Energy, Natural Resources and Environmental Economics (eBook)

Energy, Natural Resources and Environmental Economics 1
Preface 
5 
Contents 
7 
Overview of the Contributions 
11 
List of Contributors 
15 
Part I Petroleum and Natural Gas 19
Investment Strategy of Sovereign Wealth Funds 20
1 Introduction 20
2 The Development of Sovereign Wealth Funds 21
3 Investment Strategy 25
3.1 Framework for Optimal Investment Strategy 25
3.1.1 The Liability Profile 26
3.1.2 Asset Allocation Policy 26
3.1.3 Passive or Strategic/Active Investment Style 29
4 Conclusion 31
References 34
Chasing Reserves: Incentives and Ownership 35
1 Introduction 35
2 Booked Reserves 36
2.1 Differences Between PSC and Concession Reserves 38
2.2 Petroleum Reserves: Definitions 38
2.3 The Role of the Reserves Report 40
3 Shtokman 41
4 Peregrino 46
5 Conclusion 51
References 54
Elastic Oil: A Primer on the Economics of Exploration and Production 55
1 Introduction 55
2 Previous Research 59
3 NCS Exploration and Production 61
4 A Simple Model of NCS Exploration 65
5 A Simple Model of NCS Production 69
6 Concluding Remarks 72
References 73
Applied Mathematical Programming in Norwegian Petroleum Field and Pipeline Development: Some Highlights from the Last 30 Years 75
1 Introduction 75
2 Some Historic Remarks 76
3 Main Deterministic Model Assumptions 77
4 Some Representative Deterministic Modelling 78
4.1 Start of Projects 78
4.2 Alternatives 78
4.3 Production Decisions 79
4.4 Global Constraints 80
4.5 Transportation and Market Constraints 80
4.6 The Objective 81
5 Stochastic Development Planning 82
6 A Surprisingly Flexible Modelling Environment 83
7 Practical Model Usage 83
8 What About Today? 84
References 84
Analysis of Natural Gas Value Chains 86
1 Introduction 86
2 Literature Review 87
3 The Natural Gas Value Chain 88
3.1 Production and Processing 89
3.2 Transportation 89
3.3 Storage 89
3.4 Markets 89
3.5 Mathematical Models 90
3.6 System Effects 92
3.7 Markets and the Portfolio Perspective 93
3.8 Modeling Competition 94
4 Conclusions 96
References 96
On Modeling the European Market for Natural Gas 98
1 Introduction 98
2 Previous Modeling 101
3 Behavior 103
3.1 Non-Negative Sales 104
3.2 Optimization Approach 105
4 The Generalized Transportation Model 105
4.1 Noncompetitive Behavior 107
4.1.1 A Numerical Example 108
5 Network 111
6 Conclusion 114
References 114
Equilibrium Models and Managerial Team Learning 116
1 Introduction 116
2 Managerial Team Learning 117
3 VisualGas 120
4 Research Methodology 121
5 Findings and Analysis 122
5.1 Concepts Used in the Two Sessions 122
5.2 Description of the First Session (without VisualGas) 123
5.3 Description of the Second Session (with VisualGas) 124
5.4 Comparison of the Sessions 125
6 Concluding Remarks 127
References 128
Refinery Planning and Scheduling: An Overview 130
1 Introduction 130
2 Crude Oil Selection and Crude Oil Scheduling 132
2.1 Selection of Crude Oils 133
2.2 Scheduling of Crude Oils 134
3 Production Planning and Scheduling 136
3.1 Production Planning 137
3.2 Production Scheduling 139
4 Product Blending and Recipe Optimization 141
5 Discussion and Further Research 143
References 143
Part II Electricity Markets and Regulation 146
Multivariate Modelling and Prediction of Hourly One-Day Ahead Prices at Nordpool 147
1 Introduction 147
2 Nordpool and Electricity Price Data 148
2.1 Local or UTC Time 149
2.2 Price or Log-Price? 149
3 Some Stylized Facts of Electricity Prices 149
3.1 Seasonalities 150
3.2 Stationary or Non-Stationary Models 151
3.3 Distributional Assumptions 152
3.4 Jumps 152
3.5 Volatility Clustering 153
4 Literature Review 153
5 Multivariate Decomposition and Modelling 154
6 Functional Data Approach 158
6.1 Notation 158
6.2 System of Basis Functions 159
6.3 Functional Analysis of Variance (FANOVA) 160
7 Prediction via Univariate ARIMA Modelling 160
8 Multivariate Volatility 163
9 Multivariate Dynamic Modelling 164
10 Conclusion 166
References 167
Time Regularities in the Nordic Power Market: Potentials for Profitable Investments and Trading Strategies? 169
1 Introduction 169
2 Literature on Price Relationships in the Power Markets 170
3 Day-of-the-Week Pattern 172
4 The Week-End Pattern 174
5 Time-of-the-Day Pattern 175
6 Mean Reversion 177
7 Tentative Conclusions 178
References 179
Valuation and Risk Management in the Norwegian Electricity Market 181
1 Introduction 181
2 The Model 182
2.1 The Forward Market 182
3 European Option 185
3.1 Forward on a Flow Delivery 185
3.2 Call Option Valuation 185
4 Asian Option 187
5 Valuation: An Example 190
5.1 Current Term Structure 190
5.2 Volatility 191
5.3 Contract Valuation 191
6 Value at Risk 193
7 Value at Risk: An Example 195
7.1 Price Path Simulations 195
7.2 Value at Risk Calculation 195
8 Conclusions 196
References 199
Stochastic Programming Models for Short-Term Power Generation Scheduling and Bidding 200
1 Introduction 200
2 The Stochastic Programming Framework 201
3 Power Generation Scheduling in Regulated Markets 202
3.1 Thermal Unit Commitment and Hydro-Thermal Scheduling 202
3.2 Hydro Scheduling 204
4 Restructured Markets 204
4.1 Thermal Unit Commitment 205
5 Solution Approaches 206
5.1 Thermal Unit Commitment and Hydro-Thermal Scheduling 206
5.2 Hydro Scheduling 208
6 Physical Market Exchange and Bidding 209
References 211
Optimization of Fuel Contract Management and Maintenance Scheduling for Thermal Plants in Hydro-based Power Systems 214
1 Introduction 214
2 General Aspects: Fuel Supply Agreements, Maintenances, and Decision Under Uncertainty 217
2.1 Fuel Supply Agreements: Characteristics 217
2.2 Fuel Opportunity Cost 218
2.3 Decision Under Uncertainty 218
2.4 Maintenance Scheduling 219
2.5 Computational Model 219
3 Case Study 224
3.1 Case 1: Deterministic with Maintenance 225
3.2 Case 2: Stochastic with Maintenance 225
4 European Application 230
4.1 Differences and Similarities 230
4.1.1 Contract Types 230
4.1.2 Markets 230
4.1.3 Gas Contracts Portfolios 230
4.1.4 Corporate Behavior 231
4.1.5 Asset Portfolios 231
4.2 Algorithm of Future Adaptations and Developments 231
5 Conclusion 231
References 232
Energy Portfolio Optimization for Electric Utilities: Case Study for Germany 233
1 Introduction 233
2 Description of the Problem 236
2.1 Power Plant Usage 236
2.2 Energy Purchase from the Spot Market 238
2.3 Energy Purchase from the Load-Following Contract 239
3 Mathematical Formulation 241
3.1 Objective Function 241
3.1.1 Cost for the Power Generation in the Own Power Plant 241
3.1.2 Cost for the Purchase of Energy from the Spot Market 241
3.1.3 Cost for the Energy Purchase from the Load-Following Contract 242
3.2 Demand and Power Plant Constraints 244
3.2.1 Power Demand Constraints 244
3.2.2 Power Plant Constraints 245
4 Improvements of the Model Formulation 248
4.1 Assumptions and Limitations of the Model 248
4.2 Modifications 248
5 Computational Results 250
6 Conclusion 252
References 253
A Indices and Index Sets 255
B Variables 255
C Constraints 257
D Input Data and Parameters 257
Investment in Combined Heat and Power: CHP 259
1 The Economics of Cogeneration 259
2 The Demand for District Heating and Its Duration 261
3 Efficient Investments for the Production of Heat 266
3.1 The Economics of Strategy 1 269
3.2 The Economics of Strategy 2 270
3.3 Results from the Profitability Analyses 271
3.4 A Parametrical Expansion of the Capacity 272
4 Potential Investments in Cogeneration for 10 Large Swedish Municipalities 273
5 Limits for Investments in Cogeneration 276
References 278
Capacity Charges: A Price Adjustment Process for Managing Congestion in Electricity Transmission Networks 279
1 Introduction 279
2 Literature Review 281
3 The Capacity Charge Approach 283
4 A Numerical Example 287
4.1 Model and Parameters 287
4.2 Unconstrained Optimal Dispatch 290
4.3 Nodal Prices 290
4.4 Optimal Capacity Charges 291
5 An Iterative Adjustment Process 293
6 A Heuristic Procedure for Faster Convergence 299
7 Summary and Topics for Future Research 302
References 304
Harmonizing the Nordic Regulation of Electricity Distribution 305
1 Scope and Introduction 306
1.1 Background 306
2 Harmonization of Regulatory Systems 307
2.1 Current Situation 307
2.2 Effects of Harmonization 308
2.3 Analyzing the Nordic Case 309
2.4 NEMESYS View on Harmonization 310
2.4.1 Definition of DSO Task Portfolio 310
2.4.2 Regulation and Information Harmonization 311
3 Regulatory Toolbox 312
3.1 Dynamic Cost-Based Yardstick 314
3.2 Dynamic Revenue-Based Yardstick 314
3.3 Dynamic Network Auction Model 314
3.4 Quality Regulation Model 315
4 The NEMESYS Approach 315
4.1 Revenue Yardstick Model 315
4.1.1 Example 317
4.1.2 The Intuition 318
4.1.3 Incentive Effects 319
4.2 Benchmarking Model 319
4.2.1 Quality Incentive Scheme 320
4.2.2 Compensation Scheme 320
4.2.3 Regulatory Settlements 321
4.2.4 Regulatory Procedure 322
5 Stakeholder Analysis 323
5.1 Results: Stability, Quality, and Efficiency 323
5.1.1 Stakeholder Consequences 324
5.2 Further Work 324
5.2.1 Consequence Analysis 325
5.2.2 Initiative on Regulatory Enablers 325
5.2.3 Principal Challenges in the Model: Non-Profit Firms 325
6 Conclusions 326
References 327
Benchmarking in Regulation of Electricity Networks in Norway: An Overview 329
1 Introduction 329
2 Regulation of Electricity Networks After the Energy Act of 1990 330
2.1 Period I: 1997–2001 331
2.2 Period II: 2002–2006 332
2.3 Period III: 2007–2011 333
3 Benchmarking and Productivity Measurement for Regulation 335
3.1 Introduction to DEA 335
3.2 Model Specification and Data Measurement Issues 338
3.2.1 The Number of Input Factors 340
3.2.2 Capital Costs and Age Effects 342
3.2.3 Choice of Output Variables 345
3.2.4 Scale Assumption 347
3.2.5 Super Efficiency and Incentives 348
3.2.6 Calibration and Average Profitability 350
4 Concluding Remarks 353
References 353
On Depreciation and Return on the Asset Base in a Regulated Company Under the Rate-of-Return and LRIC Regulatory Models 355
1 Introduction and Overview 355
2 Four Standard Methods for Allowed Depreciation and Return on the Asset Base and Their Use in the Rate-of-Return Regulatory Model 358
3 LRIC and the Real Annuity Method 363
4 A Comparison of the Two Models 364
References 366
Part III Natural Resources and Logistics 368
Rescuing the Prey by Harvesting the Predator: Is It Possible? 369
1 Introduction 369
2 Background and Motivation 370
3 The Model 373
3.1 Numerical Example 374
4 The Rescue Operation 376
5 State-Space Analysis 377
6 Summary 383
7 Appendix: Proof of Proposition 1 384
8 Appendix: Analysis of Steady States With No Prey 385
References 387
Absorptive Capacity and Social Capital: Innovation and Environmental Regulation 389
1 Introduction 389
1.1 Previous Research on Absorptive Capacity and Social Networks 390
1.2 Theory: Absorptive Capacity and Social Capital 391
1.2.1 Absorptive Capacity 391
1.2.2 Social Capital 392
1.3 Combining Absorptive Capacity and Social Capital 393
2 Methods 394
2.1 Data and Respondents 394
2.1.1 Research Design 394
2.1.2 The Study Setting 394
2.1.3 Data Sources and Measurements 394
2.1.4 Respondents 395
2.2 Regression Analysis 396
3 Results 397
3.1 The respondents' History of Pollution Reduction 397
3.2 Test of the Hypothesis: Absorptive Capacity and Social Capital 398
4 Discussion and Conclusion 399
References 401
Issues in Collaborative Logistics 404
1 Introduction 404
2 Collaborative Logistics Problems 405
2.1 Strategic Collaboration in Logistics 407
2.2 Operational Collaboration in Logistics 408
2.3 Size of the Coalition 408
2.4 Collaboration Driver 408
2.5 Collaboration in Vertical Network 409
2.6 Research Questions 409
3 Building Coalitions 410
4 Sharing Principles 411
4.1 Game Theoretic Background 411
4.2 Quantitative Allocation Methods 413
4.2.1 Weighted Costs 413
4.2.2 Separable and Non-Separable Costs 413
4.2.3 Shapley Value 414
4.2.4 Equal Profit Method 414
5 Technology 415
6 Discussion 417
References 417
Pilot Assignment to Ships in the Sea of Bothnia 419
1 Introduction 419
2 Problem Description 421
3 Short-Term Planning 422
3.1 Work Schedule Generation 425
3.1.1 Network Flow Model Details 426
3.2 The Master Problem 427
4 Input Data Example from the Sea of Bothnia 428
4.1 Values, Costs, and Other Control Parameters 429
4.2 Reference Case Value 429
5 Column Generation Solutions 430
5.1 Some Solution Details 431
6 Conclusions 432
6.1 Future Work Suggestions 432
References 433
Transportation Planning and Inventory Management in the LNG Supply Chain 434
1 Introduction 434
2 The LNG Supply Chain 436
3 Common Problem Characteristics and Notation 437
4 A Planning Problem for a Producer 439
5 A Planning Problem for a Vertically Integrated Company 441
6 Solution Approaches 444
7 Concluding Remarks 445
References 446
Part IV General Problems and Methods 447
Optimal Relinquishment According to the Norwegian Petroleum Law: A Combinatorial Optimization Approach 448
1 Introduction 449
2 Constructing the Mathematical Model 453
3 Different Combinatorial Optimization Models 455
4 Difficulty of the Relinquishment Problem 456
5 A Small Illustrative Example 457
6 Conclusions 461
References 461
An Overview of Models and Solution Methods for Pooling Problems 463
1 Introduction 463
2 A General Pooling Model 465
2.1 Lower Supply Bound 467
2.2 Upper Demand Bound 467
2.3 Generality of the Model 468
3 Inexact Solution Methods 468
4 Exact Solution Methods 469
4.1 Flow-Based Methods 469
4.2 A Proportion Model 470
5 Conclusions 472
References 472
Cooperation Under Ambiguity 474
1 Introduction 474
2 Shadow Prices and Core Solutions 476
3 Choquet Integration 479
4 Integral Representation of Preferences 484
5 Explicit Core Solutions 486
6 Comonotone Allocations and Sunspots 489
References 493
The Perpetual American Put Option for Jump-Diffusions 495
1 Introduction 495
2 The Model 496
3 The Optimal Stopping Problem 497
4 The Solution of the Optimal Stopping Problem 498
5 Risk Adjustments 503
6 Solution When Jumps Are Negative 505
7 Conclusions 507
References 508
Discrete Event Simulation in the Study of Energy, Natural Resources and the Environment 510
1 Introduction 510
2 Project Time Planning Simulation 512
3 A Bidding Example 515
4 A Duopoly Game 517
References 521