Energy storage system design for large-scale solar PV in Malaysia: technical and environmental assessments

Journal of Energy Storage 26 (2019) 100984

Contents lists available at ScienceDirect

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

Energy storage system design for large-scale solar PV in Malaysia: technical and environmental assessments

T

⁎

Mahmoud Laajimi, Yun Ii Go

School of Engineering and Physical Sciences, Heriot-Watt University, Putrajaya, Malaysia

A R T I C LE I N FO

A B S T R A C T

Keywords: Electricity storage Large-scale Solar PV Peak generation Peak demand

The Paris agreement, signed in 2015, is a commitment by the nations to set targets to reduce greenhouse gases emissions. In line with that, Malaysia is committed to lower its greenhouse gases emissions by 45% by 2030. This target is supported by the massive projects of Large-Scale Solar PV, of which 1 GW will be operating by 2020. However, the peak generation of this huge capacity, which occurs at noon, will not meet the peak electricity demand which occurs in the morning and in the evening, and this issue, in such scale, has not been addressed yet. Besides the direct use of solar generated electricity, storing electricity at the peak generation time and delivering it at the desired time may be the best usage of such intermittent energy. This project aims to design the optimal large-scale storage system for the Malaysian scenario. A comprehensive power system is simulated through HOMER Pro, including various storage technologies in different locations, selected according to the planned Large-Scale Solar capacity, the solar irradiation and the electricity demand. The power system has been sized according to two approaches. In the first approach, the storage is sized to satisfy the night peak demand, which occurs between 8pm and 10pm. In the second approach, the daily average demand is determined, and the storage is sized to satisfy all the demand above this average. The proposed power system is feasible only in five locations under the first approach, and not feasible in all the locations under the second approach. Based on HOMER Pro simulation outcomes, the best energy storage type is the 1 MWh Zinc Bromide flow battery.

1. Introduction With the global exponential increase of the solar PV deployment, the need to eliminate its main drawback is an urgent concern. Furthermore, the electricity generated from Large-Scale Solar farms has a peak in the middle of the day, mostly at the off-peak demand. It is beneficial to try to shift a part of the energy generated during the day, to be consumed at the peak demand. By doing this, the demand from the utility will be more stable, and the use of solar energy more efficient. Multiple storage technologies can be used in large scale applications. This study aims to identify the most suitable storage solution according to the Malaysian scenario, to examine the feasibility of a power system that includes this storage solution in different locations in Malaysia, and to determine the impacts of using this power system. In the following, the large-scale storage technologies are introduced. 1.1. Pumped hydro storage According to Rehman et al [1], the main characteristics of the PHS technology are the high storage capacity and its maturity. The main

⁎

Corresponding author. E-mail address: [email protected] (Y.I. Go).

https://doi.org/10.1016/j.est.2019.100984 Received 1 July 2019; Accepted 23 September 2019 2352-152X/ © 2019 Elsevier Ltd. All rights reserved.

limitations are the elevated capital costs and the low energy density. The author affirmed that the use of this technology will decrease in the future, since the most suitable locations are already used. The use of such technology has several benefits, including the flexibility of power generation, quick response time and energy shifting capability 1.2. Lead-acid batteries According to [2], lead-acid batteries is an uncomplicated storage technology, using a liquid electrolyte, and both negative and positive poles. The lead acid batteries are characterized by the slow charging, the low cost and the low depth of discharge. 1.3. Lithium-ion batteries The Lithium-Ion technology is composed of a positive electrode, usually manufactured from phosphate, and negative electrode, usually manufactured from graphite. The charging and discharging is ensured by the movement lithium ions between the two electrodes. This technology is characterised by the high efficiency, high energy density, long

Journal of Energy Storage 26 (2019) 100984

M. Laajimi and Y.I. Go

Nomenclature PHS PV CAES LAES PHES PEM GHI

A REF NPC LCOE PD OECD TNB

Pumped Hydro Storage Photovoltaic Compressed Air Energy Storage Liquid Air Energy Storage Pumped Heat Energy Storage Proton Exchange Membrane Global Horizontal Irradiation

LSS

Storage output / LSS output Renewable Energy Fraction Net Present Cost Levelized Cost of Electricity Peak Demand Organization of Economic Co-operation and Development Tenaga National Berhad: Utility company in Peninsular Malaysia Large-scale Solar

The LAES plants are composed of three parts: Compression section, cold and warm thermal storage section and expansion section. The first pilot LAES plant has an efficiency of 8% [6], but with improvement of the cold recycling, the overall efficiency is expected to be increased to over 50% [7]. The main characteristics of LAES technology are the long discharge period capability, the high costs but very high energy density, up to 97 kWh/m3.

lifetime with very high costs [2,3]. 1.4. Hybrid flow batteries Hybrid flow batteries use two electroactive parts. Zinc-Bromide batteries are the most commonly used among all hybrid flow batteries [4]. They are composed of two tanks, one tank is filled with zinc, and the second tank is filled with bromine. Each tank is connected to an electrode, with a pump ensuring the electrolyte circulation. The main advantages of this storage technology are the low energy density, the complete depth of discharge, the long lifetime and the low cost of storage. According to [2], Zinc Bromide flow storage solutions can have the lowest cost compared to other electrochemical storage technologies.

1.9. Pumped Heat Energy Storage PHES is a thermal storage of electricity. As it is shown in Fig. 1, the PHES system consists of a compression and expansion reciprocating engines, two heat exchangers in their inlets, a cold and a hot storage, and a buffer vessel to mitigate the changes in the mass within the reservoirs during charge [8]. So far, there is only one operating PHES plant, hosted by Newcastle University. The technology is still under demonstration stage, but it is expected to have an efficiency of 70%, competing then with LAES and CAES technologies. With very high energy density, up to 430 kWh/m3, the technology can deliver very low-cost stored electricity.

1.5. Redox flow batteries Redox Flow batteries are rechargeable storage technologies, consisting of two electrodes and an electrolyte [3]. The most commonly used Redox Flow battery is the Vanadium Flow battery. It is based on the ability of vanadium to exist in different valence states. The conversion between chemical and electrical energy is guaranteed by the transfer of electrons, and thus the change of valence of the electrolyte [3]. The main characteristics of vanadium flow batteries are the complete depth of discharge, the long lifetime, the unlimited upgrade capability, the low energy density and the high cost.

1.10. Power to Hydrogen The power to Hydrogen technology consists of the production of H2 from electricity, by the means of three types of electrolysis [9]. The first is the Alkaline water electrolysis, that is composed of an aqueous potassium hydroxide electrolyte, steel perforated electrodes with catalysts made from Nickel cobalt and iron, and a diaphragm separating the electrodes. The second power to Hydrogen technology is the Proton Exchange Membrane electrolysis. The PEM technology is composed of proton-conducting polymeric membrane, not separated electrolyte and

1.6. Sodium-Sulphur batteries The Sodium-Sulphur batteries are composed of a sodium electrode, a sulphur electrode and a ceramic solid electrolyte. The main features of this technology are the high efficiency, the high energy density and the high cost compared to all battery storage technologies [2,3]. 1.7. Compressed Air Energy Storage CAES technology has been used in Huntorf, Germany, since 1978, [5]. Later in 1991, the McIntosh CAES was built in the United States. Although there is only 2 commercial CAES operating plants in the world so far, the technology is considered mature, since it has proven its capability to store and deliver energy on a large scale. The main components of CAES technology are the compressed air storage, an air compressor, a turbine and a natural gas supply. The air is compressed and stored during the peak generation time. It is expanded through a turbine, with the combustion of natural gas, to generate electricity at the desired time. The efficiency of this technology is between 42% and 60% for the existing commercial plants. Recent researches claim that it can reach 80% with few modifications on the compression process and the heat management [5]. 1.8. Liquid Air Energy Storage The LAES technology is a relatively new storage technology. However, since all its components are technologically mature, and been used widely, this technology has promising potential to be improved.

Fig. 1. Layout of PHES system [8]. Main components: cold and hot reservoirs, buffer vessel, two heat exchangers and two reciprocating engines. 2

Journal of Energy Storage 26 (2019) 100984

M. Laajimi and Y.I. Go

The second step to estimate the electricity load profile is to determine the peak demand of each province. According to the Malaysian Energy Statistics Handbook of 2017, published by the Energy Commission [29], the peak demand in Peninsular Malaysia is 17 788 MW, and 3 950 MW in Sabah & Sarawak. The peak demand of each state of Peninsular Malaysia is then calculated as follows:

diaphragm and directly-connected electrodes . The third technology is the High Temperature Water electrolysis, that utilize steam at very high temperatures, between 700 ∘C and 1000 ∘C. The use of steam ensures the supply of a part of the energy needed for the electrolysis process, but it requires stable operation, which is not usually the case of storage usage. The efficiency of this electrolysis technology can exceed 100%, while it has a maximum of 67% for the Alkaline and PEM electrolysis technologies. The main drawback of the Alkaline technology is the high minimal part load, that is between 20% and 40%, while it consists of the high costs for PEM technology [9]. The injection of the produced Hydrogen in the gas grid must be less than 5% of concentration [10].

PDState =

PDPeninsular × PopState Pop

(1)

Peninsualr

With: - PD: Peak demand - Pop: Population

1.11. Power to Methane The limit of H2 injection into the gas grid can be overcome by the conversion of Hydrogen to Methane, that can be injected without limits [10]. This conversion is done under the Sebatier process, and it requires the use of hydrogen carbon dioxide and electricity [11]. The carbon dioxide can be provided by power plants or industrial processes. The efficiency of this reaction has a maximum of 83%, 250 ∘C to 400 ∘C operating temperature and 80 bar operating pressure [11].

The population share of each state, as well as the population and the peak demand of each state are stated in Table 3. The hourly load data for a typical day in Malaysia, used in [30] and shown in Fig. 2, has been used to generate an hourly load profile. For each hour of the day, the following coefficient has been calculated:

1.12. Categorisation of large-scale storage technologies

Finally, the hourly coefficient is multiplied by the peak demand of each state, to determine its load profile:

Hourly Coefficient =

Table 1 provides a wide summary of the characteristics and parameters of the large-scale storage technologies reviewed in this study.

Hourly Load PD

(2)

(3)

HLState = HC × PDState With:

2. Methodology - HL: Hourly load - HC: Hourly coefficient from (2) - PD: Peak demand

The methodology of this study consists of the selection of the suitable locations for the storage power system. After the selection, the power system is simulated by HOMER Pro. The feasibility of the power system is determined from the simulation results. Furthermore, the results of each simulation allow to compare the projects in the feasible locations, as well as the technologies used in each location. The technical and environmental impacts of the energy storage system are examined in one of the feasible locations, for all the project lifetime.

Selangor and Sabah have by far the highest electricity load compared to the Malaysian states. Selangor is surrounded by Negeri Sembilan, Perak and Pahang. As stated in Table 2, these states have high GHI rates and the highest LSS capacities. The very high electricity load in Selangor implies a very large storage capacity. Subsequently, very large LSS capacity will be required to satisfy this storage demand. For this reason, the LSS capacity of Negeri Sembilan, Perak, Pahang and Selangor will be combined, to test whether it is sufficient to satisfy the needed storage capacity for the state of Selangor. Then, the combination of the LSS capacity of these four neighbour states with the electricity demand of Selangor is named « Group Selangor ». Such option is not possible for Sabah, due to its geographic independency from Peninsular Malaysia. Consequently, Sabah would have its own feasibility study for a storage project, charged by Sabah planned LSS capacity (72.5 MW). To summarize, feasibility studies will be carried out for 7 locations; Perak, Kedah, Negeri Sembilan, Pahang, Selangor, Group Selangor and Sabah.

2.1. Project locations selection A selection of the locations where the power system will be tested is done according to three factors: a) the planned LSS capacity, b) the global horizontal irradiation, and c) the electricity load of each location. According to the announcements of the Energy Commission of the planned LSS projects by 2020 [25,26], Perak has the largest LSS planned capacity (198.87 MW), followed by Kedah (194.89 MW), Negeri Sembilan (121 MW), Pahang (109.92 MW) and Selangor (87.94). The software PVSyst has been used to determine the global horizontal irradiance of each single project location. The monthly meteorological data were collected using the Meteonorm software available in PVSyst. An average of GHI for each state has been then calculated. The ranking of the first five states according to the LSS planned capacity, as well as the ranking of the same states according to the global solar irradiance, are presented in Table 1. The third and the sixth ranked states according to the global solar irradiance are not included in the table, due to the low planned LSS capacity in those states. Melaka that has the third solar irradiance average, will have only 6.8 MW of LSS by 2020, while Perlis, which has the sixth solar irradiance average, will have only 33 MW. One of the main design factors of an energy storage facility, is the storage capacity, that must satisfy a certain load. Consequently, the electricity load profile is a fundamental data to size a storage project. The population share of the Malaysian States, as well as the total population of Malaysia have been announced by the Department of Statistics [27,28]. These values have been used to determine the population of the Malaysian States.

2.2. Power system sizing 2.2.1. First approach: night peak demand The first approach to size the power system, is based on the Table 1 States ranking according to LSS planned capacity and Solar irradiation.

3

State

LSS

GHI

Perak Kedah Negeri Sembilan Pahang Selangor

1 2 3 4 5

4 7 2 5 1

Journal of Energy Storage 26 (2019) 100984

M. Laajimi and Y.I. Go

Fig. 2. Hourly electricity load [30].

2.2.3. Storage systems 2.2.3.1. Storage types. In order to determine the best storage technology for each location, 10 storage types have been tested. To avoid the uncertainty of the storage costs available on the internet, the generic models available in the software have been used. The energy storage costs are provided by HOMER Pro software. The simulated storage types are as follows: 2.2.3.1.1. Lead acid

principle that the energy storage is able to deliver an amount of electricity equal to the peak demand between 8pm and 10pm. From the hourly load data, estimated in 2.1., the above average energy demand between 8pm and 10pm is determined. 23

AD =

∑i = 0 HL 24

(4)

21

ND =

∑ HL 20

• Generic 1 kWh Lead Acid: A generic 12-volt lead acid battery with 1 kWh of energy storage and 0.3 kW as maximum discharge power. • Surette 4 KS 21P: A 4-V lead acid battery with 6.39 kWh of energy

(5)

The energy to be delivered by the storage is equal to:

SC1 = ND − 2 × AD

storage and 1.5 kW maximum discharge power. 2.2.3.1.2. Lithium-ion

(6)

With:

• Generic 1 kWh Li-Ion: A generic 6-volt lithium-ion battery with 1 kWh of energy storage and 3 kW as maximum discharge power. • Generic 100 kWh Li-Ion: A generic 600-volt lithium-ion battery with

- SC1: Storage capacity (kWh) under the first approach - ND: Night demand (kWh), between 8pm and 10pm - AD: Average demand (kWh)

•

The power output of the storage is equal to the maximum between the demand of 8pm to 9pm, and the demand of 9pm to 10pm. The converter is responsible for converting the DC output from the PV and the storage to AC electricity, and vice versa. The capacity of the converter is determined according to (7).

CC = Max (LSS C , SC )

100 kWh of energy storage and 300 kW as maximum discharge power. Generic 1 MWh Li-Ion: A generic 600-V lithium-ion battery with 1000 kWh of energy storage and 3000 kW as maximum discharge power. 2.2.3.1.3. Flow batteries

• Generic Vanadium Flow: A generic vanadium flow battery, in which you can size independently the power and energy of the storage. • Generic 1 MWh Zinc Bromide: A generic 600-V zinc bromide flow

(7)

With: - CC: Converter capacity (kW) - LSS C: LSS capacity (KW)

•

2.2.2. Second approach: long discharge hours The second approach to size the power system is based on the principle that the energy storage is able to satisfy all day peak demand. The average demand is calculated by (4), and the storage energy capacity is then calculated according to the following equation:

•

battery with 1000 kWh of storage capacity and 3000 kW as maximum discharge power. The cost of energy storage is RM 400/kWh (USD 97/kWh) [31]. 280 kW-1 MWh Primus Power EnergyPod: A modular 840-V zinc bromide flow battery, with 1008 kWh energy storage capacity and 420 kW maximum discharge power. Redflow ZBM2: A 48-V zinc bromide flow battery with 10.3 kWh of energy storage capacity and 5 kW maximum discharge power. 2.2.3.1.4. PHS

• Generic

(8)

245 kWh Pumped Hydro: A generic 240-V PHS with 254 kWh of energy storage capacity and 22 kW as maximum discharge power.

With SC2: Storage Capacity (kWh) under the second approach. The storage power capacity is equal to the maximum hourly above average demand. The converter size is the maximum between the LSS capacity and the highest peak demand of the day.

2.2.3.2. Storage quantities. According to the storage capacity calculated in 2.2.1 and 2.2.2, each storage type, has a different quantity. The storage type quantities are calculated with consideration to the useful

23

SC2 =

∑ HL − (24 × AD) i=0

4

<10

5–1000

1–1000

0.005–1000

0.350–5

Zinc Bromine Battery Sodium-Sulphur Battery Pumped Hydro Storage D-CAES

A-CAES

I-CAES

Liquid air energy storage Pumped heat energy storage Power to Hydrogen Power to Methane

<40

<60

5

Up to TWhs Up to TWhs

<1000

<1000

<0.600

0.150

Up to GWhs Up to GWhs Up to GWhs Up to GWhs <20

<15

Vanadium Redox Battery

<36

<5000

0.1–100

Lithium-ion battery

<40

<238

<36

Lead acid battery

Energy Rating (MWh)

<200

Power rating (MW)

Storage technology

>24

>24

<6

>24

>24

>24

>24

>24

<8

<4

<4

<6

<8

Discharge duration (hours)

<1 min

<1 min

****

100 s to 5 min

<1 min

5–15 min

10–15 min

10 s to 2 min

10–20 ms

10–20 ms

10–20 ms

10–20 ms

10–20 ms

Response time

30 years

5–30 Years

****

>20 years

20–60 years

20–60 years

20–60 years

5–15 years/ 1000–10000 cycles 5–15 years/ 12000–18000 cycles 5–8 years/2000 Cycles 5–10 years/ 2500–4000 cycles 30–60 years

3–12 years / 500–1200 cycles

Life time (year / Cycles)

Table 2 Summary of characteristics and parameters of large-scale storage technologies.

0.2%


70% 38 to 80%

∼0% ∼0%

0.5–20 kWh/ m3

1800 Wh/l

20 to 40% 18 to 36%

∼0%

70%

0–4%

****

70–430 kWh/ m3

600 Wh/l

****

97 kWh/ m3

50–70%

42–60%

∼0%

2–15 kWh/m3

1–25 kWh/ m

65–87%

∼0%

0.5–1.5 Wh/kg

3

75–86%

75%

70–85%

90–94%

75–85%

Efficiency (%)

0.05–20%

<240 Wh/kg

0.2%

0.1–0.3%

<200 Wh/kg

30–60 Wh/kg

0.1–0.3%

Selfdischarge (%)

<50 Wh/Kg

Energy density (Wh/kg)

2000 USD/kW

1000–2000 USD/kW

50–180 USD/kWh

1000–2000 USD/kw

****

****

2–140 USD/kWh

5–100 USD/kWh

1000–3000 USD/kWh

110–750 USD/kWh

600–1500 USD/kWh

1200–4000 USD/kWh

300–600 USD/kWh

Capital cost (USD)

No

No

No

No

No

Yes

Yes

Yes

No

No

No

No

No

Location dependency

Development

Development

Research/ Demonstration Research/ Demonstration Demonstration, Precommercial Demonstration

Developed

Mature

Demonstration, commercially available Demonstration, commercially available Demonstration, commercially available Commercially available Mature technology

Maturity

[9,10,13,22,24]

[9,10,14,22–24]

[8,21]

[6,7,17–20]

[5]

[5]

[5]

[1,4,18,22]

[2,3,11–14]

[2,11–14,16]

[11–15]

[2–4,11–13]

[2,4,11–13],

References

M. Laajimi and Y.I. Go

Journal of Energy Storage 26 (2019) 100984

Journal of Energy Storage 26 (2019) 100984

M. Laajimi and Y.I. Go

have an efficiency between 19.1% and 22.5%. Thus, the efficiency of the generic flat plate PV is set to 19%. The derating factor, which is a correction factor that takes into consideration the real operating conditions such as dust and wiring inefficiencies [34], is set to 80%. The panel slope is set automatically by HOMER according to each location. The lifetime of the generic PV panel is 25 years. The cost of PV panel is RM 3000/kWh (USD 727/kWh). This price is in accordance with the price of utility scale PV in Malaysia, published by SEDA in 2016 [35]. The performance degradation of the PV panel is set to 1% per year, according to [36], a study for the performance degradation in tropical countries. This parameter is introduced to the Multi Year module, which allows to generate results according to the changes that can occur from year to year, during the project lifetime [37].

Table 3 Population and estimated peak demand of Malaysian states. State

Population share (%)

Population

PD (MW)

Perak Kedah Negeri Sembilan Pahang Selangor Sabah Johor Terengganu Kelantan Perlis Penang Melaka Total

7.8 6.7 3.5 5.2 25.8 21.1 11.5 3.7 5.7 0.8 5.4 2.8 100

2,499,877 2,147,330 1,121,740 1,666,585 8,268,823 6,762,487 3,685,716 1,185,839 1,826,833 256,398 1,730,684 897,392 32,049,700

1758.51 1510.51 789.07 1172.34 5816.61 3950 2592.67 834.16 1285.06 180.36 1217.43 631.26 21,738

2.3.4. Economical inputs According to [38], the inflation rate in Malaysia is 3.1% by 2020. Since Malaysia is a non-OECD country, the discount rate for renewable energy projects is equal to 10% [39]. These parameters are introduced to HOMER to be considered in the economic analysis. The projects life time is set to 21 years, in line with the Power Purchase Agreements in Malaysia [40].

capacity of the storage type, the efficiency of storage and the efficiency of the converter. The following equation has been used to calculate the quantity of each storage type:

Q=

SC/STC DoD × ηStorage × ηConverter

(9)

With: -

3. Results

Q: Storage type quantity SC: Needed storage capacity STC: Specific storage type capacity DoD: Depth of discharge ηStorage: Specific storage type efficiency ηConverter: Converter efficiency

3.1. Feasibility study The Peak Demand Satisfaction is the ratio of the daily storage output to the daily peak demand. The Storage to LSS ratio, is the ratio of the electricity storage output to the LSS electricity output in a given location. This ratio determines the gap between the needed storage capacity and the announced LSS capacity. Among all the technical results, the PD Satisfaction and the Storage to LSS ratio are the most critical parameters. The storage systems in the two approaches are sized to satisfy the peak electricity demand, whether it is the night peak, or all along the day above average demand. Thus, satisfying the peak demand is a measurement of the performance of each system. Additionally, the storage to LSS ratio is a measurement of the feasibility of the power systems. The LSS capacities introduced in the simulations are those planned officially by 2020 [1,2]. Consequently, this parameter allows to determine whether a storage project powered by solar electricity is feasible by 2020 or not. In the following graphs, whether it is to determine the feasibility or to compare the feasible locations, the technologies that have the lowest NPC are compared. In each location, the best three types of batteries, in addition to PHS, are represented and compared.

2.3. HOMER Pro simulation 2.3.1. Power system The sized power systems under the two approaches have been simulated by HOMER Pro software. A model of the simulated power system is shown in Fig. 3. The components of the proposed power system are as follows:

• Gen: it represents the conventional power generation, named Auto • • • •

sized Genset in HOMER software. This component adjusts itself to the load. Electric load: it is the hourly load profile that has been estimated previously. PV: the photovoltaic panel. Storage: for each location, the 10 storage types have been sized and tested in HOMER. Converter: composed by an inverter and a rectifier.

3.1.1. First approach 3.1.1.1. Storage to LSS ratio. In Sabah and Selangor, the storage output exceeds the PV generation (250% and 315% respectively), which means that the LSS capacity must be significantly increased. The storage

2.3.2. Meteorological data The meteorological data of the selected LSS locations have been determined using the National Renewable Energy Laboratory data [32], that can be imported from HOMER software directly. It covers the Global horizontal irradiance, the clearness index, the wind velocity and the temperature degrees. While PVSyst has been used to select the project locations prior to the power system modeling, the data available in HOMER Pro itself has been used for the simulations. Table 4 shows the scaled annual average of GHI, retrieved from HOMER, for the selected locations. 2.3.3. PV Panel The generic flat plate PV of HOMER is used in the proposed power system. The operating temperature of the panel is 47 ∘C, and the temperature effect on power is − 0.5%/∘C. According to [33], the monocrystalline PV panels used in Malaysia

Fig. 3. Simulated Power system in HOMER Pro. 6

Journal of Energy Storage 26 (2019) 100984

M. Laajimi and Y.I. Go

feasible in Group Selangor, Kedah, Negeri Sembilan, Pahang and Perak. According to HOMER Simulation results, presented in Table 5, the NPC and LCOE of the systems using 1 MWH Zinc Bromide is the lowest among other technologies, in the 5 feasible locations. Therefore, 1 MWH Zinc Bromide battery is the most suitable storage type for largescale solar projects in Malaysia.

Table 4 Annual average GHI in the selected locations. State

GHI (kWh/m2/day)

Selangor Kedah Sabah Negeri Sembilan Pahang Perak

5.40 5.49 5.52 5.35 5.42 5.36

3.2. Technical outcomes in the feasible locations 3.2.1. Capacity shortage Power systems that contain batteries have always the same capacity shortage. The location with the lowest capacity shortage value is Negeri Sembilan (0.0331%), followed by Kedah (0.0353%) and Perak (0.0452%). Pahang and Group Selangor have the same capacity shortage value, equal to 0.0873%. PHS systems have greater capacity shortage than battery systems. It is equal to 0.942% in all the locations. The Capacity Shortage of the best simulated power systems are shown in Fig. 7.

systems in those locations are consequently not feasible with the current LSS capacities. Battery systems have the highest ratio in Group Selangor (53.54%), followed by Pahang (50.86%), Perak (42.62%), Kedah (36.84%) and finally Negeri Sembilan (31.83%). In the feasible locations, PHS systems have low ratios, not exceeding 10%. The Storage to LSS Ratios are shown in Fig. 4. 3.1.1.2. Peak demand satisfaction. In all the simulations, battery systems have a demand satisfaction near 100%, while PHS systems have a very low value that does not exceed 20% in all cases. Furthermore, the lowest state of charge of PHS systems is 80%, which means that the PHS systems are unable to deliver the stored energy in a short period of 2 h. The PDS of the best storage systems in the selected locations are shown in Fig. 5.

3.2.2. Renewable Energy Fraction The Renewable Energy Fraction is the ratio of the electricity generated by solar PV to the total energy generated by the power system. Systems with PHS have slightly better REF than those with batteries. The performance of different battery storage systems is the same in all locations. The REF of the best simulated power systems is presented in Fig. 8.

3.1.2. Second approach To satisfy the storage needed in the second approach, larger PV capacities are required. Compared to the current LSS generation, the LSS capacity must be increased to a value equal to 1.9 times the current capacity in Negeri Sembilan. The increasing ratio is higher in Kedah (2.4 times) and Perak (2.9 times). It is much higher in Pahang where the LSS capacity must be increased 3.7 times and even more in Group Selangor, up to 4.1 times. The LSS capacity must be increased 25 times in Selangor and 20 times in Sabah. Those values are in the same range for both PHS and Batteries, and this is for all the locations. Consequently, the power system is not feasible under the second approach in all the locations. The Storage to LSS Ratios are shown in Fig. 6.

3.3. Environmental outcomes in the feasible locations While adding LSS to the generation mix results in a positive impact on the CO2 emissions, the use of storage is significantly beneficial. In the feasible locations, the difference in terms of CO2 emissions between reference systems and systems with energy storage has been calculated. This allows to determine the locations that provides the highest reduction in CO2 emissions. Group Selangor simulation results show a reduction of 1.47 MtonCO2/year for PHS and 1.24 MtonCO2/year for Batteries. Perak and Kedah will have a lower emissions reduction, close to 0.43 MtonCO2/year for PHS and 0.35 MtonCO2/year for batteries. Pahang will reduce its emissions by 0.3 MtonCO2/year using PHS, and by 0.25 MtonCO2/year using batteries. And finally, Negeri Sembilan has the lowest emission reduction, that is equal to 0.2 MtonCO2/year by using PHS, and to 0.17 MtonCO2/year using batteries. A comparison between the power systems in each location is presented in Fig. 9.

3.1.3. Findings The best storage technologies are 1 MWh Zinc Bromide, 1 kWh Lithium-Ion and 100 kWh Lithium-Ion battery systems. A power system using any of these three energy storage technologies is technically

Fig. 4. Storage to LSS ratio under the first approach. 7

Journal of Energy Storage 26 (2019) 100984

M. Laajimi and Y.I. Go

Fig. 5. Peak Demand Satisfaction under the first approach.

electricity injected into the grid, starting from 7am to 6:50pm. The rest of the generated electricity is used to charge the batteries. The LSSStorage generation profile is shown in Fig. 11. The storage system being charged during the day by the solar energy, and discharged between 8 pm and 10 pm, to meet the night peak demand, will have the following annual state of charge, described in Fig. 12. The annual storage output in Pahang is 80 GWh. Since the PV performance decrease by 1% each year [36], the electricity generated for LSS PV will decrease as well. Thus, the carbon dioxide emissions reduction will decrease year after year. The evolution of CO2 emissions reduction for the whole project lifetime is resented in Fig. 13. The total emissions reduction for the whole project lifetime is 5.12 MtonCO2.

3.4. Generation profile outcomes in one of the feasible locations In this paragraph, Pahang generation profile characteristics are presented. In Pahang, and with the LSS capacity planned by 2020 (110 MW), 156.3 GWh will be generated in the first year, with a capacity factor of 16.2%. Due to the annual performance degradation of PV, the annual electricity generation will be reduced from year to year. In the 21st year of the project, the installed LSS capacity will generate 128 GWh of electricity. This generated electricity is equal to 2% of the total electricity generated by the power system. The evolution of LSS generation is presented in Fig. 10. In order to avoid the fluctuations of the solar generation, the grid injection will be done on a stable and continuous rate, while all the fluctuations will be absorbed ed by the storage. The generation of an average sunny day, multiplied by the storage to LSS ratio, is the

Fig. 6. Storage to LSS ratio under the second approach. 8

Journal of Energy Storage 26 (2019) 100984

M. Laajimi and Y.I. Go

Table 5 LCOE and NPC of the best power systems in the feasible locations. Technology

1 MWh ZB 1 kWh Li-Ion 100 kWh Li-Ion

Group Selangor LCOE (RM/ NPC (Billion kWh) RM)

Kedah LCOE (RM/ kWh)

NPC (Billion RM)

Negeri Sembilan LCOE (RM/ NPC (Billion kWh) RM)

Pahang LCOE (RM/ kWh)

NPC (Billion RM)

Perak LCOE (RM/ kWh)

NPC (Billion RM)

0.438 0.439 0.440

0.432 0.434 0.435

46.903 47.108 47.176

0.438 0.440 0.440

0.439 0.441 0.441

36.954 37.111 37.158

0.433 0.434 0.435

54.645 54.882 54.953

182.827 183.606 183.837

24.815 24.926 24.954

Fig. 7. Capacity shortage of the best storage systems in the feasible locations.

4. Conclusions

conventional power generation. The power systems are able to satisfy the night peak demand, with mature and commercially available technologies, such as Flow batteries and Lithium-Ion batteries. The energy management of the proposed power system, allows to constantly feed the solar electricity to the grid, and charge the storage without any negative impacts. Pumped Hydro Storage would be beneficial in such large scale, but its low power density holds it back from delivering the

A power system that includes a storage medium, aiming to satisfy the peak demand of the night, and consequently reduce the mismatch between the peak solar generation and the night peak demand on the scale of entire states, is technically feasible and financially profitable. It leads to important reductions in CO2 emissions, and more stable

Fig. 8. Renewable Energy Fraction of the best storage systems in the feasible locations. 9

Journal of Energy Storage 26 (2019) 100984

M. Laajimi and Y.I. Go

Fig. 9. Carbon Dioxide emissions reduction in the feasible locations.

Fig. 10. Yearly electricity generation from LSS PV in Pahang over the project lifetime.

at this stage, namely the ability of this technology to be upgraded in capacity. The installed LSS will be increased in all the states, and the same as LSS, installing more energy storage is most probably an irreversible trend. The response between different types of storage technologies should be studied as well, due to the fact of the impossibility of installing only one storage technology in all the country. The CO2 emissions reduction in the feasible locations is equal to 1.6 MtonCO2 for the first year. This reduction is equal to 0.6% of the total emissions of Malaysia in 2017 [41]. This is a pilot study of large-scale energy storage solutions in Malaysia since the announcement of Energy Commission of the planned LSS projects. We adopt the data and statistics of SEDA and Energy Commission to ensure the practicality and feasibility of the sizing approaches and proposed technical solutions. However, it is worth indicating that the system design precision of HOMER Pro software is 0.01%, which implies a high accuracy range. It will be with huge importance if in the near future, a similar study is done with another software or another methodology.

required energy to the grid in a short period of two hours only. A storage project is feasible in Group Selangor, and not feasible in Selangor alone. While Selangor has an important planned LSS capacity, the huge electricity load in this state makes it impossible to satisfy the Peak demand by solar energy. It is feasible only with the combination of the planned LSS capacity of Negeri Sembilan, Perak and Pahang, the neighbour states of Selangor. Their LSS capacity is able, if stored, to satisfy the huge night peak demand of Selangor. We can conclude that the feasibility of a storage project depends not only on the solar irradiation and the LSS capacity, but also on the demand that the storage has to satisfy. According to HOMER Pro simulations, the most suitable energy storage type is the 1 MWh Zinc Bromide flow battery. The great rated capacity of the battery is a main factor for its low price compared to other types. Furthermore, the high efficiency, the full depth of discharge, the fast charging and the excellent value for money insured by this technology at this scale make it very competitive and attractive solution. Nevertheless, some features should be considered and studied 10

Journal of Energy Storage 26 (2019) 100984

M. Laajimi and Y.I. Go

Fig. 11. Daily generation from LSS PV, injection to the grid, and storage charging.

Fig. 12. Storage annual charging from LSS PV and discharging at night peak time.

Fig. 13. Carbon Dioxide emissions reduction decrease through the project lifetime.

5. Recommendations

states of Malaysia. Further investigation of the technical feasibility of energy storage projects, as well as the economic analysis of different scales of investment should be carried out in future work. This will provide a very comprehensive idea for the solar market in Malaysia, and the technical and financial outcomes of investing in energy storage in every Malaysian state. Additionally, a safety study of the proposed energy storage solution, 1 MWh Zinc Bromide, can be carried out as well, taking the particularity of the weather conditions of Malaysia into consideration. Finally, a combination of Hybrid-flow batteries and Zinc Bromide batteries might be better for the Malaysian scenario. The feasibility of such system and its configuration should be studied in the future.

From an authority vintage point, two main things should be considered in the near future. The first is to increase the overall Large-Scale Solar PV installed capacity. The second is to keep or even reduce the electricity consumption of the country. In this way, the energy storage capacity, which depends on the electricity load, will not increase, while the LSS output will increase, hence reducing the GHG emissions of the country. Therefore, the energy efficiency act and the renewable energy act may be amended in a way that ensures a common target and a coherent development of both sectors. 6. Future work

Acknowledgement The locations investigated in this paper have been selected according to three factors; solar irradiation, estimated electricity demand and planned LSS capacity. However, LSS projects are planned in all the

This work was funded by the Energy Academy, Heriot-Watt University under the “Fledge Project”. It is a part of the research “Large11

Journal of Energy Storage 26 (2019) 100984

M. Laajimi and Y.I. Go

Scale Solar Power: Solutions and Techno Social-Economic Implications to Malaysia in Meeting World Energy Challenges by 2030”.

1016/j.apenergy.2016.12.118 [Accessed 16 Mar. 2018]. [20] Highview power storage. "Grid scale liquid energy storage", 2015. [21] A. Benato, Performance and Cost Evaluation of an Innovative Pumped Thermal Electricity Storage Power System, Elsevier, 2017 [online]Available at: http://dx. doi.org/10.1016/j.energy.2017.07.066 [Accessed 6 Mar. 2018]. [22] H. Beltran, E. Perez, N. Aparicio and P. Rodriguez, "Daily Solar Energy Estimation for Minimizing Energy Storage Requirements in PV Power Plants", 2018. [Online]. Available: http://dx.doi.org/10.1109/TSTE.2012.2206413 [Accessed: 18- Mar2018]. [23] J. Howes, Concept and development of a pumped heat electricity storage device, Proc. IEEE 100 (2) (2012) 493–503. [24] Smallbone, A., Jülch, V., Wardle, R. and Roskilly, A. "Levelised Cost of Storage for Pumped Heat Energy Storage in comparison with other energy storage technologies", 2017. [online] Available at: http://dx.doi.org/10.1016/j.enconman.2017.09. 047 [Accessed 16 Mar. 2018]. [25] Energy Commission, "Request for proposal (RFP) for the development of Large Scale Solar Photovoltaic (LSSPV) Plants in Peninsular Malaysia and Sabah for commercial operation in 2017-2018 – Announcement of Shortlisted bidders", Putrajaya, Malaysia, 2016. [26] Energy Commission, "Request for proposal (RFP) for the development of Large Scale Solar Photovoltaic (LSSPV) Plants in Peninsular Malaysia, Sabah and Labuan for commercial operation in 2019-2020 – Announcement of Shortlisted bidders", Putrajaya, Malaysia, 2017. [27] Department of Statistics Malaysia, "Current Population estimates, Malaysia, 20142016", 2016. [28] Department of Statistics Malaysia, "Current Population Estimates, Malaysia, 20162017", 2017. [29] Energy Commission, "Malaysia Energy Statistics Handbook 2017", Putrajaya, Malaysia, 2017. [30] K. Chua, Y. Lim, S. Morris, Cost-benefit assessment of energy storage for utility and customers: a case study in Malaysia, Energy Conserv. Manage. (2015) [Online]. Available https://www.sciencedirect.com/science/article/pii/ S0196890415009632?via%3Dihub [Accessed: 16-May-2018]. [31] "XE: Convert MYR/USD. Malaysia Ringgit to United States Dollar", Xe.com, 2019. [Online]. Available: https://www.xe.com/currencyconverter/convert/?Amount= 1&From=MYR&To=USD. [Accessed: 26- Jan- 2019]. [32] "National Renewable Energy Laboratory (NREL), US Department of Energy", Nrel. gov, 2018. [Online]. Available: https://www.nrel.gov/. [Accessed: 17- May- 2018]. [33] "Solar panel efficiency | solar system Malaysia", Solarsystemmalaysia.com, 2012. [Online]. Available: http://www.solarsystemmalaysia.com/tag/solar-panelefficiency/. [Accessed: 09- Jun- 2018]. [34] "PV Derating Factor", Homerenergy.com, 2018. [Online]. Available: https://www. homerenergy.com/products/pro/docs/3.11/pv_derating_factor.html. [Accessed: 17- Jun- 2018]. [35] SEDA, "National Survey Report of PV Power Applications in MALAYSIA 2016", Putrajaya, Malaysia, 2016. [36] J. Ye, T. Reindl, A. Aberle, T. Walsh, Performance degradation of various PV module technologies in tropical Singapore, IEEE J. Photovoltaics 4 (5) (2014) 1288–1294. [37] "Multi-Year Module", Homerenergy.com, 2018. [Online]. Available: https://www. homerenergy.com/products/pro/docs/3.11/multiyear_module.html. [Accessed: 23- Jun- 2018]. [38] "Malaysia Inflation Rate - Forecast", Tradingeconomics.com, 2018. [Online]. Available: https://tradingeconomics.com/malaysia/inflation-cpi/forecast. [Accessed: 23- May- 2018]. [39] Renewables 2016: Global Status Report. Available online: http://www.ren21.net/ wp-content/uploads/2016/05/GSR_2016_Full_Report_lowres.pdf (accessed on 02 June 2018). [40] "Overview of the FiT System in Malaysia", Seda.gov.my, 2018. [Online]. Available: http://seda.gov.my/overview_of_fit_concept_in_malaysia.html. [Accessed: 24- Jun2018]. [41] "Malaysia Carbon Dioxide Emissions (Yearly, Metric Tons)", Ycharts.com, 2018. [Online]. Available: https://ycharts.com/indicators/malaysia_carbon_dioxide_ emissions. [Accessed: 10-Jul-2018].

Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.est.2019.100984. References [1] Rehman, S., Al-Hadhrami, L. and Alam, M. “Pumped hydro energy storage system: a technological review”, 2015. [online] Available at: http://dx.doi.org/10.1016/j. rser.2014.12.040 [Accessed 12 Feb. 2018]. [2] Poullikkas, A. "A comparative overview of large-scale battery systems for electricity storage", 2013. [online] www.elsevier.com/. Available at: http://dx.doi.org/10. 1016/j.rser.2013.07.017 [Accessed 13 Mar. 2018]. [3] C. Zhang, Y. Wei, P. Cao, M. Lin, Energy storage system: current studies on batteries and power condition system, Renew. Sustain. Energy Rev. 82 (2018) 3091–3106. [4] International Renewable energy agency. "Electricity storage and renewables: costs and markets to 2030". Abu Dhabi, pp. 19, 50-54, 63-81, 82-86, 87, 90, 2017. [5] Budt, M., Wolf, D., Span, R. and Yan, J. "A review on compressed air energy storage: basic principles, past milestones and recent developments ", 2016. [online] Available at: http://dx.doi.org/10.1016/j.apenergy.2016.02.108 [Accessed 13 Feb. 2018]. [6] Morgan, R., Nelmes, S., Gibson, E. and Brett, G. "Liquid air energy storage – analysis and first results from a pilot scale demonstration plant ", 2015. [online] Available at: http://dx.doi.org/10.1016/j.apenergy.2014.07.109 [Accessed 17 Feb. 2018]. [7] G. Guizzi, M. Manno, L. Tolomei, R. Vitali, Thermodynamic analysis of a hybrid energy storage system based on compressed air and liquid air, Sustain. Energy Technol. Assess. 11 (2015) 159–164. [8] JD McTigue, AJ White, CN Markides, Parametric studies and optimization of pumped thermal electricity storage, Appl. Energy (2015) 800–811. [9] S. Schiebahn, T. Grube, M. Robinius, V. Tietze, B. Kumar, D. Stolten, Power to gas: technological overview, systems analysis and economic assessment for a case study in Germany, [online], Int. J. Hydrog. Energy (2015) Available at https://doi.org/ 10.1016/j.ijhydene.2015.01.123 [Accessed 13 Mar. 2018]. [10] DVGW. Technische regel, Arbeitsblatt G 260-Gasbeschaffenheit, 2008. [11] Götz, M., Lefebvre, J., Mörs, F., McDaniel Koch, A., Graf, F., Bajohr, S., Reimert, R. and Kolb, T. "Renewable power-to-gas: a technological and economic review", 2016. Available at: http://dx.doi.org/10.1016/j.renene.2015.07.066 [Accessed 13 Mar. 2018]. [12] International Energy Agency, Technology Roadmap: Energy storage, (2014) Paris, France, pp. 18,19,32. [13] International Renewable Energy Agency. "Battery Storage for Renewables: Market Status and Technology Outlook". Abu Dhabi, pp. 23–44, 2015. [14] Deloitte, Electricity Storage Technologies, Impacts, and Prospects, Deloitte Center for Energy Solutions, 2015, pp. 4–14. [15] Y. Nagaoka, Vanadium flow battery systems for renewable energies and grid controls, Energy Storage Global Conference, Sumitomo Electric Industries, Ltd, 2014. [16] Guneya, M. and Tepeb, Y. "Classification and assessment of energy storage systems", 2017. [online] Available at: http://dx.doi.org/10.1016/j.rser.2016.11.102 [Accessed 9 Apr. 2018]. [17] B. Ameel, C. T'Joen, K. De Kerpel, P. De Jaeger, H. Huisseune, M. Van Belleghem, M. De Paepe, Thermodynamic analysis of energy storage with a liquid air Rankine cycle, J. Appl. Therm. Energy 52 (2013) 130–140. [18] M. Aneke, M. Wang, Energy storage technologies and real-life applications – a state of the art review, [online], Appl. Energy (2016) Available at http://dx.doi.org/10. 1016/j.apenergy.2016.06.097 [Accessed 17 Mar. 2018]. [19] Sciacovelli, A., Vecchi, A. and Ding, Y. "Liquid air energy storage (LAES) with packed bed cold thermal storage – from component to system level performance through dynamic modelling ", 2017. [online] Available at: http://dx.doi.org/10.

12