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Planning for the integration of renewable energy systems and productive zone in Remote Island: Case of Sebira Island

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Sebira Island is an Indonesian remote island that relies on its energy source mostly from diesel-generated electricity. The island's main economic activities include activities related to the coastal and fishery sectors. This research paper evaluates long-term planning of a mini-grid hybrid energy system from solar, wind, and diesel in island level based on technical and economic aspects. The mini-grid hybrid system is designed not only to meet the electricity demands of residential, community, but also integrated with the development of productive zone to advance the local economy by enhancing the added value of local products and services. This study used TIMES model to obtain optimized energy system which will be used as an input for developing productive zone of the island. The optimization result is a portfolio of the energy system of Solar PV and wind turbine generators and Li-Ion battery storage. The total investment cost of the energy system is USD 130,000. In order for the designed hybrid-mini grid to be financially feasible, a minimum of 25% of the grant is required. Innovative financing lies in a low-rate crowdfunding scheme. Most schemed productive activities are financially feasible and competitively priced in the market. Designed energy system model integrated with the productive zone generates a yearly net profit of USD 103,700 and create jobs for 51 islanders.
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Cleaner Energy Systems 4 (2023) 100057
Contents lists available at ScienceDirect
Cleaner Energy Systems
journal homepage: www.elsevier.com/locate/cles
Planning for the integration of renewable energy systems and productive
zone in Remote Island: Case of Sebira Island
Vida Zinia Putri Hardjono
a
,
b
, Nadhilah Reyseliani
a
,
b
, Widodo Wahyu Purwanto
a
,
b
,
a
Sustainable Energy Systems and Policy Research Cluster, Universitas Indonesia, Depok, 16424, Indonesia
b
Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, 16424, Indonesia
Keywords:
Sebira Island
Hybrid mini-grid
VRE
Productive zone
Long-term planning
Sebira Island is an Indonesian remote island that relies on its energy source mostly from diesel-generated elec-
tricity. The island’s main economic activities include activities related to the coastal and shery sectors. This
research paper evaluates long-term planning of a mini-grid hybrid energy system from solar, wind, and diesel in
island level based on technical and economic aspects. The mini-grid hybrid system is designed not only to meet
the electricity demands of residential, community, but also integrated with the development of productive zone
to advance the local economy by enhancing the added value of local products and services. This study used TIMES
model to obtain optimized energy system which will be used as an input for developing productive zone of the
island. The optimization result is a portfolio of the energy system of Solar PV and wind turbine generators and
Li-Ion battery storage. The total investment cost of the energy system is USD 130,000. In order for the designed
hybrid-mini grid to be nancially feasible, a minimum of 25% of the grant is required. Innovative nancing lies
in a low-rate crowdfunding scheme. Most schemed productive activities are nancially feasible and competitively
priced in the market. Designed energy system model integrated with the productive zone generates a yearly net
prot of USD 103,700 and create jobs for 51 islanders.
1. Introduction
Energy use enhances the quality of life. In essence, energy is
the determinant for economic growth, and its importance in modern
economies and human lifestyles is indisputable ( Masud et al., 2007 ).
Safeguarding accessible and aordable energy played an essential role
in economic growth and human development ( Birol, 2007 ). Without
widely accessible, aordable energy, it is dicult for households to
climb out of the poverty cycle ( Bridge et al., 2016 ). Aside from af-
fordability and accessibility, Indonesia’s commitment to meet its Na-
tional Determined Contribution (NDC) from the COP 26 includes decar-
bonization of its energy sector which targets to reach net-zero by 2060
( Hendriwardani et al., 2022 ).
One of Indonesia’s biggest challenges as an archipelagic country is
the provision of aordable commercial energy in isolated areas. Most of
Indonesia’s poor live in regions that are dicult to access, such as on
small islands with limited access to reliable and aordable electricity
services ( Blum et al., 2013 ). Most non-electried villages in Indonesia
are too remote, complex, and expensive for grid extension ( Holland and
Derbyshire, 2009 ). On top of this is the low grid electricity tari set by
the government, which targets to ensure aordable electricity for all.
This caps revenue from electricity sales, making it dicult for an elec-
Correspondence author.
E-mail address: widodo@che.ui.ac.id (W.W. Purwanto) .
tricity producer to recover the high production and distribution costs
( The World Bank 2005 ). Hence, o-grid solutions, predominantly diesel-
based power plants, become the basic electrication solution for these
areas. This was the case for many island communities, namely, Sebira
Island.
Sebira Island is one of the small residential islands in the North
Kepulauan Seribu Administrative Region, whose residents make a liv-
ing as shermen and sh processing workers. O-grid electrication
is not economically feasible due to the high consumption and depend-
ability of diesel. There are two major challenges of Sebira Island’s en-
ergy system: the gap between the purchasing power of the islanders
and the cost of energy production and the gap between supply and the
growing electricity demand ( Suku Dinas Ketenagalistrikan, 2020 ). De-
bottlenecking strategy to improve island electrication access also in
line with the national decarbonization agenda based on World Bank
( Terrado et al., 2008 ) and IRENA ( IRENA 2016 ), is the utilization of
renewable energy-based grids, usually involving variable renewable en-
ergy (VRE) ( Blum et al., 2013 ). These grids are sucient to fulll the
energy supply for the basic livelihood of islanders and even further en-
able the realization of energy-dependent productive activities, adding
value to local products and encouraging economic growth.
The integrated energy model establishes the methodological founda-
tions of demand and supply models typically developed by focusing on
specic aspects of energy use ( Farzaneh, 2019 ), including integrating
productive energy uses. To plan for such an integrated energy system,
https://doi.org/10.1016/j.cles.2023.100057
Received 29 September 2022; Received in revised form 24 January 2023; Accepted 19 February 2023
Available online 23 February 2023
2772-7831/© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
V.Z.P. Hardjono, N. Reyseliani and W.W. Purwanto Cleaner Energy Systems 4 (2023) 100057
Nomenclature
CAPEX Capital Expenditure
DG Diesel Generator
FSE Financial Scheme for Energy System
FSP Financial Scheme for Productive Activity
GDP Gross Domestic Product
GDRP Gross Domestic Regional Product
IRR Internal Rate of Return
LCA Life Cycle Analysis
LCOE Least Cost of Electricity
LCOS Least Cost of Storage
Li-Ion Lithium-Ion Battery
MARR Minimum Acceptable Rate of Return
NPV Net Present Value
O&M Operation and Maintenance
PBP Payback Period
PLN Perusahaan Listrik Negara, Indonesian State Owned
Electricity Company
RES Reference Energy System
SPV Solar Photovoltaic Panel
VRE Variable Renewable Energy
VRLA Valve-Regulated Lead Acid Battery
WACC Weighted Average Cost of Capital
WT Wind Turbine
YoY Year-on-year
modeling tools generate recommendations for the long-term generation
capacity needed to meet energy needs ( IRENA 2017 ). Planning outlines
for optimizing an objective function within a given time horizon. Using
the techno-economic approach, the model is optimized with the objec-
tive function of minimizing total system cost resulting in a portfolio of
investment choices based on the energy produced by various compara-
tive technologies ( Watchueng et al., 2010 ).
There have been a variety of existing research and projects on
power generation systems for islands for small islands ( IRENA 2016 ;
Hivos, 2012 ; Abraham et al., 2012 ; Ocon and Bertheau, 2019 ;
Meschede et al., 2019 ; Moner-Girona, 2008 ; Purwadi et al., 2012 ;
Zafeiratou and Spataru, 2018 ; Pombo et al., 2022 ; Trondheim et al.,
2021 ; Sadeghi et al., 2022 ; Fitiwi et al., 2020 ). Most reported projects
energy systems in small islands and developing states ( IRENA 2016 ;
Moner-Girona, 2008 ) design o-grid mini systems as a part to transition
to renewable energy. Therefore is usually a hybrid system consisting of
diesel, solar, and hydropower generators or solely solar-based systems
with battery storage systems evaluated using techno-economic analysis
for short-term time horizons. In the case of previous research done in
Sebira Island ( Purwadi et al., 2012 ), demand modelling only includes
community and residential end uses without the inclusion of productive
demands. Extensive research have been done to similar neighboring is-
land country like Philippines ( Ocon and Bertheau, 2019 ) including to
develop tourism sector ( Meschede et al., 2019 ). Several research have
been done in Indonesia ( Hivos, 2012 ; Abraham et al., 2012 ) include in-
tegration of energy systems with the development of productive zone to
model 100% renewable energy islands. Research mentioned above de-
sign energy systems to meet present-date needs without considering the
long-term increase of demand. Existing research has been done to plan
for a longer-term (10-30 years) energy system. These long-term plan-
ning are usually used to evaluate schemes to achieve local policy on RE
share targets like in the case for Crete ( Zafeiratou and Spataru, 2018 ),
Cape Verde ( Pombo et al., 2022 ) Faroe Islands ( Trondheim et al., 2021 ).
Others are used to explore possible schemes which minimize energy sys-
tem costs using hybrid systems involving usually solar and wind en-
ergy like in the case of study in Iran ( Sadeghi et al., 2022 ) and Ireland
( Fitiwi et al., 2020 ). Overall, existing studies in long-term planning only
models for energy infrastructure to meet non-productive demand.
This paper proposes to bridge research gaps of existing studies by
designing a long-term planning (20 years) o-grid energy system in-
tegrated with the development of productive zone for Sebira Island.
Productive zone includes value-adding activities to goods and services
based on local resources adjusted to local needs and resources. This
study covers four productive activities to encourage the shery and
tourism activities at Sebira Island, which are: cold storage for sh pro-
duce, RO/desalination plant, solar dryer and resort. The demand mod-
eling approach is the bottom-up model, with stress on the technical side
or end-use. Optimization is done with the objective function of least cost
using VEDA-TIMES. The optimized result will be a portfolio, capacity,
and expansion of powerplant capacity and storage technology operating
at 24 hours for 2020 to 2040 to supply energy needs from household,
community and productive sector of Sebira Island.
2. Methodology
2.1. Energy system model
This study assesses the long-term planning of the redesign, assuming
greeneld of the remote energy system of Sebira Island from 2020 to
2040. This study uses greeneld assumption due to the lack of opera-
tional data of the existing energy system of the island. Demand growth
over time is based on small islanders’ activities following the histori-
cal demand-GDRP elasticity ( Badan Pusat Statistik DKI Jakarta 2021 ;
Badan Pusat Statistik DKI Jakarta 2021 ; BPS Kabupaten Administrasi
Kepulauan seribu 2021 ; BPS Kabupaten Administrasi Kepulauan seribu
2020 ). Demand is also integrated with the exogenous input of energy
usage of productive activities designed in the productive zone over the
span of a 24-hour time slice. The capacity and sizes of productive activ-
ities are designed based on the constraints of land and sh production
on Sebira Island. The supply-demand is modeled in a 24-hour time slice,
where demand is the same everyday of the year as seasonal variance is
assumed negligible due to the island’s tropical climate. Competing gen-
eration technologies involved in the model are based on energy potential
in Sebira Island.
The output of the optimized energy system is a portfolio of the en-
ergy infrastructure’s generation expansion capacity relative to the time
function. The reference energy system (RES), which shows the design of
the energy supply-demand, is shown in Fig. 1 . This research will opti-
mize three energy supply technologies, SPV (Solar PV), WT (low-speed
wind turbines), and DG (diesel generator), as electricity supply technol-
ogy. Meanwhile, the solar collector component is part of the sh drying
productive system. This portfolio of energy supply technology includes
the cost of investment, cost of energy production per kWh, and CO
2
production. The energy infrastructure investment of the optimized gen-
eration portfolio will then be input to evaluate the economic feasibility
of the energy system assessed in several nancing scenarios.
The spatial planning of the island, considering infrastructures built
on this study, is shown in Fig. 2 , as outlined in the Regional Regulation of
DKI Jakarta Number 1 of 2014 concerning spatial planning and zoning
regulations for the Kepulauan Seribu Utara region ( Pemerintah Provinsi
DKI Jakarta 2014 ).
2.1.1. TIMES Model
The Integrated MARKAL-EFOM System (TIMES) is an economic
model generator for local, national, multi-regional, or global energy sys-
tems that provides a technological foundation for depicting energy dy-
namics in multi-period time ( Loulou et al., 2004 ). Demand for end-use
energy services, estimated stock of existing energy-related equipment in
all sectors, features of present and future available technologies, and fu-
ture and possible primary energy supply sources are inputs to the TIMES
System. TIMES solve complex Linear Programming (LP) optimization
with the objective function of minimizing total system cost.
2
V.Z.P. Hardjono, N. Reyseliani and W.W. Purwanto Cleaner Energy Systems 4 (2023) 100057
Fig. 1. Reference energy system for Sebira island.
Fig. 2. Birdview spatial planning of Sebira island.
The objective function for TIMES model is minimizing total system
cost as shown in Eq. 1 .
𝑇 𝑜𝑡𝑎𝑙 𝑑𝑖𝑠𝑐 𝑜𝑢𝑛𝑡𝑒𝑑 𝑠𝑦𝑠𝑡𝑒𝑚 𝑐 𝑜𝑠𝑡𝑠 =
𝑦 𝑌 𝐸𝐴𝑅𝑆
(
1 + 𝑑
)
𝑅𝐸𝐹 𝑌 𝑅 𝑦
×𝐴𝑁 𝑁 𝐶𝑂𝑆𝑇 (
𝑦
)
(1)
y is modeled year, 𝑑is discount rate, REFYR is reference year, and
𝐴𝑁 𝑁 𝐶𝑂𝑆𝑇 ( 𝑦 ) is the annual cost of year y which sums, if any, im-
port/export, fuel, capital, O&M costs and salvage value.
Modeled system is subject to a constraint regarding the supply and
demand balance and additional user-dened input, such as the availabil-
ity of energy resources. The calculated generated electricity should be
more than or equal to the demand modeled per vintage year, as shown
on Eq. 2 .
𝐷𝐸 𝑀
𝑐,𝑠,𝑡
𝑃
𝑝 =1
𝑃 𝑅 𝐷
𝑝,𝑐,𝑠,𝑡
(2)
The model structure is as shown in Fig. 3 . Input in the energy plan-
ning system includes technical, economic, and environmental param-
eters. Technical parameters include demand relative to the time slice
function including end use demnd from productive activities, availabil-
ity and capacity factor of powerplants, lifetime, eciency, and energy
to power ratio for storage. Economic input parameters include CAPEX,
learning rate, O&M cost, and fuel cost. While environmental parame-
ters provide emission factors for technologies modeled. Parameters are
dened for existing and new technologies. The data handling processes
of input and output are done on (VEDA FE) and the VEDA Back-End
(VEDA BE), respectively ( Reyseliani and Purwanto, Oct. 2021 ). Opti-
mization results will then become input for nancial analysis, through
selection, nancially feasible schemes are selected. Electricity genera-
tion cost from the optimized scheme, becomes the input for designing
productive activities (part of O&M cost). Financial analysis will simi-
larly be done to productive activities. Collective output of energy and
productive system will be a power system portfolio, a protable scheme
of productive activity and local employment from systems.
2.1.2. Data input
2.1.2.1. Supply. Two renewable energy sources have the potential to
be used for mini-grid electricity generation on Sebira Island, PV grid
and wind turbines. It is known from the ( ESMAP 2019 ; World Bank
Group 2020 ), GHI on Sebira Island is 1,739 kWh/m
2
per year with ir-
radiation time from 6:00 am to 6:00 pm with availability factor per
hour. A mean average of 16% ( Staell and Pfenninger ) all year long is
used as a yearly assumption of the system. With an estimated 80% of
the available land allocated for power generation and a module area
specication of 120Wp/m
2
, the electric potential is obtained from a so-
lar panel with the potential capacity of 700 kW, which outputs 680.6
MWh/year ( World Bank Group 2020 ). The potential for wind power on
Sebira Island is very small due to the low wind speed, 3.46 m/s at an al-
titude of 50m with a maximum speed of 3.48 m/s at an altitude of 80 m
( World Bank Group 2020 ), resulting in an average power of 25 W/m
2
.
The average yearly wind availability factor is only 10.7% ( Staell and
Pfenninger ). Table A.1 . summarizes assumptions used.
2.1.2.2. Investment and technology parameter. Referring to the literature
on energy supply technology ( McDonald and Schrattenholzer, 2001 ;
Rubin et al., 2015 ) energy supply technology’s investment and oper-
ation cost decreases yearly. The model used to determine the value of
the reduction in the cost of the energy supplier used is the experience
curve.
𝑌 = 𝑎 𝑥
𝑏 (3)
3
V.Z.P. Hardjono, N. Reyseliani and W.W. Purwanto Cleaner Energy Systems 4 (2023) 100057
Fig. 3. Model structure.
Eq. (3) demonstrates the decrease of the cost ( Y ) is driven by a dif-
ferent cost of technology at base year ( a ) learning rate factor ( b ) for
each technology and cumulative experience or cumulative capacity in-
stalled per year ( x ). Energy system optimization using TIMES is based
on the minimum overall system cost, which is evaluated using the least
cost of electricity (LCOE), calculated using Eq. (4) . The LCOE equals
the net present value (with rate r, over the period of t) of the total cost
of building (investment cost, I
t
), operating the power generating asset
(maintenance and operation cost, M
t,
and if applicable, fuel cost, F
t
).
This number is then divided by the total electricity generation over its
lifetime (E
t
).
𝐿𝐶𝑂𝐸 =
𝑛
𝑡 =1
𝐼
𝑡
+ 𝑀
𝑡
+ 𝐹
𝑡
(
1− 𝑟
)
𝑡
𝑛
𝑡 =1
𝐸
𝑡
(
1+ 𝑟
)
𝑡
(4)
Referring to the IEA reports ( IEA and OECD NEA 2020 ; IEA 2021 ),
the annual cost reduction is depicted in Fig. 3 . Based on the curve, it is
found that the investment cost that has experienced a sharp decline is the
Solar PV mini-grid (SPV), with a learning rate of 30% from $1100/kWh
in the base year (2020) to $619/kW at the end of the planning year
(2040). The learning rate for wind turbines (WT) is 11%, and investment
costs are down from $1075/kW in the base year (2020) to $944/kW at
the end of the planning year. In contrast to renewable energy sources,
diesel generator (DG) investment costs are assumed to be constant due
to their maturity. Therefore, the investment cost per kW is constant from
the base year to 2040 at $1507/kW.
𝑙 𝑛𝐸 = 𝑙 𝑛𝑎 + 𝑏 𝑙𝑛𝑌 (5)
Unlike renewable-sourced energy generation, fuel price is the main
component that drives the LCOE of DG in addition to investment costs.
Projection of fuel price is calculated using the log-linear elasticity econo-
metric model ( Meier, 1984 ) using Eq. (5) . This demonstrates the log-
linear correlation of energy demand ( E ) with income ( Y ), where gradient
b is elasticity and a is a constant.
Using the investment cost projection in Fig. 3 and the assumption
of xed operating and maintenance (O&M) cost at 3% for DG, 2% for
SPV, and 4% for WT, and the projection of fuel prices, LCOE for each
generation technology is deduced. Using a similar method, the least cost
of storage (LCOS) is deduced as shown in Fig. 4 , with the assumption of
xed O&M cost at 2% for Li-Ion and 3% for Valve-Regulated Lead Acid
(VRLA) battery. Assumptions are summarized in Table A.1 .
Generation technology inputs in TIMES include eciency, capacity,
and emission factor. The eciency of DG is set at 35%, SPV at 21%,
and WT at 17%. The reference roundtrip eciency of Li-Ion battery
technology is 86%, and the VRLA (valve-regulated lead-acid) is 80%.
The capacity factor of DG is set to be at 23%. The availability factor
took the place of the capacity factor for renewables. Environmental pa-
rameters are measured from kg CO
2
per kWh production, referring to
each technology’s life cycle analysis (LCA) ( Mongird et al., 2020 ). Fossil
generators have much higher emission factors than renewable energy
plants. DG’s emission factor is at 0.76 kg/kWh, WT at 0.013 kg/kWh,
and SPV at 0.014 kg/kWh ( Das et al., 2018 ). Li-Ion battery technology
has a 0.09 g/kWh emission factor and a VRLA of 0.2 kg/kWh.
2.1.2.3. Demand. Energy demand for residential zones and community
zones is driven by energy consumption activity factors and energy in-
tensity. Total demand is the product of energy activity or the amount of
unit/users of energy (population) and energy intensity or the value of
energy use per unit of activity (kWh/per capita).
The residential zone energy demand behavior was adopted from the
residential model for villages by ( Blum et al., 2013 ) adapted with the
24-hour load prole trend data of the PLN feeder 24-hour load for res-
idential islands in Kepulauan Seribu Regency ( PLN, 2021 ). Community
zone demand is modeled based on typical technology used and operat-
ing hours of community facilities on Sebira Island. Fig. 8 shows the inte-
grated hourly energy use, including daily residential and community de-
mands. Residential and island community demand model assumptions
used in this study are summarized in Tables B.1 and B.2 .
Calculation of energy demand includes end-use technology for cool-
ing, including fans and air conditioners, lighting, home lighting appli-
ances, streetlights, and lighthouses. Meanwhile, other residential appli-
ances include TVs, refrigerators, rice cookers, and refrigerators. Based
4
V.Z.P. Hardjono, N. Reyseliani and W.W. Purwanto Cleaner Energy Systems 4 (2023) 100057
Fig. 4. Renewable energy supply technology investment.
on the socio-economic status of Sebira islanders, it is assumed that the
washing machine users are one for every ve houses; based on a similar
assumption, the rice cooker users are two for every ve houses. This
assumption is slightly above the projected ownership ratio for electrical
appliances for rural area ( McNeil et al., 2008 ) taking into consideration
that even though Sebira Island remote, it is regionally a part of the cap-
ital region. For community zones, the end-use technologies appliances
are TVs, computers, and pumps. Complete demand model is shown on
Tables B.1 - B.2 . The demand model reduces to an average consumption
of 576.3 kWh/year, well below the national average of 1.08 MWh/year
( Direktorat Jenderal Ketanagalistrikan 2019 ). This is in accordance with
the socio-economic prole and behavior of electricity users on the is-
land, which involves no energy-intensive activities.
The increase in the energy consumption factor is driven by popula-
tion growth at the island level, projected using the historical year-on-
year (YoY) average population growth based on 2010-2020 BPS data for
the North Kepulauan Seribu District, which is 2% ( BPS Kabupaten Ad-
ministrasi Kepulauan Seribu 2021 ). Energy intensity projection is driven
by gross regional domestic product (GRDP) growth of North Kepulauan
Seribu District, forecasted using the curve for Indonesia’s gross domestic
product (GDP) YoY growth and the historical GRDP growth in the Kepu-
lauan Seribu. The increase in energy intensity per capita is then calcu-
lated using the elasticity slope based on historical data, which shows the
relationship between GDP and electricity use per capita at the national
level for the past ten years. It is found that activity is a more inuen-
tial driving factor of demand than intensity. This is due to the larger
YoY population growth than the Kepulauan Seribu GRDP growth. Mod-
eled residential and community zone electricity consumption growth
per year driven by macroeconomic factors are obtained as exogenous
input to the energy model.
2.1.2.4. Productive zone development plan. One of the main steps in
planning is determining development activities ( Proag, 2021 ). Devel-
opment planning includes choosing between alternative options, exist-
ing limitations, and the needs of the residents. In planning the produc-
tive zone on Sebira Island, this research is carried out by rst iden-
tifying potential productive activities, determining supporting facili-
ties/infrastructure, and the development plan considering land use (spa-
tial planning) based on DKI Jakarta’s Regional Policy, as shown in Fig. 1 .
This paper covers four proposed productive systems developed on Se-
bira Island: resort construction, sh drying facilities, cold storage facil-
ity, and desalination systems (reverse osmosis). Demand induced by the
procurement of proposed productive activities of four designed systems
will then be integrated into the energy system on a time horizon from
2020 to 2040, as shown in Fig. 6 .
Time-horizon considered the replacement of facilities and prioriti-
zation of the development of productive sectors. The strategy chosen
in this research is to prioritize the improvement of supporting facili-
ties for the existing productive sector. The biggest contributor to Sebira
Island’s income is the sale of sh, especially dried salted sh from pro-
cessing fresh selar sh ( Suku Dinas Perikanan, 2020 ). Therefore, in the
productive zone development plan, the procurement of facilities for sh
drying is set as a priority. The current production of dried salted sh
is via conventional drying in the open air, causing sh products to not
thoroughly dry during the rainy season, which puts them at risk of grow-
ing mold and spoilage ( Abbas et al., 2009 ). With the construction of a
closed solar-based drying facility, through a process, as recommended
by the national standard for dried salted sh SNI 8273:2016, local pro-
duce could leverage sales in the market and be sold at a higher price
than the current.
Apart from processed selar sh, fresh sh sales rely on the conven-
tional cold storage method by temporarily storing sh in the hull of the
shing boat lled with ice cubes. Considering that the sh caught are
only shipped once a week, the conventional storage method could re-
duce the quality of the catch with the risk of spoilage. The procurement
of cold storage is expected to reduce this risk. For these reasons, solar
thermal drying facilities and cold storage are plotted to be developed
early on the roadmap.
The construction of reverse osmosis functions to sustain the provi-
sion of clean water for essential needs (i.e., residential and community
demands), and the touristic sector demands the planning of resort con-
struction. Currently, the primary clean source is obtained from pumping
groundwater, which is unsustainable since the continuous extraction of
groundwater may pose a risk of land subsidence ( Wang et al., 2019 ),
even more so for islands. There is a need to build a reverse osmosis
facility with a larger capacity than the one existing on the island be-
cause the current volume of clean water produced, 1000 liter/day, only
meets the minimum water demand for direct household consumption,
i.e., cooking and drinking. This drives the need to procure reverse os-
mosis to be included in the roadmap built prior to the development of
the touristic island resort.
The driving factors for energy activity and intensity determine the
demand for each developed productive facility. Energy activity is mod-
eled based on electronic devices used in a typical model according to
the good practices, capacity, and scale of each productive activity: solar
drying ( Aravindh and Sreekumar ), resort development ( Neufert, 1991 ),
cold storage ( Johnston et al., 1994 ), reverse osmosis for desalination
( World Bank 2019 ). Meanwhile, energy intensity is obtained from the
estimated number of users based on island population data ( BPS Kabu-
paten Administrasi Kepulauan seribu 2021 ) and, for the resort’s design,
estimated visitors’ occupancy. The typical load model developed in this
paper is adjusted to the historical data from the PLN feeder as the elec-
tricity provider for the residential island and resort island feeders in
Kepulauan Seribu ( PLN, 2021 ). The combined hourly load prole of the
residential, community and productive zones is shown in Fig. 7 , consid-
ering the annual demand growth in Fig. 8 .
5
V.Z.P. Hardjono, N. Reyseliani and W.W. Purwanto Cleaner Energy Systems 4 (2023) 100057
Table 1
Funding schemes for energy system.
Funding Schemes Description %Equity; rate% % Debt; rate% Grant (%CAPEX) % Crowd Funding; rate% Tax (%)
FSE-1 0% Grant 30 70;4 - - 25
FSE-2 25% Grant 25 50;4 25 - 25
FSE-3 50% Grant 15 35;4 50 - 25
FSE-4 75% Grant - 25;4 75 - 25
FSE-5 100% Grant - - 100 - 25
FSE-6 FS-1, with low tax rate (0.5%) 30 70;4 - - 0,5
FSE-7 100% Crowdfunding with a low rate (3%) - - - 100; 3 25
FSE-8 100% Crowdfunding with a low rate (3%)
and a low tax rate (0.5%)
- - - 100;3 0.5
FSE-9 Crowdfunding with a low rate (3%) with a
25% grant and a low tax rate (0.5%)
- - 25 75;3 0.5
Table 2
Funding schemes for productive activities.
Funding Scheme Description %Equity; rate% % Debt; rate% Grant
(%CAPEX)
Tax Rate (%)
FSP-1 0% Grant 30;12 70; 10 0 25
FSP-2 30% Grant 30;12 40; 10 30 25
FSP-3 70% Grant - 30; 10 70 25
FSP-4 100% Grant - - 100 25
FSP-5 FSP-1, with low tax rate 0.5% 30;12 70; 10 0 0.5
FSP-6 FSP-1, with low tax rate 0.5% 30;12 70; 6 0 0.5
2.2. Financial scheme
O-grid (rural or island) small-scale electrication projects involv-
ing renewable energy are not considered protable due to several nan-
cial constraints, such as a lack of project funding, especially long-term
ones ( Donastorg et al., 2017 ). This is why renewable energy projects
are harder to nd attractive nancing than conventional ones. For this
reason, planning a business scheme and an incentive policy is necessary
to realize the project ( ADB 2019 ).
In order to evaluate the nancial feasibility of the optimized energy
and designed productive systems, nancial schemes for energy (FSE)
and productive activities (FSP) are proposed in Tables 1 and 2 , re-
spectively. Financial schemes are developed based on good practices
( ADB 2019 ; Quang Cu, 2013 ). Schemes proposed to include a nancial
incentive in the form of grants to partially waive CAPEX (FSE-2, FSE-3,
FSE-4, FSP-2, FSP-3) and fully waive CAPEX (FSE-5 and FSP-4). To make
the energy system nancially feasible, other nancial incentive is used,
in the form of a low debt rate, adopted from the participative invest-
ment with a xed rate of 4% (FSE-1, FSE-2, FSE-3, FSE-4, and FSE-6)
and crowdfunding with a xed rate of 3% (FSE-8 and FSE-9) over the
loan term of 10 years. Fiscal incentive simulated takes the form of low
tax rates (FSE-8, FSE-9, FSP-5, and FSP-6), based on the Government
Regulation PP 23/2018, applicable for business entities with a gross
margin less than USD347,000. The nancial feasibility of the energy
system and productive activities studied is based on payback period,
net present value, and internal rate of return (PBP, NPV, and IRR) pa-
rameters calculated through project nancing cash ow over a 20-year
period, from 2020 to 2040.
3. Results and discussion
3.1. Installed capacity and power generation portfolio
In this study, the energy supply scheme on Sebira Island that will
be simulated is a redesigned scheme without considering the existing
energy infrastructure. The redesign scheme was chosen because it was
found that the existing generating system consisting of DG (capacity 100
kW), SPV (capacity 400 kWp), and lead-acid battery (912 kW capacity)
is oversized. The electricity production exceeds the modeled demand
as there is signicant loss due to low distribution eciency, making it
unable to be optimized ( Suku Dinas Ketenagalistrikan, 2020 ).
Optimization is carried out on the objective function of energy de-
mand and supply based on the lowest cost using TIMES-VEDA software.
Optimization output is the portfolio of generation and storage technol-
ogy. The selected power plants are SPV, WT, and Li-Ion storage technol-
ogy, which is in accordance with the lowest cost considering calculated
LCOE for generators and LCOS for storage technologies ( Fig. 5 ). DG is
not selected as a generation technology due to the high LCOE.
The development of the new capacity of the power plant shown in
Fig. 9 is driven by an increase in energy demand. Based on the optimiza-
tion, the selected main power plant is SPV due to the small potential for
wind power. It was obtained from the optimization results that the least
capacity growth occurred in 2020-2021, only requiring 11.4 kW SPV
and 1.8 kW Li-Ion batteries, driven by the demand for the construction
of a greenhouse solar dryer in 2021.
Meanwhile, the sharpest increase in capacity occurred in 2025-2030,
with a new generating capacity of 167 kW, followed by 2021-2025,
with a new generating capacity of 111kW. In these two periods, the
development of new capacity was driven by increasing energy demand
by holding two energy-intensive productive activities, namely desali-
nation facilities (2023) and island resort (2026). The RO system has
a clean water production energy rating of 7.27kWh/m
3 with a daily
production capacity of 50m
3
, increasing energy demand by 27% from
the previous year (2021). Coupled with the resort’s construction, RO
and resort will collectively absorb 38% of the island’s total energy by
2038.
The increase in generating capacity is positively correlated to the
storage capacity increase. This is because the generators used, SPV and
WT, are VRE, with varying hourly energy production due to varying
availability, making storage technology a crucial component in the en-
ergy system. However, in contrast to SPV generators, the battery’s life-
time is only ten years, which backdrops the largest capacity increase in
2040, amounting to 107 kW, due to the retirement of the 96 kW battery
capacity from 2030. This is why even though the trend of adding SPV
capacity is relatively at from 2035 to 2040, the new battery capacity
increases to 72.7 kW in 2035 and 107 kW in 2040.
Electricity production by SPV, WT, and Li-Ion discharge per year re-
sults from optimization ( Fig. 10 ) is surplus over demand from the island
model integrated with the productive zone. It was found that SPV dom-
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V.Z.P. Hardjono, N. Reyseliani and W.W. Purwanto Cleaner Energy Systems 4 (2023) 100057
Fig. 5. LCOE of SPV, WT and DG and LCOS of Li-Ion and VRLA.
Fig. 6. Sebira Island productive activity integration planning
timeline.
Fig. 7. Integrated overall load prole of Sebira Island.
inates electricity production, and the share of WT is very small (0.7%)
due to the low potential of wind power on Sebira Island. There is a gap
between the demand and supply due to loss in distribution and battery’s
round trip eciency, which cumulatively amounts to 9.44% of the total
supply.
3.2. Electricity dispatch
Results of optimized hourly energy dispatch in 2020 are shown in
Fig. 11 , and over the period 2020-2040 are shown in Fig. 12 . The SPV
generator fullls 99.3% of electricity demand and only generates elec-
tricity from 6:00 to 18:00, while WT generates electricity for 24 hours.
Electricity generated is used for direct consumption to fulll demand,
while excess is used to charge the battery. When SPV doesn’t generate
electricity at night, including peak demand at 20:00, electricity demand
is met by the discharge of the battery Fig. 13 .
3.3. Carbon footprint
In this study, the projected CO
2
emissions are only sourced from the
electricity generation system, from SPV and Li-Ion batteries. Consider-
ing that the selected technology competition is a scheme that uses 100%
renewable energy, the CO
2
produced per kWh of electricity is low. As
seen on Fig. 14 , cumulatively, the amount of CO
2
produced increases,
because it is directly proportional to the increase in the amount of en-
ergy production per year. Considering that the energy system is made
up of majorly solar PV, and Li-Ion, the carbon footprint is signicantly
lower. Based on assumption on a study on carbon footprint of energy
7
V.Z.P. Hardjono, N. Reyseliani and W.W. Purwanto Cleaner Energy Systems 4 (2023) 100057
Fig. 8. Sebira Island’s overall electricity demand growth.
Fig. 9. Capacity of generator and storage technology.
Fig. 10. Electricity production by SPV, WT, and discharge by Li-ion bat-
teries.
system, ( Das et al., 2018 ) the optimized energy system in this study re-
sults seven times less carbon footprint per kWh than a 100% DG energy
system.
3.4. Total investment cost of the system
The annual investment cost of generation and storage technology is
shown in Fig. 14 . The decrease in 2040 is due to the end of the invest-
ment period for the 275.7 kW PV mini-grid in 2020 and the commence-
ment of the investment of a 180 kW new PV mini-grid built in 2040
as a replacement. The annual investment cost is only 44% of the total
energy system cost procured in 2020. The price of the new power plant
is signicantly lower because, apart from the smaller capacity, there is
also a reduction in investment costs per kW of capacity due to the PV
mini-grid learning rate.
Based on optimization results, it was found that the largest invest-
ment in present value is in 2020, at the beginning of the planning year,
due to the installation of SPV with a capacity of 275.7 kW and Li-Ion
8
V.Z.P. Hardjono, N. Reyseliani and W.W. Purwanto Cleaner Energy Systems 4 (2023) 100057
Fig. 11. Electrical Hourly Energy Dispatch in 2025.
Fig. 12. Electrical hourly energy dispatch from 2020-2040.
Fig. 13. Total CO
2
production from energy system.
batteries of 80.2 kW. The comparison of battery and PV mini-grid in-
vestment is almost 50:50 in 2028 and 2038 because even though the
procurement of SPV capacity is always twice as large as the battery ca-
pacity, the unit price per kW battery is much more expensive. In 2028,
the investment cost battery is USD 1368/kW compared to the cost of
SPV capacity in the same year, USD 765/kW.
LCOE of the optimized energy system consists of the cost components
of energy generation and storage. The yearly LCOE of the optimized
energy system is presented in Fig. 15 . The decrease in LCOE results from
the decrease in investment cost due to the learning rate. Investment
costs are the main factor that makes up 90% of LCOE. This is because
the energy system consists of SPV and WT, therefore, it is not aected by
varying fuel costs. O&M costs are only 8-10% of the annual investment
cost.
Fig. 14. Investment Cost of Generation and Storage Technology.
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V.Z.P. Hardjono, N. Reyseliani and W.W. Purwanto Cleaner Energy Systems 4 (2023) 100057
Fig. 15. LCOE of optimized electricity system.
Fig. 16. Milestones of capital cost of energy systems and productive zones.
In this study, the projected CO
2
emissions are only sourced from the
power generation system, from SPV and Li-Ion batteries. The optimized
scheme uses 100% renewable energy, and the CO
2
produced per kWh of
electricity is low, 0.014 kg /kWh. This value is consistent from 2020 to
2040 because the energy system consists of the same components. The
carbon footprint of the current energy system is estimated to be 29 times
more than the optimized energy system in this study, at 0.404 kg/kWh,
due to the use of DG, supplying 50% of the island’s energy supply ( Yildiz
et al., 2019 ).
3.5. Financing analysis for system
The project-based cashow calculation shows the total and yearly
capital costs of the ve components of the integrated renewable energy
generation system with the productive zone systems in Fig. 16 . The ve
components of the system consist of one energy system and four pro-
ductive zone activities, or product added value activities. The system
component with the most signicant contribution of capital cost is the
resort system. The funding scheme is shown in Tables 1 & 2 . Economic
feasibility is evaluated by comparing the value of the cost of goods, sell-
ing price, IRR, MARR, and NPV of each funding scheme.
3.5.1. Energy system
The energy system portfolio studied in this paper is that of an opti-
mized portfolio for 2023. Additional energy system infrastructure to be
built consists of a PV mini-grid with a capacity of 111 kWp and Li-Ion
battery storage technology of 30 kW. The increasing demand will drive
the increase in electricity demand in this period due to the construc-
tion of cold storage and desalination (reverse osmosis) facilities and the
growing demand for residential zones.
The negative cash ow from the energy system for 2023 consists of
three components, capital costs (Capital Expenditure, or CapEx), SPV
(80%), and Li-Ion batteries (19%), and the overall operations and main-
tenance costs (O&M, 1%). The solar PV mini-grid capital costs, USD
97,850, are paid at the start of the investment because it has a lifetime
of 20 years, while Li-Ion batteries have a lifetime of 10 years, so in ad-
dition to the investment in 2023, the replacement cost of USD 23,403
will be due in 2034.
Nine funding schemes ( Table 1 ) with 3-4% lending rates, follow-
ing the eco-bank scheme to build sustainable infrastructure and partic-
ipative nancing (crowdfunding). The economic feasibility parameter
of the funding scheme is assessed from the IRR value that exceeds the
WACC, which indicates that the return exceeds the costs incurred. The -
nancially feasible scheme also has a positive prot margin, which shows
the dierence in basic costs against the base tari (USD 0.1 /kWh).
Based on Table 3 and Fig. 17 , the nancially feasible schemes are
the ones with the aid of grants (FSE-3, FSE-4, FS-5, and FSE-9). This
is because the cost of production (LCOE) scheme without grants is
higher than the regulated electricity tari by the state-owned electric
power distributor, Perusahaan Listrik Negara (PLN), of USD 0.1/kWh
( Bridle et al., 2014 ). The PLN sales tari in this paper is a standard of
the Sebira Island community’s willingness to pay. The regulated tari
will be an input to the O&M (utility cost) in the cashow of all designed
productive activities proposed.
The innovations in funding used in this study are low-interest loans,
4% with a loan term of 10 years applicable to FSE-1, FSE-2, FSE-3, and
FSE-4, and participative investment/crowdfunding schemes with a low
loan rate of 3% in FSE-7, FSE-8, and FSE-9. Even with minimal interest
and a 10-year loan term, the principal cost of the crowdfunding scheme
still exceeds the stipulated electricity sales tari, even with the scal
intervention in FSE-8. The new crowdfunding scheme is economically
feasible when assisted by a 25% grant (FSE-9). The low-interest bank
lending scheme (4%) and 25% grants (FSE-2) are not enough to cover
the high CAPEX costs, so it is not nancially feasible.
The crowdfunding scheme can be an alternative funding for projects
in remote areas to reduce the burden of local government expenditures,
which previously assumed 100% of the capital costs of energy gener-
ation. In addition to allowing government savings, crowdfunding can
benet local, individual investors to build a sense of belonging to the
project and enable the community to directly contribute to projects that
impact the development of remote areas. Increased public awareness
about renewable energy’s economic and environmental benets can be
a driving factor for the realization of similar projects.
3.5.2. Productive zone activities
Costing estimation, which consists of capital cost and O&M costs
of the development and construction of designed productive activities
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V.Z.P. Hardjono, N. Reyseliani and W.W. Purwanto Cleaner Energy Systems 4 (2023) 100057
Table 3
Financial analysis of funding schemes for energy system.
Funding Scheme IRR = WACC Production
Cost (USD/kWh)
Electricity Tari
(USD/kWh)
Prot Margin
from Cost (%)
NPV (USD) WACC (%) IRR (%) PBP (Years) Financial
Feasibility
Hybrid- Mini-Grid
Power Generator
FS-1 0.15 0.10 -46% -69 821.86 4.79% -0.48% 18.64 No
FS-2 0.11 -9% -28 199.36 3.75% 2.46% 15.24 No
FS-3 0.07 30% 26 000.15 2.40% 8.73% 10.46 Yes
FS-4 0.04 63% 89 833.20 0.75% 24.57% 3.98 Yes
FS-5 0.01 91% 135 543.44 0.00% - 0 Yes
FS-6 0.14 -39% -74 310.18 5.48% -0.31% 18.4 No
FS-7 0.12 -19% -51 685.49 2.25% -0.18% 18.22 No
FS-8 0.11 -13% -46 134.77 2.99% 0.97% 16.85 No
FS-9 0.08 16% 7 802.49 2.45% 5.48% 12.64 No
Fig. 17.