Photovoltaic Deployment Experience and Technical Potential in Indonesia’s
Java-Madura-Bali Electricity Grid
Yusak Tanoto1,4, Iain MacGill1,3, Anna Bruce2,3 and Navid Haghdadi2,3
1School of Electrical Engineering and Telecommunications, UNSW Australia
2School of Photovoltaic and Renewable Energy Engineering, UNSW Australia
3Centre for Energy and Environmental Markets, UNSW Australia
4Electrical Engineering Department, Petra Christian University Indonesia
The largest and most established electricity grid in Indonesia, the Java-Madura-Bali
(JAMALI) interconnected system, accounts for around 60% of the country’s total electricity
demand. Given continuing high demand growth and the slow addition of major generators in
recent years, energy users served by the JAMALI grid are experiencing inadequate and
unreliable electricity supply. There is growing interest in the potential of renewable energy to
help mitigate the risks of continued fossil fuel dependence, improve system reserve margins,
and diversify energy sources to enhance supply security, as well as improve environmental
outcomes. The Indonesian government’s vision to increase the renewable energy contribution
to Indonesia’s energy mix to 23% by 2025, raises the question of what role utility-scale
photovoltaic (PV) generation might play in meeting these challenges and opportunities.
Indonesia has an excellent solar resource, while PV costs have fallen markedly over recent
years. Still, the JAMALI’s present generation mix is dominated by low cost and relatively
abundant coal and gas.
This paper reviews the current status and future potential of utility PV in the JAMALI area.
Given limited existing studies on solar irradiation and PV mapping for the Indonesian context,
we first present a detailed spatial mapping of PV output potential for the JAMALI region
including hourly temporal behavior – a key factor for assessing PV integration challenges and
opportunities. This proposed spatial PV output mapping is provided at a 5 km2 resolution,
using weather data derived from the NASA MERRA-2 satellite database and the Global Solar
Energy Estimator model. The mapping provides a preliminary indication of appropriate
utility-scale PV locations in the JAMALI context. An open-source generation mix model,
NEMO, is then used to assess the potential role of utility PV in least cost future generation
portfolios for JAMALI. Results suggest that PV could play a useful role, depending on future
technology, fuel and carbon prices, and demand growth. On this basis, current policies and
regulations relevant to deployment of large scale PV are described and a number of key
challenges for PV deployment and possible opportunities for reform are identified.
The Indonesian government is facing great challenges yet also opportunities to improve the
nation’s future energy mix, contributing to international efforts to avoid dangerous global
warming while also improving energy access and security. At the 21st United Nations
Framework Convention on Climate Change (UNFCCC) Conference of the Parties (COP) in
Paris in 2015, Indonesia announced an ambitious target to reduce greenhouse gas emissions
by up to 29% compared to the business as usual level by 2030. One element of this is a target
of 23% renewables in its overall energy mix by 2025. Among all commercially available
renewable generation technologies, the role of solar photovoltaics (PV) seems certain to be
crucial in supporting efforts towards these goals, in particular because of the rapid decrease in
solar PV system prices in recent years.
Few studies, however, have been carried out to date assessing solar PV potential in Indonesia.
The energy generation potential and cost-effectiveness of grid-connected PV systems at
Indonesia’s provincial level has been assessed by Veldhuis and Reinders (2013). However,
the time interval in terms of PV potential is not clearly specified in the study. Solar radiation
analysis has been conducted by Parangtopo et al. (1984), who analyzed global and diffuse
solar irradiation (GHI and DNI) in Jakarta, while multi years solar radiation data for 10
locations in Indonesia and 2 neighboring locations in Singapore and Darwin, Australia, has
also been analyzed (Morison and Sudjito, 1992). The study found that solar radiation across
Indonesia shows a significant east-west gradient. Another study focused on the estimation of
global solar radiation in the Indonesian climatic region (Halawa and Sugiyatno, 2001). The
study presented monthly average daily global solar radiation correlations applicable to the
Indonesian climatic region using a modified version of Sayigh’s formula. Mapping of solar
irradiation for each province in Indonesia, based on an artificial intelligence technique, has
been conducted by Rumbayan et al. (2012). The study however, published only a single range
of monthly values of solar irradiation for each province using a yearly average NASA
database. Mapping of Indonesia’s long term monthly average global horizontal irradiation and
direct normal irradiation, and PV output potential funded by the World Bank’s ESMAP
program was recently published by Solargis (2017), based on their model.
While these reports provide long term averages that describe the solar resource at different
locations in Indonesia, and can be used as a preliminary step towards prospecting for PV plant
development opportunities, no analyses of temporal and spatial variability were reported in
this studies. For power system planning, the variability of the output of PV systems over time,
and the correlation of the output of PV in different locations are important in determining the
potential aggregate PV generation ramp rates that must be managed by the power system.
Given the limited studies on utilizing PV resources mapping for assessing challenges and
opportunities of large scale PV integration in Indonesia, this paper presents mapping of the
temporal variability of PV generation output in the largest interconnected power grid in
Indonesia; the Java, Madura and Bali electricity grid, or hereafter “JAMALI”. On this basis,
sites were then selected for a preliminary investigation into possible least cost future utility-
scale PV investment in JAMALI using the National Electricity Market Optimizer (NEMO)
tool (Elliston, et al, 2012)
This paper is organized as follows: the status of PV deployment in Indonesia and JAMALI is
described in the next section. PV resource mapping in JAMALI is presented in Section 3. A
simulation of a least cost generation mix in JAMALI with high PV penetration is presented in
Section 4, followed by discussions around policy recommendations, conclusions and future
work in Sections 5 and 6.
2. Status of PV Deployment in Indonesia and in JAMALI
JAMALI covers two special regions and five provinces; that is, Jakarta, Yogyakarta, Banten,
West Java, Central Java, East Java and Bali. It has about 19,500 kilometers of 150 kV and
500 kV transmission line. JAMALI relies heavily on coal (47% of total generation), gas
(21%), and a small number of hydro and geothermal power stations to meet annual demand of
more than 180 TWh, and 33.2 GW peak load in 2016. It represents around 60% of total
national electricity generation (PLN, 2017).
Despite an excellent solar resource, averaging 4.8 kWh/m2/day across the country (PLN,
2017), significant deployment of grid-connected PV has not yet occurred in Indonesia. The
International Renewable Energy Agency has estimated that there is some 39 GW of solar PV
potential in the JAMALI area (IRENA, 2017).Still, by 2015, national installed capacity of
utility-scale PV had reached only 9 MW with no installations in the JAMALI grid. During
2016, the capacity only slightly increased to 13 MW, with only 1 MW capacity in the
JAMALI area (PLN, 2017). As part of the government’s plan toward 100% electricity access
for the Indonesian people, the limited budget allocated to support PV is mainly directed
towards the promotion of off-grid PV for supporting electricity provision in rural areas, and in
particular reducing diesel consumption on isolated mini-grids. The Indonesian government
has encouraged Independent Power Producers (IPPs) to construct and operate such PV plants,
and as a result, more than 71 MW of off-grid PV capacity was installed by 2016 (DG
NREEC, 2016), compared to 13 MW on the grid.
Despite the limited budget to date, an overall national target of 6.4 GW of PV capacity by
2025 has been set by the government (IRENA, 2017). Initial support for IPPs involved a
government regulated feed-in tariff up to US$0.25/kWh with an additional incentive of
$0.05c/kWh for PV systems with minimum 40% local content (that is, 40% of the overall
value of the PV system should be produced locally). Under this framework, the maximum PV
capacity allowance for each developer is limited to a maximum 20% of the regional capacity
quota for the tender offer of 10–100 MW (MEMR, 2016). The first stage capacity quota and
feed-in price for provinces in Java are 150 MW and US$ 0.145/kWh, and 5 MW and
US$ 0.16/kWh for Bali provinces. Feed-in price for other regions, vary from US$ 0.15 –
0.25/kWh (MEMR, 2016).
More recently, following a lawsuit brought by the local PV industry over foreign IPPs, the
incentive was replaced with a different framework forcing PLN to buy PV energy from the
IPPs. Under this framework, the feed-in tariff for PV generation ranges from 85% – 100% of
the PLN regional electricity generation cost, while other rules stipulated in the previous
framework remain active.
PLN opened a tender in the first half of 2017 for 168 MW of PV capacity (The Jakarta Post,
2017), in addition to 87 MW of recently planned capacity (PLN, 2017) in Sumatra.
Nevertheless, this capacity is only a fraction of the 6.4 GW utility-scale PV goal for Indonesia
over the next 10 years, and the long-term policy for achieving this is as yet unclear.
JAMALI PV resource mapping
Given the significant deployment of utility scale PV expected in the JAMALI region, it is
important to better understand the underlying PV resource available across the region. For our
study, one-year of gridded hourly PV power output data across the JAMALI region was
obtained from the online renewable energy simulation tool Renewables Ninja (Pfenninger and
Staffel, 2016). The data covers 2015, with a spatial resolution of 0.05° x 0.05° (5 km x 5 km).
Renewables.ninja models hourly timestep PV output at a specific tilt angle based on NASA
MERRA2 direct and diffuse irradiation, and ground temperature data, using the Global Solar
Energy Estimator (GSEE) model (Pfenninger and Staffel, 2016). Figure 1 presents a map of
PV capacity factor across JAMALI in 2015, which varies between 16.9% - 18.7%. A
relatively high capacity factor can be seen in Bali (the island at the right side of Java) and in
the southeastern part of East Java province.
Figure 1. Mapping of 1-year PV capacity factor across locations in JAMALI in 2015
Of course, effective and economically efficient PV integration will depend greatly on the
patterns of PV generation across daily and seasonal cycles. A mapping of monthly PV
capacity factor is presented in Appendix 1 while the range of monthly PV capacity factors for
all JAMALI locations is presented in Figure 2. The three months with the highest monthly
capacity factors are September, October, and August, of which the highest median capacity
factor was 23.63% (September), while the lowest median capacity factor was April (11.87%).
This significant seasonal variation poses some challenges for PV integration.
Figure 2. Range of monthly PV capacity factor for all JAMALI locations in 2015
3.1. Hourly output variability
Integration of high penetrations of variable renewable energy such as PV into power systems
adds to underlying variability in demand, and increases the challenge of supply demand
matching. Changes in PV power output require other generation units to ramp up to maintain
the demand and generation balance, requiring sufficient spinning dispatchable generation to
be available. This can be challenging at high penetrations, particularly where PV plant output
is correlated and large changes in aggregate PV output occur. While managing variability and
uncertainty presents challenges over a range of different timeframes, this study evaluates the
hourly variability of PV power output. JAMALI dispatch is planned in half hourly basis. Of
course, PV and demand variability over shorter time frames also poses operational challenges.
However, higher frequency data of temporal PV power output for JAMALI is not available.
The method used in this paper for analysing variability is based on Mills and Wiser (2010).
Change in average power output is calculated as follows:
(𝑡) is the value of delta power output for 1 hour interval at a single site 𝑃 for
hour 𝑡. The standard deviation of the variability (step changes between each hour interval) is
The 99.7th percentile (three standard deviations, 3𝜎) value is used as an indicator of the range
Figure 3 shows the range of hourly variability across all locations in the JAMALI grid
expressed as 3𝜎 variability. These fall within a range of 230 – 254 kW/hr change per 1 MW
PV plant capacity. Overall, there is roughly 10% difference between locations with the lowest
and highest range of variability, roughly equivalent to the 10% difference in capacity factors
seen across the JAMALI region.
Figure 3. Mapping of 2015 hourly ramp rate variability across JAMALI (in 3𝜎)
The range of values of 3𝜎 variability across all locations in JAMALI are shown by month in
Figure 4. High variability occurred in the same months having high capacity factor, i.e.
September, October, and August. During these three months, the values of 3𝜎 variability were
0.28 – 0.3 MW/hr per 1 MW plant capacity.
One key question for integration is the effect of PV plant diversity on aggregate output; that is
how variability is impacted when PV generation is distributed across multiple, geographically
dispersed locations. As more variability of PV output in intra hour resolution is expected, the
aggregated variability of multiple disperse PV systems would be less so that intra hour
variability will be reduced using 1-hour data. To assess this, one location from each province
(the small provinces of Jakarta and Banten are merged) was selected, based on sites with the
best capacity factor. Another 19 locations with more varied capacity factors and ramp rates
were then added to these 6 provincial locations, in order to compare the cumulative
distribution delta of PV output. Figure 5 presents the cumulative delta of PV output (in
absolute values) for 6 and 25 aggregated locations.
Figure 4. Monthly maximum – minimum variability of hourly ramp rate across
locations in JAMALI in 2015, expressed in 3σ
Figure 5. Top 10% cumulative distribution of delta P for 6 aggregated sites (left) and 25
aggregated sites (right)
The top 10% of the aggregated delta P ranges between 0.17 – 0.22 MW/hr per MW installed
aggregated capacity, for both the 6 and 25 aggregated PV locations. In practice, this gives an
idea of how much adjustment would be required from other generators in terms of their hourly
output in order to maintain supply – demand balance in JAMALI. This would be equivalent to
650 – 850 MW/hr given a 3.84 GW capacity (equivalent to assuming 60% of the 6.4 GW
Indonesian 2025 PV target was deployed in the JAMALI grid, which supplies 60% of demand
in Indonesia). Figure 6 presents a comparison of the cumulative distribution of the delta PV
output for 1, 6 and 25 aggregated locations. The magnitude of delta P is slightly decreased
from 0.24 to 0.22 MW/hr per MW installed capacity from a single site to 6 aggregated
locations or more.
Figure 6. Comparison of the cumulative distribution of deltas P for 1, 6 and 25
aggregated locations with normalized absolute delta P
PV integration in JAMALI’s long-term generation mix
This Section offers insights into the potential role of large scale PV in the future JAMALI
system, through preliminary results of modelling of JAMALI’s least-cost long-term
generation expansion options. The simulation is conducted using NEMO, an open source
evolutionary algorithm-based optimization tool, which was developed and used to study
Australia’s future electricity generation mix with 100% renewable energy (Elliston, et al,
2012). Solutions for generation mix are obtained by minimising cost of energy while
complying with a set of constraints.
For this study, 2015 JAMALI hourly demand data was used (PLN, 2017) as a baseline, and
3.5% to 10% linear annual growth was added to create a projected 2030 demand profile. All
generators in 2030 are costed as new build. For this purpose, baseline technology costs for
2015 are taken from an Indonesian government agency report (National Energy Council,
2016) and technology cost scenarios for 2030 are scaled from the baseline by using 2015 –
2030 cost escalation trends provided by NREL (NREL 2017). The international trends are
considered to be relevant, since most the technologies are largely imported. Indonesia’s coal
and gas price projections for 2030 are obtained from IRENA (IRENA, 2017). A Loss of Load
Probability (LOLP) of 0.274% (equivalent with 24 hours in a year, where demand cannot be
meet by available generation) is used in all scenarios in accordance with the Indonesian
reliability standard (PLN, 2017). In addition, the carbon price and discount rate are assumed
to be US$ 25/tonne of CO
equivalent and 5% in 2030, respectively as suggested in Elliston,
et al (2012).
Coal, gas, hydro, biomass, geothermal and PV are modelled as generation mix candidates
using NEMO. Build limits (GW) are set for hydro and geothermal at 8 and 10 GW,
respectively (DG NREEC, 2016a). To model potential PV generation, the JAMALI area was
divided into 6 polygons based on provincial classification, and each polygon assigned a PV
plant candidate trace, of which the PVs build limit (GW) were capped according to each
polygon’s potential output. The provision of traces from each region to the NEMO investment
optimisation model provides an equal opportunity for PV to be built in each region. The PV
plant candidate in each polygon was chosen according to three factors: high capacity factor,
low hourly temporal variability and low spatial variability. The selected locations are
therefore mostly in the southern part of JAMALI where capacity factors are high (Figure 1).
An additional benefit of these locations is that they are relatively far away from the central
part of the islands, which have volcanoes and difficult terrain (highlands).
Other considerations for PV plant locations could include the similarity between each site’s
output (Elliston, et al., 2016). In this case, the coefficient of correlation between polygon 1
(western part of JAMALI) and polygons 2 – 6 are within 0.95 to 0.97, indicating that hourly
output is highly correlated across JAMALI. The highest coefficient of correlation is found
between polygons 3 and 4, which is 0.99, which would make a combination of PV plants at
these two locations less complementary in terms of benefiting from generation diversity.
Several scenarios that consider changes in future technology costs, future gas price and future
demand are simulated in order to reveal possible least cost generation mixes that meet the
specified reliability level.
4.2. Reference Scenario and Results
The reference scenario (Table 1) 2030 mid-level technology costs, 346.5 TWh demand (vs
166.6 TWh demand in 2015), a US$ 10.9/GJ gas price, US$ 3.5/GJ coal price, and 0.274%
Table 1. System parameters of the reference scenario
Reference scenario: LOLP 0.274%, cost scenario: 2030
demand: 346.5 TWh, gas price US$ 10.9/GJ
Unused surplus energy (TWh) 0.0
Time steps with unused surplus energy (hr) 15
Min – max shortfalls (MWh) 0.0, 696
Unserved energy (%) 0.002
Unserved total hours 24
Loss of Load (%) 0.27
Total hour of Loss of Load (hr) 24
System generation cost (US$/MWh) 50.38
Total emissions (MtCO2) 108.94
The results of simulating the reference scenario are shown in Table 2. Flexible fossil fuel-
based generators (coal fired, CCGT, and OCGT) account for 30.88% and 41.46% of the total
capacity and energy generation 2030. Geothermal and hydro contribute 25.25% and 20% of
the total energy generation, respectively. Meanwhile, PV’s contribution is quite substantial
with PV generation built in all regions except Yogyakarta. The total PV capacity and energy
generation are 28.91 GW and 46.2 TWh, respectively, accounting for 37.16% and 13.34% of
the total capacity and energy generation in the JAMALI. Under the chosen technology, fuel
and carbon costs, these results suggest that PV is already a cost effective generation option
and would be part of an economically least cost, green fields, generation mix for JAMALI.
Levelised Cost of Energy (LCOE) and capacity factors for each generator in the optimisation
are shown in Figure 7.
Table 2. Generation and capacity mix of the reference scenario in 2030
LOLP 0.274%, technology costs: 2030Mid, demand: 346.5 TWh, gas price: US$ 10.9/GJ
(Bali) Total %
Solar PV Cap (GW) 4 9 2.91 0 11 2 28.91 37.16
Energy (TWh) 6.087 14.27 4.651 0 17.95 3.248 46.21 13.34
Geothermal Cap (GW) 10 10 12.86
Energy (TWh) 87.49 87.49 25.25
Hydro Cap (GW) 8 8 10.28
Energy (TWh) 69.1 69.1 19.95
Coal Cap (GW) 22.3 22.3 28.67
Energy (TWh) 137.5 137.5 39.69
CCGT Cap (GW) 5.51 5.51 7.08
Energy (TWh) 5.585 5.585 1.61
OCGT Cap (GW) 3.07 3.07 3.95
Energy (TWh) 0.5582 0.558 0.16
Total capacity (GW) 77.79 100
Total energy supplied (TWh) 346.4 100
Figure 7. LCOE and capacity factor of the generators, given LOLP at 0.274%, cost
scenario: 2030-mid, demand: 346.5 TWh, gas price: US$ 10.9/GJ
Demand Growth and Technology Cost Scenarios
The impact of different possible future electricity demand, technology and gas prices were
also investigated. Three technology costs scenarios, i.e. 2030 low, mid and high were used
along with gas prices of US$ 7/GJ, US$ 10.9/GJ and US$ 15/GJ, and 4 demand levels, 279.9
TWh, 346.5 TWh, 493.1 TWh and 696.3 TWh (3.5%, 5%, 7.5% and 10% annual growth from
2015). Historical inter-year average demand growth rate in JAMALI was measured around
5% over 10 years, with annual load growth was ranging around those selected demand levels.
PV capacity could vary between 2 GW to 24 GW for the first demand level and between 4
GW to 35 GW for other demand levels, depending on the gas price. PV capacity would be the
lowest for all cases in the high technology cost scenario and highest for the low cost scenario.
Meanwhile, other generators’ aggregated capacity are relatively stable at 40 GW, 50 GW, 70
GW and 98 GW for demand level of 279.9 TWh, 346.5 TWh, 493.1 TWh and 696.3 TWh,
respectively, given various cost scenarios and gas price. PV’s contribution to the generation
mix could range between 5% - 14.4% for 346.5 TWh energy demand profile (5% annual
demand growth vs 2015). This accounts the highest share of PV generation mix among other
demand – cost scenario levels, despite relatively low share among other technologies. Figure
8 (left) shows PV and other generator’s aggregated capacity for all demand, all cost scenarios
and all gas prices, and (right) generation capacity mix for all demand, all cost scenarios and at
gas price US$ 10.9/GJ.
Figure 8. (left) PV Capacity (GW) versus other generators’ aggregated capacity for all
demand, cost and gas prices scenarios; (right) Energy mix (%) for all demand and cost
scenarios at a gas price US$ 10.9/GJ
Figure 9. Changes in generation costs (US$/MWh) and CO
) for all
costs, demand and gas price scenarios
Figure 9 (left) shows changes in average generation costs and CO2 emissions for all costs and
demand scenarios with the different gas prices. Average generation cost varies from 41.31 –
62 (US$/MWh) for all costs scenarios. With a 279.9 TWh annual demand, the cost ranges
between 41.31 – 48.25, while with 696.3 TWh annual demand, costs range from 58 – 62.02.
emissions could be as low as 132 MtCO
for 493 TWh demand in the high cost scenario
and a gas price of US$ 7/GJ, due to the elimination of coal fired plants in the system, replaced
by significant CCGT and a few GW of OCGT.
The results of simulations conducted for this research suggest that a substantial capacity of
PV; that is 25 – 35% of the total system GW capacity could be part of a least-cost 2030
generation mix for JAMALI under a modest carbon price, with only a relatively small
variation in PV deployment given different gas prices and projected demand under low and
medium technology cost forecasts. In terms of energy generation, PV contributes 8 – 16% of
the total mix under those scenarios. However, for all high technology costs scenarios, PV
combined capacity from all regions is found to be very low compared to other generators, i.e.
10 GW or lower and PV contributes only 5% of energy.
Given the relatively low penetration of PV by energy in all scenarios, it does not appear likely
that there would be significant integration challenges. In terms of hourly dispatch, it is likely
that much higher penetrations of variable PV could be reliably accommodated in the JAMALI
system. While greater supply-side flexibility and ramping capacities may be required to
accommodate hourly variability of PV penetration in low and medium costs scenarios,
significant geothermal and hydro can provide relatively constant energy generation and load
following, respectively, while flexibility can also be provided by gas plants and more frequent
ramping by coal fired plants. Shorter term flexibility may present challenges, but there are a
range of technology options with decreasing costs that can provide the fast response required.
Although PV integration into JAMALI of the scale found in the modelling appears both
technically and economically feasible, long-term electricity industry planning with PV
integration in the Indonesian context requires careful planning and regulation. Key existing
challenges include a lack of transparent planning and publicly accessible information
regarding costs, technical characteristics, constrains, performance of existing generators and
quality (measured) renewable energy resource data.
Meanwhile, another operational challenge would be faced by PLN following the recent
MEMR ministerial regulation (MEMR, 2017) due to the obligation to purchase the whole
energy generated by PV plants managed by IPPs. To improve the situation, in-depth studies
regarding feed-in tariff determination and framework that lead to efficiency improvement and
even encourage competition among energy producers, would be beneficial, given the potential
benefits of increasing PV integration in JAMALI.
6. Conclusion and further work
This study reviews the current status, and explores the potential for utility-scale PV
deployment, in the JAMALI areas. Recent experiences regarding PV deployment, policies
and regulations relevant to deployment of large scale PV are described and a number of key
challenges for PV deployment are identified. Mapping and analysis of PV output potential
and hourly temporal variability for JAMALI areas are presented using data obtained from
renewables.ninja based on the NASA MERRA-2 satellite database and the Global Solar
Energy Estimator model. This paper also presents preliminary simulations to explore the
potential for PV to contribute to an economically efficient generation mix in 2030. PV
capacity could vary between 2 GW to 24 GW for the lower demand growth scenario and
between 4 GW to 35 GW for other demand scenarios, dependent also on future gas prices.
Average generation cost estimates varied from 41.31 – 62 (US$/MWh) for all technology,
cost and gas price scenarios. Further work on exploring the status and sustainability of long-
term electricity planning and power sector development in Indonesia would be of great value
for policy makers. Such analysis should include consideration of broader aspects of energy
security, future uncertainties and analysis around system robustness with high penetrations of
renewables and more detailed technical constraints.
Veldhuis, A. J. and Reinders, A. M., 2013, ‘Reviewing the potential and cost-effectiveness of
grid-connected solar PV in Indonesia on a provincial level’, Renewable and Sustainable
Energy Reviews, Volume 27, p315-324.
Parangtopo, Poesposeotjipto, H. and Harsono, A. G., 1984, ‘Review and analysis of the global
and diffuse solar radiations in Jakarta, Indonesia’, Solar & Wind Technology, Volume 1, Issue
Morrison, G. L. and Sudjito, 1992, ‘Solar radiation data for Indonesia’, Solar Energy, Volume
49, Issue 1, p65-76.
Halawa, E. and Sugiyatno, 2001, ‘Estimation of global solar radiation in the Indonesian
climatic region’, Renewable Energy, Volume 24, Issue 2, p197-206.
Rumbayan, M., Abudureyimu, A. and Nagasaka, K., 2012, ‘Mapping of solar energy potential
in Indonesia using artificial neural network and geographical information system’, Renewable
and Sustainable Energy Reviews, Volume 16, Issue 3, p1437-1449.
Solargis, 2017, ‘Solar resource and photovoltaic power potential of Indonesia’, Washington
DC: World Bank (ESMAP).
P.T. PLN (Persero), 2017, ‘PLN Statistics 2016 (in Bahasa Indonesia)’, Jakarta: Corporate
Secretariat - P.T. PLN (Persero).
P.T. PLN (Persero), 2017, ‘P.T. PLN (Persero) Electricity planning 2017-2026 (in Bahasa
Indonesia)’, Jakarta: Ministry of Energy and Mineral Resources of the Republic of Indonesia.
P.T. PLN (Persero), 2017, ‘Annual demand data 2015/2016 (in Bahasa Indonesia)’, Jakarta:
PLN Java-Bali Load Dispatch Centre.
Gielen, D., Saygin, D. and Rigter, J., 2017, ‘Renewable energy prospects: Indonesia, a
REmap analysis’, Abu Dhabi: International Renewable Energy Agency.
DG NREEC, 2016a, ‘Statistics Book of New, Renewable Energy and Energy Conservation
2016 (in Bahasa Indonesia)’, Jakarta: Directorate General of New, Renewable Energy and
Energy Conservation of the Republic of Indonesia.
DG NREEC, 2016b, ‘Achievement Report 2016 (in Bahasa Indonesia)’, Jakarta: Directorate
General of New, Renewable Energy, and Energy Conservation - MEMR.
MEMR, 2016, ‘Regulation No. 19/2016 of The Minister of Energy and Mineral Resources of
The Republic of Indonesia (in Bahasa Indonesia)’, Jakarta: MEMR of The Republic of
MEMR, 2017, ‘Regulation No. 12/2017 of The Minister of Energy and Mineral Resources of
The Republic of Indonesia (in Bahasa Indonesia)’, Jakarta: MEMR of The Republic of
NREL, 2017, ‘NREL annual technology baseline (ATB)’, available at: https://atb.nrel.gov/
Pfenninger, S. and Staffel, I., 2016, ‘Long-term patterns of European PV output using 30
years of validated hourly reanalysis and satellite data’, Energy, Volume 114, p1251-1265.
Mills, A. and Wiser, R., 2010, ‘Implications of wide area geographic diversity for short-term
varability of solar power’, California: Ernest Orlando Lawrence berkeley National
Elliston, B., Diesendorf, M. and MacGill, I., 2012, ‘Simulations of scenarios with 100%
renewable electricity in the Australian National Electricity Market’, Energy Policy, Volume
National Energy Council, 2016, ‘Indonesian Energy Outlook 2016 (in Bahasa Indonesia)’,
Jakarta: Secretariat General of The Indonesian National Energy Council.
Elliston, B., Riesz, J. and MacGill, I., 2016, ‘What cost for more renewables? The incremental
cost of renewable generation - An Australian National Electricity Market case study’,
Renewable Energy, Volume 95, p127-139.
This study is financially supported by The Indonesia Endowment Fund for Education (LPDP)
Scholarship provided by the government of The Republic of Indonesia. We thank PLN for
providing JAMALI hourly demand.
Appendix 1: Mapping of monthly PV capacity factor across locations in JAMALI in 2015