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Electrical energy storage systems A comparative life cycle cost analysis (2015)
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Renewable and Sustainable Energy Reviews 42 (2015) 569–596
Contents lists available at ScienceDirect
Renewable and Sustainable Energy Reviews
journal homepage: www.elsevier.com/locate/rser
Electrical energy storage systems: A comparative life cycle cost analysis
Behnam Zakeri n, Sanna Syri
Department of Energy Technology, Aalto University, PL 14100, FIN-00076 Aalto, Finland
art ic l e i nf o
a b s t r a c t
Article history:
Received 8 May 2014
Received in revised form
1 August 2014
Accepted 5 October 2014
Large-scale deployment of intermittent renewable energy (namely wind energy and solar PV) may entail
new challenges in power systems and more volatility in power prices in liberalized electricity markets.
Energy storage can diminish this imbalance, relieving the grid congestion, and promoting distributed
generation. The economic implications of grid-scale electrical energy storage technologies are however
obscure for the experts, power grid operators, regulators, and power producers. A meticulous technoeconomic or cost-benefit analysis of electricity storage systems requires consistent, updated cost data and a
holistic cost analysis framework. To this end, this study critically examines the existing literature in the
analysis of life cycle costs of utility-scale electricity storage systems, providing an updated database for the
cost elements (capital costs, operational and maintenance costs, and replacement costs). Moreover, life
cycle costs and levelized cost of electricity delivered by electrical energy storage is analyzed, employing
Monte Carlo method to consider uncertainties. The examined energy storage technologies include
pumped hydropower storage, compressed air energy storage (CAES), flywheel, electrochemical batteries
(e.g. lead–acid, NaS, Li-ion, and Ni–Cd), flow batteries (e.g. vanadium-redox), superconducting magnetic
energy storage, supercapacitors, and hydrogen energy storage (power to gas technologies). The results
illustrate the economy of different storage systems for three main applications: bulk energy storage, T&D
support services, and frequency regulation.
& 2014 Elsevier Ltd. All rights reserved.
Keywords:
Battery energy storage
Cost of energy storage
Electricity market
Electricity storage
Renewable energy integration
Smart grid
Techno-economic analysis
Contents
1.
2.
3.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Electricity storage for a flexible power system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.
Imperatives of electricity storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.1.
Meeting demand and reliability in grid's peak hours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.2.
Liberalized electricity markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.3.
Intermittent renewable energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.4.
Distributed generation and smart grid initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.
Alternative solutions for increasing the flexibility of the power system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
EES technologies: characteristics and costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.
General considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.
Methodology in cost analysis of EES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.1.
Total capital cost (TCC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.2.
Life cycle costs (LCC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
570
571
571
571
571
571
571
572
572
572
572
572
573
Abbreviations: AA-CAES, advanced adiabatic compressed air energy storage; ALCC, annualized life cycle costs; BES, battery energy storage; BOP, balance of plant; CAES,
compressed air energy storage; CRF, capital recovery factor; D-CAES, diabatic compressed air energy storage; DG, distributed generation; DOE, The US Department of Energy;
DoD, depth of discharge; EES, electrical energy storage; FC, fuel cell; GT, gas turbine; IQR, interquartile range; LCC, life cycle costs; LCOE, levelized cost of electricity; LCOS,
levelized cost of storage; NaS, sodium–sulfur (battery); Ni–Cd, nickel–cadmium (battery); O&M, operation and maintenance; PCS, power conversion system; PEM, polymer
electrolyte membrane; PHS, pumped hydroelectricity storage; PSB, polysulfide–bromide (battery); RES, renewable energy source; RES-E, electricity from renewable energy
source; SCES, supercapacitor energy storage; SMES, superconducting magnetic energy storage; T&D, transmission and distribution; TCC, total capital costs; TSO, transmission
system operator; UPS, uninterruptible power supply; VRFB, vanadium-redox flow battery; VRLA, valve-regulated lead–acid (battery); ZEBRA, zero emission battery (NaNiCl2
battery)
n
Corresponding author. Tel.: þ 358 405007085.
E-mail addresses: behnam.zakeri@aalto.fi (B. Zakeri), sanna.syri@aalto.fi (S. Syri).
http://dx.doi.org/10.1016/j.rser.2014.10.011
1364-0321/& 2014 Elsevier Ltd. All rights reserved.
570
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
3.3.
3.4.
Methodology in review and collection of cost data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573
EES technologies and related costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577
3.4.1.
Mechanical energy storage systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577
3.4.2.
Electrochemical battery energy storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579
3.4.3.
Electric and magnetic energy storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 580
3.4.4.
Power to gas energy storage technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581
3.4.5.
Other electricity storage technologies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 582
4. Results and discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 582
4.1.
Results of the review for individual cost items . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 582
4.2.
Total capital cost (TCC) of EES systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 584
4.3.
Life cycle costs (LCC) of EES systems and uncertainty analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585
4.3.1.
Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 588
4.4.
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589
5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 590
Appendix A.
Cost elements of different EES systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 590
Appendix B.
Summary of technical characteristics of EES systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 592
Appendix C.
Total capital cost of different EES systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 592
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593
1. Introduction
Power systems are on the threshold of a new transformation by
the confluence of deploying variable renewable energy sources (RES)
and free electricity markets. High share of variable RES intensifies the
variability and intermittency of the power supply, disrupting the
optimal operation of conventional power systems and grid reliability.
Deregulated electricity markets introduce a competitive environment
for power producers resulting in high capital cost requirement for
meeting peak demands and volatile electricity prices. This new
setting has imposed technical, economic, and environmental challenges for secure supply of electricity.
Energy storage is deemed as one of the solutions for stabilizing
the supply of electricity to avert uneconomical power production
and high prices in peak times. The recent World Energy Outlook
(2013) published by International Energy Agency (IEA) predicts a
significant growth in the share of variable RES in total electricity
generation, from 6.9% in 2011 to 23.1% by 2035 within the EU [1].
Accordingly, the European Commission has recognized electricity
storage1 as one of the strategic energy technologies in SET-Plan in
achieving the EU's energy targets by 2020 and 2050 [2]. The US
Department of Energy (DOE) has also identified energy storage as
a solution for grid stability, through the Energy Storage Systems
Program (DOE OE/ESSP) for developing the energy storage technologies and systems [3].
A wide spectrum of studies address the technical features of
electrical energy storage (EES) technologies. For instance, technical
characteristics of different EES systems have been subject to study
and review in a number of contributions [4–12]. There are other
studies that have more thoroughly investigated operational features of certain EES technologies, including pumped hydroelectricity storage (PHS) [13], compressed air energy storage (CAES)
[14], different types of batteries [15–17], flywheel energy storage
[18,19], superconducting magnetic energy storage (SMES) [20],
and supercapacitor energy storage (SCES) [21]. There is also a
broad range of researches in modeling and optimization of EES in
exemplary or real power systems [22–30]. The aforementioned
and similar efforts have contributed to the better understanding of
1
The terms “electricity storage” and “electrical energy storage” are used
interchangeably in the literature and are equal in this study, representing all the
technologies that can store and then discharge back the electricity, with a reasonable response time.
technical characteristics, functional limitations, and possible
operational strategies of EES systems. Yet, further research is
required to address the barriers in large-scale deployment of EES
systems in existing energy systems.
In the absence of commercial, grid-scale adoption of the
majority of EES technologies, their economic characteristics have
remained obscure for energy system analyzers, power suppliers,
grid operators, and policy makers. Moreover, cost analysis of the
mature or commercial storage technologies, namely PHS and
CAES, cannot be easily generalized as they are site-specific technologies [9,13,31]. According to different studies [27,32–34], this
lack of adequate information regarding the economy of utility-scale
EES systems is one of the major obstacles in the establishment of
feasible business models, ownership structures, and required regulation strategies. In 2013, DOE announced four challenges in the
widespread use of EES, of which cost-competiveness is to be
addressed with focus on the life cycle costs (LCC) of EES systems
[35]. To contribute in this regard, but not limited to that, this study
provides an up-to-date, comprehensive, and comparative review of
the available literature on cost analyses, capital cost data, and life
cycle costs of different EES technologies. The focus is dedicated to
recent publications considering their methodology, applied tools, and
possible limitations.
The LCC of EES systems is directly associated with the use case
and its techno-economic specifications, e.g. charge/discharge
cycles per day. Hence, the LCC is illustratively analyzed for three
well-known applications; including bulk energy storage, transmission and distribution (T&D) support services, and frequency
regulation. Since the cost data of EES systems are rather dispersed
and varying in the literature, this study applies a robust uncertainty analysis in the determination of LCC of EES systems.
This study is structured as follows. The main imperatives for
the adoption of EES systems are briefly studied in Section 2. The
cost analysis framework is established in Section 3, with describing the methodology for the representation of cost data. The cost
elements of different EES technologies are discussed with respect
to the recent publications in this field. Section 4 presents and
discusses the results in three main parts: cost elements, total
capital costs, and the LCC of EES systems. Conclusions are drawn in
Section 5 supported with recommendations for the future work.
This study focuses on stationary, utility-scale EES systems that are
capable for supporting the grid at a reasonable response time.
Indirect energy storage processes, smart electric vehicles, thermal
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
Nomenclature
C BOP
C cap
C cap;a
C DR
C DR;a
C FOM;a
C LCC;a
C O&M;a
C PCS
CR
cost of balance of plant (€/kW)
total capital costs per unit of power rating (€/kW)
annualized value of total capital costs (€/kW-yr)
disposal and recycling costs (€/kW)
annualized disposal and recycling costs (€/kW‐yr)
fixed operational and maintenance costs (€/kW‐yr)
annualized life cycle costs (€/kW‐yr)
annualized operational and maintenance costs (€/kW‐
yr)
cost of power conversion system (€/kW)
replacement costs (€/kWh)
energy storage, and demand side management are excluded from
this study. The EES technologies that are covered in this study include
mechanical energy storage systems (PHS, CAES, and flywheel);
secondary electrochemical batteries (lead–acid, sodium–sulfur
(NaS), sodium–nickel chloride (ZEBRA), nickel–cadmium (Ni–Cd),
and Li-ion); flow batteries (vanadium-redox flow battery (VRFB),
zinc–bromine (Zn–Br), iron–chromium (Fe–Cr), polysulfide bromide
battery (PSB), and metal–air batteries); electro-magnetic energy
storage systems (SMES and SCES); and hydrogen-based energy storage
systems.
2. Electricity storage for a flexible power system
This section reviews the main imperatives for the adoption of
EES technologies. Other alternatives for EES that can contribute to
the grid stability and flexibility are also listed in Section 2.2.
Comparing advantages and limitations of these alternatives is
however beyond the scope of this study.
2.1. Imperatives of electricity storage
2.1.1. Meeting demand and reliability in grid's peak hours
Electricity demand is inherently variable in momentarily, hourly,
weekly, and seasonal time lags. It has been a tradition in power
systems to maintain the production capacity large enough to meet
the peak demands that occur just a few hours per year. This may
result in oversized, inefficient, non-environmental, and uneconomical
power systems. EES is an alternative to store the power in low
demand time to be used later in the peak hours, diminishing the
construction of extra power capacity. In some cases, for instance with
high share of nuclear power, EES has been used to firm the
production capacity to avoid part load operation or undesirable
shutdowns, offering more economical baseload production [36–38].
Not only the generation capacity, transmission and distribution
(T&D) systems are also constrained in peak hours. Since T&D
networks are traditionally designed for one-way operation, they
must be oversized to address the occasional peak hours. EES can
relieve network contingency and reduce the risk of consequences
of overloaded T&D network [39–41]. This can reduce large costs of
grid management and reliability services.
2.1.2. Liberalized electricity markets
The deregulation of electricity markets is another potential use
case for EES systems by benefiting from price arbitrage, shifting
electricity from low-demand periods to the peaks [32,40,42]. The
profitability of EES in price arbitrage depends on the level of
fluctuations in spot prices [43,44]. The use of EES in balancing
markets and other deregulated ancillary services may stack the
C R;a
C stor
C V OM
Ein
Eout
h
i
n
r
t
T
ηsys
571
annualized replacement costs (€/kW‐yr)
cost of storage section (€/kWh)
variable operational and maintenance costs (€/kWh)
input energy in one cycle (kWh)
output energy in one cycle (kWh)
discharge time (h)
interest rate ( )
number of discharge cycles per year
number of replacements
replacement period (yr)
lifetime (yr)
overall efficiency of storage system (%)
benefits, resulting in more economic attractiveness [45,46]. Adopting an optimal strategy in charge/discharge scheduling and more
improvements in price forecasting are two important parameters
in increasing the revenues from EES in price arbitrage [47,48].
2.1.3. Intermittent renewable energy
National and regional energy policies endeavor to promote the
use of renewable-based electricity (RES-E) to reduce carbon
emissions and secure local power supply [1,49]. Inherent intermittency of variable RES, namely wind and solar PV, introduces
new challenges in optimal operation of power systems, including
frequency fluctuations, voltage flicker, and the cyclic operation of
thermal power plants that are networked with high-level wind
generation [50–52]. In the energy systems employing variable RES,
other conventional generation plants are usually planned large
enough to handle the maximum load without reliance on intermittent RES [53]. It is shown that EES is needed in relatively high
shares of variable RES, even in the presence of an ideal, widely
dispatched transmission system [54,55].
EES can be employed in different ways to enhance the use of
RES-E. For instance, it can store extra, uncontrollable RES-E to be
used at desirable time, eliminating power curtailment and oversized construction of power capacity [56]. With respect to wind
power generation, EES can contribute in relieving the fluctuation
suppression, low voltage ride through, and voltage control support, resulting in smooth power output [57–59]. The recent
improvements in modeling and analysis of wind-storage systems
have contributed in better understanding of the role of storage and
its integration into the hybrid power systems [60–64]. It is further
shown that EES favors other storage technologies, namely heat
storage and gas storage, for large-scale wind integration [65]. EES
can facilitate the use of RES for secondary applications, e.g. water
desalination [66].
2.1.4. Distributed generation and smart grid initiatives
Distributed generation (DG) is one of the economic, reliable,
and efficient ways for power supply in small scales, kW to a few
MW. Not only DG dispatches the issue of power generation and
transmission leading to further resource flexibility, it is known as a
secure path for increasing the share of local RES-E [67,68].
By providing flexible power and reliability services, EES contributes
in uninterruptible power supply (UPS) and overcoming voltage
drops in these decentralized and inflexible power systems [69–72].
EES also facilitates the remote islands and microgrids with more
RES integration, resulting in higher energy security and lower
emissions [73]. In many smart grid schemes, which are seen to be
a major step in achieving sustainable energy systems, EES is
considered as an inherent solution [74–77].
572
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
2.2. Alternative solutions for increasing the flexibility of the power
system
While technical solutions are developing for power smoothing
of variable RES at equipment level, e.g. controlling the kinetic
energy of inertia, pitch angel, and DC link voltage in wind
production [57], comprehensive solutions should deal with the
problem from “system design” viewpoint. Demand side management or flexible power demand [78–80] and electric vehicles with
smart charging [81–86] are two alternatives for shifting the peaks.
Power system control, grid expansion, and more advanced methods in network management are other measures ensuring optimized power flow through the grid [87–91]. To provide the actors of
power systems with proactive decisions in optimal planning and
balancing of their energy systems, improved forecasting methods for
production and consumption are crucial [92–96]. The optimal planning and control of power generation fleet and the adoption of
combined heat and power (CHP) plants are other measures to
increase the flexibility of the power system [97,98]. Electric-tothermal energy systems are other mechanism to capture the excess
power to be utilized in the district heating networks [99]. Interconnected heat and power networks are one of the promising
solutions for the integration of RES-E by applying energy storage as
an inter-linkage [100–102].
3.1. General considerations
In general, EES technologies include two main sections: power
conversion system (PCS) and energy storage section. PCS is used to
adjust the voltage, current, and other power characteristics of the
storage based on the load requirements. PCS may consist of two
separated units for charging and discharging with different characteristics. Energy storage section is the other part of EES that is
designated to contain the storage medium, e.g. water reservoirs in
PHS. Since PCS and energy storage units have inherent inefficiencies and losses, overall efficiency (AC-to-AC) of EES technologies is
defined by Eq. (1), in which Eout and Ein are output and input
electric energy, respectively.
Eout
ðkWh=kWhÞ
Ein
To provide a uniform framework for cost comparison of different
EES technologies, first, the scope of the cost analysis should be
agreed. There are two main approaches in the literature in studying
the cost of EES technologies: total capital cost (TCC) and LCC. In this
study, both cost analyses are explained with respect to EES systems.
3.2.1. Total capital cost (TCC)
TCC evaluates all costs that should be covered for the purchase,
installation, and delivery of an EES unit, including costs of PCS, energy
storage related costs, and balance of power (BOP) costs [104]. PCS costs
of the EES system are typically explained per unit of power capacity
(€/kW). Energy related costs include all the costs undertaken to build
energy storage banks or reservoirs, expressed per unit of stored or
delivered energy (€/kWh). In this manner, cost of PCS and storage
device are decoupled to estimate the contribution of each part more
explicitly in TCC calculations. For instance, turbo-machinery related
costs of a PHS with a certain power capacity can be addressed without
considering the construction cost of reservoirs, which itself is a
complex function of volume and geological characteristics of the site.
BOP costs can be expressed per unit of power (€/kW) or energy
(€/kWh), or a certain fixed amount depending on the technology
and application [104]. BOP includes costs for project engineering, grid
connection interface and integration facilities (e.g. transformers),
Table 1
Main items in initial capital cost analysis of EES [39,105].
3. EES technologies: characteristics and costs
ηsys ¼
3.2. Methodology in cost analysis of EES
TCC elements
Cost element
Example/Notes
Power conversion
system (PCS)
Power interconnections
Converter, rectifier,
turbine/pump (PHS)
Storage section
Containment vessels
Cabling and piping
Construction and
excavation
Balance of plant
(BOP)
ð1Þ
Kaldellis et al. [103] proposes separated efficiency calculations
for power output/input and energy output/input. In that case,
overall efficiency is the product of the two items. Fig. 1 illustrates
the main sections of a typical EES system and the associated
losses.
a
Battery banks, air tanks
(CAES)
Cavern, reservoir
Project engineering
Grid connection and
system integration
ESS isolation and protective Switches, DC brakes, and
devices
fuses
Construction managementa
Land and access
Buildings and foundationa
HVAC system
Air-conditioning and
vacuum pumps
Monitoring and control
Voltage and frequency
systems
control
Shipment and installation Applicable to other
costs
sections
Those not included directly in PCS and storage related costs.
Fig. 1. Main sections of EES systems and energy losses.
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
construction management including cost of land and accessibility, in
addition to other services and assets required that are not included in
the scope of PCS and storage related costs [39]. A summary of general
cost elements of TCC analysis is provided in Table 1. More technologyspecific cost components are addressed in the corresponding section
of each EES technology.
TCC can be calculated per unit of output power rating, presented as (C cap ) in Eq. (2). While C PCS , C BOP , and C stor represent
unitary costs of PCS, BOP, and storage compartment (€/kWh),
respectively, h is the charging/discharging time.
C cap ¼ C PCS þ C BOP þ C stor nh ð€=kWÞ
ð2Þ
Considering the discharge time of the asset, C cap can be
interchangeably presented per unit of power rating or storage
capacity (€/kWh). According to [9], cost per kWh per cycle offers a
better indicator for the cost evaluation of EES systems, as it also
accounts for the life cycle numbers of EES.
3.2.2. Life cycle costs (LCC)
From ownership perspective, LCC is more important indicator to
evaluate and compare different EES systems. LCC accommodates all
the expenses related to fixed operation and maintenance (O&M),
variable O&M, replacement, disposal and recycling, in addition to
TCC. LCC can be presented in levelized annual costs (€/kW yr), which
is the yearly payment that the operator should maintain for all
services of EES, including repayment of the loan and upfront of the
capital costs. Schoenung and Hassenzahl [106] propose the term
revenue requirement in (¢/kWh), which can be used by an energy
supplier to calculate all the operating and ownership costs required
for discharging each unit of stored energy in kWh.
In some other studies, the price of electricity (and natural gas
for CAES) is excluded from cost analysis of EES. For instance,
Poonpun and Jewell [107] suggest a methodology to calculate the
added cost by storing electricity. LCC calculations can be performed,
first, by annualizing TCC ðC cap Þ, presented by (C cap;a ) in Eq. (3).
Based on the present value of money the capital recovery factor
(CRF) is calculated by applying Eq. (4), subject to the interest rate
(i) during the lifetime (T) [108].
C cap;a ¼ TCC CRF ð€=kW yrÞ
CRF ¼
ð3Þ
ið 1 þ iÞ T
ð4Þ
ð 1 þ iÞ T 1
Total annual O&M costs (C O&M;a ) can be expressed by adding
annualized costs of fixed O&M (C FOM;a ), and variable O&M (C VOM )
multiplied by yearly operating hours, as presented in Eq. (5).
C O&M;a ¼ C FOM;a þ C VOM n h
ð€=kW yrÞ
ð5Þ
The price of electricity, as well as fuel costs (for CAES system)
can be included in variable O&M costs or separately addressed.
Number of discharge cycles per year (n) is one of the applicationbased parameters in cost calculations. To accommodate the
replacement costs for replaceable EES systems, e.g. batteries, the
future cost of replacement (C R ) in €/kWh and replacement period
(t) in years should be known. Annualized replacement costs (C R;a )
can be calculated by using Eq. (6), given the number of replacements (r) during the application lifetime [106].
!
r
X
CR h
ð1 þ iÞ kt C R;a ¼ CRF ð€=kW yrÞ
ð6Þ
Zsys
k¼1
Discharge time ðhÞ and overall efficiency (ηsys ) are given for one
full cycle at the rated depth of discharge (DoD) of the batteries. All
the losses during the charge, discharge, and other losses in storage
part due to self-discharge and DoD should be reflected in the
overall efficiency. Disposal and recycling costs ( C DR ) are other cost
items that are usually neglected in the LCC analysis of EES in the
573
literature. Annualized disposal and recycling costs ( C DR;a ) can be
calculated by applying interest rate (i) and lifetime of the plant (T),
as explained in Eq. (7)
i
ð€=kW yrÞ
ð7Þ
ð1 þiÞ T 1
The annualized LCC costs (ALCC) of EES systems, presented by
C LCC;a in Eq. (8), is determined by stacking the previously discussed
cost items.
C DR;a ¼ C DR C LCC;a ¼ C cap;a þ C O&M;a þ C R;a þ C DR;a
ð€=kW yrÞ
ð8Þ
The levelized cost of electricity (LCOE) delivered by EES systems
can be then calculated by applying Eq. (9), knowing the annual
operating hours of the system in question.
LCOE ¼
ALCC
C LCC;a
¼
ð€=kWhÞ
yearly operating hours n h
ð9Þ
If the cost of charging electricity would be deducted from the
LCOE delivered by EES, the net levelized cost of storage (LCOS)
itself can be realized (Eq. (10)). This way, the cost of employing EES
can be calculated despite the price of electricity, which is inherently market-specific.
LCOS ¼ LCOE price of charging power
ð€=kWhÞ
overall efficiency
ð10Þ
The majority of the references in the literature have reported the
costs of EES based on TCC, for instance [8,9,109]. This may be based
on the notion that LCC analysis cannot be adequately established in
the absence of long-term utilization and field experiences for the
majority of EES technologies. For instance, useful lifetime and
replacement costs of emerging battery technologies in large-scale
applications are unclear and different from different suppliers. Moreover, O&M costs are heavily depended on the operational regime of
the EES system, e.g. charge/discharge cycles per day and DoD.
3.3. Methodology in review and collection of cost data
Estimating the cost of EES systems includes levels of uncertainty
and complexity. Except of some mature technologies, the use of
large-scale EES systems is scarce and the economic performance of
the existing sites is not widely reported in the literature. The cost
data are scattered, from different times and power markets, and
calculated/estimated based on different methods. Since most of the
EES technologies are in the early stages of development and
demonstration, their cost data cannot be conveniently scaled for
the larger or smaller sizes. For those cost data that are merely
reported based on the power rating of EES, the comparison and
generalization may entail errors, as the storage size can be different
for the same power rating. In this study, the authors have collected
the cost data that are accompanied with required technical data, e.g.
storage size, efficiency, and lifetime. Those reports not applicable to
grid-scale services, e.g. small-scale batteries, are excluded from cost
analysis. The cost figures are grouped with respect to the corresponding technical configuration of each EES system. For instance, the cost
of underground and aboveground CAES is reported separately.
Publications that provide cost estimations of EES systems
comprise a wide range of different approaches and tools. While
some publications [110,111] provide the cost data based on the
inputs from vendors and manufacturers, others [12,112] present
the cost data based on the review and update of different sources.
While TCC are reported frequently, the LCC of EES systems is
studied in a more limited number of publications, for example
[14,105,107,111,113], resulting in fewer samples for the calculation
of O&M and replacement costs. The data for the capital cost are
combined from both reference groups resulting in more samples.
The authors have endeavored to evaluate the source of the cost
574
Table 2
Different publications examined in this study for analyzing the capital cost and LCCs (life cycle costs) of electricity storage systemsa.
Affiliation/
country
Reference
ID
EES technologiesb
Abrams et al. (2013)
California
Energy
Commission/
USA
[114]
Lead–acid, Li-ion,
flywheel
Akhil et al. (2013)
DOE-EPRI/
USA
[111]
[115]
Auer and Keil (2012)
DB
Research/
Germany
Future
cost
estimation
Applicationc
Sensitivity
analysis
Probabilistic
model
Method for cost
estimation
Notes
– Total capital cost
(per unit of power
and energy)
– O&M costs
No
T&D support and
investment
deferral, RES
integration
No
No
Data from manufacturer
and review, analyzed by
their model
Develops an analytical cost-benefit
framework and tool for evaluating
both cost and benefits of EES
PHS, CAES, flywheel,
lead–acid, NaS, NaNiCl2,
Li-ion, Zn–Br, VRFB
– Total capital cost
(per unit of power
and energy)
– Cost of PSC and
storage
– Fixed and variable
O&M
No
Bulk energy
storage
No
No
Historical data from
plant operators and
developed modeling
framework
Including cost analysis framework
and modeling datasheets
PHS, CAES, hydrogen
(methane)
– Total capital cost
– Storage (operating)
cost
No
Bulk energy
storage
No
No
Model by DB Research
and information from
system operators
Analysis of power to gas routes. No
details about modeling approach
and cost elements
Cost items
Battke et al. (2013)
Swiss Federal [113]
Institute of
Technology
(ETH)/
Switzerland
Lead–acid, NaS, Li-ion,
VRFB
– Costs of PCS and
storage
– BOP cost
– O&M costs
No
Energy time shift,
T&D support,
frequency
regulation, userlevel storage
Yes
Yes
Data from review of
other studies and
manufacturers.
LCC modeling based on
uncertainty analysis
(Monte Carlo method)
Taking into account the uncertainty
in input data.
Assuming fixed values for cost of PCS
and O&M costs
Chen et al. (2009)
University of
Leeds/UK
[9]
– Total capital cost
(per unit of power,
energy, and cycle)
No
Energy storage
No
No
Review of extensive
references
No O&M costs
Connolly (2010)
University of
Limerick/
Ireland
[116]
PHS, CAES, flywheel,
lead–acid, NaS, NaNiCl2,
Ni–Cd, Li-ion, Zn–Br,
VRFB, PSB, SMES, SCES,
hydrogen
PHS, CAES, flywheel,
lead–acid, NaS, Ni–Cd,
Zn–Br, VRFB, PSB, SMES,
SCES, hydrogen
– Cost of PCS and BOP No
– Cost of storage part
– Fixed and variable
O&M
Bulk energy
storage, T&D
support, RES
integration
No
No
Review of other
references
A descriptive review of the costs
Danish Energy Agency
(2012)
Energi
styrelse/
Denmark
[117]
PHS, CAES, NaS, VRFB,
hydrogen
– Capital cost of
pump part
– Total capital cost
– Fixed O&M costs
Energy time shift
ancillary services
No
No
Based on different
sources and references
Grid related costs are not included
Díaz-González et al. (2012)
Energy
Research
Inst./Spain
[12]
PHS, CAES, flywheel,
lead–acid, NaS, Ni–Cd, Liion, Zn–Br, VRFB, PSB,
SMES, SCES, hydrogen
PHS, CAES, flywheel,
lead–acid, NaS, Li-ion,
VRFB, SMES
No
– Total capital cost
(per unit of energy)
Energy storage
related to wind
integration
No
No
Review of other
references
No O&M costs
– Total capital costs
No
Bulk energy
storage
No
No
Review of other sources
The market potential of EES in
the future
PHS, CAES, flywheel,
lead–acid, NaS, Ni–Cd, Liion
– Capital cost of
storage unit
No
Energy storage
No
No
From plant operators
Cost of PCS is not included in TCC.
No O&M costs
– Total capital cost
(per unit of power
No
No
No
From vendors and
operators
Include project contingency,
substation and interconnection costs
Energy Research Partnership ERP/UK
(ERP) (2011)
[53]
Electricity Storage
Association (2013)
ESA/USA
[5]
Electric Power Research
Institute (EPRI) (2010)
EPRI/USA
[110]
Yes up to
2050
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
Author (publisher), date
Renewable
integration/energy
time shift
[118]
PHS, CAES, battery, flow
battery
Yes
– Total capital cost
(per unit of power
and energy)
– Fixed O&M costs
– Variable O&M costs
Transmissionconnected bulk
energy storage
Yes
No
Using EPRI Energy
Storage Valuation Tool
(ESVT)
Inputs to the tool are provided by
California Public Utility Commission
(CPUC) with support from energy
storage and utility stakeholders
European Association for
EASE-EERA/
Storage of Energy (EASE) EU
and the European
Energy Research Alliance
(EERA) (2013)
[119]
PHS, CAES, flywheel,
lead–acid, NaS, Ni–Cd, Liion, VRFB, SCES,
hydrogen
Yes up to
– Total capital cost
(per unit of energy) 2030
and other
investments needed
for pilot plants
Long- and shortterm energy
storage
No
No
A study related to the
future market, technical,
economic, and societal
aspects of EES
Prospects of EES in 2020 and 2030.
Costs are not provided for all the
technologies
Evans et al. (2012)
Macquarie
University/
Australia
[4]
PHS, CAES, flywheel,
lead–acid, NaS, Ni–Cd,
ZEBRA, Li-ion, VRFB,
Zn–Br, Fe–Cr, SMES, SCES
– Total capital cost
(per unit of power
and energy)
No
Energy storage
No
No
Review and summary of
other references
No O&M costs
Hittinger et al. (2012)
Carnegie
Mellon
University/
USA
[120]
flywheel, NaS, Li-ion,
SCES
– Cost of PCS and
storage
– Total capital cost
– Fixed O&M costs
No
Frequency
regulation, peak
shaving, wind
integration
Yes
No
Review of other sources
Prioritizes the effective parameters
in LCC for each technology
Inage (2009)
International [121]
Energy
Agency (IEA)/
OECD
Institute for
[122]
Energy and
Transport/JRC
European
Commission
National
[14]
Technical
University of
Athens/
Greece
PHS, CAES, flywheel,
lead–acid, NaS, Ni–Cd, Liion, SMES, SCES
– Total capital cost
(per unit of power)
No
Power quality, bulk No
energy storage
No
Review of other sources
and manufacturer
reports
Develops an analysis for simulating
storage in power systems
PHS, CAES, flywheel,
lead–acid, NaS, Ni–Cd,
ZEBRA, Li-ion, VRFB,
Zn–Br, SMES, SCES,
hydrogen
CAES, hydrogen-fuel cell
– Total capital cost
(per unit of power
and energy)
No
Energy storage
No
No
Review of other
references
– Total capital costs of No
electrolysis, storage,
and fuel cells (PEM)
– O&M costs
– Replacement costs
Energy storage in
island areas
No
No
From technology
developer and
manufacturers
Kintner-Meyer et al. (2010)
Pacific
[112]
Northwest
National Lab./
USA
PHS, NaS, Li-ion
Yes
– Total capital cost
(per unit of power)
– Fixed O&M costs
– Variable O&M costs
Energy arbitrage,
balancing services
Yes
No
Review of other sources Including the combination of PHS
and cost analysis
with other technologies when
framework developed by needed for balancing services
authors
Kintner-Meyer et al. (2011)
Pacific
[123]
Northwest
National Lab./
USA
PHS, NaS, Li-ion
– Cost of PCS and BOP No
– Total capital costs
– Fixed and variable
O&M costs
Power balancing
No
No
Review of other sources
Lund and Salgi (2009)
Aalborg
University/
Denmark
CAES (underground)
No
– Total capital cost
– Fixed O&M costs
– Variable O&M costs
No
No
Typical costs for
compressor and turbine,
in combination with
[125,126]
Costs are for one plant with
different capacities for compressor
and turbine
Poonpun and Jewell (2008)
Wichita State [107]
University/
USA
PHS, flywheel, lead acid
(VRLA), NaS, Zn–Br, VRFB
– Costs of PCS and
storage
– Cost of BOP
– Cost of fixed O&M
and
replacement costs
Renewable
integration, power
regulating,
generation
capacity
Bulk energy
storage, T&D
applications
Yes
No
Review of other sources
and manufacturers
Sensitivity of the costs to the size
and cycle number
Electric Power Research
Institute (EPRI) (2013)
Joint Research Center (JRC)
(2011)
Karellas and Tzouganatos
(2013)
EPRI/USA
[124]
No
No life cycle costs for CAES
575
and energy)
– Fixed O&M costs
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
PHS, CAES, flywheel,
lead–acid, NaS, Li-ion,
Zn–Br, VRFB
576
Table 2 (continued )
Affiliation/
country
Reference
ID
EES technologiesb
Schoenung (2011a)
Sandia
National
Laboratories
(DOE)/USA
[105]
PHS, CAES, flywheel,
lead–acid, NaS, Ni–Cd, Liion, Zn–Br, VRFB, SCES
Schoenung (2011b)
Sandia
National
Laboratories
(DOE)/USA
[128]
Sioshansi et al. (2011)
National RE
lab., Uni of
Ohio/USA
Steward et al. (2009)
Tan et al. (2013)
a
b
c
Future
cost
estimation
Applicationc
Sensitivity
analysis
Probabilistic
model
Method for cost
estimation
– Capital cost of PCS,
BOP and
storage unit
– Fixed O&M costs
– Replacement costs
No
Bulk energy
storage
Yes
No
In combination with
Includes cost analysis for variable
[104,106,127] provides
speed PHS
an analytical framework
for LCC of energy storage
Hydrogen (also an
update for other
technologies)
– Capital cost of PCS,
BOP and
storage unit
– Fixed O&M costs
No
Bulk energy
Yes
storage, distributed
generation, and
wind integration
No
With review of other
Includes cost of storage tank and
sources develops a
underground storage for hydrogen.
framework for analysis of Analysis of wind curtailment costs
LCC
[45]
PHS, CAES
No
– Total capital cost
(per unit of power)
– Variable O&M costs
– Cost of upgrade
Arbitrage/capacity
payment
Yes
No
Authors' model, other
sources
Considers the effect of storage on the
system prices
National RE
Lab. (NERL),
DOE/USA
[129]
PHS, CAES, NaS, Ni–Cd,
VRFB, hydrogen (fuel cell
and gas turbine)
– Capital cost of PCS Yes
and storage unit
– Fixed O&M costs
– Variable O&M costs
– Replacement costs
Bulk energy
storage
Yes
No
Review of other sources,
a holistic analysis of
hydrogen-based systems
Includes different gas-to-power
systems for hydrogen, namely fuel
cells and gas turbines
Examines tank and caverns for
hydrogen storage
Shandong
University/
China
[130]
Flywheel, lead–acid, NaS,
SMES, SCES
No
– Total capital cost
(per unit of energy)
Power quality in
microgrids
No
No
Review of other sources
No LCC costs
No power rating
Cost items
Those publications that report the cost data of one single EES technology are not included in this table, but reported in each technology's subsection.
Those that are among examined technologies in this study.
Based on the terminology used in the reference.
Notes
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
Author (publisher), date
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
data, based on the credit of the report and reliability of the
estimation method, as well as tools, models, and estimation
procedure used in each reference.
Those publications that are reviewed in this study for collecting the
cost data of different EES systems are listed in Table 2. The main cost
items that each publication has covered, as well as their methodology,
sensitivity analysis, probability analysis based on the uncertainties, and
the inclusion of estimations for the future costs are also compared. The
efforts are made to avoid referencing those studies that are merely
citing some other available contributions, without adding new information. In some cases that a secondary reference (e.g. a review article)
adds more data to the original reference or reports some references
that were not accessible, those new additions are individually examined (if possible) and the secondary reference is also listed in Table 2.
In general, estimating the cost of EES involves both analysis and
judgment. The cost figures that are relatively unreliable, outlier, and
outdated are averted. For the sake of brevity, those publications with
the cost data of a single project are not listed in Table 2, while taken
into account in the analysis process.
3.4. EES technologies and related costs
In this section, EES technologies are briefly reviewed with
regard to their main techno-economic characteristics relevant for
LCC analysis. The reported costs in this study are in their original
values in the text body followed by their equivalent euro, if
needed. However, the results and values presented in the tables
and calculations are converted to euro and inflation-adjusted.2
3.4.1. Mechanical energy storage systems
3.4.1.1. Pumped hydroelectric storage (PHS). With a total installed
capacity of over 125 GW, PHS3 represents 3% of the total installed
electricity generation capacity in the world and 99% of the electricity
storage capacity [110]. PHS is the only commercially-proven, largescale EES with no additional fuel needs. PHS is characterized for large
power capacity (100–2000 MW), long lifetime, relatively long
discharge time, and high efficiency. This has favored PHS over other
alternatives for bulk energy storage, from daily energy time shift to
seasonal storage. PHS's duty can be extended to include ancillary
services, e.g. frequency regulation, in turbine mode. The use of variable
speed pumping may introduce new capabilities and flexibility for
ancillary services in charging phase as well (pump mode). In a study
by EPRI [132], the profitability of PHS is examined by considering
multiple services, including energy arbitrage, frequency regulation,
and spinning and non-spinning reserve capacity.
While installation of new PHS plants inclined in early 90s due to
the environmental concerns and scarcity of favorable sites, new
projects are again proposed after recent projections for the future
development of RES and free electricity markets. For instance in the
EU, there were 7.4 GW proposed projects for the period of 2009–
2018, increasing the total installed capacity of PHS by 20% in
the region. In the US, the new project proposals were 30 GW by
2009 [45]. Despite its relatively low generating costs, PHS has shown
to be capital intensive. Prior to the required capital investment;
siting, environmental impacts, permitting, land demand, project
contingency, and long-lead construction time of PHS plant are the
main barriers in further adoption of PHS in the power systems.
Recent technological advances and new tools might further ease the
discovery of potential sites for PHS in the future [133].
2
Annual inflation rate of 2.45% (based on the average rate of the EU for the
period of 2003–2013), 1d ¼ 1.21€, and 1€¼ 1.34$ are applied throughout this study
[131].
3
In this study, PHS mainly refers to the pure PHS (also called off-stream or
closed-loop), in which the output power is solely generated by returning the
pumped water back to the lower reservoir, river, or sea.
577
To address the above-mentioned challenges, some of new PHS
projects are proposed incorporating innovative solutions, e.g. making
PHS reservoirs as wastewater treatment storage bodies [134], using
piston-floated mechanism in an underground water-filled shaft [135],
undersea PHS connected to offshore wind plants [136], water-filled
balloon under pressure caused by sand [117], and underground PHS.4
In a study by Pickard [137], the feasibility of underground PHS is
examined from technical, economic, and environmental aspects,
indicating that excavation costs comprise 82% of the TCC for such
systems. Based on [134], 25% of the permitted PHS projects in the US
by 2010 are those with at least one underground reservoir.
The construction and installation costs of PHS are estimated to
be as twice as conventional hydropower plants with similar
capacity, while operating costs are almost equal [117]. The cost
of PCS may increase by 30–40% by applying variable speed
pumping in PHS plants [138,139]. The main cost elements in the
construction of a PHS plant are listed in Table 3.
TCC is highly depended on topographical and geological characteristics of the examined site. The long lead time of PHS project
may result in the intensification of initial cost estimations. For
instance, despite the details in project contingency, the capital costs
of a 1000 MW upgrade PHS project was announced 810 M€ in 2009
[36], but was modified later to 1700 M€ in 2014 [140]. The project
contingency of PHS is deemed to be typically in the range of 10–15%
and the accuracy of cost estimations may vary between 20 and
þ25% [110]. There is no significant cost reduction forecast for the PCS
section as it already consists of mature technologies. Regarding
the storage reservoirs, the estimated costs vary more significantly,
from 10 $/kWh (7.5 €/kWh) [107] to 169 $/kWh (126 €/kWh) [111].
In addition to the specific features of the site, the cost of storage
depends on the plant size, 69 $/kWh (52 €/kWh) for a 14.4 GWh
plant while 103 $/kWh (77 €/kWh) for 11.7 GWh storage capacity
[111]. The results of this study show the cost of PCS of 513 €/kW and
storage cost of 68 €/kWh, on average. More details of the results can
be seen in Section 4.1 and Appendix A.
3.4.1.2. Compressed air energy storage (CAES). With two plants in
operation, CAES is the second commercially proven, large-scale EES,
after PHS. The configuration of CAES can be in the form of diabatic
(D-CAES), with the need for additional fuel in the expansion process,
or advanced adiabatic5 (AA-CAES). The overall efficiency of D-CAES is
approximately 42%, as for Huntorf power plant in Germany (with
320 MW power rating) [115]. The efficiency can be improved by 12%point with adding a recuperator to recover the waste heat from the
gas-fired expansion process as for McIntosh CAES plant in Alabama.
The recent research focus is mainly dedicated to AA-CAES, in which
the efficiency can reach to 70% with eliminating the supplemental gasfiring procedure. This process enhancement increases the cost of
AA-CAES by 30–40% compared to the conventional counterparts [115].
Concerning the storage unit for compressed air, underground
salt caverns, natural aquifers, and depleted natural gas reservoirs
are respectively the most cost-efficient options for capacities up to
several hundreds of megawatts (discharge time of 8–26 h) [110].
Aboveground CAES (typically a pressure vessel) may have capacities of 3–15 MW (with 2–4 h discharge time), at higher costs but
easier project implementation compared to the underground type.
The energy ratio6 of aboveground CAES is 0.79–0.81, which is
typically higher than underground plants with 0.68–0.75, implying further need for additional fuel. CAES can be built in power
4
In underground PHS, the lower (or both) reservoir is located deep under the
ground level.
5
In AA-CAES, the extra heat of air compression is recovered by a thermal
storage unit to heat up the air during the expansion process.
6
Energy ratio is the ratio of energy consumption (from additional fuel like
natural gas) to the output electric energy.
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Table 3
Cost elements of pumped hydropower storage (PHS), based on [138].
Cost items
Direct costsa
Indirect costsb
Other costs
Civil works (storage section)
Power station costs
Dams, spillways, water diversion, and embankments
Intakes
Surface penstocks
Vertical shaft
Horizontal power tunnels
Steel-lined tunnel
Electromechanical works (PCS)
Transmission works
Switchyard
Planning and investigation
Environmental studies
Licensing and permitting
Preliminary and final design
Quality assurance
Construction management
Administration
Transmission interconnections
Infrastructure upgrade
Initial charging energy (filling)
Pumping
Life cycle operation and maintenance
Time cost of money
Escalations
Interest during construction
Bank fees
Depreciation
a
b
Project contingency is considered 25% for PCS and civil structure, and 35% for underground works.
Indirect costs vary between 15 and 30% of the total direct costs.
Table 4
Estimating the share of each cost item for underground CAES, based on the data from [117,141,142].
Cost item
Cavern leaching
Construction and equipment
Cushion gas for one cavern
Total energy related capital costa
Compressor part and related
construction
Turbine section
Total PCS costsb
Balance of plantc (BOP) and other
costs
Total capital costsd (TCC)
Total
(%)
4
44
28
75
13
7
20
5
100
a
Based on the figures for a greenfield plant.
Assuming equal power rating for turbine and
compressor, otherwise the costs should be scaled
accordingly.
c
Including connections, transformers, regulation, and instrument.
d
The costs are based large scale plant, assuming 4 h discharge time.
b
ratings up to 2000 MW, with flexibility in input/output power,
depending on the storage capacity.
The costs of CAES can be conveniently divided into two main
sections: storage- and power-related costs. Storage-related costs
may be inexpensive if the cavern already exists. The costs of power
trains are generally as for the conventional gas turbine plants,
including turbine, compressor, and related ancillary equipment.
The share of main capital cost items of an underground D-CAES
plant are separately illustrated for power- and energy-related
parts in Table 4.
The LCC of a CAES plant is however highly depended on the
additional fuel costs, related emission costs, and charging electricity prices. The optimal economic operation of CAES in different
electricity markets has been subject to research in a broad range of
studies [125,143–147]. CAES is capable to provide different services including energy arbitrage, reserve capacity, and wind
integration based on the structure of the electricity market [148–
150]. CAES has a typical construction time of three years, 95%
availability, and 99% reliability at starting time. The geologically
appropriate formations in the service territory of the CAES
operator is however one of the project challenges. Yet the
conventional CAES relies on fossil fuels, an issue that can be
resolved by the implementation of AA-CAES or the use of biomass-
derived gas or hydrogen instead [151]. The cost of storing air in
geologic formations is compared with as for hydrogen in Table 7,
in Section 3.4.4.
Since all the equipment items used in CAES are established
technologies, no significant reduction in cost is expected for the
near future. As the key components and controls should be
verified for the second-generation CAES, a process contingency
of 10–15% is expected for such plants [110]. The project contingency related to the site geology of underground CAES plants is
estimated to be 10%, which should be considered in project
planning and implementation. The review of publications listed
in Table 2 shows that the cost of storage may differ from 4 to 48
€/kWh, depending on the site and scale of the plant. Fixed O&M
costs are estimated in the range of 14 €/kW‐yr in [117] and [125],
while other references have estimations lower than 5 $/kW‐yr (3.7
€/kW‐yr) [110,111,118]. The results for the main cost elements of
CAES are summarized in Section 4.1 and Appendix A. The average
cost of PCS is in the range of 845 €/kW, while the storage costs
varies between 40 for aboveground and 110 €/kWh for underground storage, on average.
3.4.1.3. Flywheel energy storage. Flywheels are among mature
technologies that have been long used for different motorgenerator applications, e.g. as power buffer in electric vehicles.
Flywheels are fast-responding, in the scale of milliseconds, with
short duration discharge, in the scale of seconds to minutes, which
make them suitable for power-related services, including UPS,
frequency regulation, and integration of intermittent RES [110].
The most common application is to play role as a ride-through to
switch between different sources of power. Having relatively high
energy efficiency (typically higher than 85%), long life cycles
(hundred thousand discharges and more than 15 yr) regardless
the working temperature and depth of discharge (DoD), and lower
environmental impacts, favors flywheels over the conventional
batteries in similar applications [18].
With established manufacturing technology and suppliers,
flywheels can be scaled up to tens of megawatts for grid-scale
applications, e.g. a 20 MW frequency regulation plant in Stephentown, New York, by Beacon Power [152]. The use of flywheel in
hybrid energy systems, namely wind-diesel, is also examined in
the literature [18,153]. A wind-hydrogen plant in Utsira in Norway
is equipped with a 200 kW flywheel EES that can store 5 kWh
electric energy for a few seconds. Flywheels are also employed in
the sites with the need for 24/7 power availability, e.g. data
centers, to eliminate the power outages of several seconds or
bridging to the back-up systems [154]. Based on the spinning
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
speed, flywheels can be divided to low-speed (less than 6000 rpm)
and high-speed configurations, of which the latter is equipped
with more advanced material and machinery to increase the
overall efficiency.
The cost of high-speed flywheels, manufactured with magnetic
bearing, can be 5 times higher than low-speed types. As flywheel is
targeted for power related applications, it is more accurate to calculate
the costs based on the unit of power (€/kW) rather than energy
(€/kWh). In a study by DOE [155], the use of flywheel in combination
with a valve-regulated lead–acid (VRLA) battery is examined for UPS
services. The costs of the two systems are compared and concluded
that flywheel has 57% lower LCC compared to VRLA batteries, over a
20-yr lifetime for the same service. Flywheel's TCC (purchasing and
installation) is however 42% higher than VRLA batteries. The use of
electromechanical flywheels compared to electrochemical batteries
entails less environmental hazards and safety issues related to the
batteries, including spill containment, detection of hydrogen, eyewash stations, and ventilation requirements [18].
A summary of the main LCC items related to flywheels are
presented in Table 5. While the goal is not to directly compare the
two estimations, the changes in the results due to technical and
financial assumptions seem to be remarkable. It should be noted
that these costs vary between different manufacturers and for
different applications. More particularly, the levelized costs of
stored energy may vary significantly based on the discharge time
of the flywheel [106]. Besides the cost of stored energy, O&M costs
show wider variations in the available estimations, while cost of
PCS stands around 300 €/kW. The replacement costs of high-speed
flywheel vary between 85 and 215 €/kW, given the period of 10 yr for
the replacement. For further details about the costs, see Section 4.1
and Appendix A.
3.4.2. Electrochemical battery energy storage
Rechargeable (secondary) battery energy storage (BES) comprises a wide range of technologies based on the material used in
electrodes and electrolytes, and the functioning system. Since the
purpose of this study is not to discuss the working principles and
pure technical features of each BES technology, further details in
this respect can be seen in [9,15,16,158–161]. BES systems covered
in this Section are both conventional (e.g. lead–acid, NaS, Ni–Ca)
and flow batteries (e.g. VRFB, Zn–Br) with focus on their stationary, utility-scale applications.
Table 5
A sample for main cost elements of flywheel energy storage, the range of 100300 kW (based on the data from [156,157]).
Cost item
Purchase cost (€/kW)
Installation cost (€/kW)
Total capital costs (TCC) (€/kW)
Bearing replacement costc (€/kW-yr)
Vacuum pump replacementd (€/kW-yr)
Fixed O&M (€/kW-yr)
Variable O&M (€/kW-yr)
Stand-by power consumption cost (€/kW-yr)
Make-up energy (€/kW-yr)
Annualized life cycle costs (ALCC) (€/kW-yr)
Application
UPSa
Area regulationb
290
19
309
1-3
0.7
5
–
0.5
–
38
–
–
1255
–
–
11.6
10.1
–
5.7
257
a
Costs are for uninterruptible power supply (UPS) application, 20 yr lifetime,
and 6% discount rate.
b
Costs are for area regulation application, 10 yr lifetime, 10% discount rate, and
present worth factor 0.2.
c
Considering 5 yr replacement period.
d
Considering 7 yr replacement period.
579
3.4.2.1. Lead–acid battery. Lead–acid batteries are the oldest form
of BES with established record in a wide range of applications.
Lead–acid batteries have been a common choice in microgrids or
isolated power systems, for power quality, UPS, and spinning reserve
applications [9]. Their limited life cycles ( 2500), short discharge
time, and low energy density ( 50 Wh/kg) make them not favorable
choice for energy time-shift purposes. However, large lead–acid
batteries with discharge time of hours are in operation, e.g. in Chino
project, California, with a power capacity of 10 MW and 4 h discharge
time [158]. New advances in lead–acid battery's configuration has
offered improved characteristic for the utility scale applications.
Advanced VRLA (valve-regulated lead acid) batteries equipped with
carbon-featured electrodes can reach 10 times longer life cycles
compared to the conventional ones [16].
Lead–acid batteries are among low cost EES systems. While lead
prices are directly influential in final prices, the cost varies widely
from different suppliers, depending on the configuration design, duty
cycles, and design lifetime [110]. Moreover, battery's temperature
should be kept in limits specified by the supplier ( 5 to þ40 1C)
otherwise it suffers from significant degradation in expected lifetime,
entailing extra operating costs [155]. The power related and BOP
costs of VRLA are estimated to be in the same range as flooded cell
(conventional) lead–acid batteries, but the storage compartment has
25–35% higher costs [162]. The main cost elements of advanced lead–
acid batteries are depicted in Section 4.1 and Appendix A. It should be
noted that the costs are related to those systems suitable for bulk
energy storage or T&D support services, with discharge time of
approximately 4 h. The costs for frequency regulation services are
mainly similar to those presented in [110]. The range for the
estimation of fixed O&M costs is between 3.2 and 13 €/kW‐yr, and
PCS costs are expected as of 322–400 €/kW.
3.4.2.2. Sodium–sulfur (NaS) and sodium–nickel–chloride batteries
(NaNiCl2). NaS batteries have been developed by NGK Insulators
and Tokyo Electric Power since 1987. The batteries are one of the
most proven electrochemical storage technologies in MW scale,
with projected total installations of 606 MW by 2012 [110]. NaS
batteries have shown capabilities in power quality applications
and power time shift, with relatively high overall efficiency (75–
85%), 2500–4500 life cycles, expected lifetime of 15 yr, and
discharge time up to 7 h [12,163]. The power rating is scalable,
promising more utility-scale demonstrations in the future. A largescale, 300 MW project was to be delivered to Abu Dhabi Water &
Electricity Authority in UAE [16], but later moderated to 60 MW.
As of Jan 2014, the largest NaS project (by NGK Insulators) is a
70 MW (490 MWh) battery ordered by the Italian transmission system
operator (TSO), Terna S.p.A [163]. With regard to the initial price of this
contract, a total capital cost of 1430 €/kW (204 €/kWh) is estimated for
NaS batteries, in projects with tens of megawatts scale. NaS battery
projects are reported to bear a project contingency of 1–5% depended
on the site conditions [110]. The cost data of NaS batteries show a
relatively higher consistency in the literature as they are mainly
supplied by one manufacturer. Based on the review performed in this
study, the levelized costs of PCS and storage section are on average
366 €/kW and 298 €/kWh, respectively. The main cost elements for
NaS batteries are summarized in Section 4.1 and Appendix A.
Similar to NaS, sodium–nickel–chloride batteries, known as ZEBRA
(Zero Emission Battery Research), are high-temperature batteries
(270–350 1C), in which nickel chloride is employed as the cathode
instead of sulfur [164]. They have been commercially available since
about 1995 and have been successfully employed in several mobile
applications. The focus of research is nowadays in developing
advanced ZEBRA batteries, with enhanced energy densities for
load-leveling and integration of variable RES. The cost of ZEBRA
batteries are not well established, but estimated by several vendors
for MW-scale applications. For instance, the capital cost may vary
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between 2800 and 4300 $/kW (2100–3200 €/kW) for projected installed power rating of 50 MW scale [111]. A typical lifetime of 8–10 yr
and 2600 life cycles are considered for this type of batteries.
This domain of lifetime favors ZEBRA batteries for distributed power
applications. The main cost items of ZEBRA batteries are presented in
Section 4.1 and Appendix A (PCS cost of 470 €/kW and storage cost of
510 €/kWh, on average). In general, more research is needed to
address the energy density and environmental issues of Na-ion
batteries for their large-scale adoption in the grid-scale services
[165,166].
3.4.2.3. Nickel–cadmium battery (Ni–Cd). Ni–Cd7 batteries are
among the oldest BES technologies that are further developed since
1990s. They offer relatively high energy density (55–75 Wh/kg), low
maintenance need, and life cycles between 2000 and 2500. The life
cycle is highly depended on DoD so that it can reach 50,000 cycles in
10% DoD [12]. Ni–Cd batteries have served in different applications
from power quality and emergency reserve to telecommunication
and portable services. The world's largest Ni–Cd battery, and the US
largest BES, has been in operation since 2003 in Fairbanks, Alaska
(USA), with power rating of 27 MW (15 min discharge time) capable
to boost to 40 MW (7 min) [16].
The major drawbacks of Ni–Cd are relatively high capital costs
(see Section 4.1 and Appendix A) and the problems in disposal
handling associated with the toxicity of the heavy metals (Ni and
Cd) [7]. It is also reported that the memory effect, susceptibility to
overcharging, and relatively low efficiency can be other limiting
barriers subject to more improvements in the future [160].
3.4.2.4. Lithium-ion battery (Li‐ion). The first commercial Li-ion
batteries were produced in early 1990s. They were first targeted for
portable applications but were employed in grid-scale, stationary
applications as well. High energy density ( 200 Wh/kg), long
lifetime ( 10,000 cycles), and relatively high efficiency (0.85–0.90)
have offered sufficient motives for the development of these batteries
[28]. The largest Li-ion EES serves at the Laurel Mountain Wind Farm,
in Moraine, Ohio supplied by AES Energy Storage [167]. The project is
an advanced Li-ion storage with the power rating of 32 MW (8 MWh
storage), targeted for enhancing the wind power plant's capability in
providing capacity services and grid stability for the PJM electricity
market. In general, the future perspective seems to be promising for
Li-ion batteries in grid-scale applications as the final price is declining
and the functionality is ever improving by optimizing manufacturing
costs, extending the lifetime, using new materials, and improving the
safety parameters [168]. It is estimated that the share of Li-ion
batteries in the market reaches 35 GWh by 2015, providing
frequency regulation and power quality services [110]. The results of
cost analysis show relatively consistent figures for PCS costs with 463
€/kW, including BOP cost of 80 €/kW on average (see Section 4.1 and
Appendix A for full details).
3.4.2.5. Flow batteries. Flow batteries store energy in the electrolyte
solutions, opposite to the conventional BES in which the electrodes
are responsible for this task. Hence, the ratings of power and energy
can be designed independently: energy capacity is determined by the
quantity of electrolyte stored in external tanks while power rating is
designed based on the active area of the cell compartment. It makes
flow batteries favorable for both energy and power related storage
7
Ni–Cd batteries are equipped with nickel hydroxide and cadmium hydroxide
as positive and negative electrode plates, respectively, and an alkaline-based
electrolyte [9].
applications, maintaining a high rate of discharge time up to 10 h
[9,168]. With relatively low energy density (10–75 Wh/kg) [109],
limited operating temperature range (10–35 1C) [168], and high
capital costs, VRFB8 are yet to be commercialized for grid-scale
applications. However, their flexibility in discharge time, power
rating, and energy capacity in addition to their long lifetime
(þ13,000) [117], motivates further research for developing VRFBs.
The largest reported VRFB is a 3 MW (16 min discharge time) unit at
Sumitomo's Densetsu Office, in Osaka, Japan, targeted for peak shaving
[16]. The breakdown of the cost elements of VRFB systems is
presented in Table 6, indicating en equal share for PCS and storage
costs. For MW-scale projects, a process contingency of 5–8% and
project contingency of 10–15% may increase the TCC. However, it is
estimated that the increase in the cost of flow batteries would be less
size-dependent compared to the conventional counterparts.
The R&D on new configurations and materials has attracted a
wide attention and plays a key role in the cost reduction and
performance enhancement of the flow batteries [169–171]. In [172],
the feasibility and economic features of deep eutectic solvents is
compared to liquid-ion electrolytes. The results show that while deep
eutectic solvents imply higher capital costs, they are rather environmentally benign and biodegradable, offering a widely available
raw material with marginal environmental impacts.
The results of this study show that the cost of PCS is in the range
of 424–527 €/kW for VRFB systems. The cost of other types of flow
batteries are also examined in this study, namely zinc–bromine
(Zn–Br), iron–chromium (Fe–Cr) and polysulfide–bromide (PSB).
Despite having relatively low efficiency (0.6–0.65), Zn–Br batteries
offer higher DoD, approximately fully discharged [110]. For Zn–Br
batteries the recent estimations show the cost of PCS in the range of
151–595 €/kW, with the average of 444 €/kW. The storage cost and
replacement costs (after 15 yr) are approximately 195 €/kWh, for
bulk energy storage and T&D applications with 365–500 cycles per
year. Fe–Cr flow batteries are in the first stages of R&D. In [111], the
cost of Fe–Cr batteries is estimated based on the data provided by
one manufacturer. The estimations imply relatively lower capital
costs for this configuration compared to other types of flow batteries,
approximately 1100–1360 €/kW for MW-scale applications. The cost
of PCS for this system is 360 €/kW which is lower than other flow
batteries (more details in Section 4.1 and Appendix A).
Flow batteries may have lower costs in larger scales [174] and in
long discharge times (several hours) [175], compared to other battery
types. Flow batteries have also shown to have the minimum carbonequivalent emissions during their life cycle, compared to lead–acid,
flywheel, and superconductors [176]. For further details about
historical trends in research and development of different flow
battery technologies; chemistry of their electrolyte, electrode and
membrane components; and different materials, configurations, and
applications of flow batteries refer to [175,177,178].
3.4.3. Electric and magnetic energy storage
3.4.3.1. Capacitors and supercapacitors. Capacitors9 are the most
direct method to store electricity, offering fast-response with life
cycles of tens of thousands and very high efficiency. The low energy
density of conventional capacitors has led the research on SCES
(supercapacitor energy storage), with electrochemical double layer
capacitors (DLC) and pseudocapacitors as the main configurations
[179]. The main drawback of SCES relies on their short storage
duration, low energy density, and high self-discharge loss. They are
8
In VRFB, vanadium redox couples, V2 þ /V3 þ in the negative and V4 þ /V5 þ in
the positive half-cells, produce power by exchanging H þ through a hydrogen-ion
permeable polymer membrane [160].
9
A capacitor has two metal plates separated by a dielectric, which is itself a
non-conducting medium. If one of the plates is charged with DC electricity the
other plate is induced a charge with an opposite sign [9].
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
Table 6
Breakdown of the internal cost of a sample VRFB system, data extracted from [173].
VRFB's cost item
Share of total (%)a
V2O5 (solute)
Electrolyte manufacture
Tanks
Total storage costs
Activated carbon‐felt electrode
Bipolar current collector
Frame and associated components
Ion‐exchange membrane
Electrolyte storage tanks ( 2)
Pumps ( 2)
Control system
Total flow cell costs (equivalent to PCS)
Total internal capital costs
28
10
7
45
7
2
19
2
8
7
11
55
100
a
The values are given for a system with 1.75 m2/kW electrode area, and 6.0 kg/
kWh required amount of V2O5.
mainly employed in power quality services, including ride-through
and bridging [9]. Ongoing research on materials, e.g. nanostructured
materials [180], can promote SCES in grid-scale applications. The
capital cost of capacitors is reported to be in the range of 1100–1500
€/kW [121]. Supercapacitor are also good candidates to smooth the
short-term high frequency fluctuations caused by swell effects in the
marine current systems, especially for one generator plants [181].
More details about the performance and LCC of asymmetric lead–
carbon capacitors can be seen in [127].
3.4.3.2. Superconducting magnetic energy storage (SMES). A SMES10
system is capable to store energy in a magnetic field so that it can
be instantaneously discharged back, offering electricity storage in
a pure electrical format. SMES systems are characterized for a very
high energy storage efficiency ( 97%), fast response (few
milliseconds), and long life cycles (100,000) [12]. These features
make SMES system capable in providing power quality services for
industrial consumers, carryover energy during the voltage sags
and momentary power outages, and frequency regulation [59,182].
The typical power rating is in kW to several MW but research is
carried out to improve the power rating.
The main challenges in the utilization of SMES rely on high capital
cost and environmental considerations related to the strong magnetic
fields. Typical capital cost of SMES is reported in the range of 150–250
€/kW of power rating for power quality applications [9]. Based on
[121], the present value of the LCC of a 100 MW scale SMES can be
approximately 1500 €/kW of installed capacity, assuming 30 yr lifetime and grid stabilization services. In [183], the energy costs of two
different configurations are compared (solenoid and toroid), concluding that the cost of superconductors may reduce by 85% with
increasing the storage capacity from kWh to MWh scale.
3.4.4. Power to gas energy storage technologies
It is expected that with the increase in the share of intermittent
RES in power systems, the need for long-term EES systems
becomes more urgent. Gas storage systems, in the form of
hydrogen or synthesized methane, have high energy density and
marginal losses in long-term storage. Gas storage systems are also
compatible with the existing infrastructure for natural gas storage
and transmission, as well as conversion technologies. They can be
delivered in different types at consumption terminals, including
power, heat, and transportation fuel. In principle, hydrogen can be
10
A SMES system is a device that stores energy in the magnetic field, by
converting AC to DC current flowing through a superconducting wire in a large
magnet [20].
581
produced from water and then further converted to synthetic
methane by reacting with CO2. The process of power-to-gas
conversion, energy storage, and final energy utilization by means
of gas storage systems is illustrated in Fig. 2. Gas storage systems
offer the possibility for integrating the process of carbon capture
and storage (CCS) in an efficient energy storage and power
production system. In addition to power-to-gas storage systems
based on electrolysis, biogas production and storage can be
considered as a measure to increase both the flexibility of the
power system and share of bioenergy [184].
3.4.4.1. Hydrogen storage. Hydrogen energy storage is the process
of production, storage, and re-electrification of hydrogen gas.
Hydrogen is usually produced by electrolysis and can be stored
in underground caverns, tanks, and gas pipelines. Hydrogen can be
stored in the form of pressurized gas, liquefied hydrogen in cryogenic
tanks, metal hydride or in chemical compounds (ammonia, methanol,
etc.) [117]. The existing natural gas networks are capable to store
additional hydrogen up to 5% of their capacity, without significant
degradation in the performance [185]. This way, energy can be
transmitted and delivered in higher capacities (4.5 times more
than high-voltage transmission lines) while lower transmission
losses (1% in gas pipelines while 4% in power transmission lines)
[115].
Energy density of hydrogen (can be pressurized and stored in
200 bar) is as high as Li-ion batteries, which implies the need for
significantly smaller storage reservoirs compared to PHS and CAES.
The stored hydrogen can be converted back to the electricity by
fuel cells (compatible for mobile applications), gas-fired turbines,
or gas-fired engines [186]. Today, the relatively low overall
efficiency and huge capital costs are two major barriers in
commercial implementation of hydrogen-based storage in gridscale applications. Since the cost of each power production
method varies along with their advantages and requirements, it
is not an easy task to establish consistent cost estimation for
hydrogen-based systems. The capital cost of electrolysis itself
varies among different configurations, projected 590 €/kW for
solid-oxide electrolysis plants in 2020 [117]. These plants have
power-to-hydrogen efficiency of 98% and net electrolysis efficiency
of 83%, due to their heat demand. For alkaline electrolysis, the
capital costs are in the range of 1400 €/kW while maintaining
43–66% power-to-hydrogen efficiency. Polymer electrolyte membrane (PEM) electrolyzer cell offers the power-to-hydrogen efficiency of 68–72% and net efficiency of 88% due to heat production.
The cost of storage part heavily depends on the use of available
infrastructure, for example gas employing caverns or gas pipelines, or
building new facilities. In general, it is estimated that the cost
of aboveground storage section would be around 15 $/kWh
(11 €/kWh) [128], while for the underground caverns ranging from
0.002 to 49 $/kWh (0.002–0.41 €/kWh) [129]. Table 7 compares the
cost of geologic storage caverns for CAES and hydrogen per unit of
delivered energy.
In [117], the cost of a MW-scale hydrogen plant, comprising
cavern storage and gas internal combustion engine, is estimated as
of 3055 €/kW with 35% overall efficiency (AC-to-AC). In [14], the
capital costs, O&M costs, and replacement cost of hydrogen
systems including electrolyzer (700 kW), storage tank, and PEM
fuel cells (500 kW), is compared with a CAES plant. It is concluded
that hydrogen plant has higher O&M costs but lower capital costs,
2.97 M€ compared to 5.5 M€ for CAES (500 kWh storage capacity).
The integration of hydrogen storage in different power systems
and the associated advantages and drawbacks has also been
subject for different studies [187]. In [188], the total electricity
generation cost using hydrogen storage (solid and gaseous) is
estimated to be lower than that for the system without storage
back up. However, the economic feasibility of the use of hydrogen
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B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
Fig. 2. The pathway for power to gas energy storage (hydrogen and methane), conversion, and final utilization in different sectors [119].
Table 7
The cost of storing air (in CAES) and hydrogen in different underground formations
(data from [128], adjusted to 2014-€).
which an overview of different EES technologies and comparison
between their main technical characteristics are maintained.
Cavern formation type
Air storage
(€/kWh)
Hydrogen storage
(€/kWh)
4. Results and discussion
Natural porous rock formations from
depleted gas or oil sites
Solution-mined salt caverns
Dry-mined salt caverns
Abandoned limestone or coal mines
Geologic storage of hydrogen
Rock caverns from excavation of
impervious rock formations
0.10
0.002
4.1. Results of the review for individual cost items
1.01
9.71
9.71
N/A
29.55
0.02
0.14
0.14
0.25
0.41
This Section reports the main individual cost items of the EES
technologies comparatively. While the ultimate goal is not to
prioritize the EES systems based on their associated costs, this
comparison provides the reader with the scale of magnitude and
variation in cost of different technologies. The data are collected
and examined from the references listed in Table 2, as well as
other individual projects presented under corresponding subsections for each EES system in Section 3.
The results will be presented by statistical methods to account
for the variability and disparity of the cost estimations in the
literature, in addition to the magnitude of the most likely values.
Due to the limited number of reported data and relatively high
disparity, the use of parametric methods, e.g. probability density
functions, might entail relatively significant parametric assumptions and unrepresentative values affected by outliers. The results
usually show a deviation from normal distributions and the data
are usually skewed to the near end of each range. Hence, the fivepoint descriptive (non-parametric) method is employed to report
the results. The average values are the median of each range and
the interquartile range (IQR), or so-called middle-fifty range,
represents the spectrum that contains 50% of the reported cost
data. This way, the reported average (median) is unaffected by
extreme outliers [194]. The outliers are either lower than three IQR
minus the first quartile or higher than three IOR plus the third
quartile, and excluded from the reported ranges.
The cost of PCS for different EES technologies can be compared
in Fig. 3. The range of power rating for each EES system is shown in
Appendix B, while the more detailed costs can be found in each
technology's corresponding table in the Appendix A. As the results
reveals, there is a wide range of variability in the PCS cost of some
battery technologies (e.g. ZEBRA) in the literature. More importantly, the cost of power electronics of commercial and mature
technologies (PHS and CAES) is also rather inconsistent in the
reviewed publications. This cost variability intensifies the uncertainty in investigation of LCC, even for mature and wellestablished EES systems. CAES systems show the most expensive
PCS systems averaging 845 €/kW. The lower variation in the cost
data of PCS for aboveground CAES might be the result of fewer
sources of estimation for this configuration in the literature.
The cost of storage compartment of MW-scale EES technologies
applicable for energy-related applications is illustrated in Fig. 4.
storage is highly depended on other value streams applicable in
the future [189].
3.4.4.2. Methane synthesis and storage. Hydrogen can be further
converted to methane by reacting with CO2, increasing the stability
and energy density of the stored media, from 360 to 1200 kWh/m3 at
200 bar [115]. The existing natural gas infrastructure can conveniently
store, transmit, and convert the synthesized methane back to the final
form of energy use. With the losses of 18–25% during the methane
production process, it is estimated that the AC-to-AC efficiency of this
storage process would be only 33–40% with today's technologies [115].
The cost of methane synthesis from hydrogen is around 1000 €/kW
[190].
3.4.5. Other electricity storage technologies
There are other EES systems under R&D that are not studied in
this contribution due to the lack of information about their costs
and functionality, including nano-supercapacitors, hydrogen–bromine flow batteries, advanced Li-ion batteries, novel mechanical
energy storage systems (based on gravity forces). While not
examining the costs in details, the capital cost of metal–air
batteries (Zn–air) are, however, covered in this study.
The technical features that are important and influential in cost
analysis are presented in the Appendix B. It should be noted that
the goal of this study is not to analyze the technical features of EES
as there are adequate contributions in this regard in the existing
literature. However, to provide consistency between the cost data
and the corresponding technical data, the same references that are
examined in this study for the cost data of EES systems are
reviewed to extract the technical characteristics of each EES
technology. For further details about each technology, readers
can refer to, for example [4,6–9,12,110,111,115,117,191–193], in
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
583
Fig. 3. Cost of power electronics for EES technologies, including BOP costs where applicable, based on the review of the references listed in Table 2 (the average is illustrated
above each range). More details including PCS costs of hydrogen-based systems are shown in the Appendix A.
Fig. 4. Cost of storage part (e.g. tank, reservoir, or electrolyte compartment) for different EES systems after reviewing the publications presented in Table 2 (the average is
shown above each bar). Note: costs are given for the typical size of each technology (see Appendix A and B for further details).
The calculated levelized costs are for EES systems with different
discharge times and should be considered in comparisons. The
results are reported for typical size of each technology, e.g. 8 h for
PHS while 6–7 h for NaS batteries. For further details about the
discharge time of each EES system, see the Appendix B.
The results reveals a relatively high variabilty in the cost
estimations of the storage part in battery technologies. These cost
sparity even dominate the price gap among different technologies.
The variability of the storage costs is, however, considerably lower
for the mechanical EES systems, intrducing underground CAES as
the cheapest one (40 €/kWh). Considering the fact that the cost of
storage reservoirs of PHS and underground CAES is highly
depended on the geography and geology of the site, yet the
uncertainty in the associated costs is less than batteries. This can
be attributed to the limited experience in the production and
deployment of large-scale batteries for utility-level applications,
resulting in scattered and inconsistent cost data for these technologies. Fe–Cr battery as an exception has more consistent
results, which can be the result of acquistion of the costs from
one available source. The cost of storage section for those EES
technologies that are employed in power-related applications,
namely flywheel, SCES, and SMES, is not compared in Fig. 4.
Fixed O&M costs of EES systems are illustrated in Fig. 5. The
costs are mostly for energy-related and T&D support applications
(except for flywheel). As these applications demand a working
regime of one full cycle per day, the costs migth be higher for other
applications with more frequent chrge/discharge requirements, e.
g. frequency regulation services. For further details for numeric
values, see the corresponding subsection for each technoogy in the
Appendix A. The variable O&M costs of different EES systems are
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Fig. 5. Cost of fixed O&M for EES technologies based on the review of the references listed in Table 2 (the average is illustrated above each range). For further details and also
for variable O&M costs see Appendix A.
Fig. 6. Replacement costs of battery storage systems per unit of stored energy at rated DoD based on the references presented in Table 2 (average values are illustrated above
each range).
illustrated in Appendix A. The costs of purchasing power in
charging phase is not included in the estimations, as it directly
depends on the market and application of the asset. The range of
natural gas prices in the examined literature varies between 8 and
20 €/MWh, while the emission costs were between 18 and 22
€/ton CO2. Since the costs are stacked in some literatures, it was
impossible to extract the fuel and emission costs separately.
The replacement costs of batteries are compared in Fig. 6. The
lower range of replacement costs for Fe–Cr batteries is attributed to
the one supplier price range. It is very important to notice the
replacement time of each battery, reported in the corresponding
subsection in the Appendix A. For instance, while the replacement
cost of ZEBRA batteries is on average 182 €/kWh compared to 195
€/kWh for Zn–Br batteries, the replacement time is around 8 and
15 yr, respectively.
4.2. Total capital cost (TCC) of EES systems
In this section, the TCC of EES systems are comparatively
presented per unit of power rating and storage capacity. For
estimating the TCC, a wider range of studies were available,
compared to the segregated cost elements discussed previously.
In other words, TCC can be calculated whether from the costs of
PCS, BOP, and storage part by using Eq. (2)[105, 107], or be directly
obtained from the manufacturer/literature, per unit of installed
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
585
Fig. 7. Total capital cost (TCC) of large-scale EES systems per unit of nominal power rating based on the review of the references listed in Table 2 and applying Eq. (2) (the
average values above each bar and further details including storage size in Appendix A and C).
power [112], stored energy [12], or both [9,110]. The TCC of
different grid-scale EES systems per unit of power rating are
illustrated in Fig. 7. The costs are presented for the typical storage
size of each system, which is not primarily identical among
different technologies. For example, the TCC of PHS and CAES is
equivalent to 8-hour discharge time, while the TCC of lead–acid
and VRFB are for 4 h discharge. The number of technologies
examined in this Section is more than previous analyses, as SMES,
SCES, and Zn–air are also included.
The results indicate that underground CAES offers the lowest
capital costs (893 €/kW) for bulk energy storage systems, followed
by Ni–Cd and Fe–Cr batteries, 1092 and 1130 €/kW, respectively.
For power quality applications, SCES and SMES show the lower
costs, 229 and 218 €/kW, respectively. However, it should be noted
that the range of available literature for the latter two technologies
is rather limited and cannot be conveniently generalized to gridscale applications. ZEBRA batteries are the most expensive EES
systems with a TCC of 3370 €/kW. The range of the TCC of batteries
is rather wide, which implies more uncertainty in the estimation
of the costs for utility-scale systems. Comparing the TCC of
hydrogen based technologies show that hydrogen gas turbines
(GT) would cost rather half the hydrogen-fuel cells (FC) systems.
The TCC of ESS systems based on the unit of storage capacity is also
presented in Appendix C.
4.3. Life cycle costs (LCC) of EES systems and uncertainty analysis
The LCC of EES technologies can be determined by applying the
framework presented in Section 3.2.2, having TCC, fixed and
variable O&M costs, replacement costs, and disposal/recycling
costs11, if applicable. The LCC of EES systems is directly depended
11
Due to the lack of data for disposal and recycling costs, they are not
examined in this study.
on the characteristics of the service (e.g. number of cycles per
year), the power market (e.g. interest rate and price of power), and
technological features (e.g. DoD and replacement time for batteries). The LCC of EES systems can be presented in different ways,
including discounted cost items to the corresponding net present
value (€/kW) [129]; annualized costs throughout the lifetime of
the application (€/kW‐yr)[105]; LCOE discharged by EES (€/kWh)
[111]; or LCOS (€/kWh) [107]. LCC analysis is conducted for three
main applications: bulk energy storage (energy arbitrage), T&D
support, and frequency regulation. This way, in addition to
exemplifying the framework and cost data reviewed in this study,
the effect of uncertainties in LCC analysis of EES systems are also
examined. The main features of three application categories are
summarized in Table 8 and economic assumptions are shown in
Table 9.
Different studies have investigated the LCC of EES systems in
different markets and for various applications [128,162]. The
sensitivity of the LCC of EES systems to the discharge time, capital
cost, overall efficiency, and discount rate are also discussed in
details [118,120,127,129]. However, the systematic analysis of the
uncertainties in input data and assumptions related to LCC
calculations is rather rare in the literature. Neglecting or overlooking the uncertainties in input parameters decreases the level
of the accuracy of the results of LCC analysis [195]. Among the
examined references, Battke et al. [113] has considered the
uncertainties in input parameters, including cost of storage part,
overall efficiency, lifetime, and life cycle numbers in the analysis
of LCC of four battery systems. In addition to more input parameters for uncertainty analysis, this study extends the number of
examined technologies to include mechanical storage (PHS, CAES,
and flywheel), hydrogen-based, and more electrochemical BES
systems.
The review of the cost data of EES systems reveals that the
uncertainty in PCS costs, O&M costs, and replacement costs are
also considerable for some EES technologies (see Section 4.1).
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Table 8
Three common applications of EES systems and their requirements, data from [110,127,156].
Application type
(usage frequency)
Example of application
Power
rating
Response
time
Discharge
time
Cycles per
year
Application
lifetime
EES systems
Long-duration, frequent
Bulk energy storage,
energy arbitrage
Capacity credit, spinning
reserve,
T&D support
Frequency regulation,
RES integration, power
quality
þ10 MW
min
4–8 h
250–300
20
1–10 MW
s–min
0.5–2 h
300–400
15
o 0.25 h
þ 1000
10
PHS, CAES, lead–acid, NaS, Ni–Cd, VRFB,
Fe–Cr
CAES (aboveground), lead–acid, NaS,
ZEBRA, Li-ion,
VRFB, Zn–Br, Fe–Cr, Ni–Cd, hydrogen
Flywheel, lead–acid, Li-ion
Medium duration, fast
response
Short duration, highly
frequent
0.1–2 MW ms–s
Table 9
Economic parameters and assumptions for the analysis of LCC of EES systems.
Parameter
Value
Note
Average yearly inflation ratea
Discount rateb
Charging electricity priceb,c
Power price escalation rate
Fuel costs (natural gas)d,e
Fuel cost escalation rate
Carbon emission costse
2.5%
8%
50 €/MWh
0%
20–25 €/MWh
0%
8–22 €/ton CO2
[131]
Sensitivity performed
Sensitivity performed
Assumption
Uncertain parameter
Assumption
Uncertain parameter
a
Average of the EU inflation rate for the period of 2003–2013 [131].
Otherwise mentioned in the sensitivity analyses.
Based on average of wholesale prices in EU [131].
d
Based on wholesale prices in EU (2007–2012) [131].
e
Notice that these values are used in this study for uncertainty analysis and they are not necessarily consistent with those
reported in reviewed publications.
b
c
Fig. 8. Comparing the results of cost calculations with and without uncertainty analysis for VRFB (vanadium-redox) systems.
Therefore, this study examines all the cost segments in uncertainty
analysis, as well as overall efficiency, fuel and emission costs for
CAES, lifetime and life cycle numbers, and replacement period
(11 uncertain input parameters). Employing the Monte Carlo
method and MATLAB simulation tool, the uncertainty analysis in
LCC is conducted by assigning stochastic values for the uncertain
input parameters, from the corresponding ranges. This method is
recommended for the modeling of systems with varying several
deterministic input parameters simultaneously [196]. For the cost
items, the uncertainty in input data is examined for the corresponding interquartile ranges (IQR) presented in Section 4.1,
which represent the middle 50% threshold of the probable values.
For the remaining parameters, the uncertainty in input data is
stochastically selected from those ranges presented in Tables 9 and B.1
of Appendix B.
In Fig. 8, the distribution of the results of uncertainty analysis
for VRFB systems is illustrated, after 10,000 simulation runs. The
results of uncertainty analysis can be properly fitted by a normal
probability distribution, indicating a mean value of 706 €/kW‐yr
for this technology. However, without uncertainty analysis, the
ALCC of VRFB system is 663 €/kW‐yr, which shows 6.5% error and
it even does not lie in the one standard deviation (SD) threshold
of the corresponding probabilistic distribution after considering
uncertainties.
In general, conducting uncertainty analysis in LCC calculations
shows changes from 17% to þ39% compared to the results
obtained without considering the effect of uncertainties. For
instance, in calculating ALCC for lead–acid batteries, the results
with uncertainty analysis indicate differences of 17% in bulk
energy storage, 11% in T&D support, and þ13% in frequency
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
587
Fig. 9. The annualized life cycle costs (ALCC) of EES systems in bulk energy storage and related uncertainties, considering 250 cycles per year, 8% interest rate and 8 h
discharge time (see Tables 8 and 9 for other input parameters). The average values are shown above each bar.
Fig. 10. The annualized life cycle costs (ALCC) of EES systems and related uncertainty for T&D support applications, with 400 cycles per year, 8% interest rate and 2 h
discharge time (see Tables 8 and 9 for other input parameters).
regulation, compared to the results for the same technology
without uncertainty analysis.
For each of three applications mentioned in Table 8, the results
are first presented and discussed for ALCC (€/kW‐yr). The share of
capital cost, charging electricity costs, and O&M costs (including
replacement costs) are separately shown for each technology.
These results are presented by boxplots, indicating the range of
95% threshold, 68% likelihood or one standard deviation band
(1SD), as well as the average values (arithmetic mean). The
annualized ALCC of EES systems applicable for bulk energy storage
(energy arbitrage) are illustrated in Fig. 9. PHS offers the minimum
costs (240 €/kW‐yr) for this service, with relatively low inconsistency. The costs of fuel and emissions decrease the profitability
of CAES, while it is the cheapest technology in terms of capital
costs. The uncertainty in LCC of lead–acid batteries is rather high,
due to more dispersed input cost elements and diverse suppliers.
The ALCC of EES systems applicable for T&D support services
are shown in Fig. 10. While aboveground CAES surpasses other
technologies for this service with lower costs (160 €/kW‐yr), Li-ion
batteries show rather the highest costs (493 €/kW‐yr). The widest
uncertainty in LCC is however related to hydrogen–FC systems.
The replacement costs of batteries may impose extra costs that
make them unfavorable for highly frequent charge/discharge
Fig. 11. The annualized life cycle costs (ALCC) and its uncertainty for EES systems
applicable in frequency regulation and power quality services, 1000 cycles per year,
8% interest rate and max 15 min discharge time (see Tables 8 and 9 for other input
parameters).
services. Comparing those EES systems applicable for both examined applications, NaS and lead–acid show lower costs for T&D
support compared to bulk energy storage. This can be attributed to
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the lower discharge time requirements, shorter application lifetime, and consequently less operating cycles.
The ALCC of fast-responding EES systems applicable for frequency
regulation and similar services are depicted in Fig. 11. The results
indicate that flywheel offers lower LCC while having the highest
capital costs. Those technologies with higher number of life cycles
are more favorable for this service to avert the costs of replacement.
4.3.1. Sensitivity analysis
In this section, the dependency of LCC on interest rate, electricity
prices, and discharge time is examined. The results are presented in
levelized cost of electricity (LCOE) delivered by the EES technology.
This way, the grid operators and decision makers are able to compare
the power prices discharged by EES with those of other alternatives
relevant to the market in question. The electricity prices are varied in
the range of 0-100 €/MWh and interest rate from 6% to 10%. Zero
charging power prices are chosen to quantify the net internal costs
added by storing and discharging power through EES systems. The
authors suggested the term levelized cost of storage (LCOS) to account
for the net internal costs of EES systems without including the
influence of price of charging electricity (see Eq. (10)). The results for
the bulk energy storage are illustrated in Fig. 12, showing the LCOS
for different EES systems at the left end of each red bar
(upper sensitivity bar).
The results indicate that the LCC of those EES systems that are
subject to significant replacement costs during the lifetime are
more sensitive to the interest rate, e.g. Ni–Cd, VRFB and lead–acid.
The LCOE delivered by PHS, as the most cost-efficient technology is
in the range of 120 €/MWh, with charging prices of 50 €/MWh.
The LCOE discharged by EES systems in T&D support services and
the corresponding sensitivity analyses are shown in Fig. 13.
LCC shows wider sensitivity to the electricity prices for those EES
systems with relatively low efficiencies. For example, the LCOE for
hydrogen–FC increases from 480 to 600 €/MWh, if power prices rise
from 50 to 100 €/MWh. Aboveground CAES offers the most costefficient option with LCOE totaling 202 €/MWh for this application.
The sensitivity of LCC of EES systems for power quality and frequency
regulation applications is comparatively lower to power prices due to
the lower power consumption (Fig. 14). Since these technologies are
Fig. 12. Levelized cost of electricity (LCOE) delivered by EES systems in bulk energy storage and the sensitivity of LCOE to interest rate and electricity prices.
Fig. 13. Levelized cost of electricity (LCOE) delivered by EES systems in T&D support and similar services, and the sensitivity of LCOE to interest rate and electricity prices.
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
not relevant for energy-related service, the cost figures presented in
the sensitivity analysis are not in the form of LCOE.
The levelized costs (LCOE and LCOS) of EES systems are also
sensitive to the discharge time. The LCOS of different EES systems
applicable for bulk energy storage are illustrated in Fig. 15, for
different discharge times up to 8 h. The price of charging power is
not included in the costs to show the pure value of each EES
systems. The results indicate that the rank order of the batteries is
different based on the discharge time. For example, Ni–Cd shows
the lower costs compared to lead–acid and VRFB in one-hour
discharge time, while it is the most expensive EES system in
discharge times higher than 3 h. PHS offers the minimum costs
with the LCOS of 54 €/MWh. The LCOE of each EES system can be
produced by adding the cost of charging power to the LCOS values
shown in Fig. 15, subject to EES inefficiencies (see Eq. (10)).
589
The future cost of EES systems is also provided in some references,
predicting cost reductions due to further commercialization [112,117].
However, the wide usage of EES systems raises questions regarding
their environmental aspects [198], material intensity [199–201],
and other societal impacts [40,202]. The extensive employment of
EES systems can affect the electricity prices by smoothing the high
peaks and troughs, deteriorating the expected revenues. According to
Awad et al. [207], the clearing market price of electricity can be
furthermore influenced by the location (in power transmission versus
distribution level) and size (distributed versus central) of the EES
systems. Therefore, any holistic approach should consider the impact
of large-scale EES systems on the energy system to evaluate the
associated costs and potential benefits [203–206]. As the integration
of EES systems is linked with the large-scale penetration of variable
RES, the associated policy and market barriers should be addressed
by considering all the direct and indirect, system-level impacts of EES
technologies [208–210].
4.4. Discussion
5. Conclusions
While the cost of discharged power is not the only criterion for
the selection of EES systems, it compares different alternatives in
fulfilling the target services in a market, based on the associated
benefits [197]. The cost of pilot plants can be higher due to the
administrative expenses, as well as the lack of economy of scale.
Fig. 14. Sensitivity of ALCC to interest rate and electricity prices for EES systems in
frequency regulation and similar services.
Fig. 15. The levelized cost added by storage (LCOS) to the price of charging power,
in different discharge times per one cycle (bulk energy storage with 250 cycles per
year, interest rate 8%).
The LCC of different grid-scale EES technologies were analyzed
by conducting an extensive review of the existing literature,
considering uncertainties in cost data and technical parameters.
The results reveal that the cost estimations/projections of the EES
systems are rather dispersed and inconsistent among different
references. The cost estimations rely on assumptions and scaling
the size, the case for most of battery systems, which reduces the
consistency among different sources of data. Most of the EES
systems are in formative stages of commercialization and those
commercial plants are mainly site-specific resulting in more
inconsistency in the cost data. Hence, a robust LCC analysis should
account for the uncertainties.
The cost items of EES systems were first separately analyzed. CAES
has the highest costs for PCS (845 €/kW) while NiCd batteries offer the
minimum power interface costs (240 €/kW). However, electrochemical batteries show higher costs for storage compartment (up to 800
€/kWh for Li-ion). Hydrogen-based and underground CAES have
lowest costs of storage, 4 and 40 €/kWh, respectively. More details
of the cost elements are presented in Appendix A for each technology.
In terms of TCC (total capital cost), underground CAES (with 890
€/kW) offers the most economical alternative for bulk energy storage,
while SMES and SCES are the cheapest options in power quality
applications. However, the cost data for these electro-magnetic EES
systems are rather limited and for small-scale applications. The TCC of
hydrogen-based systems indicate a large difference between gas
turbine (1570 €/kW) and fuel cell systems (3240 €/kW). TCC of
different EES systems are illustrated in more details in Appendix C.
In the calculation of LCC, the effect of uncertainties is different
and can affect the results by 5–17% in most of the examined cases.
The results indicated that mechanical energy storage systems,
namely PHS and CAES, are still the most cost-efficient options for
bulk energy storage. PHS and CAES approximately add 54 and 71
€/MWh respectively, to the cost of charging power. The project's
environmental permitting costs and contingency may increase the
costs, however. The uncertainty in LCC of CAES may increase if the
fuel and emission costs could not be consistently established for
the application's lifetime.
Among the commercialized batteries, NaS offers relatively lower
LCC in both energy arbitrage and T&D support applications. However,
the uncertainties in the cost of batteries are rather wide, even larger
than difference in costs between different technologies. Hence, the
most optimal option should be selected based on other technical and
project-specific characteristics. Since replacement costs comprise a
major and distinctive part of LCC of the batteries, optimal cycle
numbers that can make the highest revenues should be defined,
based on the service requirements and the regime of charge and
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discharge (DoD). Flywheels offer the most cost-efficient option in
power quality and frequency regulation applications (discharge time
up to a few minutes), with lower operational costs. Hydrogen-based
storage and other EES systems with relatively low efficiency demonstrate higher sensitivity to the electricity prices, indicating the need
for more R&D to become economically competitive.
This study aims at providing a milestone for future researches
that examine the integration of EES systems by contributing in costbenefit analysis. The analysis can be improved in the future by
realization of more demonstration plants and establishment of the
costs at different stages throughout the plant's lifetime. Considering
more parameters in LCC of the batteries can improve the practicality
of the results, e.g. dependencies between DoD and life cycle numbers
or optimal life cycle numbers based on the service lifetime.
electricity is not included in variable O&M costs in the following
tables, as it depends on the market in which the EES is adopted.
See Tables A1–A12
Table A3
Main cost items of flywheel energy storage systems.
Cost item
Average
Middle fifty range, IQR
Range
PCSa (€/kW)
Storage sectionb(€/kWh)
Fixed O&M (€/kW -yr)
Variable O&M (€/MWh)
Replacement costsc (€/kW)
287
2815
5.2
2.0
151
284–356
1030–18159
4.8–5.6
1.1–2.9
118–184
263–470
865–47764
4.3–6.0
0.2–3.8
85–216
a
Including BOP costs.
As flywheel systems are typically employed for power quality applications
with discharge time of seconds up to 30 min, the direct use of storage cost may
entail ambiguity.
c
Every four years, given based on the unit of power rating.
b
Acknowledgments
The authors would like to present their sincere appreciation
for the supports provided by STEEM project (Sustainable Transition of European Energy Markets) and the Aalto Energy Efficiency
Research Program.
Appendix A. Cost elements of different EES systems
The main cost items of each EES systems are depicted in separate
tables to compare the values and variability of the cost items in the
literature for each individual technology. The results are calculated
based on the detailed review of the references reported in Table 2.
The average is the median of each range, and outliers are not
included in the ranges. It should be noted that the price of purchasing
Table A4
Main cost items of lead–acid battery systems.
Cost item
Average
Middle fifty range, IQR
Range
PCS (€/kW)
BOP (€/kW)
Storage sectiona (€/kWh)
Fixed O&M (€/kW -yr)
Variable O&M (€/MWh)
Replacement costsb(€/kW)
378
87
618
3.4
0.37
172
322–440
65–108
264–661
3.3–6.1
0.35–0.49
157–264
195–594
43–130
184–847
3.2–13.0
0.15–0.52
50–560
a
Mainly for MW-scale systems with rated DoD of 80%, used for bulk energy
storage and T&D support (discharge time 4 h).
b
Every 8 yr for the mentioned application (365 cycles per year).
Table A1
Main cost items of PHS systems.
Cost item
Average
Middle fifty range, IQR
Range
PCS (€/kW)
BOP (€/kW)
Storage sectiona (€/kWh)
Fixed O&Mb (€/kW-yr)
Variable O&M (€/MWh)
513
15
68
4.6
0.22
410–805
9–22
41–115
3.9–7.7
0.20–0.79
373–941
3–28
8–126
2.0–9.2
0.19–0.84
a
Mainly for storage sizes with 8 h discharge time.
Major fixed O&M is expected every 20 yr totaling 84 €/kW of installed
capacity.
b
Table A5
Main cost items of NaS (sodium–sulfur) battery systems.
Cost item
a
PCS (€/kW)
Storage sectionb (€/kWh)
Fixed O&M (€/kW-yr)
Variable O&M (€/MWh)
Replacement costsc (€/kW)
Average
Middle fifty range, IQR
Range
366
298
3.6
1.8
180
314–553
277–358
3.3–16.5
0.3–4.6
180–307
241–865
180–563
2.0–17.3
0.3–5.6
180–443
a
Including BOP costs, which is estimated in the range of 80 €/kW.
Mainly for MW-scale systems with rated DoD of 80%, used for bulk energy
storage and T&D support (discharge time 6–7.2 h).
c
Every 8 yr for the mentioned application (365 cycles per year).
b
Table A2
Main cost items of first-generation CAES systems.
Cost item
PCS (€/kW)
a
Storage section (€/kWh)
Fixed O&Mb (€/kW -yr)
c
Variable O&M (€/MWh)
a
Type of CAES
Average
Middle fifty
range, IQR
Range
Aboveground
Underground
Aboveground
Underground
Aboveground
Underground
Aboveground
Underground
846
843
109
40
2.2
3.9
2.2
3.1
825–866
696–928
97–120
30–47
2.2–3.0
2.6–4.0
2.1–2.6
2.6–3.6
804–887
549–1014
86–131
4–64
2.2–3.7
2.0–4.2
1.9–3.0
2.2–2.5
Mainly for storage sizes with 8 h discharge time.
Major fixed O&M is expected every 5 yr totaling 67 €/kW of installed
capacity.
c
As natural gas prices are not equal in different studies, the variable O&M costs
entails more uncertainty. On average, the fuel costs are in the range of 8–20 €/MWh
and emission cost is 18–22 €/ton CO2, for example see [110,111].
Table A6
Main cost items of Ni–Cd (nickel–cadmium) battery systems.
Cost item
a
PCS (€/kW)
Storage sectionb (€/kWh)
Fixed O&M (€/kW -yr)
Replacement costsc (€/kW)
Average
Middle fifty range, IQR
Range
239
780
11
525
213–279
571–1020
5–19
502–549
206–329
564–1120
4–24
478–573
b
a
Including BOP costs.
Mainly for MW-scale systems with rated DoD of 80%, used for bulk energy
storage and T&D support (discharge time 2–4 h).
c
Every 10 yr for the mentioned application (365 cycles per year).
b
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
591
Table A7
Main cost items of sodium–nickel chloride (NaNiCl2) battery systems, known as ZEBRA.
Cost item
Average
Middle fifty range, IQR
Range
PCSa (€/kW)
Storage sectionb (€/kWh)
Fixed O&M (€/kW -yr)
Variable O&M (€/MWh)
Replacement costsc (€/kW)
472
509
5.5
0.6
182
379–611
410–723
3.7–7.1
0.41–1.0
148–202
335–638
366–778
3.3–7.2
0.38–2.1
107–202
a
b
c
Including BOP costs.
Mainly for MW-scale systems with rated DoD of 80%, used for bulk energy storage and T&D support (discharge time 5 h).
Every 8 yr for the mentioned application (365 cycles per year).
Table A8
Main cost items of Li-ion battery systems.
Cost item
a
PCS (€/kW)
Storage sectionb (€/kWh)
Fixed O&M (€/kW-yr)
Variable O&M (€/MWh)
Replacement costsc (€/kW)
a
b
c
Average
Middle fifty range, IQR
Range
463
795
6.9
2.1
369
398–530
676–1144
4.9–11.2
0.99–3.6
284–505
241–581
470–1249
2.0–13.7
0.4–5.6
187–543
Including BOP costs of 80 €/kW.
Mainly for MW-scale systems with rated DoD of 80%, used for bulk energy storage and T&D support (discharge time 0.5–2 h).
Every 5 yr for the mentioned application (365–500 cycles per year).
Table A9
Main cost items of VRFB (vanadium-redox) battery systems.
Cost item
a
PCS (€/kW)
Storage sectionb (€/kWh)
Fixed O&M (€/kW-yr)
Variable O&M (€/MWh)
Replacement costsc (€/kW)
a
b
c
Average
Middle fifty range, IQR
Range
490
467
8.5
0.9
130
478–518
440–536
4.3–16.1
0.5–1.2
114–165
472–527
433–640
3.4–17.3
0.2–2.8
111–192
Including BOP costs approximately 25 €/kW.
Mainly for MW-scale systems with rated DoD of 80%, used for bulk energy storage and T&D support (discharge time 4 h).
Every 8 years for the mentioned application (365–500 cycles per year).
Table A10
Main cost items of zinc–bromine (Zn–Br) battery systems.
Cost item
a
PCS (€/kW)
Storage sectionb (€/kWh)
Fixed O&M (€/kW-yr)
Variable O&M (€/MWh)
Replacement costsc (€/kW)
a
b
c
Average
Middle fifty range, IQR
Range
444
195
4.3
0.6
195
343–470
178–314
3.6–5.4
0.4–1.0
148–198
151–595
178–530
3.2–6.9
0.3–2.0
101–201
Including BOP costs approximately 25 €/kW.
Mainly for MW-scale systems with rated DoD of 80%, used for bulk energy storage and T&D support (discharge time 2–5 h).
Every 15 yr for the mentioned application (365 cycles per year).
Table A11
Main cost items of iron–chrome (Fe–Cr) battery systems.
Cost item
Average
Middle fifty range, IQR
Range
PCSa (€/kW)
Storage sectionb (€/kWh)
Fixed O&M (€/kW -yr)
Variable O&M (€/MWh)
Replacement costsc (€/kW)
362
145
3.3
0.4
29
333–393
126–152
2.8–4.0
0.2–0.6
24–33
326–523
64–156
2.7–6.9
0.1–1.0
14–38
a
b
c
Including BOP costs approximately 25 €/kW.
Mainly for MW-scale systems with rated DoD of 80%, used for bulk energy storage and T&D support (discharge time 2–5 h).
Every 15 yr for the mentioned application (365 cycles per year).
592
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
Table A12
Main cost items of hydrogen-based EES systems.
Cost item
Configuration
Average
Middle fifty range, IQR
Range
PCSa (€/kW)
Hydrogen-FCb
Hydrogen-GTc
2465
1548
1630–3884
1359–2673
1383–4453
1102–3362
Storage sectionb (€/kWh)
Aboveground
Underground
130
3.7
128–132
0.2–11.6
125–134
0.02–12.4
O&M (€/kW -yr)
Hydrogen-FCb
hydrogen-GTc
25
35
24–39
25–45
16–44
23–48
a
b
c
Including BOP costs approximately 25 €/kW.
Electrolysis and fuel cell.
Electrolysis and small-to medium scale gas turbine.
Appendix B. Summary of technical characteristics of EES
systems
See Table B1
Table B1
Technical characteristics of electrical energy storage (EES) systems, based on the review of the references in Table 2.
EES technology
Power range
(MW)
Discharge time
(ms–h)
Overall efficiency
PHS
CAES (underground)
CAES (aboveground)
Flywheel
Lead–acid
NaS
NaNiCl2 (ZEBRA)
Ni–Cd
Li-ion
VRFB
Zn–Br
Fe–Cr
PSB
SMES
Capacitors
SCES
Hydrogen (fuel cell)
10–5000
5–400
3–15
Up to 0.25
Up to 20
0.05–8
50
Up to 40
up to 0.01
0.03–3
0.05–2
1–100
15
0.1–10
Up to 0.05
Up to 0.3
0.3–50
1–24 h
1–24 h
2–4 h
ms–15 m
s–h
s–h
2–5 h
s–h
m–h
s–10 h
s–10 h
4–8 h
s–10 h
ms–8 s
ms–60 m
ms–60 m
s–24 h
0.70–0.82
0.7–0.89
0.70–0.90
0.93–0.95
0.70–0.90
0.75–0.90
0.86–0.88
0.60–0.73
0.85–0.95
0.65–0.85
0.60–0.70
0.72–0.75
0.65–0.85
0.95–0.98
0.60–0.65
0.85–0.95
0.33–0.42
Power density
(W/kg)
Energy density
(Wh/kg)
Storage durability
Self-discharge
(per day)
Lifetime (yr)
Life cycles
(cycles)
0.5–1.5
30–60
Negligible
Small
Small
100%
0.1–0.3%
20%
15%
0.2–0.6%
0.1–0.3%
Small
Small
Small
10–15%
40%
20–40%
Negligible
50–60
20–40
20–40
15–20
5–15
10–15
15
10–20
5–15
5–10
5–10
10–15
10–15
15–20
5–8
10–20
15–20
20000–50000
413,000
413,000
20,000–100,000
2000–4500
2500–4500
2500–3000
2000–2500
1500–4500
10,000–13,000
5000–10,000
410,000
2000–2500
4100,000
50,000
4100,000
20,000
1000
75–300
150–230
150–200
50–1000
50–2000
166
45
5–100
30–50
150–250
100–140
15–300
150–350
10–35
30–85
h–months
h–months
h–days
s–min
min–days
s–h
s–h
min–days
min–days
h–months
h–months
500-2000
100,000
800–23,500
500
0.5–5
0.05–5
2.5–50
100–10,000
h–months
min–h
s–h
s–h
h–months
Appendix C. Total capital cost of different EES systems
See Table C1
Table C1
Total capital cost (TCC) of grid-scale EES systems based on the review of the sources listed in Table 2.
EES technology
PHS
CAES
Flywheel
Lead–acid
NaS
Ni–Cd
ZEBRA
Li-ion
VRFB
Zn–Br
PSB
Fe–Cr
Zn–air
Supercapacitors
Configuration
Conventional
Aboveground
Underground
High-speed
Advanced
–
–
–
–
–
–
–
–
–
Double-layer
Total capital costa (TCC), per unit of power rating €/kW
Total capital costa (TCC), per unit of storage capacityb €/kWh
Min
Average
Max
Min
Average
Max
1030
774
1286
590
1388
1863
2279
874
2109
1277
1099
927
1376
1313
214
1406
893
1315
867
2140
2254
3376
1160
2512
1360
1132
1093
1400
1364
229
1675
914
1388
1446
3254
2361
4182
1786
2746
1649
1358
1308
1425
1415
247
96
48
210
1850
346
328
596
973
459
257
170
1071
527
262
691
137
92
263
4791
437
343
699
1095
546
307
220
1147
569
271
765
181
106
278
25049
721
398
808
1211
560
433
281
1153
611
417
856
B. Zakeri, S. Syri / Renewable and Sustainable Energy Reviews 42 (2015) 569–596
593
Table C1 (continued )
EES technology
SMES
Hydrogen
Configuration
–
Fuel cellc (FC)
Gas turbined (GT)
Total capital costa (TCC), per unit of power rating €/kW
Total capital costa (TCC), per unit of storage capacityb €/kWh
Min
Average
Max
Min
Average
212
2395
1360
218
3243
1570
568
4674
2743
5310
399
227
6090
540
262
Max
6870
779
457
a
It should be noted that the capital costs are calculated based on typical discharge time (storage size) for each technology, which is not necessarily the same among
different EES systems (for typical discharge time, see the corresponding table for each technology in Appendix A). Minimum and maximum values are the bands of
interquartile range (middle-fifty likelihood) and the average value is the median of whole sample, excluding outliers. It should be noted that the costs of grid
interconnections and infrastructure requirements are not included in this estimation.
b
For the batteries, the storage capacity is equivalent to the rated DoD.
c
Electrolysis and fuel cell with steel tank storage system.
d
Electrolysis and small-to medium scale gas turbine with underground storage.
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