ICT for electric vehicle integration with the smart grid / edited by Nand Kishor and Jesús Fraile-Ardanuy.

Format:

Book

Author/Creator:

Fraile-Ardanuy, Jesus, author.

Contributor:

Kishor, Nand, editor.

Series:

IET transportation series.

IET transportation series

Language:

English

Subjects (All):

Electric vehicles--Power supply.

Electric vehicles.

Physical Description:

1 online resource (xiv, 426 pages).

Edition:

1st ed.

Place of Publication:

London, England : The Institution of Engineering and Technology, [2020]

Summary:

This book provides a basis for full integration of electric vehicles into the smart grid, through the use of ICT tools. It looks at transport and energy system modelling, simulation and optimisation processes; vehicle on-line optimal control, estimation and prediction; energy system strategic planning; and services such as smart charging.

Contents:

Intro

Title

Copyright

Contents

Preface

1 Advanced mobility communication

1.1 Introduction

1.2 Background

1.2.1 Cooperative ITS regulatory framework

1.2.2 State on cooperative ITS standardisation

1.2.3 Cooperative ITS developments and initiatives

1.2.4 3GPP initiatives

1.2.5 LTE-V and CV2X

1.3 Target services

1.4 Cooperative ITS current state

1.4.1 Standardisation in vehicular communications

1.4.2 List of related standards for cooperative ITS

1.5 5G and future mobility

1.6 Conclusions

References

2 Grid integration and management of EVs through machine-to-machine communication

List of abbreviations

2.1 Introduction

2.2 M2M in distributed energy management systems

2.3 M2M communication for EVs

2.3.1 M2M communication architecture (3GPP)

2.4 Electric vehicle data logging systems

2.4.1 Data logging system

2.4.2 Hardware description

2.4.3 DLS operation

2.5 Scalability of electric vehicles

2.5.1 Radio resource and IP connection of LTE

2.5.2 Radio access of M2M

2.5.3 LTE user-plane protocol

2.5.4 Analytical model

2.5.5 Simulation and performance evaluation

2.6 M2M communication with scheduling

2.6.1 LTE scheduling

2.6.2 LTE popular scheduling algorithms

2.6.3 Performance evaluation

2.7 Conclusion

3 Electrical vehicles charging and discharging scheduling for the cloud-based energy management service

List of abbreviation

3.1 Introduction

3.3.1 Framework and utilized data

3.3.2 Procedure

3.2 Cloud computing

3.3 Cloud-based energy management service

3.4 Electrical vehicles for the cloud-based energy management service

3.5 Scheduling results discussion

3.6 Conclusion

References.

4 Multi-criteria optimization of electric vehicle fleet charging and discharging schedule for secondary frequency control

Nomenclature

4.1 Introduction

4.1.1 Motivation

4.1.2 Literature review

4.1.3 Contribution

4.1.4 Chapter structure

4.2 Optimization problem

4.2.1 Frequency regulation

4.2.2 Business model

4.2.3 ICT architecture

4.2.4 Optimization objectives

4.3 Multi-objective optimization

4.3.1 Fuzzy multi-criteria decision-making

4.3.2 MAUT

4.4 Case studies

4.4.1 Belman-Zadeh approach

4.4.2 MAUT methodology

4.5 Conclusion

Acknowledgments

5 Power-demand management in a smart grid using electric vehicles

5.1 Introduction

5.2 Power-demand management for single customer: Energy-resource-management technique

5.2.1 Load-management algorithm

5.2.2 EV charge management

5.2.3 Case studies

5.2.4 Comparison with an artificial neural network technique

5.3 Power-demand management for single customer: Load-management technique

5.3.1 HEMS model

5.3.2 Scheduling model

5.3.3 Case study

5.4 Power-demand management for multiple customers

5.4.1 General overview

5.4.2 Aggregated EV and power-demand management algorithm

5.4.3 Case studies

5.5 Conclusion

6 Energy management of a small-size electric energy system with electric vehicles, flexible demands, and renewable generating units

6.1 Introduction

6.2 Notation

6.2.1 Indices

6.2.2 Sets

6.2.3 Parameters

6.2.4 Optimization variables

6.3 Modeling of electric vehicles

6.3.1 Individual modeling

6.3.2 EV aggregator

6.4 Deterministic energy-management problem

6.4.1 Objective function

6.4.2 Power balance constraints

6.4.3 Network constraints

6.4.4 Electric vehicle constraints.

6.4.5 Demand constraints

6.4.6 Renewable production constraints

6.4.7 Formulation

6.5 Uncertainty characterization

6.6 Stochastic energy-management problem

6.7 Summary and conclusions

6.8 GAMS codes

6.8.1 Illustrative example 6.1

6.8.2 Illustrative example 6.2

6.8.3 Illustrative example 6.3

6.8.4 Illustrative example 6.5

7 Peer-to-peer energy market between electric vehicles

List of parameters and variables

7.1 Introduction

7.2 Activity-based model

7.3 Consumption model and drivers classification

7.3.1 Consumption model

7.3.2 Drivers classification

7.4 Intermediate charging process optimization

7.5 Peer-to-peer trading system

7.5.1 Determining the final price for the P2P trading system each TAZ and each time period

7.5.2 Quadratic programming formulation

7.5.3 Proposed algorithm for solving the P2P market trading

7.6 Results of the P2P energy market

7.6.1 Individual analysis of vehicles from sets A and B

7.6.2 Electricity price analysis at TAZ level

7.7 Long-term peer-to-peer energy market

7.8 Conclusions

8 Dispatch of vehicle-to-grid battery storage using an analytic hierarchy process

8.1 Introduction

8.2 Battery characteristics

8.3 Dispatch strategy of electric vehicle battery storage

8.3.1 AHP hierarchy process

8.3.2 Determination of the dispatch action

8.4 Simulation test

8.5 Sensitivity analysis

8.6 Conclusions

9 Electric vehicles as distributed energy storage for local energy management

9.1 Introduction

9.2 System description

9.2.1 Building PV generation and electricity consumption

9.2.2 Grid electricity price

9.2.3 Mobility information

9.3 Optimization modelling

9.4 Scenarios and results

9.5 Conclusions

10 Contribution of electric vehicles to power system ancillary services beyond distributed energy storage

10.1 Introduction

10.2 Contribution of electric vehicles to the power system frequency control

10.2.1 Theoretical background

10.2.2 Case study

10.3 Contribution of electric vehicles to voltage control

10.3.1 Theoretical background

10.3.2 Unbalanced three-phase power flow

10.3.3 Contribution of electric vehicles to voltage control

10.3.4 Case study

10.4 Conclusion

Acknowledgements

11 Electric vehicles for renewable energy integration in isolated power systems

11.1 Introduction

11.2 El Hierro's electrical system description

11.3 Data description

11.3.1 Electric power system data

11.3.2 Mobility data

11.4 Optimization algorithm for night charging

11.5 Scenarios

11.5.1 Scenario 1. Base scenario. No VE-No PHEP

11.5.2 Scenario 2. Base scenario+VEs-No PHEP

11.5.3 Scenario 3. Base scenario+VEs-PHES

11.6 Conclusion

12 A solar- and wind-powered charging station for electric buses based on a backup batteries concept

12.1 Introduction

12.1.1 Related works

12.2 Methods and data

12.2.1 Methods and problem formulation

12.2.2 Input data

12.3 Discussion and results

12.3.1 Self-sufficiency and reliability of supply

12.3.2 Economic analysis

12.3.3 Environmental impact

12.3.4 Impact on the grid

12.3.5 Future works

12.4 Conclusion

13 Deploying stochastic coordination of electric vehicles for V2G services with wind

Notation

13.1 Introduction

13.2 Test system

13.3 Probabilistic model of wind power

13.4 Stochastic load modeling of EV

13.4.1 Stochastic adaptive fuzzy model of EVs

13.4.2 Charging level and type of EVs

13.4.3 Initial SOC and EVs load profile.

13.5 Financial and operational modeling

13.5.1 Real-time pricing policy

13.5.2 Degradation cost of EVs battery

13.5.3 Frequency regulation

13.5.4 Spinning reserves

13.6 Formulation of charging/discharging strategy

13.6.1 Charging/discharging energy of EVs

13.6.2 Optimizing strategy and function

13.6.3 ESPSO

13.7 Case studies and discussion

13.7.1 Comparison of load profiles of EVs on different modeling schemes

13.7.2 Impact of wind and EV penetration

13.8 Conclusion

14 Optimal location and charging of electric vehicle with wind penetration

14.1 Introduction

14.2 Test system

14.3 Sensitivity analysis

14.3.1 Node selection

14.3.2 System operation algorithm

14.4 Stochastic modeling for EVs load demand

14.4.1 Charging level and type of EVs analysis

14.4.2 Stochastic fuzzy modeling

14.4.3 Initial SOC and EVs load profile

14.5 Peak load shifting optimization

14.5.1 EV charging optimization according to load curve

14.5.2 EV charging optimization according to real-time price

14.5.3 EV charging optimization according to wind power and RTP

14.5.4 Charging/discharging shifting

14.6 Conclusion

15 Optimal coordination of vehicle-to-grid batteries and renewable generators in a distribution system

15.1 Introduction

15.2 General description of the electricity network and agents

15.3 Formulation of DMOCOP

15.3.1 Objectives

15.3.2 The AHP

15.3.3 Constraints

15.4 A* optimal dispatch procedure

15.5 The application of A* search to optimal decentralized coordination of EVs and RGs in a distribution network

15.5.1 Stochastic modelling of uncertainties

15.5.2 Simulation results

15.6 Complexity discussion

15.7 Conclusion

Index.

Notes:

Description based on print version record.

Description based on publisher supplied metadata and other sources.

ISBN:

1-83724-796-X

1-78561-763-X

OCLC:

1223097684