Advances in Energy Systems E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1482

Veröffentlichungsjahr: 2019

Beschreibung

A guide to a multi-disciplinary approach that includes perspectives from noted experts in the energy and utilities fields Advances in Energy Systems offers a stellar collection of articles selected from the acclaimed journal Wiley Interdisciplinary Review: Energy and Environment. The journalcovers all aspects of energy policy, science and technology, environmental and climate change. The book covers a wide range of relevant issues related to the systemic changes for large-scale integration of renewable energy as part of the on-going energy transition. The book addresses smart energy systems technologies, flexibility measures, recent changes in the marketplace and current policies. With contributions from a list of internationally renowned experts, the book deals with the hot topic of systems integration for future energy systems and energy transition. This important resource: * Contains contributions from noted experts in the field * Covers a broad range of topics on the topic of renewable energy * Explores the technical impacts of high shares of wind and solar power * Offers a review of international smart-grid policies * Includes information on wireless power transmission * Presents an authoritative view of micro-grids * Contains a wealth of other relevant topics Written forenergy planners, energy market professionals and technology developers, Advances in Energy Systems is an essential guide with contributions from an international panel of experts that addresses the most recent smart energy technologies.

Leseprobe

Table of Contents

Cover

List of Contributors

Preface

PART I: ENERGY SYSTEM CHALLENGES

1 Handling Renewable Energy Variability and Uncertainty in Power System Operation

INTRODUCTION

THE CHALLENGES OF RES IN POWER SYSTEM OPERATION

ADVANCES IN RENEWABLE ENERGY FORECASTING

THE IMPORTANCE OF GENERATION FLEXIBILITY

METHODS FOR HANDLING THE VARIABILITY AND UNCERTAINTY FOR STEADY‐STATE OPERATION

THE ROLE OF STORAGE DEVICES

ACTIVE AND REACTIVE POWER CONTROL OF RES

MARKET RULES AND PRODUCTS FOR DEALING WITH VARIABILITY AND UNCERTAINTY

EMERGENT APPROACHES

CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

FURTHER READING

2 Short‐Term Frequency Response of Power Systems with High Nonsynchronous Penetration Levels

INTRODUCTION

FREQUENCY RESPONSE EVOLUTION WITH INCREASED VARIABLE GENERATION

POTENTIAL FREQUENCY RESPONSE SOLUTIONS

GRID CODE REQUIREMENTS AND ANCILLARY SERVICE MARKETS

ISSUES RESULTING FROM NONSYNCHRONOUS FREQUENCY RESPONSE

CONCLUSIONS

REFERENCES

3 Technical Impacts of High Penetration Levels of Wind Power on Power System Stability

INTRODUCTION

SYSTEM MODELING

FREQUENCY CONTROL AND INERTIAL ISSUES

TRANSIENT STABILITY AND FAULT RIDE‐THROUGH

VOLTAGE STABILITY

SMALL SIGNAL STABILITY AND SUBSYNCHRONOUS INTERACTIONS

CONCLUSIONS

REFERENCES

4 Understanding Constraints to the Transformation Rate of Global Energy Infrastructure

INTRODUCTION

WHAT IS POSSIBLE? – HISTORICAL (AND FUTURE) CONTEXT

WHAT EXTRA BURDENS DOES AN ENERGY TRANSFORMATION INTRODUCE?

HOW SIGNIFICANT IS THE EARLY REPLACEMENT CHALLENGE?

SENSITIVITY ANALYSIS

CONCLUSIONS

REFERENCES

5 Physical and Cybersecurity in a Smart Grid Environment

INTRODUCTION

MAJOR INTRUSION INCIDENTS

SMART GRID VULNERABILITIES

SECURITY CONTROLS FOR THE SMART GRID

ENHANCEMENT OF THE SMART GRID SECURITY

PHYSICAL AND CYBERSECURITY INTERDEPENDENCY

CONCLUSION

ACKNOWLEDGMENTS

REFERENCES

6 Energy Security

INTRODUCTION

DEFINING ENERGY SECURITY

THREATS TO AVAILABILITY

THREATS TO AFFORDABILITY

THREATS TO EFFICIENCY

THREATS TO STEWARDSHIP

CONCLUSION

REFERENCES

FURTHER READING

7 Nuclear and Renewables

INTRODUCTION

STATUS AND PERSPECTIVES OF NUCLEAR POWER

RENEWABLE ENERGIES

CONCLUSION

REFERENCES

FURTHER READING

PART II: PERSPECTIVES ON GRIDS

8 Smart‐Grid Policies

INTRODUCTION

BARRIERS AND DRIVERS IMPACTING THE DEPLOYMENT OF SMART GRIDS

SMART‐GRID POLICIES OF THE UNITED STATES

SMART‐GRID POLICIES OF THE EUROPEAN UNION

SMART‐GRID POLICIES OF EAST ASIA

INTERNATIONAL COLLABORATION

CONCLUSIONS AND RECOMMENDED FUTURE POLICY DIRECTIONS

ACKNOWLEDGMENTS

REFERENCES

9 A View of Microgrids

INTRODUCTION

DISTRIBUTED ENERGY RESOURCES

ACTIVE DISTRIBUTION NETWORKS

TECHNICAL REQUIREMENTS FOR MICROGRID OPERATION

MICROGRID DEPLOYMENT ROADMAP

CONCLUSIONS

REFERENCES

10 New Electricity Distribution Network Planning Approaches for Integrating Renewables

INTRODUCTION

DISTRIBUTION PLANNING IN THE SG ERA

MODERN DISTRIBUTION PLANNING TOOLS FOR RES INTEGRATION

APPLICATIONS OF PLANNING TOOLS FOR RES INTEGRATION

CONCLUSIONS

REFERENCES

FURTHER READING

11 Transmission Planning for Wind Energy in the United States and Europe

INTRODUCTION

TRANSMISSION PLANNING FOR ENERGY RESOURCES

REGIONAL PLANNING EFFORTS – STATUS AND PROSPECTS

LOOKING INTO THE FUTURE

CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

FURTHER READING

12 Opportunities and Barriers of High‐Voltage Direct Current Grids

INTRODUCTION

PRIORITY CORRIDORS: LINKING LARGE RENEWABLE ENERGY SOURCES (RES) GENERATION WITH CONSUMPTION CENTERS

THE SUPERGRID VISION

BARRIERS AND FACILITATORS

CONCLUSION

REFERENCES

13 Wireless Power Transmission

INTRODUCTION

HISTORY OF THE WPT

INDUCTIVE COUPLING AND RESONANCE COUPLING

MICROWAVE POWER TRANSMISSION

CONCLUSIONS

REFERENCES

PART III: FLEXIBILITY MEASURES

14 The Role of Large‐Scale Energy Storage Under High Shares of Renewable Energy

INTRODUCTION

GENERAL CHARACTERISTICS OF THE POWER GRID

POWER QUALITY

FEATURES OF VARIATIONS DUE TO RENEWABLES

PROSPECTS OF ENERGY STORAGE GROWTH

ENERGY STORAGE SYSTEMS

CONCLUSIONS

REFERENCES

FURTHER READING

15 The Role of Electric Vehicles in Smart Grids

INTRODUCTION

APPROACHES FOR THE INTEGRATION OF ELECTRIC VEHICLES INTO POWER SYSTEMS

ELECTRIC VEHICLE OPERATION OBJECTIVES AND ROLES

STORAGE FOR MARKET PARTICIPATION AND PROFIT MAXIMIZATION

UPCOMING CHALLENGES FOR THE INTRODUCTION OF LARGE‐SCALE EV ADOPTION AND MANAGEMENT

CONCLUSION

REFERENCES

16 Use of Electric Vehicles or Hydrogen in the Danish Transport Sector in 2050?

A SYSTEMS APPROACH TO DECARBONIZE THE TRANSPORT SECTOR

THE STREAM MODEL

DESCRIPTION OF THE 2050 SCENARIOS

SCENARIO RESULTS

WHICH TECHNOLOGICAL PATH SHOULD THE INNOVATION FOLLOW? SENSITIVITY ANALYSIS OF COST DRIVERS

CONCLUSIONS

REFERENCES

17 Comparison of Synthetic Natural Gas Production Pathways for the Storage of Renewable Energy

INTRODUCTION

TECHNOLOGICAL OVERVIEW

BIOCHEMICAL SNG PRODUCTION

THERMOCHEMICAL SNG PRODUCTION

ELECTROCHEMICAL SNG PRODUCTION

ALTERNATIVE/HYBRID CONCEPTS

DISCUSSION AND COMPARISON OF CONCEPTS

CONCLUSION

REFERENCES

18 Storage and Demand‐Side Options for Integrating Wind Power

INTRODUCTION

STORAGE AND DEMAND‐SIDE OPTIONS FOR INTEGRATING WIND POWER: OVERVIEW OF TECHNOLOGIES

WIND GENERATION INTEGRATION ISSUES RELATED TO DR AND STORAGE

MODELING THE BENEFITS OF STORAGE AND DEMAND‐SIDE OPTIONS TO FACILITATE WIND INTEGRATION

AREAS FOR FUTURE RESEARCH IN STORAGE AND DEMAND‐SIDE OPTIONS AS THEY RELATE TO WIND POWER

CONCLUSIONS

REFERENCES

19 On the Long‐Term Prospects of Power‐to‐Gas Technologies

INTRODUCTION

CURRENT CHALLENGES IN THE ELECTRICITY SYSTEM AND THE ROLE OF P2G

THE COSTS OF HYDROGEN AND METHANE IN P2G

USE OF HYDROGEN AND METHANE IN THE TRANSPORT SECTOR

ECONOMIC PERSPECTIVES FOR P2G TECHNOLOGIES FROM TECHNOLOGICAL LEARNING UP TO 2050

CONCLUSIONS

APPENDIX

REFERENCES

FURTHER READING

20 Wind Integration

INTRODUCTION

THE CHALLENGE OF WIND POWER TO POWER SYSTEMS

WIND IMPACTS ON BALANCING AND RESERVES

BALANCING COSTS OF WIND POWER

CURTAILMENTS OF WIND POWER GENERATION

WIND IMPACTS ON THE TRANSMISSION GRID

CAPACITY VALUE OF WIND POWER

CONCLUSION AND OUTLOOK

ACKNOWLEDGMENTS

REFERENCES

21 Quantifying the Variability of Wind Energy

THE IMPORTANCE OF WIND VARIABILITY

AN OVERVIEW OF DIFFERENT SCALES OF VARIABILITY

WIND SPEED DISTRIBUTIONS

LONG‐TERM TRENDS

FUTURE TRENDS

THE IMPACT OF VARIABILITY ON WIND POWER

CONCLUSION

REFERENCES

FURTHER READING

22 Capacity Value Assessments of Wind Power

INTRODUCTION

CAPACITY VALUE OF WIND POWER

SUMMARY AND FUTURE WORK

ACKNOWLEDGMENTS

REFERENCES

23 Hydropower Flexibility for Power Systems with Variable Renewable Energy Sources

INTRODUCTION

HYDROPOWER FLEXIBILITY

PRACTICAL EXPERIENCE IN USING HYDRO FLEXIBILITY FOR INTEGRATION OF VARIABLE GENERATION

STUDYING POSSIBILITIES OF HYDROPOWER IN WIND INTEGRATION

SIMULATION CHALLENGES

CONCLUSIONS

REFERENCES

24 Contribution of Bulk Energy Storage to Integrating Variable Renewable Energies in Future European Electricity Systems

INTRODUCTION

ANALYSIS OF THE EUROPEAN ELECTRICITY MARKET REGIONS

SETUP AND INPUT DATA

RESULTS FOR THE CENTRAL WESTERN EUROPE REGION

RESULTS FOR THE IBERIAN PENINSULA

ROLE OF CROSS‐BORDER TRANSMISSION GRID EXPANSION AND EXTREME WEATHER EVENTS IN THE EUROPEAN ELECTRICITY MARKET REGIONS

SUMMARY AND CONCLUSIONS

REFERENCES

FURTHER READING

25 Characterization of Demand Response in the Commercial, Industrial, and Residential Sectors in the United States

OVERVIEW OF DEMAND RESPONSE IN THE UNITED STATES

END USES CONSIDERED FOR DR

DR ATTRIBUTES

CONCLUSION

ACKNOWLEDGMENTS

REFERENCES

FURTHER READING

26 Simplified Analysis of Balancing Challenges in Sustainable and Smart Energy Systems with 100% Renewable Power Supply

INTRODUCTION

KEEPING THE CONTINUOUS SHORT‐TERM POWER BALANCE

LEVEL 1 ANALYSIS: MAXIMAL SHARE OF VARIABLE RENEWABLE

LEVEL 2 ANALYSIS: TRANSITION DIAGRAMS

LEVEL 3 ANALYSIS: TIME SERIES ANALYSIS

CONCLUSIONS

REFERENCES

PART IV: CHANGING ELECTRICITY MARKETS

27 Who Gains from Hourly Time‐of‐Use Retail Prices on Electricity? An Analysis of Consumption Profiles for Categories of Danish Electricity Customers

INTRODUCTION

DATA FOR HOURLY ELECTRICITY CONSUMPTION

AVERAGE HOURLY CONSUMPTION AND SPOT MARKET PRICE PROFILES

SEASONAL VARIATIONS IN HOURLY CONSUMPTION PROFILES FOR AGGREGATED CATEGORIES OF CUSTOMERS

THE AVERAGE TIME‐OF‐USE SPOT MARKET PRICE FOR CATEGORIES OF CUSTOMERS

THE VARIATION OF THE AVERAGE TIME‐OF‐USE SPOT MARKET PRICE PAID BY INDIVIDUAL CUSTOMERS WITHIN CATEGORIES

CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

APPENDIX

28 Designing Electricity Markets for a High Penetration of Variable Renewables

INTRODUCTION

THE DISTINGUISHING CHARACTERISTICS OF VARIABLE RENEWABLE TECHNOLOGIES

VARIABILITY AND UNCERTAINTY

LOW SHORT‐RUN MARGINAL COSTS

NONSYNCHRONOUS GENERATION

CONCLUSION

REFERENCES

FURTHER READING

29 Multivariate Analysis of Solar City Economics

INTRODUCTION

BRIEF REVIEW OF RECENT “SOLAR CITY” ASSESSMENT LITERATURE

ANALYTIC APPROACH

PROJECT FINANCE ANALYSIS

REGRESSION RESULTS

RESULT OF THE MODEL ROBUSTNESS CHECK

CONCLUSION

ACKNOWLEDGMENTS

REFERENCES

30 The Influence of Interconnection Capacity on the Market Value of Wind Power

INTRODUCTION

BACKGROUND

MODEL DESCRIPTION AND DATA

RESULTS

CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

31 Research with Disaggregated Electricity End‐Use Data in Households

INTRODUCTION

MOTIVATION

OUR FOCUS AND ITS CONTEXT

STUDIES UNDER INVESTIGATION

THEME 1 – METHODS

THEME 2 – KEY FINDINGS

THEME 3 – LOOKING FORWARD

THE EMERGING ENERGY AGENDA

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

ACKNOWLEDGMENTS

REFERENCES

FURTHER READING

32 Household Electricity Consumers' Incentive to Choose Dynamic Pricing Under Different Taxation Schemes

INTRODUCTION

DETERMINING THE ATTRACTIVENESS OF DYNAMIC PRICING

THRESHOLD BENEFIT LEVELS OF CONSUMERS

RESULTS

DISCUSSION

CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 FRT voltage and time values for European grid codes.

Chapter 2

Table 2.1 Ireland and Northern Ireland system frequency ranges.

Table 2.2 Frequency response products.

Chapter 4

Table 4.1 Unit operations required for different electricity supply t...

Chapter 5

Table 5.1 Comparison of the main properties of commercial and researc...

Table 5.2 Verification technologies for access control.

Table 5.3 Research projects and programs on physical security.

Table 5.4 Cyberattack penetration times and success rates.

Table 5.5 Success rates of the SPSM system in five intrusion paths.

Chapter 6

Table 6.1 Life expectancy of proven fossil fuel and uranium resources...

Table 6.2 Number of people without access to electricity and dependen...

Chapter 7

Table 7.1 Share of nuclear electricity of

total final energy consumpt

...

Chapter 8

Table 8.1 Net metering policies in four US states.

Table 8.2 Interconnection standards and rules in four US states.

Table 8.3 Smart metering targets in four US states.

Table 8.4 Dynamic pricing policies in four US states.

Table 8.5 Smart Grid legislation and regulations in the European Unio...

Table 8.6 Smart‐grid policies to tackle barriers and leverage drivers...

Table 8.7 Status, targets, policy drivers, and emphases by country. ...

Chapter 9

Table 9.1 Possible microgrid architectures and their characteristics

Chapter 10

Table 10.1 Network and no‐network solutions in modern distribution pl...

Table 10.2 The most important features of modern planning with partic...

Table 10.3 Network costs comparison in the whole planning period with...

Table 10.4 Different scenarios for active service remuneration.

Table 10.5 Average values in the optimal Pareto set of the OFs and of...

Chapter 11

Table 11.1 Planned electrical circuits in the Spanish transmission sy...

Chapter 14

Table 14.1 The electricity generation mix in WEU in the BLUE map scen...

Chapter 17

Table 17.1 Range of requirements of various European countries for in...

Table 17.2 Typical biogas composition with characteristics.

Table 17.3 Typical compositions of product gases from different gasif...

Table 17.4 Typical contaminants present in biomass gasification synth...

Table 17.5 Comparison of commercial electrolysis technologies

[46–48]

...

Table 17.6 Definition of TRLs (technology readiness levels) by the Eu...

Table 17.7 Overview of mean values and ranges for all data sets.

Chapter 18

Table 18.1 Provision of system services from storage and demand‐side ...

Chapter 19

Table 19.1 Investment costs in hydrogen and methane production plants...

Table 19.2 Efficiency of electrolysis and methanation (for two capaci...

Table 19.A1 Selected power‐to‐gas operational projects in Europe

[8]

. ...

Chapter 23

Table 23.1 Annual hydro and pumping generation (TWh) versus installed...

Chapter 24

Table 24.1 RES‐E capacities of the CWE region and Iberian Peninsula in th...

Table 24.2 Contribution of transmission grid expansion for the mitigation...

Chapter 25

Table 25.1 Summary of DR participation in wholesale markets in the Un...

Table 25.2 Differences between OpenADR 2.0 and SEP 2.

Table 25.3 Common and evolving grid services and their attributes.

Table 25.4 End uses by sector considered for their flexibility.

Table 25.5 DR attributes mapped on a flexibility scale.

Table 25.6 Resources with integrated thermal mass or process storage,...

Table 25.7 Cost of enabling demand response resources.

Table 25.8 Attributes of DR resources.

Chapter 26

Table 26.1 Maximal share of variable renewables for some countries.

Table 26.2 Used data in application of third level analysis on a futu...

Table 26.3 Cost calculation for surplus and deficit under described a...

Chapter 27

Table 27.1 The effect of a time‐of‐use hourly pricing contra a fixed ...

Table 27.2 Variation in average time‐of‐use price for individual cust...

Table 27.A1 The effect of a time‐of‐use hourly pricing contra a fixed...

Chapter 29

Table 29.1 Overview of the input data.

Table 29.2 Inputs for hard and soft cost percentage for each city in ...

Table 29.3 Overview of regression results by city.

Table 29.4 Overview of the regression results of the combined regress...

Table 29.5 Overview of the PB in each location.

Chapter 30

Table 30.1 Summary of default parameter settings and assumptions for ...

Chapter 31

Table 31.1 Basic information regarding studies under investigation.

Table 31.2 Monitoring information regarding studies under investigati...

Chapter 32

Table 32.1 Used hourly consumption data set.

Table 32.2 Switching cost estimates.

Table 32.3 Average benefits of switching to dynamic pricing under dif...

List of Illustrations

Chapter 1

Figure 1.1 Generic FRT voltage versus time characteristic curve.

Figure 1.2 (a) Forecast intervals, centered in the median, and limited by their...

Figure 1.3 Generation variability of solar power due to clouds: (a) hourly aver...

Figure 1.4 Example of the expected energy not supplied as a function of the res...

Figure 1.5 Wind power reduction in Portugal due to extreme wind speed condition...

Figure 1.6 Architecture of a wind power dispatch center.

Chapter 2

Figure 2.1 Frequency response categories.

Figure 2.2 Illustrative frequency trace following event.

Figure 2.3 Ireland and Northern Ireland system stored energy duration curve.

Figure 2.4 Ireland and Northern Ireland system RoCoF 2020.

Figure 2.5 Emulated inertia response shape and variation with wind speed (GE fi...

Figure 2.6 Illustrative droop characteristic.

Figure 2.7 Simulated frequency response of the CAISO system incorporating droop...

Figure 2.8 Simulated frequency response following disturbance with units having...

Figure 2.9 Impact of large aggregate energy recovery from active controls on sy...

Chapter 3

Figure 3.1 Inertia duration curves for the all‐island system in Ireland.

Figure 3.2 Representative schematic of power system operational analysis.

Figure 3.3 Frequency response of Light Spring Hi‐Mix case – DG trip versus two ...

Figure 3.4 Maximum RoCoF for all‐island (Ireland) system for the year 2020 foll...

Figure 3.5 Distribution of frequency nadirs of European Continental synchronous...

Figure 3.6 Low voltage‐ride through requirement from wind power plants in vario...

Figure 3.7 Fault ride through certified wind power in Spain.

Figure 3.8 Load‐induced voltage collapse in heavy summer base case, Midway‐Vinc...

Figure 3.9 Reactive power versus voltage analysis (QV curves) for 400 kV busbar...

Figure 3.10 Voltage security boundary of the Western Danish power system, depen...

Figure 3.11 Measured quantities [(phase currents (blue) and voltages (magenta)]...

Chapter 4

Figure 4.1 Historical rates of installing new fossil‐ and nuclear‐fueled electr...

Figure 4.2 Historical evolution of the automobile industry in China

[49]

shown a...

Figure 4.3 The changing role for electricity, comparing forecasts for business‐...

Figure 4.4 Relative importance of the regions adopted in this model for the for...

Figure 4.5 Cumulative capacity of coal‐fired power plant as a function of its a...

Figure 4.6 Early replacement forecasts for (a) United States and EU, and (b) In...

Figure 4.7 Early replacement forecasts for the world, obtained by aggregating t...

Figure 4.8 Overall (averaged) rate of early replacement for the global aggregat...

Figure 4.9 Minimum age of coal‐fired power plant at the time of retirement obta...

Chapter 5

Figure 5.1 Cyber‐physical system.

Figure 5.2 Typical architecture of the surveillance system.

Figure 5.3 Cybersecurity controls for the smart grid.

Figure 5.4 Typical flowchart of image processing for detection.

Figure 5.5 Three‐dimensional substation scene.

Figure 5.6 Substation layout and the intrusion path.

Figure 5.7 Substation information and communications technology model.

Figure 5.8 Frequency variations.

Figure 5.9 Current variations on lines 26–28 and 28–29.

Figure 5.10 Voltage variations at bus 29.

Figure 5.11 Load variations at bus 28.

Figure 5.12 Architecture of the substation physical security monitoring system.

Figure 5.13 Procedure of the system response.

Chapter 6

Figure 6.1 Major global energy reserves for the top 15 countries, 2008.

Chapter 7

Figure 7.1 Nuclear reactors and net operating capacity in the world since 1954.

Chapter 8

Figure 8.1 Smart grid – a vision for the future.

Chapter 9

Figure 9.1 Organization of conventional electric power systems.

Figure 9.2 Integration of distributed generation in electrical power systems.

Figure 9.3 Microgrid architecture.

Figure 9.4 Low voltage microgrid test network.

Figure 9.5 Frequency versus active power droop.

Figure 9.6 Control and management architecture of a multimicrogrid system.

Chapter 10

Figure 10.1 Graphical representation of modern concepts in smart distribution, ...

Figure 10.2 Load and generation modeling suitable for active distribution netwo...

Figure 10.3 Probabilistic network design, based on the concept of acceptable ri...

Figure 10.4 Comparison of different approaches in the distribution network desi...

Figure 10.5 General flowchart for the technical validation of a network configu...

Figure 10.6 Example of MV distribution network referred to an existing industri...

Figure 10.7 Financial exchanges among distribution system stakeholders.

Chapter 11

Figure 11.1

European Wind Energy Association

(

EWEA

) 2030 offshore grid vision.

Figure 11.2 Joint Coordinated System Plan (JCSP) high‐voltage direct current (H...

Figure 11.3 Texas Competitive Renewable Energy Zone (CREZ) locations.

Figure 11.4 RES‐driven transmission lines included in the Portuguese National T...

Figure 11.5 Spatial distribution of the wind power to be injected in the transm...

Figure 11.6 Grid extension and annual costs of the different technologies: basi...

Figure 11.7 Optimal grid example for the North Sea region. Green, optimized int...

Figure 11.8 Cross‐border links with strong economic expansion benefits identifi...

Chapter 12

Figure 12.1 Potential of green generation for Europe determining the future tra...

Figure 12.2 A possible configuration of the North Sea Supergrid provided by the...

Figure 12.3 Concept of a “EUMENA Supergrid” based on HVDC power transmission as...

Figure 12.4 Projected supertransmission lines linking the hydropower stations i...

Chapter 13

Figure 13.1 SPS image.

Figure 13.2 Inductive coupling.

Figure 13.3 Magnetic resonance coupling.

Figure 13.4 Coupling transmission efficiency of resonant coupling.

Figure 13.5 Concept of ubiquitous power source.

Figure 13.6 Wireless charging experiment of mobile phone in ubiquitous power so...

Figure 13.7 Wireless building using microwave power transmission.

Figure 13.8 Beam efficiency at the far field and the near field using

τ

pa...

Chapter 14

Figure 14.1 CO

2

reduction during 2005–2050 based on the BLUE Map scenario

[1]

.

Figure 14.2 Example of long‐ and short‐term variations of wind power

[3]

.

Figure 14.3 Comparison of daily load curves

[5]

.

Figure 14.4 Comparison of power generation mix

[6]

.

Figure 14.5 Comparison of frequency controllers.

Figure 14.6 Comparison of the ramp rates of four power plants.

Figure 14.7 Normalized operation curve of a wind turbine.

Figure 14.8 Distribution of wind speed–Weibull distribution: (a) features of We...

Figure 14.9 Statistical data of daily irradiation.

Figure 14.10 Estimated probability distribution from actual data.

Figure 14.11 Wind farm smoothing effect on net power variation

[7]

.

Figure 14.12 Smoothing effect of wind power in Germany

[7]

.

Figure 14.13 Concept of smoothing effect on photovoltaic (PV) output.

Figure 14.14 Example of smoothing effect of photovoltaic (PV) power

[8]

.

Figure 14.15 Adjustable speed rate and operational load range of gas turbine (G...

Figure 14.16 Combining variable renewables with gas turbine (GT).

Figure 14.17 Relationship between net wind power variation and necessary storag...

Figure 14.18 Growth of necessary energy storage capacity worldwide during 2010–...

Figure 14.19 Comparison of energy storage technologies

[9]

.

Figure 14.20 Arial view of a seawater‐pumped hydropower plant

[10]

.

Figure 14.21 Aerial view of Huntorf

[11]

.

Figure 14.22 6.4 kW modules

[12]

.

Figure 14.23 Matrix flywheel system showing flywheel structure (left) and the f...

Figure 14.24 Principle of vanadium redox flow cell.

Figure 14.25 Principle of capacitors.

Figure 14.26 10 000 kVA system for instantaneous voltage drop compensator.

Figure 14.27 Demonstration wind farm project at Nishime in Akita, Japan

[14]

.

Figure 14.28 Smoothed output obtained with lead acid battery cells.

Chapter 15

Figure 15.1 Aggregator integration framework for future power system operation.

Figure 15.2 Typical charge curve of a lithium‐ion battery.

Figure 15.3 Decentralized control architecture for electric vehicles.

Figure 15.4 Loading of two transformers feeding a low voltage network in a metr...

Figure 15.5 Loading of 400 V distribution lines in a low voltage network in a m...

Figure 15.6 Voltage levels in a low‐voltage network in a metropolitan area at p...

Figure 15.7 Loading of transformers and distribution lines on the 11/22 kV netw...

Figure 15.8 Line loading at the 150 kV network level. The impacts of uncontroll...

Figure 15.9 Transformer loading at the 150 kV of uncontrolled plug‐in electric ...

Figure 15.10 Fraction of vehicles driving or parked on a typical weekday in Swi...

Figure 15.11 Load profiles resulting from different smart‐charging schemes.

Figure 15.12 Vehicle‐to‐grid service for balancing a renewable energy source pr...

Figure 15.13 Plug‐in electric vehicle demand in the 11/22 kV distribution netwo...

Figure 15.14 Plug‐in electric vehicle demand in the 11/22 kV distribution netwo...

Figure 15.15 Plug‐in electric vehicle (PEVs) and controllable loads are used to...

Chapter 16

Figure 16.1 Fuel use today in Denmark (2012). Measured as fuel consumption in p...

Figure 16.2 Model structure in STREAM

[12]

.

Figure 16.3 Fuel use in transport sector in the carbon‐neutral scenario in 2050...

Figure 16.4 Fuel use in the electric vehicle scenario in 2050. Measured as fuel...

Figure 16.5 Fuel use in the hydrogen scenario in 2050. Measured as fuel consump...

Figure 16.6 Mix of electricity production in 2050 in the different scenarios.

Figure 16.7 Power generation and consumption with and without flexible demand f...

Figure 16.8 Mix of district heating production in 2050 in the different scenari...

Figure 16.9 Total annual system costs (mill €) in 2050 for the carbon‐neutral s...

Figure 16.10 Cost difference between the electric vehicle scenario (EVS) and th...

Figure 16.11 Power generation and consumption with and without flexible demand ...

Figure 16.12 Cost difference between the hydrogen scenario (H

2

S) and the carbon...

Figure 16.13 Sensitivity analysis for the electric vehicle scenario (EVS) compa...

Figure 16.14 Sensitivity analysis for the hydrogen scenario (H

2

S) compared to t...

Chapter 17

Figure 17.1 Natural gas consumption per sector in the EU in the year 2011

[10]

.

Figure 17.2 Historic development of power production from natural gas and bioga...

Figure 17.3 Natural gas prices in Japan, the European Union, and the United Sta...

Figure 17.4 Simplified process scheme for biochemical SNG production.

Figure 17.5 Simplified amine scrubbing process flowsheet.

Figure 17.6 Relative permeation rate of biogas molecules.

Figure 17.7 Simplified process scheme for thermochemical SNG production.

Figure 17.8 Gasifier types. Entrained flow (left), fixed bed (middle), and flui...

Figure 17.9 Lurgi methanation process flowsheet.

Figure 17.10 Typical process flowsheet of the Bio‐TREMP process by Haldor Topsø...

Figure 17.11 Simplified process scheme for electrochemical SNG production.

Figure 17.12 Exemplary experimental characteristic U‐j‐curves for AEL (Casale C...

Figure 17.13 Dynamic operation of 250 kW methanation unit

[60]

.

Figure 17.14 Comparison of energy input and output of the three SNG pathways [

L

...

Figure 17.15 Overview of specific biochemical SNG production costs in €ct/kWh

SN

...

Figure 17.16 Mean‐specific production costs for SNG produced via the three path...

Figure 17.17 Mean‐specific investment costs for SNG production via the three di...

Chapter 18

Figure 18.1 Worldwide installed capacity of different storage technologies.

Figure 18.2 NERC and NAESB categorization of demand‐side options.

Figure 18.3 DR in the United States.

Figure 18.4 Power draw from a 3 kW water heater and associated AGC signal.

Figure 18.5 Flexibility supply curve showing cost and increasing requirement wi...

Figure 18.6 Usage of emerging flexible resources to shift, curtail, or increase...

Chapter 19

Figure 19.1 Development of electricity from variable renewable energy sources s...

Figure 19.2 Basic principle of the P2G process: Converting electricity into hyd...

Figure 19.3 Historical development of P2G conversion technologies from laborato...

Figure 19.4 Example: Electricity generation from variable renewables (wind, pho...

Figure 19.5 Electricity generation from renewable energy source over a year and...

Figure 19.6 Survey on storage options depending on capacity and discharging tim...

Figure 19.7 The chain for storing electricity as hydrogen or methane and re‐ele...

Figure 19.8 The chain for producing methane and re‐electrification, efficiencie...

Figure 19.9 Investment costs in electrolysis and methanation, as well as total ...

Figure 19.10 Large system: The total cost of methane depending on capital costs...

Figure 19.11 Total production costs of hydrogen and methane depending on the fu...

Figure 19.12 The chain for the use of renewable energy source via hydrogen and ...

Figure 19.13 Energy (fuel) costs of mobility per 100 km depending on full‐load ...

Figure 19.14 Total mobility costs per 100 km in 2016 depending on full‐load hou...

Figure 19.15 Large storage types: Future perspectives of the investment cost of...

Figure 19.16 Small storage types: Future perspectives of the investment cost of...

Figure 19.17 Large storage types: Perspectives for the total costs of hydrogen ...

Figure 19.18 Small storage types: Perspectives for the total costs of hydrogen ...

Figure 19.19 Future perspectives of the investment costs for long‐term storage ...

Figure 19.20 Development of total costs of several technologies for long‐term s...

Figure 19.21 Fuel/energy costs of mobility per 100 km based on average of EU co...

Figure 19.22 Total specific costs per 100 km in 2050 depending on full‐load hou...

Chapter 20

Figure 20.1 Impacts of wind power on power systems, displayed by time and spati...

Figure 20.2 Outline of possible active power control functions from wind power ...

Figure 20.3 Wind power will add to the variability that power systems experienc...

Figure 20.4 Variability of wind power will smooth out with aggregation of wind ...

Figure 20.5 Predictability of wind power is better for shorter time horizons an...

Figure 20.6 Decrease of forecast error of prediction for aggregated wind power ...

Figure 20.7 Results for the increase in reserve requirement due to wind power, ...

Figure 20.8 Results from estimates for the increase in balancing and operating ...

Figure 20.9 Capacity credit of wind power, results from eight studies

[1, 18, 33

...

Chapter 21

Figure 21.1 The Van der Hoven spectrum of wind speeds at Brookhaven National La...

Figure 21.2 Power spectrum density of wind speeds based on data collected at ST...

Figure 21.3 Histogram of observed wind speeds with three theoretical distributi...

Figure 21.4 Trends in observed surface wind speeds (in m/s per annum).

Figure 21.5 Robust coefficient of variability (RCoV) across the USA.

Figure 21.6 Projected changes in wind speed across Brazil by 2091–2100 under th...

Figure 21.7 Correlations (

ρ

) between pairs of wind farm sites in Texas com...

Figure 21.8 (a) Average correlation length and (b) autocorrelation time inferre...

Chapter 22

Figure 22.1 Summary of results for the capacity value of wind power for several...

Figure 22.2 Example of effective load‐carrying capability.

Figure 22.3 Map of US Western Interconnection. Shaded areas show zones.

Figure 22.4 Capacity value calculated on the basis of LOLE, LOLH, and EUE for s...

Figure 22.5 Impact of interconnection on resource adequacy in the US Western In...

Figure 22.6 Comparison of WECC simplified rule for wind capacity value with ful...

Figure 22.7 Comparison of WECC simplified rule for solar capacity value with fu...

Figure 22.8 Peak demand (MW) for Sweden, 1991–2011.

Figure 22.9 Multiple‐year ELCC results for 1000‐MW wind power in Ireland.

Figure 22.10 Multiple‐year ELCC results from Finland using real data (green) an...

Figure 22.11 Multiple‐year ELCC results from Finland using NASA/MERRA ReAnalysi...

Chapter 23

Figure 23.1 Hydro production versus annual consumption in 2013 (The German and ...

Figure 23.2 The existing hydropower installed capacities per type of hydropower...

Figure 23.3 The existing hydropower capacity within each interconnected region ...

Figure 23.4 Canadian Hydroelectric Power in 2011 by Province. Capacity and Perc...

Figure 23.5 Share of load covered by wind in 2013 in western Denmark.

Figure 23.6 Storm situation 2005.

Figure 23.7 Development of the average total electricity generation balance of ...

Figure 23.8 Example of wind curtailment during high wind power forecast error p...

Figure 23.9 Net load and hydropower generation in Spain on November 2, 2010.

Figure 23.10 Load, net load, and wind generation for the Hydro‐Quebec system du...

Figure 23.11 (a) Reservoir handling and (b) Hydro production in Norway 2010

[52]

Figure 23.12 (a) Reservoir handling and (b) Hydro production in Norway 2030

[52]

Figure 23.13 Norwegian pumping strategies versus offshore wind power variations...

Figure 23.14 The scenario of generation profile for a wet windy day in 2011

[36,

...

Figure 23.15 BPA Big Ten (Inset: Pacific Northwest Region).

Chapter 24

Figure 24.1 (Pumped) hydro energy storage [(P)HES] potential in Europe until th...

Figure 24.2 Clustering of countries to nine different European electricity mark...

Figure 24.3 Classified load duration and residual load curves for the CWE regio...

Figure 24.4 Age structure of the TPP portfolio in the CWE region.

Figure 24.5 Coverage of the 2030 residual load of the CWE region with existing ...

Figure 24.6 Coverage of the 2030 residual load after PHES optimization in the C...

Figure 24.7 Storage capacity of the PHES system in the CWE region in the year 2...

Figure 24.8 Classified load duration and residual load curves for the CWE regio...

Figure 24.9 Coverage of the 2050 residual load of the CWE region with existing ...

Figure 24.10 Coverage of the 2050 residual load after PHES optimization in the ...

Figure 24.11 Classified load duration and residual load curves for the Iberian ...

Figure 24.12 Coverage of the 2030 residual load of the Iberian Peninsula with e...

Figure 24.13 Coverage of the 2030 residual load after PHES optimization in the ...

Figure 24.14 Classified load duration and residual load curves for the Iberian ...

Figure 24.15 Coverage of the 2050 residual load of the Iberian Peninsula with e...

Figure 24.16 Coverage of the 2050 residual load after PHES optimization in the ...

Figure 24.17 Contribution of transmission grid expansion for mitigation of vari...

Chapter 25

Figure 25.1 Demand‐side activities. Increasing levels of granularity of control...

Figure 25.2 Three different scenarios of DR logic implementation.

Figure 25.3 An office building using global temperature adjustment strategy to ...

Figure 25.4 Demand response at a wastewater facility from pumps, centrifuges, a...

Figure 25.5 Attributes that impact demand response.

Chapter 26

Figure 26.1 Load transition: change of electric load during 2011 during 1 hour ...

Figure 26.2 Net load transition: change of electric consumption during 2011 dur...

Figure 26.3 I Swedish hydropower transitions from hour to hour for 2008 (a) and...

Figure 26.4 Swedish hydropower transitions within 4 hours for 2008 (a) and 2011...

Figure 26.5 First approach to power supply in the example during a week with hi...

Figure 26.6 Resulting power supply in the example during a week with high wind ...

Figure 26.7 Duration curve of solar and wind power spillage.

Figure 26.8 Resulting power supply in the example during a week with low wind a...

Figure 26.9 Duration curve of extra need.

Figure 26.10 Transition curves for hydropower in high solar‐wind example (a) an...

Figure 26.11 Duration curve for hydropower at high share of wind + solar. Minim...

Chapter 27

Figure 27.1 Hourly electricity consumption by categories of customers, February...

Figure 27.2 Average consumption and spot market price profiles for workdays and...

Figure 27.3 Average monthly load profiles.

Figure 27.4 Average monthly consumption profiles for categories of customers.

Figure 27.5 Average monthly spot market prices.

Figure 27.6 Variation in the average time‐of‐use price for individual customers...

Figure 27.7 Consumption profiles for a greenhouse and a mink fodder producer, y...

Chapter 28

Figure 28.1 Characterizing renewable technologies.

CST

refers to

concentrated s

...

Figure 28.2 The distinguishing characteristics of renewable technologies, and t...

Figure 28.3 Typical categories of frequency control ancillary services.

Chapter 29

Figure 29.1 Monte Carlo assessment of project finance. *NREL's System Advisor M...

Figure 29.2 Results of the Monte Carlo analysis using a 90% interval around the...

Figure 29.3 Multivariate regression results for each city for the seven variabl...

Figure 29.4 Histogram overview of model residuals for all cities combined for a...

Chapter 30

Figure 30.1 Welfare effect of interconnector capacity in a two‐market system.

Figure 30.2 Exemplary illustration of the impact of wind power on the welfare g...

Figure 30.3 Supply representation in the two‐system model.

Figure 30.4 Dependency of baseload price and market value of wind power on the ...

Figure 30.5 Effect of wind power variability on the baseload price in the case ...

Figure 30.6 Import/export case – market values and baseload prices depending on...

Figure 30.7 Import/export case – total generation cost depending on the interco...

Chapter 32

Figure 32.1 Danish household electricity price (4000 kWh yr

−1

) in 2013

[20

...

Figure 32.2 Supply cost results of load shift simulations under different prici...

Figure 32.3 Simulated savings of different load shift simulations with unit and...

Figure 32.4 Simulated savings of different load shift simulations with unit and...

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Advances in Energy Systems

The Large‐scale Renewable Energy Integration Challenge

Edited by

PETER D. LUND

Aalto University, Finland

JOHN BYRNE

University of Delaware, USA

REINHARD HAAS

Vienna University of Technology, Austria

DAMIAN FLYNN

University College Dublin, Republic of Ireland

This edition first published 2019© 2019 John Wiley & Sons Ltd

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

The right of Peter D. Lund, John Byrne, Reinhard Haas & Damian Flynn to be identified as the authors of the editorial material in this work has been asserted in accordance with law.

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Library of Congress Cataloging‐in‐Publication Data

Names: Lund, Peter D., editor.Title: Advances in energy systems : the large‐scale renewable energy integration challenge / edited by Peter D. Lund (Aalto University School of Science, Finland) [and three others].Description: Hoboken, NJ : Wiley, [2019] | Includes bibliographical references and index. |Identifiers: LCCN 2018046918 (print) | LCCN 2018047879 (ebook) | ISBN 9781119508335 (Adobe PDF) | ISBN 9781119508328 (ePub) | ISBN 9781119508281 (hardcover)Subjects: LCSH: Renewable energy sources. | Power resources–Forecasting.Classification: LCC TJ808 (ebook) | LCC TJ808 .A3838 2019 (print) | DDC 333.79/4–dc23LC record available at https://lccn.loc.gov/2018046918

Cover Design: WileyCover Images: (Left top) © Eviart/Shutterstock, (Right top) © Stocksolutions/Shutterstock, (Left bottom) © metamorworks/Shutterstock, (Right bottom) © guteksk7/Shutterstock

List of Contributors

Amela Ajanovic

F. M. Andersen

Göran Andersson

Atle Rygg Årdal

Hans Auer

Ricardo Bessa

Marilyn A. Brown

Daniel Burke

Alexander Buttler

John Byrne

Luis Carr

Gianni Celli

Debora Coil‐Mayor

Nicolaos A. Cutululis

Lewis Dale

Salvatore D'Arco

Jan Dobschinski

Robert Entriken

Peter B. Eriksen

Ana Estanqueiro

Hossein Farahmand

Sebastian Fendt

Damian Flynn

Alain Forcione

Bethany Frew

Matthias Gaderer

Matthias D. Galus

Andrew Garnett

Emilio Ghiani

Madeline Gibescu

Emilio Gómez‐Lázaro

Chris Greig

Reinhard Haas

Anca D. Hansen

Ove Hoegh‐Guldberg

Hannele Holttinen

Daniel Huertas‐Hernando

Eduardo Ibanez

Shin‐ichi Inage

Kenneth B. Karlsson

Jonas Katz

Ben Kaun

Sila Kiliccote

Kyung N. Kim

Lena Kitzing

Juha Kiviluoma

Magnus Korpas

Thilo Krause

Joe L. Lane

H. V. Larsen

Warren Lasher

Joohee Lee

Chen‐Ching Liu

Joao A. P. Lopes

Peter D. Lund

Andre G. Madureira

Sergio M. Martinez

Manuel Matos

Nickie Menemenlis

Eric McFarland

Lutz Mez

Michael Milligan

Carlos Moreira

P. E. Morthorst

Carlo Obersteiner

Daniel Olsen

Mark O'Malley

Antje Orths

Dale Osborn

Paul Parker

Mary A. Piette

Fabrizio Pilo

Amalia Pizarro

Zakir Rather

Barry Rawn

Tobi Reid

Jenny Riesz

Erkka Rinne

Luis Rodrigues

Serafin van Roon

Ian H. Rowlands

Lisa Ruttledge

Diego Schmeda‐Lopez

Jürgen Schmid

Sascha T. Schröder

Jeongseok Seo

Naoki Shinohara

Bernardo Silva

Klaus Skytte

Simon Smart

Charles Smith

Lennart Söder

Michael D. Sohn

Gian G. Soma

Poul Sorensen

Benjamin K. Sovacool

Hartmut Spliethoff

Alexandru Stefanov

Morten Stryg

Job Taminiau

John Tande

Thomas Trotscher

Aidan Tuohy

Frans Van Hulle

Marina González Vayá

Ye Wang

Simon Watson

Jing Xie

Karl A. Zach

Robert Zavadil

Shan Zhou

Preface

The global energy system confronts huge challenges in the coming decades. The present‐day fossil‐fuel‐based energy production, which dominates the energy scenery, needs to be replaced by clean energy options to meet the climate change mitigation targets set in Paris in December 2015. At the same time, the demand for energy continues to grow, mainly due to growing prosperity in the less developed world. One of the main challenges will indeed be to secure a clean energy path to the future in the emerging economies, unlike the industrialized countries in the past.

Although global carbon dioxide emissions have increased almost by half since the days when the first climate agreement, under the UN auspice, was established in the 1990s, positive news is starting to emerge. In recent years global CO2 emissions have been stabilized, but these now need to be sent on a declining trajectory. Much of the positive development can be contributed to the rapid market share of renewable energy sources, notably solar and wind power. The cost of these technologies is becoming competitive with their fossil counterparts. More importantly, future prospects for renewable energy technologies are bright: there still remains potential for major technology developments, efficiency improvements, and cost reductions, which together could make renewable energy the mainstream energy solution.

Indeed, respected energy scenarios, for example those developed by the International Energy Agency (IEA), indicate that in the power (electricity) sector, which is of upmost importance with respect to emissions, a significant share of future generation capacity investments will be concentrated in solar and wind power by the middle of this century. We are already witnessing that these variable renewable electricity forms deliver a major share of the national electricity supply in some countries, such as Denmark, Germany, Ireland, Italy, and the United Kingdom. In the long‐term, more countries are envisioned to satisfy their clean energy demands through renewable energy.

Although renewable energy may play an important role across the entire energy system, it is particularly in the electricity sector where the new technologies will play a dominant role. Moreover, electricity demand is growing much faster than primary energy demand, due to electrification within our societies and everyday life, which stresses the role of electricity in the future energy system. Inherently, most new renewable power production technologies, such as solar and wind, but also marine power, do not rely on a supply of fuel, meaning that their instantaneous power production depends on the prevailing and time‐varying weather conditions. Thus, when transitioning to large‐scale deployment of renewable energy from variable sources, a key challenge will be matching supply of power against demand, on a range of time scales from seconds to hours, days, and weeks.

Large‐scale renewable electricity schemes in conjunction with existing energy systems can cause a range of different systemic issues, which need to be solved to make the best use of clean energy. Bridging the “new” and “old” energy is necessary, and both will need to coexist for some time, although the share from renewable sources will increase. An energy transition cannot be an on–off change, where we switch from old to new overnight! Integration of renewables into the energy system will thus be a critical issue, and of growing importance, in the coming years. We claim here that integration, in broad terms, will actually be the new hot topic in energy, if it is not already, which is not only linked to innovative technology solutions but which will also reshape energy markets, challenge existing business models for companies, and even integrate the consumer in a pivotal role.

Energy system integration of renewable energy is a wide field which covers themes ranging from modifying existing energy systems to better match renewables characteristics, introducing new flexibility measures and the evolution of energy‐limited technologies, exploiting communications and IT advancements within a smarter grid, and reforming markets, incentives, regulation and policy frameworks to obtain an operational, robust and economically viable energy resource portfolio.

The energy transition ahead is a huge challenge to all market actors. No one will be left untouched: policy makers, energy planners, businesses, developers, academia, and even end users need to be re‐educated to understand the new rules of the game. This book aims to provide timely guidance on how to prepare for the turbulence, which rushes toward us, presenting implementable solutions and proposing successful pathways moving forward.

This book addresses the key areas of large‐scale renewable energy integration and provides an authoritative overview on both the challenges and potential solutions. The book discusses the system challenges associated with renewables, grids, flexibility options, and markets, which encompass the central “integration” themes. The book is a collection of 32 authoritative contributions from specialists in the relevant disciplines. Through this collective push, our aim is to offer the reader a fresh and skillful insight to the multidisciplinary topic.

The original inspiration for the book came through the publisher, when John Wiley & Sons established the Wiley Interdisciplinary Reviews: Energy and Environment journal, which mainly publishes review‐type articles in energy and environment. Mr. Tony Carwardine, now retired from Wiley, suggested selecting collections of articles from the journal to create reference works in topical areas. The first book published in this manner was Advances in Bioenergy – The Sustainability Challenge (ISBN 9781118957875) in 2016. Advances in Energy Systems – The Large‐scale Renewable Energy Integration Challenge is the second book in this series.

The editors wish to thank Sandra Grayson, Louis Manoharan, Adalfin Jayasingh, Shalisha Sukanya and Peter Mitchell from Wiley for their great help and assistance during the process of finalizing this book.

Peter D. LundJohn ByrneReinhard HaasDamian Flynn

PART IENERGY SYSTEM CHALLENGES

1Handling Renewable Energy Variability and Uncertainty in Power System Operation

Ricardo Bessa, Carlos Moreira, Bernardo Silva and Manuel Matos

INESC TEC, INESC Technology and Science (Formerly INESC Porto) and FEUP, Faculty of Engineering, University of Porto, Porto, Portugal

Concerns about global warming (greenhouse‐gas emissions), scarcity of fossil fuel reserves, and primary energy independence of regions or countries have led to a dramatic increase of renewable energy sources (RES) penetration in electric power systems, mainly wind and solar power. This has created new challenges associated with the variability and uncertainty of these sources. Handling these two characteristics is a key issue that includes technological, regulatory, and computational aspects. Advanced tools for handling RES maximize the resultant benefits and keep the reliability indices at the required level. Recent advances in forecasting and management algorithms provide a means to manage RES. Forecasts of renewable generation for the next hours/days play a crucial role in the management tools and protocols of the system operator. These forecasts are used as input for setting reserve requirements and performing the unit commitment (UC) and economic dispatch (ED) processes. Probabilistic forecasts are being included in management tools, enabling a move from deterministic to stochastic methods, which lead to robust solutions. On the technological side, advances to increase mid‐merit and base‐load generation flexibility should be a priority. The use of storage devices to mitigate uncertainty and variability is particularly valuable for isolated power systems, whereas in interconnected systems, economic criteria might be a barrier to invest in new storage facilities. The possibility of sending active and reactive control set points to RES power plants offers more flexibility. Furthermore, the emergence of the smart grid concept and the increasing share of controllable loads contribute with flexibility to increase RES penetration levels.

INTRODUCTION

The integration of renewable energy sources (RES) in a generation portfolio conveys several benefits, such as a reduction in greenhouse gases emissions and in the country's dependency on imported energy, and it decreases spot prices. However, generation from RES (i.e. wind, solar, hydro, wave, geothermal, and biomass) can be variable and uncertain, in contrast to conventional generation (e.g. coal thermal plants, combined and open cycle gas turbines). Nevertheless, many power systems have had hydropower for a long time in their portfolio, and system operators (SOs) already have appropriate procedures for its utilization regarding the need to manage its variability and uncertainty. Note that hydropower is more flexible than other RES (such as wind and solar), in particular power plants with a reservoir. The installation of pumped storage units also facilitates water management. Conversely, geothermal generation is invariable, which might create problems because it is incapable of following load variations. The variability of hydropower, biomass, and geothermal is more apparent on yearly and seasonal timescales (run‐of‐river hydropower can also present daily variability), whereas the variability of wind and solar covers all timescales (including daily, hourly, and minutes variability).

At present, the penetration of wind and solar generation in many power systems has attained a high level, and this has created new challenges when operating the power system. In order to meet these challenges, the state‐of‐the‐art encompasses new technological and computational advances for dealing with the variability and uncertainty of RES, particularly regarding wind and solar generation, since hydro variability has for a long‐time been tackled in power systems.

New forecasting and decision‐aid algorithms, including stochastic information, can improve the ability of a power system to cope with variable and uncertain generation coming from RES, without excessive extra operational cost while maintaining reliability standards. On the technological side, new technological advances to enhance the flexibility of conventional power plants (namely, base‐load and mid‐merit units) are essential. Primary frequency control provided by new RES power plants or the use of storage devices are also relevant research areas.

This article describes developments in several interdisciplinary topics related with managing high penetrations of solar and wind, and points toward research trends for the next years. First, the challenges introduced by RES (in the remainder of the chapter only wind and solar are considered) in power system operation are discussed. Then, an overview of the advances in renewable energy forecasting is presented. Renewable energy forecasts are an important input to methods for setting reserve requirements, defining the commitment schedule and performing congestion detection, which are reviewed. Consideration is also given to the electricity market role and the value of storage devices for interconnected and isolated systems. On the technological side, the importance of flexibility (from conventional generators and storage units) in power system operation is described, and some challenges and technological solutions unrelated to resource variability are reviewed, and the capability of active and reactive power control is analyzed.

THE CHALLENGES OF RES IN POWER SYSTEM OPERATION

Main Challenges

The intrinsic variability and uncertainty of RES create several challenges in power system operation and planning[1]. At every instant, generation must follow load variations in order to maintain the generation‐load balance. The variable nature of RES (e.g. rapid generation ramps) represents a challenge, in particular, for systems without hydropower, as it introduces variations in the generation side that can only be smoothed within the physical constraints of the conventional power plants (e.g. ramping up and down, minimum generation limits). In general, the available ramping rates of flexible generation units and fast‐starting units (e.g. hydropower) are used for accommodating this variability. Technological solutions such as control schemes for wind power active and reactive power set points smoothen the impact of variability. For example, a dispatch center for RES with the ability to control the active and reactive power output was created in Spain[2].

RES uncertainty also creates imbalances between generation and load as it is not possible to know (with certainty) the RES generation levels for the next hours/days. These imbalances originating from forecast errors are handled with additional generation capacity (which is an ancillary service). Computational algorithms such as forecasting algorithms and large‐scale stochastic optimization (instead of deterministic tools/rules) have been developed for including information about uncertainty in the decision‐making processes. The importance of new and advanced forecasting algorithms for RES, not only for the SO but also for wind power producers (in particular when trading wind power in the market), is shown by the proliferation of companies that sell this service[3]. Storage units can also play an important role in handling RES variability and uncertainty on different timescales.

If these new solutions are not adopted, variability and uncertainty of RES could lead to situations with high operational cost. For instance, curtailment of renewable generation during low load periods and the startup of expensive fast‐starting units lead to a cost increase. Moreover, even with a perfect forecast for the next hours/days, it is necessary to schedule flexible generation units for accommodating the generation ramps.

Ela and O'Malley[4] presented a simulation framework for assessing the impact of wind power variability and uncertainty on several timescales. The results showed that the imbalance impacts increase with longer dispatch resolutions (ranging from five