Techno-economic assessment of energy storage systems using annualized life cycle cost of storage (LCCOS) and levelized cost of energy (LCOE) metrics

Techno-economic assessment of energy storage systems using annualized life cycle cost of storage (LCCOS) and levelized cost of energy (LCOE) metrics

Journal of Energy Storage 29 (2020) 101345 Contents lists available at ScienceDirect Journal of Energy Storage journal homepage: www.elsevier.com/lo...

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Journal of Energy Storage 29 (2020) 101345

Contents lists available at ScienceDirect

Journal of Energy Storage journal homepage: www.elsevier.com/locate/est

Techno-economic assessment of energy storage systems using annualized life cycle cost of storage (LCCOS) and levelized cost of energy (LCOE) metrics

T



Mostafa H. Mostafaa, Shady H.E. Abdel Aleemb, , Samia G. Alic, Ziad M. Alid,e, Almoataz Y. Abdelazizf a

Department of Electrical Power and Machines, Ain Shams University, Cairo, Egypt Mathematical and Physical Sciences, 15th of May Higher Institute of Engineering, Cairo, Egypt Department of Electrical Power and Machines, Kafrelsheikh University, Cairo, Egypt d Electrical Engineering Department, College of Engineering at Wadi Addawaser, Prince Sattam bin Abdulaziz University, Saudi Arabia e Electrical Engineering Department, Aswan Faculty of Engineering, Aswan University, Egypt f Faculty of Engineering and Technology, Future University in Egypt, Cairo, Egypt b c

A R T I C LE I N FO

A B S T R A C T

Keywords: Batteries Energy storage Levelized cost of energy Life cycle cost of storage Renewable energy sources Techno-economic metrics

Energy generation from renewable energy sources (RESs) is rapidly developing across the world to improve the performance of power networks and increase the share of RESs in world energy production. In this regard, energy storage (ES) technologies are the key enablers for reliable use of renewables because they introduce many benefits for modern power systems. However, the choice of a suitable technology depends on several technoeconomic metrics, which require the decision-maker to investigate the applicability of the technology and whether it offers promising benefits or not. Hence, this paper presents an ES cost model that considers long-term, medium-term, and short-term ES applications, technologies and technical characteristics in an integrated framework that consider the ES technical and economic characteristics supported by in-market insight, including capital costs of the technologies; operation and maintenance costs; replacement costs during the lifetime of the system; and disposal and recycling costs, based on the current ES costs. Two key metrics, namely the annualized life cycle cost of storage (LCCOS) and the levelized cost of energy (LCOE), are used to make proper ES operational choices while complying with their technical and operational performance limits. Further, a sensitivity analysis of the governing factors that affect the storage cost is presented to introduce a powerful decision tool to empower techno-economic assessment of ES systems using the proposed cost models.

1. Introduction In response to environmental and social initiatives, as well as technical and economic development, energy generation from renewable energy sources (RESs) is rapidly developing across the world [1] to improve the performance of power networks and increase the share of RES in the world energy production [2]. Besides, both the global commitment of countries to curb the use of high-carbon fossil energy resources because of their depletion and the instability of their global price are helping to promote energy generation from RESs [3]. For instance, in 2017, the energy produced from RESs represented around 18.1% of world energy production. This percentage has increased at the end of 2019 to be more than 26% of world energy production [4]. The main goal of power system operators is to enhance the stability,



reliability, and power quality performance levels of the systems and increase energy efficiency in an environmentally friendly cost-effective framework [5]. But, many factors affect energy generation from RESs, such as intermittency and geographic limitations, in addition to the incomplete flexibility to balance the production and consumption within a long period of time with the increased RESs share for power system operators. Accordingly, there is a high need for energy storage (ES) [6,7]. ES is one of the promising facilities that can support system reliability to increase its resiliency to recover quickly from disruptions and enhance its hosting capacity, to facilitate the integration of high penetration levels of RESs into clean and sustainable modern power systems not associated with a carbon footprint [8]. In 2017, the power capacity of ES systems was 1.4 GW and the energy stored was 2.3 GWh.

Corresponding author. E-mail address: [email protected] (S.H.E. Abdel Aleem).

https://doi.org/10.1016/j.est.2020.101345 Received 6 December 2019; Received in revised form 4 February 2020; Accepted 3 March 2020 2352-152X/ © 2020 Elsevier Ltd. All rights reserved.

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empower techno-economic assessment of ES systems using two key metrics, namely the annualized life cycle cost of storage (LCCOS) and the LCOE, to enable proper ES operational choices while complying with their technical and operational performance limits to be used easily in long-term ES planning models. The total cost of ES technology includes not only the storage container, power converter, protection devices, cooling system, and other elements inherent to the technology itself but also market energy price, regulation of usage, and other elements related to the operation of storage. Therefore, all these elements should be taken into consideration to reach the correct economic feasibility of each ES technology. In general, the techno-economic assessments of ES technologies should be including the capital, fixed and variable operation and maintenance, replacement, and end-of-life (disposal and recycling) costs. The sum of all these elements is named the total life cycle cost of storage. It is usually expressed in an annualized form, LCCOS in €/kW-year, to give a yearly figure of the total life cycle cost of the storage technology. LCCOS allows a cost comparison of technologies in different system designs and various operation modes such as different power and energy rating. Thus, for decisionmakers, LCCOS becomes a useful metric to address the economic feasibility of the ES. Moreover, dividing LCCOS by the number of yearly operating hours of the system, one can determine a second metric, which is the widely utilized “levelized cost of energy,” LCOE, expressed in c€/kWh. The LCOE gives the economic resources that the storage operator needs to charge the storage system per energy unit that it delivers. It represents an appropriate tool to compare the cost of electricity storage technologies having the same number of yearly operating hours [35–37]. This motivates the authors to determine the possibility of each technology to offer the lowest LCCOS and LCOE in ES applications and technologies. Second, a sensitivity analysis of the dominant factors that affect the storage cost is presented. Third, the Matlab code of the formulated cost model is made freely available in [38]. The rest of the paper is organized as follows: Section 2 presents an overview of the ES technologies. Section 3 discusses the applications and benefits of ES technologies. The mathematical formulation of the cost models of ES technologies is detailed in Section 4. In Section 5, the results obtained are presented and discussed for high, medium and low power storage technologies. Besides, the sensitivity analysis of the dominant factors that affect the storage cost is presented and discussed, and finally, Section 6 presents a brief summary of the work done and the conclusions drawn from the study.

These values are estimated to reach 8.6 GW and 21.6 GWh by 2022 for the power capacity and energy stored, respectively [9]. ES technologies are transformative technologies that will shape power markets because they can introduce many benefits for power systems, such as enhancing reliability and power quality; increasing the share of RESs; mitigating issues related to the intermittency of RESs; increasing system stability; following loads and shaving the peak ones; managing energy and reducing the total operation cost of power systems; supporting spinning reserve and relieving transmission congestion [10–13]. Besides, there are many ES technologies, with different power and energy capacities, that can be integrated into power systems. However, the choice of a suitable technology depends on several techno-economic metrics. A detailed study should be performed to determine the applicability of the technology (the quality of being appropriate according to the application type and period) and whether it offers promising benefits or not in relation to market price and performance constraints, as well as technical, economic, cyclability, operability and maintainability aspects [14,15]. In the literature, many studies have investigated the technical characteristics of different ES technologies to understand the applicability, operability and maintainability aspects of each ES technology [15–30]. However, few studies such as [31–34] concentrate on the complementarity of the ES cost models that consider the ES technical and economic characteristics in an integrated framework to foster ES applications. In [31], the levelized costs for five hybrid ES system is determined by Gbadegesin et al. based on the technology, installation, operation and maintenance and replacement costs over a project lifetime of twenty years, considering the degradation effects of the hybrid ES systems. The main aim was to exploit the complementary features of each storage technology and develop the technical and economic characteristics of the ES system. Gbadegesin et al. analyzed the levelized costs of the hybrid storage system by calculating the levelized cost of energy (LCOE). However, uncertainties in technical factors of each storage system were not included in their analysis. In [32], Jülch presented economic feasibility of eight electricity storage technologies for long-term and short-term durations in terms of the levelized cost of storage (LCOS), in which Jülch calculated LCOS to compare between the different ES technologies, depending on the plant configuration and the number of operating hours per year. LCOS is analyzed using various data of each storage system. The presented sensitivity analysis showed that the electricity price and amount of energy discharged are the most effective factors for LCOS calculated for a storage system. However, the replacement costs of each storage system were not included in the presented economic feasibility. In [33], Zakeri and Syri presented a life cycle cost analysis of different ES technologies, considering capital costs, operational and maintenance costs, and replacement costs, in which comprehensive literature research of the technical characteristic of different storage system technology and their main benefits was presented. They analyzed the cost of different ES systems for three applications: energy arbitrage, transmission and distribution support, and frequency regulation. The sensitivity of the life cycle cost of the different ES technologies used in regard to electricity price, discharge time, and the interest rate was analyzed. However, the impact of life cycles on the lifetime of the ES system was not included while calculating the ES cost. In [34], Bhattacharjee and Nayak presented a technoeconomic and environmental analysis of pumped storage technology integrated with a solar PV system. The economic feasibility of the system was analyzed based on the net present value (NPV) and LCOE, but the NPV and LCOE were not studied in detail. To interpolate these shortcomings, first, this study presents an ES cost model that considers long-term, medium-term, and short-term ES applications, technologies and technical characteristics in an integrated context supported by in-market insight, based on the current ES costs from recently published studies, as the ES costs have changed rapidly in the past few years. The main goal of this work is to establish a powerful decision tool to

2. ES technologies ES systems can be divided into three categories according to their power and energy capacities, in which the first category expresses the long-term or high power storage technologies such as pumped hydroelectric storage (PHS) and compressed air energy storage systems (CAES), the second expresses the medium-term or medium power storage technologies such as hydrogen-based energy storage (HES), leadacid (LA), nickel-cadmium (NiCd), sodium-sulfur (NaS), vanadium redox (VR), zinc-bromine (ZnBr), and lithium-ion (Li-ion) batteries, and the third category expresses the short-term or low power storage technologies such as flywheel energy storage (FWES), superconducting magnetic energy storage (SMES) and supercapacitor energy storage (SCES) technologies [14,32,33]. The size of storage technology is a dominant factor in practice. As shown in Fig. 1, the size of ES can be addressed by relating the power density (the amount of power stored in an ES system per unit volume) to the energy density (amount of energy stored in an ES system per unit volume) for the different ES technologies. One can see that the volume of ES decreases with the increase of power density and energy density; therefore, the smallest volume of storage (top right corner) can be found at the highest power and energy densities available. Thus, the highly compact technologies suitable for volume-limited applications can be found at the top right corner of the figure. In contrast, the most 2

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Fig. 1. Power density versus energy density of different ES technologies [15].

from minutes to few hours: overground small-scale CAES, LA, Li-ion, NiCd, and ZnBr; (3) discharge duration can vary within a range from hours to days: PHS and large-scale underground CAES. Fig. 4 illustrates the various scales of the nominal discharge time duration versus the power delivered for different ES technologies. The nominal discharge time of storage depends on the rated power, in which at the peak load, the rated power is high; thus nominal discharge time of storage is short. In contrast, the nominal discharge time is long when the rated power of ES is low. To sum up, one can see from Figs. 3 and 4 that the time period relies on the scale of the rated power considerably. The techno-economic specifications of the different ES technologies used in this work are summarized below.

massive volume of storage (Fig. 1, bottom left corner) can be found at the lowest power and energy densities available. PHS and CAES have lower power and energy densities; thus they are mainly utilized in stationary ES applications because they need large reservoirs. One can see that most batteries have relatively moderate energy densities and power densities. It should be noted that a higher energy density does not mean a higher power density, as batteries have a higher energy density than capacitors because they have the capability to store more energy than capacitors; but the capacitors have a higher power density than batteries as they can discharge this energy more quickly. The Liion battery has both a high energy density and a high power density, which leads to widespread utilization in portable devices and another promising potential in transportation and other small-scale ES applications. The weight of the storage technology is also an essential factor that plays an important role in choosing the proper ES technology for many applications using specific energy (energy per unit mass) and specific power (power per unit mass) of storage. Specific energy and specific power of ES technologies are acknowledged to obtain the total energy and power per unit weight, as shown in Fig. 2. For instance, ES technology that has higher specific power and higher specific energy is appropriate for lightweight applications and activities. Generally, choosing the appropriate ES technology for a particular application based on ES weight comes first in priority, and is secondly taken into account the economic aspects of the selected ES technology among other applicable ES technologies. The ES technologies, which have high specific power and specific energy, is situated at the top right corner of Fig. 2. In contrast, the considerable weight of ES is situated at the bottom left corner, as shown in Fig. 2. One can see that SMES and SCES have high specific power but low specific energy; because of their fast response time, they are more suitable for power quality and frequency fluctuation applications. The power rating, energy capacity and time of discharge of different ES technologies are shown in Fig. 3. It is clear that the discharge duration of ES depends on its power rating and energy capacity. The nominal discharge time duration of ES technologies at rated power can vary within a range from seconds to hours [14,15]. ES technologies can be categorized by the nominal discharge time at rated power: (i) discharge duration can vary within a range from seconds to less than 1 h: FWES, SMES and SCES; (ii) discharge duration can vary within a range

2.1. Pumped hydroelectric storage A PHS converts the energy from the kinetic energy of water into mechanical energy that is then converted into clean electricity, with no need for fuel to operate [34]. It has a large storage capacity (greater than 50 MW), high efficiency (70–85%), low energy capital cost, a long lifetime (40–60 years), and limited need for power electronic converters. PHS can be used to store the energy for a long time (from hours to months), in which the stored energy can be used in specific months, or whenever needed. The life cycle of PHS is around 10,000–50,000 cycles and its energy density is around 0.5–1.5 Wh/L. The discharge time of PHS is around 1 h to 24 h [2,14,15,33]. 2.2. Compressed air energy storage CAES is used to store energy during off-peak time by compressing air to about 75 bars in a reservoir or a cavern using an electric compressor. Then, the highly pressurized air is used to generate electricity during a peak time [24]. CAES has many advantages, such as a large ES capacity, low energy capital cost, fast start-up, long lifetime (20–40 years), limited need for power electronic converters and low storage losses. CAES can store energy for more than a year. There are two types of CAES, in which the first type expresses the underground CAES (UCAES) and the second type expresses the aboveground CAES (ACAES). The power range of UCAES is 5–400 MW and its discharge time is 1 h to 24 h. But, the power range of ACAES is 3–15 MW and its 3

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Fig. 2. Specific power versus specific energy of different ES technologies [15].

2.4. Sodium sulfur batteries

discharge time is 2 h to 4 h. The efficiency range of CAES is between 70–90% and its life cycle is 13,000+ cycles [2,14,15,33].

NaS batteries are one of the most attractive battery technologies, being made up of inexpensive materials, in which the liquid sulfur serves as the positive electrode and the liquid sodium serves as the negative electrode [13]. The advantages of NaS batteries are high ES capacity, long cycling life (2,500–4,500 cycles), high efficiency (75–90%), and lightweight. However, the main drawback of NaS batteries is to maintain the high temperature required for operation as they operate at high operating temperature (300–350 °C), in addition to the safety issues associated with the reactivity of the contents, i.e. the corrosive nature of sodium polysulfides. The lifetime of NaS batteries is around 10–20 years. The time of discharge varies from seconds to hours [2,14,15,33].

2.3. Lead-acid batteries The oldest and most developed battery type is the LA battery. LA has many advantages such as the low capital cost, the diverse capacity range, and good performance in a variable temperature range [40]. LA batteries also have some disadvantages such as the short lifetime (5–15 years), low life cycle (500–1800 cycles), and low energy density (50–90 Wh/kg), in addition to which they are large and heavy batteries. The efficiency of LA batteries is around 70–90%, and their time discharge is around seconds to hours [2,14,15,33].

Fig. 3. Power rating, energy capacity and time of discharge of ES technologies [15]. 4

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Fig. 4. Discharge duration versus rated power of various ES technologies [39].

2.5. Lithium-ion batteries

2.8. Vanadium redox batteries

In cobalt-based lithium-ion batteries, lithium-cobalt-oxide and carbon are used as the positive and negative electrodes, where lithium salt and an organic solvent are used as an electrolyte. Recently, nanomaterials have been used to improve the electrochemical performance of this type of batteries. Because of their high energy density (150–500 Wh/kg), light weight, high open-circuit cell voltage, high efficiency (85–97%), enhanced safety, low self-discharge rate (5–10% per month), long cyclability (1,000–10,000 cycles), and little to no maintenance, lithium-ion batteries are popular in the markets and transportation applications. However, Li-ion batteries have disadvantages such as high capital costs, frequent charging needs associated with long charging times, and poor cycle stability. The lifetime of Li-ion batteries is around 5–15 years and time of discharge varies from minutes to hours [2,14,15,33].

One of the mature flow-based types of battery is the VR as it can be used in many applications because its power and energy ratings are independent so they can be optimized according to the type of application, essentially in solving power quality problems and responding to variable RESs generation [41]. The efficiency of VR batteries is around 65–85%, the lifetime is around 5–10 years, the life cycle is about 10,000–13,000 cycles, and the time of discharge varies from seconds in small-scale applications up to ten hours in medium-scale applications [2,14,15,33]. 2.9. Zinc-bromine batteries ZnBr batteries belong to the flow-based batteries type. They have many advantages such as chemical acceptance, electrochemical reversibility at the electrodes, long cycling life (5,000–10,000 cycles) and low-cost materials. The efficiency of ZnBr batteries is around 60–85%, lifetime around 5–10 years, and the time of discharge varies from seconds to around 10 h [2,14,15,33].

2.6. Nickel-cadmium batteries In nickel-cadmium batteries, cadmium and nickel-oxide hydroxide are used as positive and negative electrodes. Potassium hydroxide is used as the electrolyte [14]. The NiCd batteries have many advantages such as low internal impedance, high strength, charge retention, and flat discharge relative to LA. On the other hand, cadmium and nickel are heavy metals that affect human health. Also, these batteries have many disadvantages such as low energy density (15–300 Wh/kg) and high capital cost. Their efficiency is about 60–85%. The lifetime of NiCd batteries is around 10–20 years, and the time of discharge ranges from a few seconds to hours [2,14,15,33].

2.10. Flywheel energy storage FWES is a mechanical-based battery since it stores the kinetic energy that accelerates the rotor. The stored energy is proportional to the squared speed. The rotor weight plays an important role in FWES performance as the lighter the rotor, the higher the speed and ES capability. FWES operates as a generator during peak time to feed the electrical load and support the network. During off-peak times, FWES operates as a motor to store energy. FWES batteries have many advantages such as low maintenance, high efficiency (93–95%), and long cyclability (20,000–100,000 cycles). The lifetime of FWES is about 15–20 years, and the time of discharge is from a few milliseconds to 15 min. FWES batteries can be utilized in power quality enhancement and frequency regulation since these batteries are characterized by their fast response (milliseconds) and short duration of discharge (seconds to minutes) [2,14,15,33,42].

2.7. Hydrogen-based energy storage In HES, the surplus of energy, usually generated from renewables, is used in a power electrolysis process to separate hydrogen to be then stored in hydrogen tanks for later use or re-electrification through hydrogen-based gas turbines. The efficiency of HES is about 20–66%, and the life cycle is about 1,000–20,000 cycles. The lifetime of HES is around 5–20 years, and the time of discharge varies from seconds to hours [2,14,15,33].

2.11. Superconducting magnetic energy storage Superconducting magnets are grid-enabling storage units that store 5

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the electrical energy from the grid-side within the magnetic field created by the flow of dc current in a superconducting coiled wire, to be discharged to enhance the power quality of a system when needed. The superconducting coil is cryogenically cooled beyond its superconducting temperature to achieve near-zero loss of energy in order to get high efficiencies (up to 97%), as well as enabling storage. The lifetime of SMES is around 15–20 years, and the time of discharge ranges between a few milliseconds and around 8 seconds, and its life cycle is 100,000+ cycles [2,14,15,33].

Fig. 5 shows the ES applications whilst Fig. 6 shows a classification of these applications according to their power ratings [14]. Also, Table 1 presents the ES technology and its suitable application. 3.1. Electric energy time shift and cost of operation reduction ES is used to store the electrical energy when the market kWh price is low and then to sell the stored energy when the market kWh price rises. As a consequence, ES systems help to support a reduction of the total operation cost of an electrical system by considering their time of charging/discharging and the market kWh price variation [14,44].

2.12. Supercapacitor energy storage Supercapacitors are used to store energy in applications that need rapid charge/discharge cycles; this is why they are usually used to enhance power quality, provide backup power, and support voltage, since they are characterized by their high response, long cyclability, and high efficiency (90–97%). The main disadvantages of SCESs are their low energy density and high self-discharge loss [43]. The lifetime of SCES is around 10–30 years, time of discharge ranges from a few milliseconds to around 60 min, and its life cycle is 100,000+ cycles [2,14,15,33].

3.2. Peak load shaving

3. Applications and benefits of ES technologies

3.3. Load following

This section highlights some of the important applications, revenue streams, and benefits of utilizing ES systems. Further, the applications and benefits of integrating ES technologies in power systems are discussed. ES facilities can be used at different voltage levels from the generation systems to the end-users as they have significant impacts and revenue streams on the different system types, such as system security, reliability, stability, power quality, hosting capacity, peak load serving, and others. However, each ES technology is proper for specific benefits. Accordingly, we first presented the different revenue streams and benefits of all the ES technologies, and then we identified the appropriate ES techniques that can achieve these benefits.

At all times, the electrical demand is subject to frequent changes during the day. Thus, the demand changes should be securely controlled using adequate reserve sources. As one of the ancillary services, many ES technologies have a fast response to loads variation. Thus, they can be used to cover the frequent changes in supply and demand [45].

This application is useful in deferring electric load peaks by charging the ES system during off-peak hours and discharging it during onpeak hours. Also, this application helps to store the extra energy produced by RESs during the off-peak time and to deliver it to the electrical loads during peak time, instead of using high-carbon or expensive lowcarbon technologies [14,40]. In addition, this helps enhance system efficiency due to the uniformity of loads.

3.4. Reliability improvement ES systems can be utilized to improve system reliability by preventing or decreasing the number of interruptions that may occur and

Fig. 5. Energy storage applications. 6

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Fig. 6. ES technologies applications according to power ratings. Table 1 ES technologies and their suitable applications [3,11,12,14,21,22,32,33]. Application Objective/Type

Long-term PHS CAES

Medium-term HBS LA

NiCd

NaS

VR

ZnBr

Li-ion

Short-term FWES

SMES

SCES

Electric energy time shift Peak load shaving Load following Reliability enhancement Spinning reserve Frequency fluctuation Power quality RESs time shift Renewable capacity firming Transmission congestion relief Substation on-site power

S S N N N S N S S N S

S N N N N N N N N S S

N N S S S S S S S S S

N S S S S S S S S S S

S S S S N S S S S N S

S S S S N S S S S N S

N N S S N S S S S S S

N N S S N S S N N N N

N N N N N S S N N N N

N N N N N S S N N N N

S S S S S S N S S N S

N N S S S S S S S S S

S = suitable application. N = NOTsuitable application.

Fig. 7. Structure of the total life cycle cost of a storage system. Fig. 8. Structure of the capital cost of a storage system.

speeding up the restoration procedures, in addition to supporting system reserve requirements to balance generation and load [46]. Besides, ES systems can help smart grids to build an efficient self-healing power system that can isolate any faulted component in the system and restore automatically, without human support [14]. Besides, ES helps support the transition from the preventive to the corrective N-1 security operation.

specified committed generators to cover power shortages during peak time or abnormal conditions (imbalances). An ES system is used to store the extra energy during the off-peak time and discharge this energy when needed with few-to-no pollutants [47]. 3.6. Frequency fluctuation Continuous changes during the day between the energy generation and electrical consumption lead to system frequency fluctuations, particularly with the presence of RESs, as clouds and temperature alter the feed-in from PV systems and the wind does not always blow at the same

3.5. Spinning reserve support Spinning reserve is considered a free generating capacity of 7

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Table 2 Main cost items of ES technologies [14,15,18–20,32,33]. Type of ES technology

PHS UCAES ACAES HES VR ZnBr NaS LA NiCd Li–ion SMES FWES SCES

CS (€/kWh) Range

IQR

Avg

Cpc (€/kW) Range

IQR

Avg

Cbp(€/kW) Range

IQR

Avg

Cfixed (€/kW) Range IQR

Avg

5–126 2–120 86–131 2–13.6 150–1000 178–530 180–563 165–847 564–1120 470–3800 500–1,080,000 200–150,000 100–94,000

41–115 30–47 97–120 2–9.75 440–536 178–314 277–358 190–510 571–1020 676–1144 55,000–100,000 70,000–120,000 40,000–90,000

68 40 109 3.7 467 195 298 264 780 795 72,000 90,000 57,000

373–4300 400–1550 804–887 500–4453 472–1500 151–595 241–3000 195–600 206–329 241–4000 196–2412 30–700 100–800

410–805 696–928 825–866 1630–3884 478–518 343–470 314–553 322–440 213–279 398–530 210–600 100–350 120–211

513 843 846 2465 490 444 366 378 239 463 300 150 150

3–28 3–28 3–28 10–40 10–40 10–40 70–120 43–130 70–120 70–120 100–500 50–300 10–100

9–22 9–22 9–22 12–30 12–30 12–30 75–110 65–108 75–110 75–110 75–110 90–190 15–50

15 15 15 25 25 25 80 87 80 80 150 125 25

2–9.2 2–4.2 2.2–3.7 16–44 3.4–17.3 3.2–6.9 2–17.3 3.2–13 4–24 2–13.7 4–6.5 4.3–6 1–5.9

4.6 3.9 2.2 25 8.5 4.3 3.6 3.4 11 6.9 5 5 5

3.9–7.7 2.6–4 2.2–3 24–39 4.3–16.1 3.6–5.4 3.3–16.5 3.3–6.1 5–19 4.9–11.2 4.2–5.8 4.8–5.6 3.2–5.5

Fig. 9. Schematic diagram of the proposed cost model of the of ES technologies.

ancillary services to support the frequency, such as the frequency primary reserve (containment reserve), secondary reserve (restoration reserve) and tertiary reserve (replacement reserve), as well as voltage stability [48].

speed, which affects the energy generation and in turn the grid frequency. This frequency fluctuation must be controlled and maintained within its permissible limits. ES systems have a quick response to the frequency fluctuation and can be utilized to introduce different 8

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Table 3 The common data and coefficients for ES systems. Parameter

Value

y (year) Belec (€/kWh) i Velec VFC

25 0.1 0.085 0.03 0.01

Table 6 Data for short-term ES systems.

Table 4 Data for long-term ES systems. Characteristic

PHS

UCAES

ACAES

P (MW) E (MWh) cs (€/kWh) cpc (€/kW) cbp (€/kW) cfixed (€/kW) Brep(€/kWh) Sdis (pu) μ (%) DODmax (%) d (day) hd (hour) nc (cycles)* N A (year) Bgas(€/GJ) Gr (MJ/kWh) Vgas

100 800 68 513 15 4.6 – 1.3 × 10−4 80 90 365 8 50 × 103 0 50 NA⁎⁎ NA NA

100 800 40 843 15 3.9 – 0 75 60 365 8 25 × 104 0 40 9.97 4.43 0.03

100 800 109 846 15 2.2 – 7.5 × 10−3 75 60 365 8 25 × 104 0 40 9.97 4.43 0.03

⁎ ⁎⁎

Characteristic

SMES

FWES

SCES

P (MW) E (MWh) cs (€/kWh) cpc (€/kW) cbp (€/kW) cfixed (€/kW) Brep (€/kWh) Sdis (pu) μ (%) DODmax (%) D(day) nc (cycles) n (required cycles)* H (hour)⁎⁎ hd (hour) N A (year)

1 0.01 72 ×103 300 150 5 72 × 103 0.3 95 80 365 5 × 105 19 × 103 0.01 0.5 1 20

1 0.01 90 × 103 150 125 5 90 × 103 1 93 80 365 1 × 105 19 × 103 0.01 0.5 1 20

1 0.01 57 × 103 150 25 5 57 × 103 0.4 95 75 365 5 × 105 19 × 103 0.01 0.5 0 25

⁎ n represents the number of cycles required in a short-term ES system application per year. This is because of the frequent operation of short-term ES technologies. ⁎⁎ H represents the discharge time in hours.

3.8. Power quality and hosting capacity enhancement Nowadays, enormous research work is going on in the area of power quality. Power quality issues include harmonics, sub-harmonics, supraharmonics and inter-harmonics, voltage sags and swells, flickers, interruptions, frequency fluctuation and imbalance [49]. The effects of RESs-based generation technologies on the power quality of a system depend on various factors: type, location, method of connection, control strategy, interface, voltage level, and size of the RESs units [50]. Fortunately, ES can be employed to support the power quality of a system and enhance [1,2] its performance by controlling the voltage and driving it to be within permissible limits, thus allowing the increase of the system's hosting capacity [2,51]. Besides, ES is of high necessity to introduce other ancillary services such as the reduction of voltage and frequency fluctuations and supporting the voltage stability of the electrical systems. The power rating of these ES technologies is usually less than 1 MW [14,51].

nc represents the number of cycles specified for an ES technology. Not applicable.

3.7. Renewable energy time-shift On the one hand, fossil fuel-based energy generation resources have many disadvantages, but are not intermittent and can be turned on or off at any time. On the other hand, renewable-based energy generation resources are intermittent (not available 24/7, year-round) and their output power fluctuates, which increases the need for a reserve in the system, as well as flexible units to be connected. ES provides flexibility and can be efficiently used to cover RESs intermittency while increasing the share of RESs. Also, ES systems can level the load curve by storing the electric energy produced by RESs during low load periods and delivering it during high load periods [14,40].

3.9. Substation on-site power ES systems can provide power to the switching components and substation communication and control systems during blackouts or emergencies [40].

Table 5 Data for medium-term ES systems. Characteristic

HES

VR

ZnBr

NaS

LA

NiCd

Li-ion

P (MW) E (MWh) cs (€/kWh) cpc(€/kW) cbp(€/kW) cfixed (€/kW) Brep(€/kWh) Sdis (pu)

10 20 3.7 2465 25 25 413 0

10 20 467 490 25 8.5 467

10 20 195 444 25 4.3 195

10 20 264 378 87 3.4 264

10 20 780 239 80 11 780

10 20 795 463 80 6.9 795

μ (%) DODmax (%) d (day) hd(hour) nc (cycles) N A (year)

45 100 365 2 2 × 104 1 15

8.3 × 10−3 80 100 365 2 13 × 103 2 10

8.3 × 10−3 70 80 365 2 5 × 103 3 7

10 20 298 366 80 3.6 298 0.2

2 × 10−3 70 80 365 2 2500 3 7

3 × 10−3 85 80 365 2 3500 2 9

2 × 10−3 90 80 365 2 4500 2 10

9

90 80 365 2 4500 2 12

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Fig. 10. The cost elements share within the LCCOS framework for a long-term ES project.

life cycle cost of storage technology. LCCOS in €/kW-year is usually expressed in an annualized form, as given in (1), to give a yearly figure of the total life cycle cost of a storage technology. In which LCCOS relates information on the operational factors around an ES, in addition to its techno-economic specifications.

Table 7 LCOE calculated for the long-term ES technologies.

LCOE (c€/kWh)

PHS

UCAES

ACAES

15.67

22.82

24.43

LCCOS = Ccap + COM + Crep + CEL 3.10. Renewable capacity firming

(1)

where, for an ES technology, Ccap is the capital cost, COM is the total operation and maintenance cost during the lifetime of the system in a project, Crep is the replacement cost, CEL is the disposal and recycling costs when the ES system reaches its end-of-life. Furthermore, a levelized cost of energy (LCOE) in c€/kWh, can be obtained by dividing LCCOS by the number of operating hours of the ES system in a year. Thus:

ES technologies can be used to support RESs in order to secure a constant supply. The output power of PV is affected by the solar radiation and ambient temperature of the atmosphere [6]. Also, the output power of WTs is affected by wind speed and direction in a specific location. Thus, ES helps support greater utilization of wind and solar energy and decrease periods of low generation from renewables [40,52].

LCOE = 100 ×

3.11. Transmission congestion relief

LCCOS hd × d

(2)

where hd is the number of operating hours per day and d is the number of operating days per year.

Grid congestion occurs due to transmission constraints. ES can be used to protect electrical networks from this problem by optimally allocating ES systems near to heavy loads to shift energy and avoid inefficiencies by efficiently making use of the power available, without violating the system constraints, to reduce the congestion while delaying the need for expansion of such systems, i.e. to postpone operators’ plans for network reinforcements [52,53].

4.1. Capital costs initial The initial capital cost (Ccap ) of an ES technology is comprised of the costs of the primary components such as the storage container, power converter, transformer, protection devices, cooling system, and initial can be divided into three main parts as given in (3), in others. Ccap which the first part (CSC) expresses the storage container cost in €, the second part (CPC) expresses the power conversion cost in €, and the third part (CBP), the so-called balance of plant costs, expresses the cost in € of the protective devices, cooling system, and others. Fig. 8 shows the structure of the capital cost of a storage system.

4. Cost models of ES technologies In general, the total cost of an ES technology, which is called LCCOS, includes capital, fixed and variable operation and maintenance, replacement, and end-of-life (disposal and recycling) costs, and it is suitable to show the economic expenditure during the lifetime of a storage technology in a project. Fig. 7 shows the main elements of the

initial Ccap = CSC + CPC + CBP

(3)

The annualized capital cost per power capacity (Ccap) of the ES

Fig. 11. Impact of variation of the project time span on LCCOS for long-term technologies. 10

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Fig. 12. Variation of LCCOS of long-term ES technologies with the cost of the charged power.

ES technology (CFOM) can be expressed as follows:

technology is calculated by using a ratio to determine the present cost value of a series of equal annual costs over a fixed amount of time, which is called the recovery factor (RF). RF can be expressed as given in (4).

i (1 + i) y RF = (1 + i) y − 1

CFOM = cfixed × FA

where cfixed is the factor of the fixed operation and maintenance cost (€/kW). However, if the ES technology will be used in short-term applications, another factor should be taken into consideration when calculating the fixed operation and maintenance costs, because short-term ES technologies must be ready in a standby mode to supply the load as fast as possible to prevent any possible failure in the electric power system st ) of buying energy to or the equipment. Thus, an annualized factor (CFOM compensate self-discharge losses resulting from the standby mode of short-term ES technologies has to be added in (10) as follows:

(4)

where i and y are the discount rate and lifetime of the project, respectively. Ccap is expressed as given in (5), where P is the rated power in kW.

Ccap =

initial Ccap

P

× RF

(5)

The cost of the storage container (CSC) depends considerably on the storage capacity. It can be represented by (6).

E ⎞⎟ CSC = cs ⎛⎜ μ × DODmax ⎠ ⎝

st CFOM = cfixed FA + CFOM

d S st CFOM = Belec × ⎛ ⎞ × ⎛ dis ⎞ × FB ⎝ 24 ⎠ ⎝ 100 ⎠

(6)

(13)

where Belec is the market energy price in (€/kWh), Sdis is the self-discharge ratio per day, d is the total number of working days per year, and Velec is the rate of variation of Belec. Secondly, the annualized variable operation and maintenance cost of the ES technology can be represented as follows:

(7)

d h CVOM = Belec × ⎛ ⎞ × d × FB μ ⎝ 24 ⎠

(14)

where hd represents the number of working hours per day. Besides, a compressed air ES storage technology uses natural gas, and this should be included in the variable operation and maintenance cost; therefore, (14) can be generalized to be:

(8)

where cbp is the plain cost of the balance of plant per power rating (€/kW).

d h d × hd × Gr CVOM = ⎜⎛Belec × ⎛ ⎞ × d × FB ⎟⎞ + ⎛Bgas × × Fgas ⎞ 24 μ 106 ⎠ ⎝ ⎠ ⎝ ⎝ ⎠ (15)

4.2. Operation and maintenance cost The operation and maintenance costs are categorized into a fixed cost that does not depend on the operation phase of the ES technology during the lifetime of the project, and a variable cost that depends on the operation phase of the ES technology. First, the annualized fixed cost of the ES technology per power rating (€/kW-year) can be formulated using the annualizing factor (FA), in which the rate of variation of the fixed operation and maintenance cost (VFC) is included, as given in (9).

so that m=y (1 + Vgas )m ⎞ ⎛ Fgas = RF ⎜ ∑ (1 + i)m ⎟ ⎝ m=1 ⎠

(16)

where Bgas and Fgas are the natural gas cost per gas unit (€/GJ) and the annual gas price factor, respectively. Gr and Vgas are the rate of gas consumption (MJ/kWh) and the rate of variation of the gas price. Finally, the total operation and maintenance cost of an ES technology (COM) during the whole lifetime is given in (17).

m=y

(1 + VFC )m ⎞ ⎛ FA = RF ⎜ ∑ (1 + i)m ⎟ ⎝ m=1 ⎠

(12)

m=y

(1 + Velec )m ⎞ ⎛ FB = RF ⎜ ∑ (1 + i)m ⎟ ⎝ m=1 ⎠

where cpc is the plain cost of the PCS per power rating (€/kW). Finally, the balance of plant costs such as the power transformer, protective devices, cooling system, and other costs can be represented according to the power capacity of the ES technology as given in (8).

CBP = cbp × P

(11)

so that

where cs is the cost of the storage container per capacity unit in €/kWh, E is the storage capacity in kWh and μ is the round-trip efficiency of the ES technology, and DODmax is the maximum depth of discharge of the storage technology. The PCS cost (CPC) depends on the rated power of the ES technology, and it can be expressed by (7).

CPC = cpc × P

(10)

(9)

Hence, the annualized fixed operation and maintenance cost of the

COM = CFOM + CVOM 11

(17)

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Fig. 13. Variation of LCCOS with efficiency and time of discharge of long-term ES technologies: (a) PHS, (b) UCAES, and (c) ACAES.

4.3. Replacement cost

Crep = Brep ×

Generally, the lifetime of the project is more than the lifetime of the storage containers (due to aging effects, type of technology, usage style, and so forth). For that reason, the replacement cost of the container should be included in the dynamic cost model of the ES technologies. It should be mentioned that this is not the case for the lifetime of the converters or the balance of the plant equipment, as they can cover the lifetime of the project completely. Thus, the annualized replacement costs can be formulated as follows:

h × N × Frep μ

(18)

so that the annual replacement cost factor (Frep) is expressed by (19) as m=N

1 ⎛ ⎞ Frep = RF ⎜ ∑ m×A ⎟ + (1 i ) ⎝ m=1 ⎠

(19)

where Crep is the total replacement cost of the ES technology, Brep is the replacement cost coefficient in €/kWh, N is the number of replacements during the lifetime of the project, and A is the period until the 12

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Fig. 14. The cost elements share within the LCCOS framework for a medium-term ES project.

Fig. 15. Impact of variation of the project time span on LCCOS for medium-term technologies.

Fig. 16. Variation of LCCOS of medium-term ES technologies with the cost of the charged power.

13

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represent the replacement cost of these types of technologies.

Table 8 LCOE calculated for the medium-term ES technologies.

LCOE (c€/kWh)

Crep = Drep × N × Frep

HES

Li-ion

ZnBr

NiCd

VR

LA

NaS

44.28

61.1

46.64

64.11

44.41

58.68

28.98

(20)

where Drep is expressed in €/kW, as these types of technologies are valued in terms of their power capacities. 4.4. End-of-life cost

replacement of the ES technology is required. For hydrogen-based systems, the most critical components to be replaced are the electrolyzer and the fuel cell. Thus, (20) is used to

An ES technology may use expensive rare earth materials that should be recycled along with strengthened plans for environmental

Fig. 17. Variation of LCCOS with efficiency and time of discharge of medium-term ES technologies: (a) HES, (b) VR, (c) ZnBr, and (d) NaS. 14

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Fig. 18. Variation of LCCOS with efficiency and time of discharge of medium-term ES technologies: (a) LA, (b) NiCd, and (c) Li-ion.

4.5. Formulation of the cost model of ES technologies

protection and pollution control. This is why the end-of-life cost (CEL) of ES technologies is very important from the socio-environmental perspective. Although it is not addressed in many works, the disposal and recycling of an ES technology should be included in the economic analysis of ES technologies. In its simplest form, CEL in €/kW-year is formulated as follows:

CEL = BEL × RF

In this work, the cost model of different types of ES technologies, including updated data integrity and detailed economic metrics and operation factors, is formulated in the Matlab environment to ensure the proper functioning of the cost model. The ES systems can be classified into three categories according to their power and energy capacities, in which PHS and CAES is classified as long-term ES since they have both large power, greater than 50 MW,

(21)

where BEL is the end-of-life cost coefficient (in €/kW). 15

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Fig. 19. Variation of LCCOS with lifetime and life cycles of HES.

Fig. 20. Variation of LCCOS with lifetime and life cycles of VR.

ES technology. The main factors cost of each ES, which is related to LCCOS, are shown in Table 2. Also, the IQR and Avg used are given in Table 2. Fig. 9 shows a schematic diagram of the proposed cost model of the ES technologies. The Matlab code of the formulated cost model is made available in [38]. Table 3 provides the common data and coefficients along a project lifetime such as the discount rate, electricity price, rate of variation of the electricity cost, and rate of variation of the fixed operation and maintenance cost. Based on [14,15,18–20,32,33], the technical parameters required to perform the cost model of the long-term ES technologies such as PHS, UCAES, and ACAES are summarized in Table 4. It can be seen from Table 4 that PHS and CAES have large power capacities, long lifetime, long discharge duration, very small self-discharge ratios, and high efficiency. The storage container cost of PHS and UCAES is low (€68/kWh and €40/kWh) since it depends considerably on the geography of the location, but this is not the case for the ACAES (€109/kWh). Table 5 provides the cost and technical parameters of medium-term

and energy storage capacities, greater than 100 MWh, HES, LA, NiCd, NaS, VR, ZnBr, and Li-ion batteries are classified as medium-term ESs since they have power rating from 1–50 MW and energy capacities from 5–100 MWh, and FWES, SMES, and SCES are classified as short term ESs since they have low power rating, around 1 MW, and low energy capacities around 3 kWh. There is a wide range of variabilities in the ES cost factors as capital, fixed and variable operation and maintenance, replacement, and endof-life costs. The range of values shows how spread out the entirety of our dataset is. However, an ES cost model will be quite sensitive to outliers of the range of values (extreme points). Hence, to solve the problem with the presence of outliers, the interquartile range (IQR) of data has been used in this study. The IQR indicates the middle (50%) of the data set. Before determining the IQR, we first need to know the values of the first quartile and third quartile which depend on the value of the median. Once we have determined the values of the first and third quartiles, the IQR will be determined as the difference between them. Then, the average value (Avg) of the IQR range is determined to be used as a single representative value to calculate the LCCOS of each 16

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Fig. 21. Variation of LCCOS with lifetime and life cycles of ZnBr.

Fig. 22. Variation of LCCOS with lifetime and life cycles of NaS.

noted that the replacement cost is considerable in the LCCOS value at the end of the useful life of the technology. However, the lifetime of PHS is 50 years, and the lifetime of CAES (whether ACAES or UCAES) is 40 years, and the project lifetime is less than 40 years. Therefore, the replacement costs of the long-term ES technologies will equal zero. The variable operating and maintenance cost depends on the usage of the storage system throughout its life span. It relates to the price of electricity, as well as fuel cost for CAES technology. Thus, it has a strong effect on the LCCOS since it includes the cost of purchasing electrical energy, as well as fuel costs for CAES storage. It is notable from Fig. 10 that PHS provided the lowest LCCOS (€457.6/kW) compared to the LCCOS value calculated for the CAES technology. Besides, PHS provided the lowest LCOE compared to the corresponding values calculated for the UCAES and ACAES technologies, as shown in Table 7. It is reasonable that both LCCOS and LCOE calculated for CAES technology are high, as CAES needs fuel to run. Also, the LCCOS and LCOE values are higher in ACAES than UCAES due to the arrangement needed in the ACAES installation. Figs. 11–13 show the sensitivity analysis results for the three longterm ES technologies to explore the most-efficient ES relative to the

ES technologies such as HES, VR, ZnBr, NaS, LA, NiCd, Li-ion based on the data available in [14,15,18-20,32,33]. Replacement costs are high in medium-term ES systems due to the short lifetime of the storage container. Also, for HES-based systems, the value of the specific cost per power capacity is higher than the specific cost per power capacity of the other technologies because of the additional cost of the electrolyzer and the fuel cell. Table 6 presents the cost and technical parameters of short-term ES technologies such as SMES, FES, and SCES. 5. Results and discussion In this section, the results of the cost model obtained for the different ES technologies are presented and discussed. 5.1. Results of long-term ES systems The elements share of the cost within the LCCOS framework is collectively shown in Fig. 10 for the two long-term ES technologies under consideration. The results obtained show a comparatively large difference between the LCCOS values of PHS and CAES. It should be 17

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Fig. 23. Variation of LCCOS with lifetime and life cycles of LA.

Fig. 24. Variation of LCCOS with lifetime and life cycles of NiCd.

Fig. 13 shows the variation of the LCCOS values with different efficiency and time of discharge values for the considered long-term ES technologies (according to their characteristics shown in Table 4). The range of efficiency and time discharge is selected according to the characteristic of each ES technology given in Section 2. Assuming that the ES will be discharged once per day during peak hours, it is clear from Fig. 13 that LCCOS considerably decreases with the increase in storage efficiency. For example, increasing efficiency from 70% to 85%, would make the LCCOS for PHS decreases from €519.1/kW to €432.3/ kW, for UCAES, LCCOS decreases from €699/kW to €612.9/kW, and for ACAES, LCCOS decreases from €749.4/kW to €654.1/kW at time of discharge of 8 h. Also, it can be seen from the same figure that LCCOS decreases with the decrease of the time of discharge of the storage system considerably. For example, decreasing the time of discharge from 8 h to 3 h would make the LCCOS for PHS decreases from €457.6/ kW to €188.6/kW at storage efficiency 80%, for UCAES, LCCOS decreases from €666.5/kW to €278.2/kW at storage efficiency 75%, and for ACAES, LCCOS decreases from €713.4/kW to €290.8/kW at storage efficiency 75%. Typically, LCCOS decreases with increased storage

time horizon of the project, cost of the charged power, storage efficiency, and discharging time of the technology. In the analysis, the time horizon of the project, cost of the charged power, storage efficiency, and discharging time of the technology were uniformly varied to show their impacts on LCCOS. It is clear from Fig. 11 that whenever the project lifetime increases, the capital cost of the storage system decreases; but the operation and maintenance cost will increase. Also, the project lifetime is still less than the lifetime of these types of technologies. Thus, LCCOS lightly decreases with the increase of the project lifetime. For example, increasing the lifetime of project 10 years (from 25 years to 35 years), decreasing LCCOS for PHS by 1.5%, for UCAES by 0.1%, and for ACAES by 1.9%. Fig. 12 shows the LCCOS values for different electricity prices (between €50/MW and €150/MW). It is clear that LCCOS is most sensitive to the change in electricity prices. For example, a 50% decrease in the electricity price, decreasing the LCCOS for PHS by 43.7%, for UCAES by 32%, and for ACAES by 29.9%. Generally, the change in electricity price has a significant influence on the resulting LCCOS since it has been affected by the variable operation and maintenance cost of ES. 18

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Fig. 25. Variation of LCCOS with lifetime and life cycles of Li-ion.

efficiency due to reduced cost of the storage container and variable operation and maintenance cost of the ES technology. On one hand, it can be seen from the same figure that in case of low efficiency and a long time of discharge, the LCCOS value increases significantly to its highest value, in which LCCOS is determined as €519/kW, €699/kW and €749.4/kW for PHS, UCAES, and ACAES, respectively. On the other hand, in case of high efficiency and short time of discharge, the LCCOS will have the lowest values, in which LCCOS is determined as €229.7/kW, €246.1/kW and €259.4/kW for PHS, UCAES, and ACAES, respectively. To sum up, LCCOS decreases with the decrease of the time of discharge of the storage system due to the reduced variable operation and maintenance cost of the ES technology.

5.2. Results of medium-term ES systems Fig. 26. The cost elements share within the LCCOS framework for a short-term ES project.

The elements share of the cost within the LCCOS framework is collectively shown in Fig. 14 for the seven medium-term ES technologies under consideration. It is clear from Figs. 15 and 16 that the variable operation and maintenance costs for medium-term ES technologies are less than the corresponding costs calculated for long-term ES technologies, and this is due to the fact that the discharge time of medium-term-based types is less than the discharge time of long-termbased types. However, one can see that the cost of buying energy for HES is relatively higher than for other medium-term ES technologies. This is because of the low efficiency in comparison with the other

Table 9 LCOE calculated for the short-term ES technologies.

LCOE (c€/kWh)

SMES

FESS

SCESS

36.92

59.02

54.03

Fig. 27. Impact of variation of the project time span on LCCOS for short-term technologies. 19

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Fig. 28. Variation of LCCOS of short-term ES technologies with the cost of the charged power.

characteristic of each ES technology given in Section 2. It is clear from Figs. 17 and 18 that LCCOS considerably decreases with the increase in storage efficiency. For example at time of discharge set to 2 h, LCCOS for HES decreases from €374.3/kW to €268.3/kW, if the efficiency increases from 35% to 65%, LCCOS for VR decreases from €391.6/kW to €307/kW, if the efficiency increases from 65% to 85%, LCCOS for ZnBr decreases from €393.2/kW to €284.7kW, if the efficiency increases from 60% to 85%, LCCOS for NaS decreases from €249.3/kW to €211.5/kW, if the efficiency increases from 75% to 90%, LCCOS for LA decreases from €410.5/kW to €324.4/kW, if the efficiency increases from 70% to 90%, LCCOS for HES decreases from €651.6/kW to €468/ kW, if the efficiency increases from 60% to 85% and LCCOS for Li-ion decreases from €451.8/kW to €407.5/kW, if the efficiency increases from 85% to 95%. Hence, one can see that LCCOS decreases with increased storage efficiency due to the reduced cost of the storage container and variable operation and maintenance cost of the ES technology. Also, it can be seen from the same figures that LCCOS considerably decreases with the decrease of the time of discharge of the storage system. For example, decreasing the time of discharge from 8 h to 3 h would make the LCCOS for HES decreases from €323.2/kW to €167.1/kW at storage efficiency 45%, for VR decreases from €324.2/ kW to €68.4/kW at storage efficiency 80%, for ZnBr decreases from € 340.5/kW to € 63.9/kW at storage efficiency 70%, for NaS decreases from €211.5/kW to €46.2/kW at storage efficiency 90%, for LA decreases from €410.5/kW to €71.5/kW at storage efficiency 70%, for NiCd decreases from €468/kW to €82.5€/kW at storage efficiency 85%, and for Li-ion decreases from € 428.4/kW to €80.6/kW at storage efficiency 90%. On a hand, it can be seen from the same figure that in case of low efficiency and a long time of discharge, the LCCOS value increases significantly to its highest value, in which LCCOS is determined as €374.3/kW, €391.6/kW, €393.2/kW, €249.3/kW, €410.5/kW, €651.6/ kW, and €451.8/kW for HES, VR, ZnBr, NaS, LA, NiCd, and Li-ion, respectively. On the other hand, in case of high efficiency and short time of discharge, the LCCOS will have the lowest values, in which LCCOS is determined as €160.2/kW, €66.3/kW, €57/kW, €46.2/kW, €60.9/kW, €82.5/kW, and €77.9/kW for HES, VR, ZnBr, NaS, LA, NiCd, and Li-ion, respectively. The reason behind this is that LCCOS decreases with the decrease of the time of discharge of the storage system due to the reduced variable operation and maintenance cost of the ES technology. Finally, each ES has a broader range of LCCOS due to the fact that the efficiency and time of discharge can vary widely and the LCCOS is mostly dependent on these ranges. The ES degradation is mainly caused by two reasons: calendric aging (lifetime) and cyclic aging (life cycle). Lifetime and life cycle of the ES vary from one ES to another; therefore, the different values of

medium-term ES technologies. Table 8 shows the values of LCOE calculated for the different medium-term ES technologies. It is notable from Fig. 14 and Table 8 that NaS technology provides the lowest LCCOS and LCOE values compared to the values provided by the other technologies because of its long lifetime, high efficiency, and low replacement costs. Hence, one can say that NaS technology is the most economic medium-term ES type. One can see from Table 5, which presents the data for medium-term ES systems, that the lifetime for HES equals 15 years and for Li-ion equals 10 years. Therefore HES needs to be replaced once during the lifetime of the project, but Li-ion needs to be replaced twice during the lifetime of the project. This is what made the LCCOS and LCOE for HES are lower than their corresponding values for Li-ion. In addition, replacement cost for HES is lower than the corresponding value for Li-ion. Figs. 15 and 16 show the sensitivity analysis results for the seven medium-term ES technologies to explore the most-efficient ES relative to the time horizon of the project, cost of the charged power, storage efficiency, and discharging time of the technology. Fig. 15 illustrates the impact of variation of the project time span on LCCOS for the medium-term ES technologies. Due to the limited lifetime and life cycle of this type of technology, they should be periodically replaced. Therefore, replacement costs will increase with an increase in the lifetime of the project. The same figure shows that we need to determine the optimal project time span to achieve the global minimum LCCOS based on the lifetime and replacement cost of the storage. For example, the optimal project time span to achieve the global minimum LCCOS for HES is 30 years, for VR, it is 20 years, for ZnBr, it is 20 years, for NaS, it is 20 years, for LA, it is 10 years, for NiCd, it is 15 years, and finally, for Li-ion, it is 20 years. Fig. 16 shows the LCCOS values for different electricity prices (between €50/MW and €150/MW). As before, it is clear that the change in electricity price has a significant influence on the resulting LCCOS. For example, a 50% decrease in the electricity price, decreasing LCCOS for HES by 27.5%, for VR by 14.4%, for ZnBr by 12.6%, for NaS by 21%, for LA by 13.9%, for NiCd by 12.2%, and for Li-ion by 10.4%. The change in electricity price has a significant influence on the resulting LCCOS since it has been affected by the variable operation and maintenance cost of ES. There is a wide range of variability in the feature values of each ES technology, such as lifetime, life cycle, efficiency, and time duration. Therefore, the values of these ranges should be included in the model to clarify the extent of impacts of each factor on the value of LCCOS. Figs. 17 and 18 show the variation of the LCCOS values with different efficiency and time of discharge values for the considered medium-term ES technologies (according to their characteristics shown in Table 5). The range of efficiency and time discharge is selected according to the

20

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Fig. 29. Variation of LCCOS with efficiency and time of discharge of short-term ES technologies: (a) SMES, (b) FWES, and (c) SCES.

For example, HES returns LCCOS value between €569.2/kW and €319.3/kW, VR has LCCOS between €907.4/kW and €324.1/kW, ZnBr has LCCOS between €514.4/kW and €236.1/kW, NaS has LCCOS between €351.3/kW and €211.5/kW, LA has LCCOS between €943.4/kW and €416.9/kW, NiCd has LCCOS between €468/kW and €248.4/kW, Li-ion has LCCOS between €1310/kW and €258.9/kW. ES systems show a more comprehensive range of LCCOS due to the fact that the lifetime and life cycles can vary widely and the LCCOS is mostly dependent on these values. Fig. 25 show that LCCOS of Li-ion ES at high lifetime and

lifetime and life cycles of each ES should be taken into consideration because they have a significant influence on the LCCOS. Sometimes, ES might need to be replaced before the end of the considered its lifetime due to its limited life cycles. Figs. 19–25 show the variation of the LCCOS values with different lifetime and life cycles values for the considered medium-term ES technologies. The ranges of lifetime and time cycles are selected according to the characteristic of each ES technology, given in Section 2. It is clear from the same figures that LCCOS is most sensitive to the storage lifetime and storage lifecycles. 21

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long life cycles is around €258.9/kW; therefore, this value is less than other values returned by HES, VR, and LA. When considering lifetime and life cycle scales in analyzing LCCOS for the Li-ion ES, LCCOS was lowest for a long lifetime and high life cycle, but it was expensive for a short lifetime or low life cycle. Figs. 20 and 21 show that VR and ZnBr have a long cycling life; therefore, they do not need to be replaced until their end of life. Finally, it is clear from the obtained results that at high life cycles, ES does not need to be replaced until its end of life. Hence, one can say that the short lifetime or low life cycles make the value of LCCOS high.

location, but this is slightly high for the ACAES since it not depends on the geography of the location. Also, the variable operating and maintenance cost of ES technologies is directly proportional to the nominal discharge time duration. Thus, the variable operation and maintenance costs for long-term ES technologies are higher than the corresponding costs calculated for medium-term and short-term ES technologies, and this is due to the fact that the discharge time of long-term-based types is more than the discharge time of medium-term and short-term-based types. Therefore, the variable operating and maintenance cost of longterm ES technologies are the most dominant in LCCOS calculation. For medium-term ES technologies, replacement costs are the significant costs due to the short lifetime of the storage container. Thus, replacement costs of medium-term ES systems have a significant influence on the LCCOS calculated. Also, for HES-based systems, the value of the specific cost per power capacity is higher than the specific cost per power capacity of the other technologies because of the additional cost of the electrolyzer and the fuel cell used. However, one can see that the cost of buying energy for HES is relatively higher than other mediumterm ES technologies. This is because of the comparatively low efficiency in comparison with the other medium-term ES technologies. For short-term ES technologies, the variable operating and maintenance cost is low since their time of discharge is short, and the replacement cost is low since their lifetime is high; therefore, the capital cost of short-term ES technologies is the most dominant in LCCOS calculation. Finally, it should be mentioned that cost reduction curves are not used in this work because the experience curve effect can sometimes come to an abrupt stop with unrealistic results, particularly with the new products or processes introduced in the ES markets, the rapid development of the technologies and the variability in in-market insights that is the main cost driver for the ES technology. From the authors’ point of view, expanding rapidly to realize experience curve benefits is a high-risk strategy as the experience strategy should be evaluated in a stable market environment in the presence of mature technologies, which have not yet been achieved in dynamic ES markets.

5.3. Results of short-term ES systems Here, the elements share of the cost within the LCCOS framework is collectively shown in Fig. 26 for the three short-term ES technologies under consideration. It is notable from Fig. 26 that SCES provided the lowest LCCOS (€67.385/kW) compared to the LCCOS values calculated for the SMES and FWES technologies. Besides, SCES provided the lowest LCOE compared to the corresponding values calculated for the SMES and FWES technologies, as shown in Table 9. Hence, one can say that the SCES technology is the most economic short-term ES type because of its long lifetime, high life cycle, and high efficiency. However, the lifetime of SCES is 25 years, and the assumed project lifetime is 25 years. Therefore, the replacement cost of the SCES technology is equal to zero. Figs. 27–29 show the sensitivity analysis results for the three shortterm ES technologies to explore the most-efficient ES relative to the time horizon of the project, cost of the charged power, storage efficiency, and discharging time of the technology. In the analysis, the time horizon of the project, cost of the charged power, storage efficiency, and discharging time of the technology were uniformly varied to show their impacts on LCCOS. Fig. 27 that whenever the project lifetime increases, LCCOS decreases until reaching the ES end-of-life, then LCCOS slightly increases due to the replacement cost. Fig. 28 shows the LCCOS values for different electricity prices (between €50/MW and €150/MW). It is clear that the change in electricity price has a significant influence on the resulting LCCOS. For example, a 50% decrease in the electricity price, decreasing LCCOS for SMES by 10.9%, for FESS by 10.7%, and for SCESS by 16.2%. Fig. 29 shows the variation of the LCCOS values with different efficiency and time of discharge values for the considered short-term ES technologies (according to their characteristics shown in Table 6). The range of efficiency and time discharge is selected according to the characteristic of each ES technology given in Section 2. It can also be seen that LCCOS increases slightly with the increase of the available storage efficiency, which is due to the fact that the available efficiency of short-term-based types is very high. For example at time of discharge set to 2 h, LCCOS for SMES decreases from €98.6/kW to €97.1/kW, if the efficiency increases from 95% to 97%, LCCOS for FESS decreases from € 107.7/kW to €105.9/kW, if the efficiency increases from 93% to 95% and LCCOS for SCESS decreases from €70.3/kW to €66.3/kW, if the efficiency increases from 90% to 97%. Also, it can be seen from the same figure that LCCOS decreases with the decrease of the time of discharge of the storage system considerably. For example, decreasing the time of discharge from 0.01 h to 0.001 h would make the LCCOS for SMES decreases from €98.6/kW to €32.5/kW at storage efficiency of 95%, for FWES, it decreases from €107.7/kW to €28.2/kW at storage efficiency of 93%, and for SCES, it decreases from €70.3/kW to €66.3/ kW at storage efficiency of 95%. It can be seen from the same figure that in case of low efficiency and a long time of discharge, the LCCOS values are high and equal to €98.6/kW, €107.7/kW, and €70.3/kW for SMES, FWES, and SCES, respectively. But, in case of high efficiency and low time of discharge, the LCCOS has the lowest values as €32.3/kW, €27.9/kW, and €19.5/kW for SMES, FWES, and SCES, respectively. To conclude, for long-term ES technologies, the capital cost of PHS and UCAES is low since it considerably depends on the geography of the

6. Conclusions This study provides an energy storage ES cost model that considers three categories of ES, different ES technologies with different time duration, efficiency, market price based on the current ES costs, and project lifetime in an integrated framework that consider the ES technical and economic characteristics supported by in-market insight. LCCOS and LCOE metrics were presented and discussed for different ES technologies based on the current technical and economic data. LCCOS allows a cost comparison of technologies in different system designs and various operation modes such as different power and energy rating. Thus, for decision-makers, LCCOS is a useful metric to address the economic feasibility of the ES since it includes not only the storage container, power converter, protection devices, cooling system, and other elements inherent to the technology itself but also market energy price, regulation of usage, and other elements related to the operation of storage. Besides, LCOE gives the economic resources that the storage operator needs to charge the storage system per energy unit that it delivers. It represents an appropriate tool to compare the cost of electricity storage technologies having the same number of yearly operating hours. The presented ES cost model is employed to serve as decisionmaking support by reviewing the costs and performance of the different ES technologies while considering a sensitivity analysis of the governing factors that affect the storage cost to introduce a powerful decision tool to empower techno-economic assessment of ES systems using the proposed cost models. We outlined the main findings of this study as follows: ü There are many benefits of ES technologies; however no one type of ES technology achieves all these benefits, but each ES is appropriate 22

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ü

ü

ü

ü

ü

influence the work reported in this paper.

to achieve specific benefits. Thus, the selection of the ES type relates to the desired benefits. Further, we can decide which one of these technologies has the lowest cost or LCCOS and LCOE metrics. Of the two long-term ES technologies, PHS technology provides the lowest LCCOS and LCOE values compared to the LCCOS and LCOE values calculated for the other technologies. Also, for long-term ES technologies, the capital cost is low since it considerably depends on the geography of the location. Also, the variable operating and maintenance cost are the most dominant in LCCOS calculation due to the long discharge time of long-term ES technologies. Of the seven medium-term ES technologies, NaS technology provides the lowest LCCOS and LCOE values compared to the other technologies because of its long lifetime, high efficiency, and low replacement costs. Also, for medium-term ES technologies, replacement costs are the significant cost component due to the short lifetime of the storage container. Thus, replacement costs of medium-term ES systems have a significant influence on the LCCOS calculated. Of the three short-term ES technologies, SCES technology provides the lowest LCCOS and LCOE values compared to the other technologies. Also, for short-term ES technologies, the variable operating and maintenance cost is low since their time of discharge is short, and the replacement cost is low since their lifetime is high; therefore, the capital cost of short-term ES technologies is the most dominant in LCCOS calculation. From the results obtained for the three ES categories, LCCOS depends substantially on the project lifetime, the electricity price, discharge time, life cycle, and efficiency of the storage. The results obtained show that costs vary significantly in the different studies in the literature due to dependence on the input parameter assumptions such as project lifetime, life cycle, efficiency, discharge time, and market price, which reduces the uniformity of the results among the different sources.

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