Data Science of Renewable Energy Integration: The Nexus of Energy, Environment, and Economic Growth (Evolutionary Economics and Social Complexity Science, 30)

This book covers various data scientific approaches to analyze the issue of grid integration of renewable energy for which the grid flexibility is the key to cope with its intermittency. It provides readers with the scope to view renewable energy integration as establishing a distributed energy network instead of the traditional centralized energy system. Specifically, quantitative valuation system-wise of the levelized cost of energy, which includes both initial cost and various operational costs, enables readers to optimize energy systems in order to minimize economic cost and environmental impact. It is noted, however, that the high cost of integrating renewable energy on a large scale might slow economic growth considerably. Topics addressed in the book also include statistical comparative study of the relationship between energy and economic growth, a graphical model of determinant factors for foreign direct investment in renewable energy, the coupled oscillator model and unit commitment model to capture intermittency of renewable energy, and the network model of evolving micro-grids. The book explains desired innovation to reduce the integration cost significantly using innovative technologies such as energy storage with hydrogen production and vehicle-to-grid technology. Illustrated by careful analysis of selected examples of renewable integration using different types of grid flexibility, this volume is indispensable to readers who make policy recommendations to establish the distributed energy network integrated with large-scale renewable energy by disentangling the nexus of energy, environment, and economic growth. Foreword Preface Acknowledgments Contents Part I Current World 1 Introduction: Today's Our System 1.1 Man-Made Systems 1.1.1 Sustainability 1.1.2 Beyond Reductionism 1.2 Centrally Managed Power System 1.2.1 Energy Balance Table 1.3 Elementary Power System Engineering 1.3.1 Mechanism of Frequency Control 1.3.2 Power System Stability 1.4 Intermittent Renewable Energy 1.5 Summary References 2 Statistical Analysis 2.1 Elementary Statistics 2.1.1 Statistical Quantities 2.1.2 Probability Distribution 2.1.3 Correlation Coefficients 2.2 Statistical Estimation 2.2.1 Parent Population and Parameter 2.2.2 Point Estimation 2.2.3 Maximum Likelihood Estimation 2.2.4 Interval Estimation 2.2.5 Hypothesis and Statistical Test 2.3 Regression Analysis 2.3.1 Single Regression Analysis 2.3.2 Analysis of Variance 2.3.3 Interval Estimation of Parameters 2.3.4 Multiple Regression Analysis 2.3.5 Test of Parameter Estimation 2.4 Time Series Analysis 2.4.1 Origins of Temporal Variations 2.4.2 Stationary Process and Auto-correlation 2.4.3 AR(p) Model 2.4.4 Model Selection Using Box–Jenkins Method 2.4.5 MA(q) Model 2.4.6 ARIMA(p,d,q) Model 2.4.7 Model Selection Using Information Criterion 2.5 Summary References 3 Fluctuation and Correlation of Renewable Energy 3.1 Principal Component Analysis 3.1.1 Basic Concept of PCA 3.1.2 Maximization of V[f] 3.1.3 Important Indices 3.2 Solar Photovoltaic Power 3.2.1 Cross-correlation of PV Output Fluctuation 3.2.2 System-Wide Output Fluctuation 3.2.3 Random Matrix Theory 3.2.4 Data Analysis 3.2.5 Estimation of Forecast Error 3.3 Wind Power 3.3.1 Random Walk Model of Wind Speed Time Series 3.3.2 Correlation of Wind Speed in Europe 3.4 Summary References Part II Toward Decarbonized World 4 Optimization and Dynamical System 4.1 Optimization Techniques 4.1.1 Linear Programming 4.1.2 Simplex Method 4.1.3 Combinatorial Programming 4.1.4 Metaheuristic Optimization 4.2 Dynamical System 4.2.1 Harmonic Oscillator 4.2.2 Attractors 4.3 Kuramoto Oscillator Model 4.3.1 Collective Motions 4.3.2 Synchronization in Physical and Biological Systems 4.3.3 Kuramoto Oscillator 4.3.4 Concept of Phase Time Series 4.3.5 Measure of Synchronization 4.4 Kuramoto Model with Inertia 4.4.1 Formulation 4.4.2 Illustrative Example 4.5 Analysis of Generator Synchronization 4.5.1 Synchronization 4.5.2 Fluctuating Electricity Market 4.6 Summary References 5 Grid Flexibility 5.1 Technologies for Balancing Power 5.1.1 Gass Turbine Materials 5.1.1.1 Alloy Design 5.1.1.2 Design Algorithm 5.1.1.3 Evaluation of Physical Quantities 5.1.1.4 Design Rule and Fitness 5.1.1.5 Results 5.1.2 Electricity Storage 5.1.3 Demand Side Management 5.2 Simple Model of Smart Grid 5.2.1 Assumptions 5.2.2 Formulation of Linear Programming 5.2.3 Parameters 5.2.4 Load Curve and Load Shifting 5.2.5 Results and Discussions 5.2.5.1 BASE and BLUE Scenarios 5.2.5.2 BASE Smart Grids 5.2.5.3 BLUE Smart Grids 5.2.5.4 Electricity Storage for Renewable Integration 5.2.5.5 Break-Even Point Analysis 5.3 Summary References 6 Grid Integration of Renewable Energy 6.1 Unit Commitment Model 6.1.1 Power Grid with Fluctuated Renewables 6.1.2 Model: Objective Function 6.1.3 Model: Global Constraints 6.1.4 Model: Local Constraints for Thermal Power Plants 6.2 Application 1: A Small Power Grid 6.2.1 Analyzed Power Grid 6.2.2 Results and Discussions 6.2.2.1 Reference Case 6.2.2.2 Demand Response 6.2.2.3 Effects of the Forecast Error 6.2.2.4 Effects of Sudden Decrease in Wind Power 6.2.2.5 Demand Response and a Sudden Decrease in Wind Power 6.2.3 Summary 1 6.3 Application 2: Two Large Power Grids 6.3.1 Integrated Operation of Power Grids 6.3.2 Analysis of Tokyo and Tohoku Power Grids 6.3.2.1 Analysis Condition 6.3.2.2 Thermal Power Unit Operation Plan for the Period of Least Demand 6.3.3 Wind Power Curtailment in Tohoku System 6.3.4 Tokyo–Tohoku Grid Interconnection 6.3.5 Summary 2 References 7 Evolving Microgrid Network and Power Market 7.1 Complex Networks 7.1.1 Adjacency Matrix 7.1.2 Node Degree 7.1.3 Path Length and Clustering Coefficient 7.1.4 Weight Matrix 7.1.5 Node Strength 7.1.6 Small-World Network 7.1.7 Strogatz β Model 7.1.8 Scale-Free Network 7.1.9 Barabasi–Albert Model 7.2 Microgrids 7.2.1 Small Island Developing States 7.2.2 Renewable Energy in Microgrid 7.2.2.1 Evolving Microgrid 7.2.2.2 Microgrid Model 7.2.3 Analysis Conditions 7.2.3.1 Representative Case 7.2.3.2 Parameters on the Supply Side 7.2.4 Estimation of System-Wise LCOEs 7.2.5 Nexus of Energy, Environment, and Economic Growth 7.2.6 Evolving Microgrid for SIDS Economies 7.3 Summary References 8 Foreign Direct Investment in Renewable Energy 8.1 Causal Inference 8.1.1 Exploratory Factor Analysis 8.1.2 Structural Equation Modeling 8.1.3 Illustrative Example 8.2 Carbon Pricing and Investment to Developing Countries 8.2.1 Development Assistance by Governments 8.2.2 Foreign Direct Investment by the Private Sector 8.2.3 Types and Objectives of Foreign Direct Investment 8.2.4 Foreign Direct Investment and New Trade Theories 8.2.5 Carbon Pricing Mechanism 8.2.6 Carbon Pricing and FDI 8.3 Investment and Development in Renewable Energy 8.4 Precedent Studies on the Determinants of FDI 8.4.1 Traditional FDI Determinants 8.4.2 Determinants of FDI in the Renewable Energy Sector 8.5 Analysis of FDI in Wind Power Plants 8.5.1 Data of Wind Power Plants 8.5.2 Analysis Results 8.6 Summary References Part III Our Future World 9 Hydrogen Energy Storage and Nuclear Energy 9.1 Hydrogen as Energy Storage 9.2 Analysis Conditions 9.2.1 Load Duration Curve 9.2.2 Time and Generation Technologies 9.2.3 Model Variables 9.2.3.1 Operating Output 9.2.3.2 New Installed Capacity 9.2.3.3 Fuel Cost 9.2.3.4 Pumped Storage Demand 9.2.3.5 Hydrogen Production 9.2.3.6 Hydrogen Consumption 9.2.3.7 LNG Consumption 9.2.3.8 Coal Consumption 9.2.4 Model Parameters 9.2.4.1 Existing Installed Capacity 9.2.4.2 Installed Capacity Limit 9.2.4.3 Facility Utilization Rate Limit 9.2.4.4 Facility Availability at Peak Demand 9.2.4.5 Annual Expense Ratio 9.2.4.6 Unit Construction Cost 9.2.4.7 Supply Reserves 9.2.4.8 Fuel Price by Power Source 9.2.4.9 Operating Years 9.2.4.10 Decommissioning Cost Rate 9.3 Analysis Model 9.3.1 Setting of Analysis Conditions 9.3.1.1 Renewable Energy 9.3.1.2 Hydrogen Production 9.3.1.3 Installed Capacity 9.3.1.4 Next Generation Nuclear Reactors 9.3.1.5 CO2 Emission 9.3.1.6 Four Future Scenarios 9.3.2 Linear Programming Model 9.3.2.1 Objective Function 9.3.2.2 Constraints 9.4 Analysis Results 9.4.1 Combination of Power Generation Sources in 2009 9.4.1.1 Change of Settings 9.4.1.2 Result 9.4.2 Scenario 1: HTGR-SI Cycle, Without Emission Limit 9.4.3 Scenario 2: HTGR-SI Cycle, with Emission Limit 9.4.4 Scenario 3: Solar-AWE, Without Emission Limit 9.4.5 Scenario 4: Solar-AWE, with Emission Limit 9.5 Discussion 9.6 Summary References 10 Blockchain Energy Trade System 10.1 Crypto Asset 10.1.1 Blockchain Technology and the Crypto Asset Bitcoin 10.1.1.1 From Web1 to Web2 10.1.1.2 From Web2 to Web3 10.1.1.3 Centralized and Distributed Systems 10.1.1.4 Blockchain and P2P 10.1.1.5 Cryptographic Hash Function 10.1.1.6 Common Key Cryptography 10.1.1.7 Public Key Cryptography 10.1.1.8 Elliptic Curve Cryptography 10.1.1.9 Electronic Signature 10.1.1.10 Blockchain Concept 10.1.1.11 Blockchain Structure and Features 10.1.1.12 Transaction Approval: Proof of Work 10.1.1.13 Proof of Work 10.1.1.14 Problems with PoW 10.1.1.15 Other Consensus Algorithms 10.1.2 Ripple's Crypto Asset XRP 10.1.3 Using Blockchain Technology to Solve Global Issues 10.2 Energy Trading System EDISON-X 10.2.1 System Architecture 10.2.2 Web Application 10.2.3 Monthly Process 10.2.3.1 Beginning of the Month 10.2.3.2 During the Month 10.2.3.3 End of the Month 10.2.4 Demonstration Experiment 10.3 Topological Characterization of Acquired Data 10.3.1 Hypergraph of Transactions 10.3.2 Cavity Detection Using Persistent Homology 10.4 Summary References 11 V2G: Vehicle to Grid 11.1 System Dynamics 11.1.1 Predator–Prey Model 11.1.2 Stability of Dynamical System 11.1.3 Feedback of Dynamical System 11.2 Electric Vehicle 11.2.1 G2V and V2G 11.2.2 Charging Power 11.2.3 Discharging Power 11.2.4 Simple Grid-EV Model 11.3 Summary References 12 Conclusion: Prospects for the Future 12.1 Society with Distributed Autonomous Organizations 12.2 Decarbonized Society 12.3 Alternative to Economic Growth A Time Series of Wind Speed B Random Walk Model for Wind Speed C Correlation of Wind Speed Index