Energy Storage in Power Systems E-Book

Francisco Díaz-González

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

Seitenzahl: 464

Veröffentlichungsjahr: 2016

Beschreibung

Over the last century, energy storage systems (ESSs) have continued to evolve and adapt to changing energy requirements and technological advances. Energy Storage in Power Systems describes the essential principles needed to understand the role of ESSs in modern electrical power systems, highlighting their application for the grid integration of renewable-based generation.

Key features:

• Defines the basis of electrical power systems, characterized by a high and increasing penetration of renewable-based generation.
• Describes the fundamentals, main characteristics and components of energy storage technologies, with an emphasis on electrical energy storage types.
• Contains real examples depicting the application of energy storage systems in the power system.
• Features case studies with and without solutions on modelling, simulation and optimization techniques.

Although primarily targeted at researchers and senior graduate students, Energy Storage in Power Systems is also highly useful to scientists and engineers wanting to gain an introduction to the field of energy storage and more specifically its application to modern power systems.

Leseprobe

ENERGY STORAGE IN POWER SYSTEMS

Francisco Díaz-González

Catalonia Institute for Energy Research, Spain

Andreas Sumper

Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments, Universitat Politècnica de Catalunya, Barcelona, Spain

Oriol Gomis-Bellmunt

Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments, Universitat Politècnica de Catalunya, Barcelona, Spain

This edition first published 2016 © 2016 John Wiley & Sons Ltd

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Library of Congress Cataloging-in-Publication Data

Names: Díaz-González, Francisco. | Sumper, Andreas. | Gomis-Bellmunt, Oriol. Title: Energy storage in power systems / Francisco Díaz-González, Andreas Sumper, Oriol Gomis-Bellmunt. Description: Chichester, West Sussex : John Wiley & Sons, Inc., 2016. | Includes index. Identifiers: LCCN 2015044575 | ISBN 9781118971321 (cloth) Subjects: LCSH: Energy storage. | Electric power systems--Reliability. | Peak load. Classification: LCC TK2980 .D53 2016 | DDC 621.31/26--dc23 LC record available at http://lccn.loc.gov/2015044575

A catalogue record for this book is available from the British Library.

Cover image: Martin Barraud/Getty

To our wives and daughters –Rocío, Sara, Marta, Sofía,Sílvia, Clara, and Rita.

Contents

Foreword

Preface

1 An Introduction to Modern Power Systems

1.1 Introduction

1.2 The Smart Grid Architecture Model

1.3 The Electric Power System

1.4 Energy Management Systems

1.5 Computational Techniques

1.6 Microgrids

1.7 The Regulation of the Electricity System and the Electrical Markets

1.8 Exercise: A Load-Flow Algorithm with Gauss–Seidel

2 Generating Systems Based on Renewable Power

2.1 Renewable Power Systems

2.2 Renewable Power Generation Technologies

2.3 Grid Code Requirements

2.4 Conclusions

3 Frequency Support Grid Code Requirements for Wind Power Plants

3.1 A Review of European Grid Codes Regarding Participation in Frequency Control

3.2 Participation Methods for WPPs with Regard to Primary Frequency Control and Synthetic Inertia

3.3 Conclusions

Notes

4 Energy Storage Technologies

4.1 Introduction

4.2 The Description of the Technology

4.3 Power Conversion Systems for Electrical Storage

4.4 Conclusions

5 Cost Models and Economic Analysis

5.1 Introduction

5.2 A Cost Model for Storage Technologies

5.3 An Example of an Application

5.4 Conclusions

6 Modeling, Control, and Simulation

6.1 Introduction

6.2 Modeling of Storage Technologies: A General Approach Orientated to Simulation Objectives

6.3 The Modeling and Control of the Grid-Side Converter

6.4 The Modeling and Control of Storage-Side Converters and Storage Containers

6.5 An Example of an Application: Discharging Storage Installations Following Various Control Rules

6.6 Conclusions

7 Short-Term Applications of Energy Storage Installations in the Power System

7.1 Introduction

7.2 A Description of Short-Term Applications

7.3 An Example of Fluctuation Suppression: Flywheels for Wind Power Smoothing

7.4 Conclusions

8 Mid- and Long-Term Applications of Energy Storage Installations in the Power System

8.1 Introduction

8.2 A Description of Mid- and Long-Term Applications

8.3 Example: The Sizing of Batteries for Load Following in an Isolated Power System with PV Generation

8.4 Conclusions

References

Index

EULA

List of Tables

Chapter 1

Table 1.1

Chapter 3

Table 3.1

Table 3.2

Chapter 4

Table 4.1

Table 4.2

Chapter 5

Table 5.1

Table 5.2

Table 5.3

Table 5.4

Table 5.5

Table 5.6

Table 5.7

Chapter 6

Table 6.1

Table 6.2

Table 6.3

Chapter 7

Table 7.1

Table 7.2

Table 7.3

Table 7.4

Chapter 8

Table 8.1

Table 8.2

Table 8.3

List of Illustrations

Chapter 1

Figure 1.1

The European Conceptual Model, modified from NIST.

Figure 1.2

The Smart Grid plane.

Figure 1.3

The layers of the Smart Grid.

Figure 1.4

The basic structure of a power system.

Figure 1.5

A time-horizon perspective of power system studies.

Figure 1.6

The operating principle of an EMS.

Figure 1.7

The six-bus system for the load-flow study.

Chapter 2

Figure 2.1

The installed renewable energy capacity worldwide between 2000 and 2013.

Source

: International Renewable Energy Agency (IRENA). Reproduced with permission of International Renewable Energy Agency (IRENA).

Figure 2.2

The installed renewable energy capacity worldwide between 2000 and 2013 without considering hydropower plants.

Source

: International Renewable Energy Agency (IRENA). Reproduced with permission of International Renewable Energy Agency (IRENA).

Figure 2.3

The ranking of the renewable power capacity for various countries.

Source

: International Renewable Energy Agency (IRENA). Reproduced with permission of International Renewable Energy Agency (IRENA).

Figure 2.4

The cumulative installed power generation capacity (GW).

Source

: World Energy Council (2013)

World Energy Perspective: Cost of Energy Technologies

, http://www.worldenergy.org/publications/2013/world-energy-perspective-cost-of-energy-technologies/ (accessed May 28, 2015).

Figure 2.5

The installed electricity capacity versus net generation, 2011.

Source

: World Energy Council (2013)

World Energy Perspective: Cost of Energy Technologies

, http://www.worldenergy.org/publications/2013/world-energy-perspective-cost-of-energy-technologies/ (accessed May 28, 2015).

Figure 2.6

The globalized LCOE for Q2 2013 (US$/MWh).

Source

: World Energy Council (2013)

World Energy Perspective: Cost of Energy Technologies

, http://www.worldenergy.org/publications/2013/world-energy-perspective-cost-of-energy-technologies/ (accessed May 28, 2015).

Figure 2.7

The evolution of cumulative installed wind power from 1996 to 2013.

Source

: Adapted from GWEC.

Figure 2.8

The cumulative (top) and new (bottom) installed wind power, December 2013.

Source

: Adapted from GWEC.

Figure 2.9

The evolution of cumulative installed wind power from 1996 to 2013.

Source

: International Renewable Energy Agency (IRENA). Reproduced with permission of International Renewable Energy Agency (IRENA).

Figure 2.10

A conceptual scheme of renewable power generation technology based on rotative electrical generators (top) without and (bottom) with a gearbox.

Figure 2.11

The conceptual scheme of renewable power generation technology based on rotative electrical generators with power electronics converters.

Figure 2.12

The conceptual scheme of the horizontal-axis onshore wind turbine.

Figure 2.13

The conceptual scheme of the horizontal-axis offshore wind turbine.

Figure 2.14

The SCIG fixed-speed wind turbine, with a multiple-stage gearbox.

Figure 2.15

The SCIG limited variable-speed turbine, with a multiple-stage gearbox.

Figure 2.16

The DFIG variable-speed turbine, with a multiple-stage gearbox.

Figure 2.17

The SCIG variable-speed turbine, with a multiple-stage gearbox.

Figure 2.18

The WRSG variable-speed turbine, with direct drive.

Figure 2.19

The PMSG variable-speed wind turbine, with direct drive.

Figure 2.20

The PMSG variable-speed wind turbine, with a single-stage gearbox.

Figure 2.21

The –λ curve.

Figure 2.22

The generated power curve.

Figure 2.23

The conceptual scheme of a PV renewable generation system.

Figure 2.24

A PV power plant based on a central inverter.

Figure 2.25

A PV power plant based on string inverters.

Figure 2.26

A PV power plant based on module inverters.

Figure 2.27

The PV panel model.

Figure 2.28

The simulation model employed for the analysis of PV modules.

Figure 2.29

The

V

–

I

and

P

–

V

characteristics obtained.

Chapter 3

Figure 3.1

The definition of the concepts: particular values of time frames and frequencies follow the ENTSO-E recommendations.

Source:

Adapted from ENTSO-E, 2009 [48].

Figure 3.2

The equivalences between the ENTSO-E (2009) [48], ENTSO-E (2012) [49], and Irish [45] regulations. Equivalences with the Spanish, German, and UK Grid Codes are also depicted.

Figure 3.3

The droop characteristic for the activation of primary reserves according to the requirements set out by the Irish regulations for WPPs.

Source:

Adapted from EirGrid, 2013 [45].

Figure 3.4

A representation of the primary, secondary, and high-frequency response capabilities according to UK Grid Code.

Source:

Adapted from National Grid plc, 2012 [55].

Figure 3.5

The minimum active power regulation levels for primary, secondary, and high-frequency response capabilities (i.e., activation of primary reserves) for WPPs in the event of a system frequency deviation of 0.5 Hz according to the UK Grid Code.

Source:

Adapted from National Grid plc, 2012 [55].

Figure 3.6

The active power – frequency response droop characteristic according to the ENTSO-E network code.

Source:

Adapted from ENTSO-E, 2013 [44].

Figure 3.7

Power–rotor-speed curves for various values of the pitch angle and deloaded options for a 1.5 MW wind turbine (wind speed 10 m/s).

Figure 3.8

An example of a control scheme for a wind turbine for primary frequency control support. It includes the primary frequency control droop, the pitch control, and the rotor speed control.

Figure 3.9

Deloaded optimum power curves for the deloaded operation of a 1.5 MW DFIG-based wind turbine.

Figure 3.10

The determination of the electromagnetic torque setpoint from an MPT algorithm and an additional control loop for synthetic inertia.

Source:

Adapted from Ramtharan, Ekanayake, and Jenkins, 2007 [79].

Chapter 4

Figure 4.1

The catalog of storage technologies.

Figure 4.2

The operating principle of pumped hydroelectric storage (PHS).

Figure 4.3

The operating principle of compressed air energy storage (CAES).

Figure 4.4

The operating principle of a battery.

Figure 4.5

A typical voltage–discharge profile for a battery cell.

Figure 4.6

Typical voltage profiles for different discharge rates.

Figure 4.7

A schematic of a NaS battery cell and module.

Source:

The cell graph on the left is adapted from NGK Insulators, Ltd, http://www.ngk.co.jp/english/ (accessed April 22, 2015) [100]; and the photograph of the battery module on the right is courtesy of Wen, Z., Cao, J., Gu, Z.,

et al

. (2008) Research on sodium sulfur battery for energy storage.

Solid State Ionics

,

179

, 1697–1701 [101]. Reproduced with permission of Elsevier.

Figure 4.8

A schematic of an Li-ion battery cell (top) and a Saft Li-ion battery pack in the IREC laboratory (bottom).

Source:

Scheme courtesy of Bruce, P.G. (2008) Energy storage beyond the horizon: rechargeable lithium batteries.

Solid State Ionics

,

179

, 752–759 [106]. Reproduced with permission of Elsevier. Bottom photograph courtesy of IREC, Catalonia Institute for Energy Research, http://www.irec.cat/ (accessed April 22, 2015) [108].

Figure 4.9

The operating principle of flow batteries.

Figure 4.10

The zinc–bromine flow battery shown on the right builds up to the containerized system shown on the left.

Source:

Redflow Limited (2015) [113], http://www.redflow.com.au/ (accessed April 22, 2015). Reproduced with permission of Redflow Limited.

Figure 4.11

The concept of the RFC.

Figure 4.12

The discharge time at rated power of the considered ESSs.

Source:

Díaz-González

et al

., 2012 [42]. Reproduced with permission of Elsevier.

Figure 4.13

A comparison of the energy efficiencies for various kinds of storage, according to the data presented in Table 4.2.

Figure 4.14

The illustrative topology of a flywheel-based ESS.

Figure 4.15

The illustrative topology of an SMES system. The liquid helium is contained in the two tanks on the left, while the tank on the right contains the superconducting coil.

Source:

Nielsen, 2010 [176]. Reproduced with permission of K.E. Nielsen.

Figure 4.16

The illustrative topology of a supercapacitor, depicting the electrical double layers at each electrode/electrolyte interface.

Figure 4.17

Supercapacitor modules from Maxwell Technologies, Inc., in a configuration on a test bench in the IREC laboratory.

Source:

Adapted from IREC, 2015 [108].

Figure 4.18

The capacitance and the ESR as temperature-dependent characteristics.

Source:

Adapted from Maxwell Technologies, Inc., 2015 [148].

Figure 4.19

The topology of a solar power plant with a storage system based on molten salt.

Source:

Adapted from Solar Millennium AG, 2015 [183].

Figure 4.20

The power-to-gas concept.

Source:

Adapted from Grond, Schulze, and Holstein, 2013 [186].

Figure 4.21

The normal topology for power conversion systems for storage not synchronized with the network.

Figure 4.22

The normal topology for the GSC.

Figure 4.23

The three-level neutral point clamped inverter.

Figure 4.24

Buck DC–DC converters for energy storage.

Figure 4.25

A graphical representation of the Siemens SIESTORAGE containerized solution.

Source:

Adapted from Siemens AG, 2015 [192].

Figure 4.26

ABB’s example of a multi-megawatt medium-voltage energy storage solution.

Source:

Adapted from Wade

et al

., 2009 [196].

Figure 4.27

A flywheel-based storage plant for frequency control (20 MW/5 MWh).

Source:

Beacon Power, LLC, 2015 [169]. Reproduced with permission of Beacon Power, LLC.

Figure 4.28

A thyristor-based battery charger and DC power supply system.

Source:

Adapted from Schaefer, Inc. catalog; Schaefer, Inc., 2015 [197].

Figure 4.29

An IGBT-based half-bridge DC–DC converter with galvanic isolation.

Figure 4.30

An uncontrolled single-phase unidirectional battery charger.

Source:

Adapted from Davis

et al

., 1999 [200].

Figure 4.31

A vehicle-to-grid charging point topology with a buck/boost DC–DC converter.

Figure 4.32

A vehicle-to-grid charging point topology without a buck/boost DC–DC converter.

Figure 4.33

A vehicle-to-grid charging point topology without a buck/boost DC–DC converter: the IREC test bench for research into second-life batteries from electric vehicles.

Figure 4.34

The topology of a double conversion uninterruptible power supply (UPS) system.

Figure 4.35

The concept of a BMS.

Chapter 5

Figure 5.1

The concept of life-cycle costs.

Figure 5.2

The relationship between the lifetime of a lead–acid battery, expressed in terms of cyclability, and the DoD.

Source:

Adapted from Power-Sonic Corporation, 2015 [207].

Figure 5.3

The annualized life-cycle costs for long-term ESSs. The systems are rated at 100 MW/600 MWh, and are deeply discharged once per day throughout the project horizon.

Figure 5.4

The annualized life-cycle costs for long-term storage systems. The systems are rated at 10 MW/10 MWh, and are deeply discharged once per day throughout the project horizon.

Figure 5.5

The annualized life-cycle costs for short-term storage systems. The systems are rated at 1 MW/0.003 MWh, taking 50 000 equivalent discharge cycles per year into consideration.

Chapter 6

Figure 6.1

The organization of the contents for describing the modeling and control of various kinds of storage system attached to their corresponding power conversion systems.

Figure 6.2

The modeling of the GSC circuit. The voltages

u

labc

correspond to the grid-side terminals, while the voltages are at the converter terminals.

Figure 6.3

The GSC controller.

Figure 6.4

The block diagram for design of the current control loop.

Figure 6.5

The DC-link voltage control scheme.

Figure 6.6

The equivalent electrical circuit of a supercapacitor.

Figure 6.7

The equivalent electrical circuit of a supercapacitor connected to a DC–DC converter.

Figure 6.8

The cascaded control system for the SSC of the supercapacitor.

Figure 6.9

The inductor current control scheme.

Figure 6.10

A summary of battery models.

Figure 6.11

The discharge curve of a battery cell.

Figure 6.12

The CC/CV charging method.

Figure 6.13

The control scheme for the SSC attached to the battery.

Figure 6.14

The machine-side converter circuit.

Figure 6.15

The control scheme for the machine-side converter.

Figure 6.16

The block diagram control methodology for the stator currents.

Figure 6.17

A rotational speed control scheme.

Figure 6.18

The voltage, active power, and current for the supercapacitor module while charged and discharged in voltage control mode.

Figure 6.19

The voltage, active power, and current for the supercapacitor module while charged and discharged in current control mode.

Figure 6.20

The voltage, active power, and current for the supercapacitor module while charged and discharged in power control mode.

Figure 6.21

A typical voltage-discharge curve for a battery cell (the discharge rate is 0.33 C).

Figure 6.22

The battery voltage, current, and charge (in Ah) while being charged in CC/CV charging mode.

Figure 6.23

The rotational speed, active power, and current for the flywheel while charged and discharged in speed control mode.

Figure 6.24

The rotational speed, active power, and current for the flywheel while charged and discharged in constant-current (or torque) control mode.

Figure 6.25

The rotational speed, active power, and current for the flywheel while charged and discharged in power control mode.

Chapter 7

Figure 7.1

The frequency–power spectrum of a three-bladed 1.5 MW wind turbine in the partial-load operating region.

Source:

Díaz-González

et al.

, 2013. Reproduced with permission of Elsevier.

Figure 7.2

A conceptual diagram of a flywheel energy storage system (FESS) for wind power smoothing.

Figure 7.3

The reference power, the actual power delivered by the flywheel, and the optimal reference angular speed, ω*

fw

, corresponding to a wind profile of a 7.5 m/s mean wind speed and 0.05 pu of turbulence.

Figure 7.4

The relationship between the flywheel reference mean speed and the mean wind power obtained by analysing the optimal results. Each cross corresponds to the mean value of for all cases evaluated for each value of the mean wind power.

Figure 7.5

The energy management algorithm of the flywheel. Note the input signals of the algorithm (

P

wt

and ω

fw

) and the output

T

*

fw

.

Figure 7.6

The asymptotic diagram of the frequency responses of the transfer functions and

T

.

Figure 7.7

The scheme of the experimental setup.

Figure 7.8

The flywheel test bench in the IREC laboratory. From left to right: 1, grid-side converter; 2, oscilloscope; 3, DC link; 4, machine-side converter; 5, autotransformer; 6, measurement devices; 7, PMSM; 8, rotating disk.

Source

Adapted from IREC, 2015 [108].

Figure 7.9

The experimental setup in the IREC laboratory. From left to right: 1, wattmeter; 2, coupling transformer; 3, power converter of the wind turbine emulator “active front end”; 4, “emulator” power converter; 5, CAN bus port.

Source

Adapted from IREC, 2015 [108].

Figure 7.10

The scaled magnitude of the power output of a 1.5 MW wind turbine and its fluctuating components with a cutoff frequency of 0.4 Hz.

Figure 7.11

Bode diagrams of the closed loop transfer function

T

(from to ω

fw

) and the transfer function (from

d

* to ω

fw

)

Figure 7.12

The power of the wind turbine emulator, the flywheel, and the net power exchanged with the network. The average rotational speed of the flywheel is rad/s.

Figure 7.13

The instantaneous power of the wind turbine emulator (blue line), as well as the power profiles of the flywheel and at the network terminals, subtracting the standing losses of the flywheel. The average rotational speed of the flywheel is 220 rad/s.

Figure 7.14

The RMS electric currents of the wind turbine emulator, the flywheel, and the network. The average rotational speed of the flywheel is rad/s.

Figure 7.15

The rotational speed of the flywheel in response to a step-profiled average reference speed from 220 to 270 rad/s. The DC-link voltage of the flywheel test bench is presented in the bottom subplot.

Figure 7.16

The spectrum of the net power exchanged with the network without flywheel support, as well as with a flywheel at different average values of the SoC. The average power losses of the flywheel have been subtracted.

Chapter 8

Figure 8.1

A graphical description of the concept of a load-following application.

Figure 8.2

The layout of an isolated power system with PV generation and storage.

Figure 8.3

The sizing procedure.

Figure 8.4

Typical daily load profiles for each of the months of the year.

Figure 8.5

I

–

V

and

P

–

V

curves for the PV generating system.

Figure 8.6

Typical daily PV generation profiles. The number accompanying each of the months in the legend sorts the months per magnitude of peak PV power generation.

Figure 8.7

A comparison between typical daily PV generation and load profiles.

Figure 8.8

Typical daily current demand profiles for the battery bank considering the contribution of the PV system.

Figure 8.9

Typical daily current demand profiles for the battery bank without considering the contribution of the PV system.

Figure 8.10

Typical daily current demand profiles for the battery bank without considering the contribution of the PV system.

Figure 8.11

The charge accumulated by the battery bank while storing as much PV generation as possible.

Figure 8.12

The charge accumulated by the battery bank while storing as much PV generation as needed.

Figure 8.13

Typical daily current demand profiles for the battery bank with limited PV energy storage.

Guide

Cover

Table of Contents

Preface

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Foreword

In response to the move in Europe towards a more sustainable, reliable, and cost-efficient society, European energy policy has set ambitious goals for the European electricity system, fixing the objective of at least 80% decarbonization by 2050.

Distribution networks represent 95% of the electricity grids in Europe. They are therefore a precondition for the retail markets (and also for wholesale) and for the sustainable development of cities and communities, new jobs, and growth.

The transition to a low-carbon society will boost Europe’s economy thanks to increased innovation and investment in clean technologies and low- or zero-carbon energy. A low-carbon economy implies a much greater need for renewable energy sources (RES), which are often geographically distributed (90% of the RES in the European Union (EU) are connected to the distribution networks), and also the integration of electric vehicles, which will represent a big shift in demand. Complementary IT solutions are being introduced to electricity networks at both the transmission and the distribution level, adding communication, sensors, and automation to actively manage the new and variable generation and demand. We call these Smart Grid technologies.

Distribution system operators (DSOs) and transmission system operators (TSOs) are responsible for the security of supply and the quality of service on their respective networks. It is EU policy that is driving the need for a reengineering of our electricity networks. New system challenges, including at the distribution level, lead to new network challenges for the pan-European transmission network. Hence, each DSO and TSO in the EU will have to evolve progressively from a “business as usual approach” to a “proactive approach” in order to avoid becoming a bottleneck in the future European electricity system.

It is perhaps surprising that the technologies required to address the new network challenges are, for the most part, not where the research and development (R&D) efforts are most needed. Overall, such inevitable evolution will also require the adaptation of existing regulatory regimes and business models more than technologies.

If the EU is to complete a real internal energy market, regulated companies must play a market facilitation role. TSOs, DSOs, regulators, power generators, retailers, traders, industrial consumers, and storage and RES project developers are all playing key roles in delivering an efficient electricity market. To reach the right setup, however, will involve a multidisciplinary approach to research activities, whereby network operators, manufacturers, and economists must cooperate closely in addressing the many barriers that have been identified - regulatory barriers being an important hurdle to jump.

National regulation still in operation continues to be based on the former design of electricity systems: predictable, controllable, and centralized energy generation, delivering power one-directionally through transmission and then distribution lines, with network charges calculated according to this split.

Now, more and less predictable sources of energy such as wind and solar are being generated locally and connected directly to distribution and sometimes transmission networks (larger plants). This means less controllable generation of energy, the need for bidirectional power flows and the transformation of ordinary consumers to “prosumers.” One of the key objectives of network operators, therefore, is to be able to use innovative approaches that are applicable in multivendor environments.

To expand on this last point, the extension and reinforcement of networks in the volume and at the rate required in the lead-up to 2020, 2030, and 2050 will be a costly endeavor. As a result, the DSO–TSO community has identified a number of grid users – demand-side response, electricity storage modules, large consumers, and even aggregated household consumer generators – as potential offerers of what are called system flexibility services, which could, in conjunction with smart technologies, reduce the need for investment in traditional assets.

It is not, however, so easy to make use of such flexibility services under the relatively new laws of the Third Energy (Liberalization) Package, which have imposed a separation of all market activities (generation and retail) and energy networks (transmission and distribution). If one considers this in the context of developing a real market for such services, the need for R&D to address the possible setups and business models becomes ever more apparent. Then there are difficult questions around the funding of R&D and demonstrations of innovative developments under the national regulatory frameworks (not at all available, in many cases).

The next step for the electricity networks R&D roadmap, under development from 2015, is the integration of R&D on storage technology applications into the existing roadmap - storage being able to offer an important form of flexibility. The European Commission is attributing an increasing amount of importance to the integration and alignment of R&D efforts, as well as to its policies in general. This is why, before influencing the calls under the EUs new R&D funding framework to 2020, Horizon 2020, the content of the roadmap is assessed alongside the R&D roadmaps for other energy sectors under the umbrella of the European Commissions EUs Strategic Energy Technology Plan (SET-Plan). The end result is the “Integrated Roadmap,” designed as the feed-in document for the Horizon 2020 annual work programs.

Storage is therefore becoming an unavoidable part of the power system, to ensure security of supply and as a crucial form of flexibility.

However, as indicated above, the regulatory frameworks in Europe are not adapted, in the majority of cases, for network operators – and certainly not for DSOs – to integrate storage into their networks; and this despite the considerable economic expenditure being devoted to research by these companies when allowed to do so by the national regulatory authorities.

The costs also remain a main reason for the lack of storage integration in the networks, and are still too high for a strong business case to be made at present. However, in places with a high renewable energies (RES) penetration, storage will be needed whatever the cost. Especially for DSOs, grid-optimized storage can help to address RES peak production and therefore congestion.

The question as to whether network operators will be able to own storage under strict regulation for high-risk and emergency situations, but operated by the market in all normal circumstances, is an issue that is and will continue to be the subject of interesting discussions for some time to come.

Ms. Ana Aguado Cornago Secretary General of the European Distribution System Operators for Smart Grids (EDSO) Brussels, June 2015

Preface

From the outset, the electric power system has been designed to maintain a balance between generation and consumption in real time. This implies severe constraints regarding the short- and long-term operation of the system in terms of security, stability, and the sizing of the units. The current design paradigm is now challenged by the massive rollout of storage units in the power system. In recent years, the electric power system has been undergoing a transition caused by the massive introduction of intermittent renewable generation, which causes a need to incorporate advanced supervision and control features into the classical network operation. With the exponentially increasing numbers of units to be supervised and controlled, advanced computational methods combined with intelligent algorithms will enable the future Smart Grid. Energy storage has not been an initial driver that has triggered the Smart Grid, but it is now definitively a key part of the Smart Grid, not only facilitating the change of technology and design, but also the overlying business models.

The Smart Grid is somehow a starting point that is enabling the massive rollout of storage, leveraging the participation of novel players in the electricity markets who have different business objectives. One important feature of energy storage in power systems is the ability to smoothen intermittent renewable generation, both for large and small-sized operations. The massive rollout of renewables will drive the use of different (centralized or decentralized) storage solutions, which will create a sufficient market size for the storage technology and push the development of the technology.

The origin of this book can be traced back to 2009, when Francisco joined the Catalonia Institute for Energy Research (IREC) to start his doctoral thesis. Andreas and Oriol became his supervisors, and rapidly decided to focus the efforts on the utilization of energy storage technologies in wind power plants. We had gained some experience working in We had some experience working in the Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments, Technical University of Catalonia (CITCEA–UPC) and IREC on electrical systems and on grid integration of wind farms in some projects with Ecotecnia (which was acquired by Alstom, becoming the wind division of the Alstom group). At that time, we started to move away from the concept of the wind farm to the more appropriate term “wind power plant.” Wind power was no longer a fancy green alternative source of energy, which could generate power when the wind blew. It was now part of a massive business, which already bore a very serious level of responsibility in the operation of the whole power system. Transmission system operators were drafting very demanding grid codes, in which wind farms were treated as dependable power plants.

We remember having discussions with some engineers in Ecotecnia (Alstom Renewables, wind division) about the possibility of incorporating energy storage in the wind turbines in order to provide ancillary services. Additionally, these devices could be used for other purposes, as power smoothing, correction of production forecasts, and energy market operations. While the potential of energy storage was evident, there were differing opinions on where to locate the storage, what technology to use, and how to size such energy storage systems. Some engineers supported the idea of wind turbines equipped with energy storage devices that could allow the smooth provision of power adjusted to the forecasted production levels and that could eventually provide ancillary services to the grid. Others argued that it made more sense to operate a single, larger energy storage device at the wind power plant level and provide the same services in an aggregated manner. Other colleagues stressed that eventually energy storage should be deployed on the demand side, close to the consumer, and that it should be combined with demand-side management. Finally, other engineers defended the idea that the optimal solution was to locate the energy storage devices in the distribution substations.

During the realization of the doctoral thesis, some contributions were made on the modeling and control of energy storage systems, especially flywheels combined with wind power plants. Francisco built a scaled test rig with which he could gain some practical experience and demonstrate the possibility of power smoothing using a flywheel. We also realized that there were some impressive advances in the development of energy storage technologies and also on different applications in electric power systems. For example, energy storage was being considered as the only possible solution for preventing rapid power drops in large photovoltaic power plants and in renewable power plants in general. Energy storage was also the backbone of the microgrid concept (which is absolutely necessary to balance power flows) and the lung of the Smart Grid of the future.

By the time the thesis came to an end and was successfully defended in September 2013, we realized that we were starting to understand the potential of energy storage in power systems with a high penetration of renewable energy. Our beliefs regarding the huge potential of energy storage utilization in future power systems triggered the idea of expanding the work done in the doctoral thesis, and in other projects that we had been developing, and start the adventure of writing a book on the topic. At that time, we probably did not appreciate the massive amount of work that was awaiting us when we began the preparation of this book in April 2014.

Let us move forward to spring 2015, at which time we were working to submit the manuscript to the publisher on time. We were writing this preface in the hope and belief that this book could provide some useful guidance to engineers and professionals interested in the utilization of energy storage in power systems that are rich in renewable energy sources. Nowadays, we often hear news stories about paradigm shifts and energy revolutions that will eventually change the way in which we understand electric power in our society. In all these communications, energy storage is part of the equation. We are not certain how future electrical energy systems will be shaped, but we trust that energy storage will play an important role.

According to the scope of the book, its contents are divided into eight main chapters. Chapter 1 first introduces readers to modern power systems. Electric power systems are experiencing a dramatic transformation from the conventional vertically integrated approach with few control actors, towards a system with a high penetration of renewable (and intermittent) generation and, as a consequence, a highly controlled system at any voltage level. As previously noted, such a transformation suggests the introduction of the term “Smart Grid,” and this is one of the main concepts underpinning future power system architectures. The Smart Grid architecture is defined in terms of domains, zones, and layers, and these are presented in the chapter. After the presentation of the power system architecture, the chapter continues with the presentation of energy management systems and the fundamentals of power system analysis. In this regard, basic concepts on optimization methods and optimal power flow computational techniques are presented. Viewed together, this results in a didactic approach to an understanding of the fundamentals of power systems. Moreover, though, the chapter also includes a practical example on load-flow calculation.

One of the main drivers of power system transformation is the field of renewable generation, and as such this is presented in Chapter 2. The chapter first discusses the contribution of the various forms of renewable energy in the worldwide energy mix. After this presentation, the chapter classifies the renewable power generation technologies into those based on rotative electrical generators, mechanically coupled to turbines or similar devices (e.g., wind turbines and hydropower); and those based on static power generation sources, producing electricity without any moving devices (e.g., photovoltaics). With regard to the former, the chapter describes wind turbine topologies in detail, and offers two numerical examples on the calculation of the power generated by both fixed- and variable-speed turbines. Finally, with regard to static renewable-based generating technologies, the chapter introduces the concept of photovoltaic generation and proposes a calculation on the analysis of PV panels. The chapter concludes with a brief presentation of the grid code requirements for the grid connection of renewables.

With the stepwise displacement of conventional generating plants by nonsynchronized renewable-based ones, the net level of synchronous power reserves in the system becomes reduced, and this can affect the frequency control in the system. For such reasons, and according to some European grid codes, wind power plants are required to provide power reserves in the same way as conventional generating units. As a contribution to the description of the requirements for the grid connection of renewables, Chapter 3 presents an extensive literature review on the European grid codes with regard to frequency support. While the chapter looks specifically at wind power plants, the results can be exported to other renewable energy generation technologies. Apart from discussing on grid codes, the chapter includes an extensive literature review on control methods for operating wind turbines, so that they can maintain a predetermined level of power reserves, thus enabling them to participate in tasks related to frequency control.

The three chapters described above serve as a good introduction to electric power systems and renewable generation. These subjects are quite pertinent, and even somehow unavoidable, for a proper understanding of the concepts presented in the rest of the book, which are all centered around energy storage technologies in power systems.

The first chapter on energy storage is Chapter 4. This chapter offers a review of the energy storage technologies that can be potentially included in the electric power system. The chapter covers a great number of technologies, such as pumped hydroelectric storage, compressed air and hydrogen-based systems, secondary batteries, flow batteries, flywheels, superconducting magnetic storage, supercapacitors, and even (although tangentially) the field of thermal storage and the power-to-gas concept. For each technology, the description includes the operating principles, the main components, and the most relevant technical characteristics. The chapter emphasizes the main differences amongst the technologies in a comprehensive manner, including some tables and graphics based on the data collected from several publications and from manufacturers’ datasheets. The final part of the chapter discusses power conversion systems for grid connection and the control of storage not synchronized with the network.

Following the description of the technology in Chapter 4, the book tackles the formulation of cost models for the economic assessment of storage technologies. A cost model considering capital, operation and maintenance, replacement, and also end-of-life costs is introduced, based on the literature. The model is demonstrated by means of a numerical example. In this example, the life-cycle costs of different storage systems – both in themselves and while providing various services in the power system – are calculated and evaluated.

The study of the inclusion of storage technologies in the power system usually requires the development of simulation platforms to virtually validate various concepts centered on the design and operation of the technology prior to the commissioning of the system. Accordingly, Chapter 6 presents averaged dynamic models, based on electrical equations, for different storage technologies such as batteries, supercapacitors, and flywheels, as well as for their corresponding power conversion systems. Ultimately, the contents of this chapter can be adopted as a practical approach to the modeling of storage systems. To demonstrate the correctness of the models and of the corresponding control algorithms for the power conversion systems to which the storage containers are attached, the chapter includes various numerical examples. These examples plot the behavior of the storage systems modeled in charge and discharge processes.

In this way, Chapters 4–6 describe the basis for storage technologies and/or offer tools for studies related to the application of the technology. The last two chapters, Chapters 7 and 8, deal specifically with the applications that energy storage systems could potentially provide in the electric power system. Since the power systems of the future will surely be characterized by increasing penetration rates of renewables, most of the storage applications discussed in these chapters are closely related to renewable generation. Chapter 7 presents the potential for short-term applications; that is, for those applications requiring storage in order to rapidly inject or absorb power, over short periods of time, for different purposes. Conversely, Chapter 8 refers to potential mid- and long-term applications: that is, those applications requiring the storage systems to continuously exchange power with the network over periods of hours or even days, for balancing and generation time-shifting purposes.

Both chapters include a numerical example, thus contributing to the practical scope of the book. With regard to short-term applications, a specific example on wind power smoothing with flywheels is offered. This example includes the formulation and solution of an optimization problem, which determines the theoretical optimal operation of the flywheel while providing this service. From the results of this optimization problem, a control algorithm for the flywheel to be executed in real time is derived and also validated using laboratory-scale equipment. Ultimately, the proposed exercise is a good example of the combination of different analytical tools; that is, modeling, optimization, and experimental validation. Finally, the example in Chapter 8 proposes the sizing of a battery bank and its attached power conversion system, building up an isolated power system with PV generation.

For us, writing this book has required tremendous personal effort, but it would not have been possible without the invaluable support received from a number of colleagues, in various forms. We would first like to acknowledge, with thanks, the support received from our colleagues at IREC and CITCEA–UPC: this work is the product of our professional activity over recent years, and throughout this time we have gained experience and knowledge from all of them.

Particularly related to the book, we thank Cristina Corchero and Joana Aina Ortiz for providing us with data for simulations. We thank Jordi Pegueroles and Fernando Bianchi for the design of control algorithms; José Luís Domínguez, Mikel de Prada, and Eduardo Prieto for the figures in Chapter 2; and Gerard del Rosario and Ramón Gumara for the information on laboratory equipment.

In addition, we would like to thank Ms Ana Aguado Cornago for writing the foreword to this book.

Finally, we are also grateful for the permissions received from many authors, institutions, and companies to use figures in the book. In particular, we like to acknowledge the permissions received from IRENA, Redflow Limited, Beacon Power, the World Energy Council, and Knut Erik Nielsen.

We hope that the book will prove to be useful for researchers and engineers. Comments from, and discussions with, readers with diverse backgrounds will be highly appreciated.

Francisco, Andreas, and OriolBarcelona, June 2015

1An Introduction to Modern Power Systems

1.1 Introduction

Power systems are complex structures composed of an enormous number of different installations, economic actors, and – in smaller numbers – system operators. In the traditional approach, the system is dominated by economies of scale. This means that for steadily increasing consumption, a large power generation capacity is installed, mainly nuclear, coal- or gas-fired thermal, and hydroelectric. In order to guarantee the reliability of such a system, a meshed transmission grid at high voltage has to be installed, into which the generators feed. Underlying this transmission system, function of the distribution grid is to conduct the power flow at lower voltage levels to customers, at medium or low voltage. The described power flow is mainly unidirectional, from the generators to the customers, who are connected at medium or low voltage. Only a few customers are connected at high voltage, due to their high loads. Such a system is easy to control, as most of the players (the customers) are passive, only a few actors (generators and system operators) are needed to centrally control the system, and the interfaces are well defined. The most extended economic model in this context is the vertically integrated utility. However, some of the deep fundamentals on which this structure is based can be envisioned, moving from these vertically integrated utilities to the Smart Grid distribution system [1]:

Economies of scale are no longer applicable to the power system generation, due to the dramatic growth of distributed generation.

The costs of the various renewable energy technologies have declined steadily due to technological advances.

Increased environmental concerns on the part of customers and legislators.

Regulation is enabling the emergence of different players on the electricity market (retailers, energy service providers, etc.)

These fundamental changes are causing a shift from the vertically integrated approach with few control actors towards a system with a high penetration of renewable (and intermittent) generation and, as a consequence, a system that needs to be highly controlled at all voltage levels. The increasing use of renewable energy not only helps to alleviate fuel poverty, but also promotes decentralized power generation, thereby reducing the dependence on conventional grid-based energy sources. It provides electricity from small-scale generation and microgeneration; working towards reducing the increasing electricity consumption and supplying any surplus generation to the grid. Therefore, microgeneration is a key power generation trend for smart communities, both rural and urban. Distributed generation from micro–combined heat and power (CHP) installations and renewables such as small-scale wind turbines and solar photovoltaics (PV) plays a strong role in this ecosystem. New generation units from renewable energy sources must be established; however, as a result of stochastic generation, those energy resources are intermittent, and possible output fluctuations have to be balanced [2]. Energy storage applications will be used to cope with this problem [3]. All of this leads to the approach to make the grid intelligent: the Smart Grid. A Smart Grid is an electricity network that can intelligently integrate the actions of all of the users connected to it – generators, consumers, and those that do both – in order to efficiently deliver sustainable, economic, and secure electricity supplies [4]. A Smart Grid uses sensing, embedded processing, and digital communications to enable the electricity grid to be observable (able to be measured and visualized), controllable (able to be manipulated and optimized), automated (able to be adapted and to self-heal), and fully integrated (fully interoperable with existing systems, and with the capacity to incorporate a diverse set of energy sources) [5].

One prominent set of actors in modern power systems are “prosumers” (“proactive consumers”). Prosumers are common consumers who become active to help to personally improve or design the goods and services available in the marketplace, transforming both it and their own role as consumers [6]. The strategic integration of prosumers into the electricity system is a challenge. As prosumers are acting outside the boundaries of the traditional electricity companies, ordinary approaches to regulating their behavior have proved to be insufficient. The aggregated potential of flexibility makes the role of the prosumer important for energy systems with high and increasing shares of fluctuating renewable energy sources. To involve different prosumer segments, both utilities and policy need to develop novel strategies. The benefits for prosumers in modern power systems can be summarized as follows:

Economic

. The Smart Grid offers the possibility of involving customers, their flexibility being used as an instrument to shed loads and secure stability. It is assumed that customers will allow the distribution system operator (DSO) access to their home automation systems, and that a value chain that links households with the transmission system operator (TSO) via the DSO will be created in such a way that the flexibility can be used systematically, as can the compensation flowing in the other direction.

Incentives

. Incentives may attract customers into a demand–response regime and into distributed energy resources (DER) programs without the need for a proper compensation structure. Poor quality of supply can also be a trigger, especially when there is only one utility operating. Local DER solutions are thus a good option, although the levelized energy costs could be much higher than the supply costs from a centralized utility. Other incentives, such as environmental and social sustainability concerns, comfort, convenience, and so on, could also be drivers.

Technical

. Energy storage for electricity is the main key to assuring the stability of a system with intermittent generation, at least for short periods. Ownership models and options for placement in the grid will drive very different solutions. It will be possible for electric cars to supply to the grid (vehicle to grid), which will add to the additional power system storage capability. As long as the distribution operator is in control of, or owns, these facilities, they will be operated in a different manner than if the storage is owned and operated by the community or by a third party working partly on their behalf.

The community

. With DER and Smart Grid technologies, communities will gain substantial market power. Traditionally, the utility was in charge of upgrading the infrastructure in order to cater for a sufficient supply capacity and to assure quality. To build a community solution for local supply by means of Smart Grid technologies and DER seems to be the solution for future expansion, at least in rural areas.

Market and trading

. New local markets and trading will arise, based on real-time trading, in order to balance the system. The flexibility of customers, local generators, and storage systems will create value on the market to balance the intermittency of renewable generation.

Social

. A new form of social cooperation and commitment can be created. For example, customers could start to cooperate to assure that surplus energy that cannot be fed into the system is provided to neighbors and others who are in a position to benefit.

1.2 The Smart Grid Architecture Model

The Smart Grid Architecture Model (SGAM) framework has been developed by the Joint Working Group on standards for Smart Grids, from CEN/CENELEC/ETSI. Its methodology is intended to present the design of Smart Grid use cases by a holistic architectural definition of an overall Smart Grid infrastructure. Apart from addressing the system architecture through a reference architecture, it also provides an overarching standardization process. The major elements of the described reference architecture are as follows:

A high-level framework model (the European Conceptual Model) that is an adapted version of the US NIST (National Institute of Standards and Technology) model, and which bridges between the two models, as shown in

Figure 1.1

.

The SGAM framework as a three-dimensional model with interoperability layers and Smart Grid zones and domains, and that will assist in the architectural design of Smart Grid use cases.

Representations of stakeholder views of Smart Grids.

The core of the framework is the Smart Grid plane. In this plane, the power system equipment and energy conversion (electrical processes) viewpoints are linked with the information management viewpoints. These viewpoints can be divided into the physical domains of the electrical and energy processes and the hierarchical zones (or levels) for their management. Figure 1.2 shows the domains and zones of the Smart Grid plane in two dimensions.

Figure 1.1 The European Conceptual Model, modified from NIST.

Figure 1.2 The Smart Grid plane.

The different domains represent the power system equipment and energy conversion factors divided into the following subgroups:

Bulk generation

. This domain represents the bulk generation of electricity by power plants. It embodies “classical” power system generation, such as by thermal, nuclear and hydropower plants, as well as large-sized renewable generation such as offshore wind farms and large-scale PV power plants. These facilities are typically connected to the transmission system.

Transmission

. This domain represents the necessary infrastructure and organization for the transmission of large amounts of power over great distances.

Distribution

. This domain represents the necessary infrastructure and organization for the distribution of electricity to the final customers.

DER

. This domain represents generation by means of distributed energy resources, typically using small-scale generation technologies based on renewable energy resources. The range of such generators is typically from 3 kW up to 10 MW; they are connected directly to the distribution grid and can be controlled by the DSO.

Customer premises

. This domain includes the industrial, commercial, and home facilities where the electricity users interact with the distribution system. In the classical approach, this is where the consumers are located (households, industrial plants, shopping malls, etc.). In the prosumer approach, small-scale generation, electric vehicles, demand response, batteries, and so forth can also be hosted.