An optimal renewable energy mix for Indonesia

Indonesia has experienced a constant increase of the use of petroleum and coal in the power sector, while the share of renewable sources has remained stable at 6% of the total energy production during the last decade. As its domestic energy demand undeniably continues to grow, Indonesia is committed to increase the production of renewable energy. Mainly to decrease its dependency on fossil fuel-based resources, and to decrease the anthropogenic emissions, the government of Indonesia has established a 23 percent target for renewable energy by 2025, along with a 100 percent electrification target by 2020 (the current rate is 80.4 percent). In that respect, Indonesia has abundant resources to meet these targets, but there is - inter alia - a lack of proper integrated planning, regulatory support, investment, distribution in remote areas of the Archipelago, and missing data to back the planning. To support the government of Indonesia in its sustainable energy system planning, a geographic explicit energy modeling approach is applied. This approach is based on the energy systems optimization model BeWhere, which identifies the optimal location of energy conversion sites based on the minimization of the costs of the supply chain. The model will incorporate the existing fossil fuel-based infrastructures, and evaluate the optimal costs, potentials and locations for the development of renewable energy technologies (i.e. wind, solar, hydro, biomass and geothermal based technologies), as well as the development of biomass co-firing in existing coal plants. With the help of the model, an optimally adapted renewable energy mix – vis-à-vis the competing fossil fuel based resources and applicable policies in order to promote the development of those renewable energy technologies – will be identified. The development of the optimal renewable energy technologies is carried out with special focus on nature protection and cultural heritage areas, where feedstock (e.g., biomass harvesting) and green-field power plant sites will be limited – depending on the protection type and renewable energy technology. The results of the study provide indications to the policy makers on where, how and which technologies should be implemented, and what kind of policy support would be needed in order to increase and meet the Indonesian renewable energy target and to increase the energy access for all

Carbon-negative emissions: Systemic impacts of biomass conversion: A case study on CO2 capture and storage options

This paper is a contribution to the ongoing debate on carbon-negative energy solutions. It deals with biomass conversion in dedicated biopower plants equipped with CCS (BECCS), or co-fired plants retrofitted with CCS in order to generate negative CO2-emissions. In this context, bioenergy refers to the use of biomass to generate electricity (i.e. biopower) in compliance with the needs of nations and regions without seasonal space heating demand. In this paper, direct-fired and co-fired systems will be addressed, combined mainly with post-combustion flue gas cleaning. The question is which CCS alternative should be preferred in order to obtain negative emissions: either building multiple smaller biopower units, or employing co-firing of biomass and coal in existing large coal power plants. Based on efficacy and the potential for mitigating greenhouse gas emissions as key indicators, some major differences between the alternatives are shown. In the event that a coal power plant equipped with CCS is readily available, more net electric energy (in MWh) can be provided from the feedstock of biomass than would be obtainable from an independent BECCS plant, although the amount of CO2 captured and stored from the biomass (per tonne) will be essentially the same. Further case-specific cost-benefit analyses will be required to determine the feasibility of carbon-negative energy solutions. Although the study is carried out from the perspective of actual biomass sources as regards biomass composition and available technology (i.e. expected efficiency levels) using Indonesian agricultural residues, its main conclusion is fairly general

Modeling wildfire dynamics using FLAM coupled with deep learning methods

We improve the accuracy of modeling burned areas using the FLAM model by identifying the hidden relationships between human and natural impacts on wildfire suppression efficiency using the deep learning-based methods. The wildfire climate impacts and adaptation model (FLAM) is able to capture impacts of climate, population, and fuel availability on burned areas. FLAM uses a process-based fire parameterization algorithm with a daily time step. The model uses daily temperature, precipitation, relative humidity and wind speed to assess climate impacts on ignition probability and fire spread. The key features implemented in FLAM include fuel moisture computation based on the Fine Fuel Moisture Code (FFMC) of the Canadian Forest Fire Weather Index (FWI), and a procedure to calibrate spatial fire suppression efficiency. The coupled FLAM and deep learning approach consists in the following steps. First, using FLAM we calibrate the suppression efficiency map by comparing model output with observed burned area (satellite data). Secondly, we use deep learning methods to identify and assess the drivers behind the calibrated map. The features used in the analysis include several socio-economic factors, including accessibility, GPP, land use maps, as well as burned areas and other parameters modeled by FLAM. Our approach allows classifying those features by their importance and find correlations between them. Finally, we implement the output of deep learning network to estimate the spatial suppression efficiency within FLAM (instead of calibrating it), and validate the approach using observed burned area. The proposed approach is implemented using the Google Earth Engine platform that provides flexibility in terms of input data sets and visualization tools. We will present the case study for Indonesia at 0.083 arc degree spatial resolution. It is planned to consider climate change impacts in more detail. Modeling burned areas and suppression efficiency can help the implementation of fire prevention policies for decision maker and provide important information for building adequate and cost-efficient fire response infrastructure

Negative Emissions on South East Asia: Renewable Energy Optimization with BECCS for Indonesia

Indonesia, on the one hand, is a tropical country with large biomass productivity and increasing oil and gas sector activities. On the other hand, it is the 3rd largest GHG emitter globally and some 90% of its emissions are generated from massive land-use change. However, Indonesia has also developed very ambitious climate targets aiming at up to 41% emission reduction by 2020. These targets need to be balanced with an envisaged GDP growth by 7% and projected 5 times higher energy consumption in 2050. To decrease its fossil fuel dependency and emissions, the government of Indonesia has decided to increase the renewable energy supply from 6% to 23% by 2025, along with a 100 percent electrification target by 2020. Furthermore, BECCS (i.e. the combination of forest based bioenergy with carbon capture and storage) is seen as a promising tool to bridge between the various future challenges Indonesia is facing and at the same time to deliver large quantities of negative emissions needed by the end of this century. But - irrespectively of Indonesia’s abundant resources to meet ambitious renewable energy and mitigation targets - there is lack of proper integrated planning, regulatory support, investment, distribution in remote areas of the Archipelago, and missing data to back the planning. To support the government of Indonesia in its sustainable energy systems planning, a geographic explicit energy modeling approach is applied. IIASA’s BeWhere Model identifies the optimal location of energy conversion sites based on the minimization of the supply chain costs. The model incorporates the existing fossil fuel-based infrastructures, and evaluates the optimal costs, potentials and locations for the development of renewable energy technologies (i.e. wind, solar, hydro, biomass and geothermal based technologies), as well as the development of biomass co-firing in existing coal plants. An optimally adapted renewable energy mix – vis-à-vis the competing fossil fuel based resources – is identified. In addition, the in situ BECCS capacity for different scenarios is assessed for Indonesia. Special focus is put on nature protection and cultural heritage areas, where feedstock (e.g., biomass harvesting) and green-field power plant sites will be limited – depending on the protection type and renewable energy technology. First results of the study provide indications on where, how and which technologies should be implemented. Moreover, the assessment indicates that the BECCS potentials vary substantially over the different scenario assumptions. Sustainable biomass feedstock production, energy demand and supply as well as competing industries and existing transport infrastructure are key to achieve an optimal BECCS solution. Clean energy access for all with special emphasis on remote areas and small islands in Indonesia turns out to be especially interesting from a socio-economic, emission savings and innovation perspective

Negative Emissions and Interactions with other Mitigation Options: A Bottom-up Methodology for Indonesia

BECCS (here the combination of forest-based bioenergy with carbon capture and storage) is seen as a promising tool to deliver the large quantities of negative emissions needed to comply with ambitious climate stabilization targets. However, a land-based mitigation option such as large-scale bioenergy production (without CCS) might interfere with other land-based mitigation options popular for their large co-benefits such as reduced emissions from deforestation and degradation (REDD+). We develop a systems approach to identify and quantify possible trade-offs between REDD+ and BECCS with the help of remote sensing and engineering modeling and apply this for illustration to Indonesia. First results indicate that prioritizing REDD+ does imply that there the BECCS potential remains limited. Further research is needed to take into account opportunities where the two options could be deployed synergistically, capitalizing on co-benefits. BECCS and REDD+ must be evaluated from a portfolio perspective, as estimating their potentials independently will not take such opportunities into account

The potential of Landsat time series to characterize historical dynamic and monitor future disturbances in human-modified rainforests of Indonesia

In this study we demonstrated for the first time the potential of using full time series from high spatial resolution (30 m) Landsat satellites, covering a period from 1987-2017, for characterizing historical dynamics in Indonesian humid tropical rainforests. Our special focus was on mapping forest disturbance and post-disturbance regrowth, which in turn can potentially be used to map primary (undisturbed) forests, secondary (disturbed/degraded) forests, and forest land converted to oil palm plantation. We applied the Breaks For Additive Season and Trend (BFAST) Monitor framework for continuous change detection; BFAST is a generic and transparent method, which can be used for near-real-time monitoring. To verify our approach, a preliminary spatial accuracy assessment was carried out for disturbance detection using 418 sample pixels interpreted from very high spatial resolution images acquired through Digital Globe viewing service. Besides, we identified the sources of detection errors and approaches to overcome them. Implementation of the potential map product in existing international and national policies will be discusse

Modeling Burned Areas in Indonesia: The FLAM Approach

Large-scale wildfires affect millions of hectares of land in Indonesia annually and produce severe smoke haze pollution and carbon emissions, with negative impacts on climate change, health, the economy and biodiversity. In this study, we apply a mechanistic fire model to estimate burned area in Indonesia for the first time. We use the Wildfire Climate Impacts and Adaptation Model (FLAM) that operates with a daily time step on the grid cell of 0.25 arc degrees, the same spatio-temporal resolution as in the Global Fire Emissions Database v4 (GFED). GFED data accumulated from 2000–2009 are used for calibrating spatially-explicit suppression efficiency in FLAM. Very low suppression levels are found in peatland of Kalimantan and Sumatra, where individual fires can burn for very long periods of time despite extensive rains and fire-fighting attempts. For 2010–2016, we validate FLAM estimated burned area temporally and spatially using annual GFED observations. From the validation for burned areas aggregated over Indonesia, we obtain Pearson’s correlation coefficient separately for wildfires and peat fires, which equals 0.988 in both cases. Spatial correlation analysis shows that in areas where around 70% is burned, the correlation coefficients are above 0.6, and in those where 30% is burned, above 0.9

Supplementary material to "Spatio-temporal assessment on the impact of palm oil-based bioenergy deployment on cross-sectoral energy decarbonization"

This archive contains the input datasets and the mathematical formulation of the BeWhere Malaysia model. The spatial datasets presented were compiled at 0.25-degree resolution (25 km x 25 km), subjected to the planning period of 2020-2050. The model has been used to generate spatio-temporal techno-economic outputs for the scenario analysis involving the comparisons of national policies on CO2 emission reduction and renewable energy production in promoting bioenergy uses in Malaysia in the long term