Problem Reformulation
185(5)
9.3.2 Discussion
190(1)
9.4 Case Study
190(8)
9.4.1 Illustrative Example
190(6)
9.4.2 Case Study
196(2)
9.5 Summary
198(5)
10 Managing Renewable Energy Uncertainty in Electricity Market
203(30)
10.1 Overview
203(2)
10.2 Market Model for Wind Power
205(6)
10.2.1 Market Model
205(2)
10.2.2 Assumptions in This Study
207(1)
10.2.3 Trading Wind Energy in Electricity Market
207(1)
10.2.4 Reserve Purchasing for a WPP
208(1)
10.2.5 WPP's Revenue Combining Energy Bidding and Reserve Purchasing
209(2)
10.3 Optimal Wind Power Bidding Strategy
211(7)
10.3.1 Uncertainty Model for Wind Power
211(1)
10.3.2 Expected Revenue for a WPP
211(2)
10.3.3 Value of Reserve Purchasing for a WPP
213(3)
10.3.4 Optimal Bidding Strategy
216(2)
10.3.5 Discussion
218(1)
10.4 Case Study
218(10)
10.4.1 Wind Power Probabilistic Forecasts and Market Prices
219(1)
10.4.2 WPP's Optimal Bidding and its Benefit
219(2)
10.4.3 Sensitivity to Wind Power Uncertainty
221(1)
10.4.4 Sensitivity to Market Prices
222(1)
10.4.5 Analysis of WPP's Revenue
223(1)
10.4.6 Risk Analysis
224(1)
10.4.7 Discussions
225(3)
10.5 Summary
228(5)
11 Tie-Line Scheduling for Interconnected Power Systems
233(14)
11.1 Overview
233(1)
11.2 Problem Statement
234(2)
11.2.1 Assumptions
234(1)
11.2.2 Model Framework
235(1)
11.3 Notations
236(1)
11.4 Model Formulation
237(3)
11.5 Case Study
240(5)
11.5.1 IEEE 118-Bus System
240(1)
11.5.2 Case List
241(1)
11.5.3 Results and Discussions
242(3)
11.6 Summary
245(2)
IV Long-Term Planning Optimization
247(116)
12 Power System Operation Simulation
249(26)
12.1 Overview
249(2)
12.2 Power System Operation Simulation Model
251(5)
12.2.1 Framework
251(1)
12.2.2 Detailed Model of Daily Operation Simulation Module
252(1)
12.2.2.1 Notations
252(1)
12.2.2.2 Model Formulation
253(3)
12.3 Wind Power Impacts on Conventional Unit Operating Cost
256(5)
12.3.1 Mechanism
256(1)
12.3.2 Evaluation Metrics
257(1)
12.3.3 Compensation Mechanism
258(1)
12.3.4 Case Study
258(3)
12.4 Pumped Storage Planning with Wind Power Integration
261(4)
12.4.1 Overview of Operation Simulation Results
262(1)
12.4.2 Wind Power Curtailment
262(1)
12.4.3 Savings in Thermal Generation Operating Costs
263(1)
12.4.4 Comparison of Operating and Investment Costs
264(1)
12.5 Grid-Accommodable Wind Power Capacity Evaluation
265(7)
12.5.1 Definitions of Grid-Accommodable Wind Power Capacity Evaluation Model
265(2)
12.5.2 Evaluation Methodology
267(3)
12.5.3 Case Study
270(2)
12.6 Summary
272(3)
13 Capacity Credit of Renewable Energy
275(32)
13.1 Introduction
275(3)
13.2 DPSO-Based Capacity Credit Calculation
278(5)
13.2.1 Definition and Calculation Framework
278(1)
13.2.2 Overall Calculation Process
279(2)
13.2.3 Detailed Calculation Process
281(2)
13.3 Analytical Model of the Renewable Energy Capacity Credit
283(10)
13.3.1 Power System RF
283(1)
13.3.2 Analytical Model of the Renewable Energy Capacity Credit Based on the RF
284(2)
13.3.3 Formulation Based on the ELCC
286(1)
13.3.4 Integrating the Value of the Capacity Credit from Different Definitions and RF Values
286(1)
13.3.5 Rigorous Method for Calculating the Capacity Credit
287(3)
13.3.6 Analysis of the Factors Influencing Renewable Energy Capacity Credit
290(3)
13.4 Case Study
293(10)
13.4.1 Basis Data
293(1)
13.4.2 Comparison of Different Methods
294(4)
13.4.3 Influence of Spatial Correlation of Wind Farm Output on the Wind Power Capacity Credit
298(1)
13.4.4 Influence of Spatial Correlation between the Load and Power Output on the Wind Power Capacity Credit
299(2)
13.4.5 Effect of the Virtual Unit Setting
301(2)
13.5 Summary
303(4)
14 Sequential Renewable Energy Planning
307(20)
14.1 Introduction
307(1)
14.2 Wind Power Capacity Credit
308(1)
14.3 Problem Formulation
309(3)
14.3.1 Assumptions and Notations
309(1)
14.3.2 Notations
310(1)
14.3.3 Objective Function
311(1)
14.3.4 Constraints
312(1)
14.4 OO Theory Based Approach
312(3)
14.4.1 OO Theory
312(2)
14.4.2 Crude Evaluation Model Used for Capacity Credit
314(1)
14.4.3 Procedures
314(1)
14.5 Illustrative Example
315(5)
14.5.1 Data and Settings
315(2)
14.5.2 OPC Shape Determination
317(1)
14.5.3 Number of Solutions for Accurate Evaluation
317(1)
14.5.4 Solutions
317(3)
14.5.5 Comparison with Genetic Algorithm (GA)
320(1)
14.6 Application to Ningxia Provincial Power Grid
320(3)
14.6.1 Wind Power Planning in Ningxia Province
320(3)
14.6.2 Results and Discussion
323(1)
14.7 Conclusions
323(4)
15 Generation Expansion Planning
327(18)
15.1 Overview
327(1)
15.2 Basic Ideas
328(2)
15.2.1 Wind Power Output Modeling Method
329(1)
15.2.2 Group Modeling Method
329(1)
15.3 Model Formulation
330(5)
15.3.1 Notations
330(1)
15.3.2 Objective Function
331(1)
15.3.3 Constraints Conditions
332(3)
15.3.4 Model Structure
335(1)
15.4 Case Study Analysis
335(8)
15.4.1 Basic Data
335(1)
15.4.2 Analysis of Basic Planning Results
336(2)
15.4.3 The Impact of the Number of Wind Power Scenarios
338(2)
15.4.4 The Impact of Extreme Scenarios
340(1)
15.4.5 The Impact of Power Grid Transmission Capacity
341(2)
15.5 Summary
343(2)
16 Transmission Expansion Planning
345(18)
16.1 Overview
345(1)
16.2 Problem Description
346(6)
16.2.1 Assumption
346(1)
16.2.2 Model Structure
347(1)
16.2.3 Notations
347(2)
16.2.4 Model Formulation
349(3)
16.3 Illustrate Example
352(4)
16.3.1 Basic Settings
352(2)
16.3.2 Results
354(2)
16.4 Case Study on Northwestern China Grid
356(4)
16.4.1 Data
356(2)
16.4.2 Results
358(2)
16.5 Summary
360(3)
Index 363
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.
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