Indonesian Rural Electrification

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Indonesian Rural

Electrification

What is the most sustainable solution?

Claudia Vannucchi

Master of Science Thesis

KTH School of Industrial Engineering and Management

TRITA-ITM-EX 2020:611

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Master of Science Thesis -

TRITA-ITM-EX

2020:611

Indonesian Rural Electrification

What is the most sustainable solution?

Claudia Vannucchi

Approved 5 March 2021

Examiner

Prof. Viktoria Martin

Supervisors

Dilip Khatiwada (KTH) Bruno Pechiné (EIFER)

Commissioner

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Abstract

The Sustainable Development Goal n°7 is calling for a prompt response to guarantee affordable and clean energy for all. While the electrification rate is rapidly increasing around the world, much work still remains to achieve electricity access in remote areas or Non-Interconnected Zones, such as the numerous small islands that compose Indonesia. This thesis work sought to understand which standalone microgrid design would represent the most sustainable solution for a rural electrification challenge, where the final scope is to provide 24 h/d stable and reliable electricity connection to the local communities of Sulawesi, Indonesia. To achieve such a result, two diametrically opposed microgrid layouts are outlined in terms of renewables share: a Business-As-Usual Scenario, in which the microgrid is powered by a standard diesel set, and an integrated renewable-based scenario, in which the microgrid envisions the implementation of biopower, PV system and Li-ion batteries as a storage option. A thorough comparison on a series of Key Parameter Indicators (KPIs), such as Carbon Footprint, Levelized Cost Of Electricity and job creation, led to the identification of the renewable-based scenario as the most sustainable option. This system layout resulted in a biomass powered electricity production covering 80% of the total electricity demand, with the remaining 20% supplied by solar power and storage means and a LCOE of 0.18 USD/kWh. At the price of a higher upfront cost than the one of BAU case, the renewable-based alternative entitles a higher profitability when compared to the business-as-usual one, together with reduced carbon dioxide emissions and a higher number of jobs directly created.

Keywords: biomass power, rural electrification, Indonesia, Calliandra, Sulawesi, Indonesia, microgrid, renewable microgrid, sustainable development

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Sammanfattning

Hållbarhetsmål nr 7 kräver ett snabbt svar för att garantera överkomlig och ren energi för alla. Medan elektrifieringsgraden snabbt ökar runt om i världen, återstår mycket arbete för att nå elåtkomst i avlägsna områden eller icke-sammankopplade zoner, såsom de många små öarna som utgör Indonesien. Detta avhandlingsarbete försökte förstå vilken fristående mikronätdesign som skulle representera den mest hållbara lösningen för en elektrifieringsutmaning på landsbygden, där det slutliga utrymmet är att tillhandahålla 24 timmars stabil och pålitlig elanslutning till lokalsamhället Sulawesi, Indonesien. För att uppnå ett sådant resultat beskrivs två diametralt motsatta mikronätlayouter när det gäller andelen förnybara energikällor: ett Business-As-Usual-scenario, där mikronätet drivs av en standarddiesel och ett integrerat förnyelsebaserat scenario, där microgrid ser implementeringen av biokraft, solcellssystem och litiumjonbatterier som ett lagringsalternativ. En noggrann jämförelse av en serie nyckelparametrar (KPI), såsom koldioxidavtryck, nivåiserad elkostnad och skapande av jobb, ledde till att det förnyelsebaserade scenariot identifierades som det mest hållbara alternativet. Systemlayouten resulterade i en biomassadriven elproduktion som täckte 80% av det totala elbehovet, med de återstående 20% som levereras av solenergi och lagringsmedel och en LCOE på 0,18 USD / kWh. Till priset av en högre kostnad i förskott än i BAU-fallet ger det förnyelsebaserade alternativet högre lönsamhet jämfört med det som vanligt, tillsammans med minskade koldioxidutsläpp och ett högre antal direkt skapade jobb.

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Acknowledgements

This thesis work has been developed at the division of Energy Systems, Department of Energy Technology of KTH Royal Institute of Technology that I sincerely thank for the assistance provided throughout the process.

I would like to express my deepest gratitude to my company supervisor, Bruno Pechiné, who guided me throughout this thesis project and helped me shape, together with the support of the amazing EIFER team and community, the solution that hereby is presented. I wish to acknowledge also the help and fundamental contribution from the local community collaborators that answered the submitted surveys and whose directions and insights framed the final result.

I also wish to extend my special thanks to the amazing people that I had the privilege to meet during my bachelor in Bologna, for always bringing me a smile, even when I felt homesick. I would like to thank that incredible family that my SELETCS’s coursemates have become during this master program. I am absolutely grateful for the new friends that I have met while writing my thesis here in Karlsruhe and with whom I shared a lot of laughs and various lockdowns.

Last but not least, I would like to show my deepest appreciation for the support received by my loving family. I wouldn’t have made it this far without the help of each and every one of you. You have always believed in my possibilities and always supported me even when my dreams have dragged me far away from you. I am especially grateful for the great encouragement to pursue my studies from my dad, Valeriano, and my younger sister, Martina, who have been involved in every aspect of my studies and thesis thanks to daily extensive, and surprisingly long, calls. I would finally like to extend a special thank to my beloved grandmother, Siberiana, to whom this thesis work is dedicated, for the love and kindness that she granted me during her entire life.

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List of Abbreviations

Acronym Description

BAU Business-as-usual

BS Baseline Scenario

CAPEX Capital Expenditure

CF Carbon Footprint

EIFER European Institute For Energy Research

FW Fresh Waste

GHG Greenhouse Gas

HH Household

LCOE Levelized Cost Of Electricity

MCA Multi Criteria Analysis

MSW Municipal Solid Waste

O&M Operation and Maintenance

OFMSW Organic Fraction of Municipal Solid Waste

OPEX Operational Expenditure

RS Renewable-based Scenario

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List of Tables

Table 1 - Policies Related to Renewable Energy 18

Table 2 - Downdraft and BFB Gasifiers Comparison 28

Table 3 - Study Cases Summary 33

Table 4 - CAPEX and OPEX values for different Bioenergy Conversion

Technologies 37

Table 5 - Energy Plantation Growth Time 38

Table 6 - Home Appliances 45

Table 7 - Forecasted Development of the Islands’ Electricity Demand and

Employment Rate 50

Table 8 - Service building evolution over the first five years of the project 51

Table 9 - Available ha to be used for Energy Plantation 54

Table 10 - Secondary Forest Data 55

Table 11 - Bioenergy Conversion Technology MCA

60

Table 12 - Biomass Resources MCA 61

Table 13 - Diesel Generator 1 Specifications 63

Table 14 - Diesel Generator 2 Specifications 64

Table 15 - Power Plant Manpower Requirements 65

Table 16 - Gasifier and Gas Engine Specifications 67

Table 17 - PV Panels Specifications 68

Table 18 - System Battery Specifications 68

Table 19 - Power Plant Manpower Requirements 71

Table 20 - Scenario Comparison 72

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Table 22 - Key Parameters for Biogas Production from Cow Manure 79

Table 23 - Key Parameters for Biogas Production from MSW 79

Table 24 - Key Parameters for Biogas Production from Fish Waste 80 Table 25 - Key Parameters for Biogas Production from Slaughterhouse Waste 81

Table 26 - Theoretical Electricity Production from Biogas 82

Table 27 - Host Bioenergy Installations Projects Specifications 96

Table 28 - Study Cases Summary 100

Table 29 - Ankur Scientific Technologies - Bamboo Gasifier 103

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List of Figures

Figure 1 - Project Location 20

Figure 2 - Average Temperature and Precipitation for Sulawesi islands 21

Figure 3 - Simple Schematic of a Biomass Stoker Boiler 24

Figure 4 - Simple Schematic of an Updraft Bed Gasifier 25

Figure 5 - Simple Schematic of a Downdraft Bed Gasifier 26

Figure 6 - Simple Schematic of a Bubbling Fluidized Bed Gasifier 27 Figure 7 - Simple Schematic of a Circulating Fluidized Bed Gasifier 27

Figure 8 - Methodology Approach 34

Figure 9 - BAU System Schematic 40

Figure 10 - RB Scenario System Schematic 41

Figure 11 - Use Pattern Probability of Occurrence with 6 h/d Diesel Microgrid 46 Figure 12 - Use Pattern Probability of Occurrence of Workers with 24 h/d

Electricity Access 47

Figure 13 - Use Pattern Probability of Occurrence of Non-Workers with 24 h/d

Electricity Access 48

Figure 14 - Daily Electricity Demand - Year 1 49

Figure 15 - Seasonal Electric Demand Variation - Year 1 50

Figure 16 - Daily Electricity Demand - First 5 Years of the Project 52

Figure 17 - Total Yearly Electricity Demand Growth 52

Figure 18 - Average Monthly Global Solar Irradiance for South-East Sulawesi

Islands 53

Figure 19 - Gliricidia sepium used as Cattle Forage 56

Figure 20 - Calliandra Plantation in Indonesia 57

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Figure 22 - Baseline Scenario Associated CO2 Emissions 64

Figure 23 - Baseline Scenario LCOE 64

Figure 24 - BAU Scenario Cash Flow 66

Figure 25 - RB Scenario Expected Yearly Electricity Production 69

Figure 26 - RB Scenario Associated CO2 Emissions 70

Figure 27 - RB Scenario LCOE 70

Figure 28 - RB Scenario Cash Flow 72

Figure 29 - RB Scenario with Biogas Power Plant 78

Figure 30 - Evolution of Collected OFMSW 80

Figure 31 - Evolution of Collected Fish Waste 81

Figure 32 - Evolution of Collected Slaughterhouse Waste 82

Figure 33 - Schematic of a 1.2 MWe Rice Husk Gasification and Power Generation

Plant 97

Figure 34 - Oil Palm Plantation in Indonesia 109

Figure 35 - Bamboo Forest in Indonesia 110

Figure 36 - Leucaena Leucocephala Tree and Forage 112

Figure 37 - Sesbania Grandiflora Tree, Flowers and Leaves 114 Figure 38 - Hectares of Calliandra to Feed One Animal for One Year 121 Figure 39 - Number of Calliandra Trees to feed One Animal for One Year 122

Figure 40 - Number of Animals fed per ha of Calliandra 122

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1 Introduction

In recent years, a higher awareness has been brought to the necessity for a more sustainable development, in line with nature laws and respectful of the surrounding environment. In this frame of mind, the setting of the so-called Sustainable Development Goals (SDGs) in 2018 [1] has traced the path for achieving sustainable development. In particular, the SDG n°7 on “Affordable and clean energy for all” set forth by the United Nations includes targets such as ensuring “universal access to affordable, reliable and modern energy services” and increasing “substantially the share of renewable energy in the global energy mix” by 2030 [2]. Unfortunately much work still remains to achieve universal access to electricity, especially when referring to rural areas or non-interconnected zones (NIZs), such as islands.

When focusing on South-East Asia, Indonesia definitely stands out as one of the countries with the highest electrification rate, as high as 98.7% [3]. However, this figure doesn’t imply that the available electricity access is as reliable and as constant as it is expected to be. If Indonesian NIZs are considered, 10.4 million Indonesians are still lacking an electricity connection and, where that connection exists, it might be accessible just for a few hours per day. Thus, Indonesian rural electrification has been under the spotlight of several research institutes, where the quest for the most sustainable development solution has been posed to help the country through a required energy transition from a coal-based energy mix to a more renewable oriented energy production [4].

Among these research institutes, the European Institute For Energy Research (EIFER) has been investigating which rural electrification solution would be the most sustainable for Indonesian NIZs with the goal of identifying a suitable option that could be transferred to similar cases. In order to validate and apply the knowledge gained by this thesis work, a real-life project provided by EIFER over a six-month internship period has been taken as a case study example of average villagers' needs and demands of Indonesian rural areas. Therefore, this thesis work will focus on assessing which microgrid solution would be the most suited to foster local sustainable development of remote areas of Indonesia through a thorough research and available literature review that will culminate in the application of such insights to the case study.

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An opportunity was identified within the enormous biomass potential of South-East Asia and the lack of available literature on the establishment and evolution of energy plantation in the selected context. An energy plantation is defined as the practice of planting trees or other biomass resources, purely for their use as fuel [5]. Thus, this thesis work will focus on providing useful insights and missing aggregated knowledge on the utilization of energy plantations for rural electrification of remote areas of South-East Asia, in particular, research will space from different woody sources, fast growing species and Municipal Solid Waste (MSW) and will seek to identify the best fitting one for the selected environment. As a matter of fact, this thesis work is meant to answer the following questions:

● Which type of microgrid would be the best option to provide rural electrification to remote areas of South-East Asia? One based on biopower, PV system and batteries, orone based on a standard diesel set?

● When biomass power is selected as a source of electricity generation, which technology and biomass feedstock need to be selected?

● Will the establishment of an energy crop be feasible in terms of workforce needed, time and cost in remote areas of South-East Asia?

● Which environmental, economic and social impact would the implementation of an integrated microgrid system have?

The findings of the aforementioned research will be presented over 6 different sections: Section 1 provides a general introduction to the research context together with thesis’s motivation, questions, and objectives and background and study case description, Section 2 presents a brief and summarized literature review, while Section 3 illustrates the methodology followed during the thesis work and key data sources, Section 4 displays the obtained results, whose roadmap and implementation plan is outlined in Section 5, and finally, Section 6 will provide conclusions and recommendations for future work.

1.1 Background

1.1.2 Country Context

Electricity access in Indonesia has been constantly increasing every year, reaching an extraordinary figure of 98.7% in 2017 [3], thanks to policies implemented by the local government. Despite this incredible result, 10.4 million Indonesians, mainly inhabitants of rural areas or Non-Interconnected Zones (NIZs), were still lacking an electricity connection in 2017.

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sugar, rice, corn, solid agricultural biomass, and forestry residue [6]. Traditional biomass power generation has been indeed widely implemented for centuries, in the form of traditional combustion of fuelwood, however, this technique has been proven to be an inefficient and environmentally harmful way of producing electricity. Furthermore, where an electricity connection is available in remote areas, this is usually provided thanks to inefficient diesel-based microgrid systems, which not only have a high environmental impact for the local environment, but, given a high fuel price fluctuations, is also associated with a certain level of uncertainty [7]. Because of these reasons, there is a great opportunity for utilizing bioelectricity as a primary energy resource in microgrid-based applications for Indonesian rural areas and NIZs [7]. In particular, the integration of bioelectricity-based solutions in renewable-based microgrids offers the advantage of benefitting from a quite stable energy generation source, thus overcoming the intermittency issue usually associated with other kinds of renewable energies [8]. Being biomass resources storable, the risk of intermittent generation is avoided, hence, the bioenergy-based part of the microgrid can provide stable generation for baseload demand.

1.1.3 National Energy Policies & Regulations

Since the oil crisis of the 1980s, Indonesian government has been trying to diversify its energy production, including more renewables in the picture [9].

The adopted energy policies were focusing on four main aspects: 1. Energy Diversification

2. Rational Energy Pricing 3. Energy Sector Reform 4. Rural Electrification

Some of the relevant energy policies applied in Indonesia are presented in Table 1 and even though there are many different regulations, the general scope of these policies is to boost biomass based energy production on a macro level and to create job and local development in the form of growing economies and business opportunities on a micro level.

Table 1 - Policies Related to Renewable Energy [9], [10]

Year Title Purpose

2020 Presidential Regulation _{to be approved}

To attract investors in renewable energy projects by tackling the bankability and the financial close issues with the introduction of the Feed-in Tariff (FiT) policy,

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2019 Regulation n° 13-16

To ease administration process and cost for small scale rooftop PV users and for industrial solar energy

prosumers

2016 Regulation n° 4 To favour the implementation of renewable energy in the _{electricity infrastructure acceleration [11]}

2013 Regulation n° 19

The electricity generated from biomass or renewable sources using gasification is priced at 1,450.00 IDR/kWh and it is bought from the National Electricity Company [12]

2010 Electricity Law (Law n° _30/2010)

To invite private companies to participate in electricity supply

To give higher priority for the use of renewable energy and clean technology for electricity supply;

To encourage more utilization of small-scale distributed power generation from renewable sources such as from biomass energy

2007 Energy Law (Law n° _30/2007)

To regulate renewable energy development and energy efficiency policy, particularly by increasing the utilization of renewable energy and provide incentives for

renewable energy developers for a certain period of time

2006

Presidential Regulation n° 5 on National Energy Policy,

To set energy diversification targets for 2025; including 5% biofuel, and 5% geothermal and other renewables such as biomass

To set an energy conservation target of reducing energy intensity by 1% per year

2006 Presidential Decree n° 1 on Supply and Use of Biofuel,

To set a target for biofuel utilization

To set the guidance for multi-sector coordination in biofuel development

2006

Ministerial Regulation n°2 on Medium Scale Power Generation from Renewable Energy Sources

To extend the same price guidelines as Ministerial Decree n° 1122/2002 for projects from 1 MW to 10 MW

2005

Blueprint of National Energy Implementation Program 2005–2025

To delineate measures for the enhancement of energy supply security

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issued by Minister of Energy and Mineral Resources

To design programs to phase out subsidies and improve energy efficiency

2005 Government Rule n°3 on _{Supply of Electricity}

To support Law n° 15/1985 on electricity, which was reenacted in late 2005 following a Constitutional Court ruling that annulled Law n° 20/2002 on electricity;

To regulate the partnership between independent power producers (IPPs) partnered with PLN to develop electricity projects; an exception is given to companies that

generate power for their own use or those using renewable energy; this way they can set up plants independently without having to partner with PLN

2004 Ministerial Decree n°2 on _{Green Energy Policy}

To optimize the utilization of renewable energy To utilize energy technology efficiently, both from renewable and non-renewable energy

To increase public awareness in energy efficiency

2002

Ministerial Decree n° 1122 on Small-Scale Distributed Renewable Power Plant

To give incentive on the development of renewable energy for small-scale distributed power plants which require PLN (State Electricity Company) to purchase electricity generated from renewable energy sources by non-PLN producers for projects of up to 1 MW capacity; In the energy sector, Indonesia has embarked on a mixed energy use policy, establishing the development of clean energy as a national policy directive under the Government Regulation no°19 of 2014 [13]. This policies are the key to transform the primary energy mix as follows between 2025 and 2050:

➔ New and renewable energy at least 23% in 2025 and at least 31% in 2050 ➔ Oil should be less than 25% in 2025 and less than 20% in 2050

➔ Coal should be minimum 30% in 2025 and minimum 25% in 2050 ➔ Gas should be minimum 22% in 2025 and minimum 24% in 2050

➔ Emission reduction: reduce unconditionally 29% of its GHG emissions against the BAU scenario by the year of 2030

1.2 Study Case Description

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situation of small islands around Sulawesi. The study case will envision the project to be developed across two islands that share similar energy needs and that are close enough to have consistent economic ties.

1.2.1 Site

The study case project is located in the Indonesian region of Sulawesi (Figure 1), specifically considering islands of South-East Sulawesi [14].

Figure 1 - Project Location [14]

1.2.2 Population

The local population of 10,000 people is distributed across 2,200 households over the two islands and it is mainly constituted by fishermen and farmers. The current low-employment rate is reflected in the percentage of population that stays at home during the workweek and the weekends, as a matter of fact, during working days 87% of the inhabitants spend the entire day in the HH, while 100% of the population is expected to be at home on weekends. As of today, the only electricity connection made available to the two islands is provided by a diesel generator capable of supplying electricity for just 6 h/d [Source: Literature review and EIFER data].

1.2.3 Conditions

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a precipitation average of 150 mm, thus, the dry season in these islands is identified from May to October, while the wet season occurs from November to April. On average, the yearly precipitation can range between 800-3,000 mm/yr [15].

Figure 2 - Average Temperature and Precipitation for Sulawesi islands [16]

1.3 Objective

The scope of this thesis work is to identify the best possible design for a rural electrification challenge in which the final aim is to provide 24 h/d stable and reliable electricity connection to the average local community of Sulawesi islands in Indonesia, that comprises 10,000 inhabitants. By completing this challenge, this thesis work seeks to answer the presented thesis questions, such as determining which type of microgrid would be the most sustainable option to provide rural electrification to remote areas of South-East Asia. To provide an univoque answer to this complex research question, two solutions will be proposed: a Business-As-Usual Scenario, in which the microgrid is diesel powered, and a more innovative scenario, in which the microgrid is entirely powered by available renewable resources.

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biomass based electricity production has been selected as a core component of the integrated renewable-based microgrid scenario. Thus, it is within the objective of this thesis to determine which technology and biomass feedstock would represent the best fit for the selected location. PV panel and Li-ion batteries as a storage option have been selected, together with biomass power, to supply the total electricity demand thanks to favourable energy policies and ease of operation and maintenance.

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2 Decentralized Biopower Generation - A review

This chapter will present a brief and summarized review of the state-of-the-art of decentralized biopower generation, with a special focus on the current situation of South-East Asia. Thanks to a thorough literature review whose extended version is presented in Appendix A, it was possible to provide missing aggregated knowledge over the topic of biopower based microgrids in South-East Asia, that allowed to determine which biopower conversion technology and biomass feedstock would be the best fit for the selected location. In the summarized cases, biopower is often the only source of electricity generation, since it offers a stable and reliable power output that guarantees quality electricity supply to the local community. Furthermore, biomass powered electricity generation allows to overcome the intermittency issue usually associated with other forms of renewable electricity production.

2.1 Biopower Conversion Technology

A series of different biopower conversion technologies are available for bioelectricity generation, among which biomass gasifiers and CHP plants (boilers + steam turbines) have been considered relevant for the project site, since the initial literature review led to the conclusion of such technologies as the more widely spread in microgrid applications in South-East Asia.

A brief description of both technologies will be given in the following paragraphs.

2.1.1 Biomass Boiler & Steam Turbine

A biomass boiler is basically a combustion chamber in which biomass is burned in order to produce steam. The main process characterizing a biomass boiler is the combustion process, for which common temperature ranges are above 1,600°C [17].

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Figure 3 - Simple Schematic of a Biomass Stoker Boiler [18]

Biomass boilers and steam turbines are a well known and proven technology that has been applied for decades in power generation projects [8], [19], thus they are believed to be reliable and easy to operate. A higher fuel flexibility is the result of such a long experience, that is derived by a higher tolerance of moisture content, as a matter of fact it can handle biomass with a moisture content up to 50%. Generally speaking, regardless of the project’s electricity demand, projects involving this technology stand out for a higher bankability, thanks to the low risk associated with these projects [8], [19][20].

However, if the project size (electricity demand in kW) is considered, boilers are less suitable for small scale applications in remote areas, since their size ranges between 4-30 MW. Furthemore, a slower adaptability to load fluctuations makes these technologies not the preferred option for microgrid applications [19], [21], [22].

According to IRENA’s estimates for bioenergy related technologies, the total capital cost of a stocker boiler ranges between 1,900-4,200 $/kWe, while the fixed and variable operational expenditure are 3.2% of CAPEX and 4.0-4.93 $/kWhe respectively [20].

2.1.2 Biomass Gasifier

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and fluidized bed [23]. However, fixed bed gasifiers can be further divided into downdraft and updraft architectures. In a similar way, a fluidized bed can be either bubbling (BFB) or circulating (CFB).

In an updraft reactor, the flow is counter current, which means that the feedstock is introduced from the upper part of the reactor, the gasifying agent from the bottom and the biochar is collected at the bottom while the bio-syngas is extracted from the upper part of the system (Figure 4). It is a simple and low cost technology that can be fed with high humidity and high content ash, however, the produced syngas has a high concentration of tar [8].

Figure 4 - Simple Schematic of an Updraft Bed Gasifier [8]

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Also downdraft reactors are a well known and low cost technology, but their design is more complex and they require a feedstock with low humidity. On the other hand, the obtained syngas has a lower concentration of tar, when compared to updraft configuration [8].

Figure 5 - Simple Schematic of a Downdraft Bed Gasifier [8]

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Figure 6 - Simple Schematic of a Bubbling Fluidized Bed Gasifier [8]

The process encountered in a CFB reactor is similar to the one previously described, with the only difference that in this case the matter collected from the cyclone filter is recirculated to the reactor, thus allowing for a higher syngas quality and for a higher efficiency (Figure 7). This enables the use of various feedstock, including also sawdust [8].

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A more thorough comparison between downdraft and bubbling fluidized bed could be performed in order to assess which one is the best technology for the selected location. A brief summary of such comparison is presented in Table 2, while the extended explanation can be found in the paragraph following the table.

Table 2 - Downdraft and BFB Gasifiers Comparison

Parameter Downdraft BFB

Moving Parts NO YES

Syngas Tar Content Lower Higher

Syngas Treatment Very Simple and Cheap Longer and more expensive

Fuel Flexibility Lower Higher

Moisture Content Flexibility

[8], [19] Lower Higher

Application Range [25] Not advised above 1.5 MWe Preferred above 1.5 MWe Worldwide use in Rural

Electrification Projects

Widely used Proven Technology

Used in several projects Less Proven Technology

Maturity Higher Lower

Capital Cost [USD/kWe] 2,100 3,500-4,300

Fixed Operational Cost

[% Capital Cost] 3-6 3-6

Variable Operational Cost

[USD/KWhe] 4.0 4.0

While fixed bed has the advantage of not having moving parts and to provide a syngas with a lower tar content, it can provide less power than the one of a CFB and it is difficult to scale and demanding on the feedstock properties, while fluidized bed are easy to scale and are very tolerant on the fuel side, but they require a longer and more expensive bio-syngas treatment.

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[19][20][25][26][27][28]. On the other hand, downdraft gasifiers offer a limited fuel flexibility, both in size and in moisture content that has to be kept below 15% [8], [19].

This technology usually comes in smaller sizes when compared both to boilers or to fluidized bed reactors, this can be both an advantage and a disadvantage with regards to the project requirements.

Generally speaking, the majority of commercially available downdraft gasifiers have a capacity lower than 1.5 MWe, with a capital cost of 2,100 USD/kWe and a fixed and variable operational cost of 3-6% of CAPEX and 4.0 USD/kWhe respectively [19].

As for fluidized bed gasifiers, several rural electrification projects have seen the implementation of this technology, however, it is of common agreement that FB are less mature than boilers and downdraft reactors, thus projects involving the use of such a technology are considered to be more risky when it comes to obtain a financial loan from a bank. Nevertheless, when the power required is higher than 1.5 MWe they are considered to be the best option [19][20][25][26][27][28]. They offer a higher fuel flexibility, both in particles and in moisture content, since they can handle feedstock with up to 20% moisture content, and good load following capacities [20].

According to IRENA’s estimates for bioenergy related technologies, the total capital cost of a fluidized bed gasifier ranges between 3,500-4,300 USD/kWe, while the fixed and variable operational expenditure are 3-6% of CAPEX and 4.0 USD/kWhe respectively [20].

2.2 Biopower Projects Case Studies

An extensive literature review has been performed to collect knowledge on biopower related projects in NIZs, with a special focus on Indonesia, although projects in other countries have been included in the research when deemed to be relevant (see Appendix A). A series of study cases have been examined, both from the academic and operational perspective, in particular, the research was focused on electricity only applications in the size range of 0.5-1.5 MWe with a variety of different feedstocks, since the best one for the location has yet not been determined.

The theoretical advantages of gasifier based microgrids, such as lower LCOE than traditional systems, lower impact on the environment and local community development, have been mirrored in the actual real life operation [19]. Some of these real life cases will be briefly described in the following paragraphs.

2.2.1 Indonesia

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generation, electricity grid, electrical installation, commissioning and training, was of 12.5 M USD, while the operational cost after commissioning has turned out to be 0.07 USD/kWh. The most interesting outcome from the project is the validation of an innovative business model that creates synergies between the community, the state-owned utility company (PNL) and Clean Power Indonesia. All the electricity produced by the microgrid is entirely purchased by PNL, that also owns the system, and then sold to the community, whereas the community sells all the bamboo feedstocks to the state-owned company. PNL purchases the electricity at a price of 0.15 USD/kWh and sells it to the community at a price of 0.031 USD/kWh, corresponding to the customer willingness to pay. This is possible thanks to the heavy subsidies given by the local government. The 20 years payment agreements established between the local community and the other two partners allows the inhabitants to realize excess income that fostered the economic development of West Sumatra. Furthermore, 450 new jobs were created through the new microgrid system and 3,000 ton of CO2eq/yr are avoided and carbon sequestration is obtained by the bamboo plantation [Source: Feedback Session with Clean Power Indonesia Engineers and [29]] . Another interesting fact is that Ankur Technologies (the gasifier manufacturer) needs to guarantee the gasifier operation, but the local community takes care of the system’s maintenance.

In Kundur Island (Riau, Indonesia), PT Prima Gasifikasi is in the advanced stage of developing a biomass gasification power plant deploying a fluidized bed gasifier [30]. The electricity demand in the island is relatively high for a microgrid configuration, as a matter of fact the demand is estimated to be between 2-7 MW with a peak load around 6.00 pm. In this context, the biomass power plant will be providing the base load.

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freely provide them with biochar for their plantations. According to more recent sources [31], the power plant has been in operation using calliandra produced from the owned land concession and locally sourced firewood since the plantation was not sufficient to provide the required 40 t/d of biomass. According to the company claims, roughly 18k USD are spent every month to purchase the required fuel for the power plant.

In the case of Sumba Island (Indonesia), a biomass gasification plant of 1 MW was built in 2015 to attract industries and to supply renewable energy to the city of Waikabubak [30]. The power plant cost approximately 2-2.5M USD and was provided by the Indonesian government and subsequently handed over to the district government of Sumba Barat. Similarly to other cases in Indonesia, a 20 yr agreement has been signed with PLN that is also in charge of connecting the power plant to the existing grid [30]. However the project encountered some issues related to the concession of Calliandra plantation permit. One of the project partners, PT Usaha Tani Lestari holded a concession for a 4700 ha area and the project started in 2007 but it took nearly 9 years to get the concession for the energy plantation. Therefore, when the operation started in 2015, the first 20 ha were planted on the concession land. The main problem was land claim by the local community for different reasons: agricultural purposes, for grazing their buffalos and for ceremonial purposes. In the end, just 2000 ha could be used for energy crops. As the plant was ready for operation, feedstock had to be sourced locally in order to provide the required 30 t/d.

2.2.2 Philippines

In a populated barangay village in the municipality of Barataza, in the province of Palawan, the Rio Tuba microgrid started its operation in 2005 [32]. PowerSource Philippines Inc. (PSPI) developed the project to power 24/7 a village of 1,976 households in the Philippines.

The inhabitants of the village were powering their businesses through old diesel engines, however, due to a constant increase in diesel price and maintenance costs, they suffered from the high expensive operating cost that they were facing in order to sustain their primary source of income. In addition to that, the location was deemed to be unviable for grid extension, thus another solution was seeked by PSPI.

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To maintain the microgrid operation, PSPI hired local residents of the barangay, in particular: ● 8 linemen to maintain the reliability of the distribution network;

● 4 generator unit operators to maintain the reliability of adequate generation production; ● 1 billing assistant;

● 1 finance officer;

● 1 cashier responsible for issuance of monthly billing statements and collections; ● 1 security guard to protect the facility;

● 1 site supervisor for the supervision of the operations of the microgrid facility;

Furthermore, the economic development generated additional job creation, as a matter of fact it was possible to power a cold storage and mini-ice plant modules, internet access and communication modules. The total cost of the system was 514,000 USD of which 70% was financed through debt, while the rest through equity. The cost of producing electricity with the microgrid was found to be 0.686 USD/kWh while the price paid by the customer was 0.171 USD/kWh which was possible thanks to subsidies from the Subsidized Approved Retail Rate (SARR). Consumers paid based on their monthly consumption that was read by linemen at the locally installed device at each consumer. At the beginning of the project, the microgrid was serving 1,885 households, presently, the number of customers increased to 1,967 with a 4% yearly demand growth.

A crucial lesson learnt from this project is the fact that subsidies for remote areas with lesser consumption and revenue potential for investors have a significant impact on the economic sustainability of the project.

2.2.3 Biopower Projects Case Studies Summary

Table 3 represents a summary of the analysed study cases and summarizes existing biomass

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Table 3 - Study Cases Summary

Study Case System Startup Feedstock Characteristics

Mentawai, Indonesia [29]

Downdraft +

Gas Engine 2017 Bamboo

700 kW; CAPEX = 12.5M USD OPEX = 0.07 USD/kWh 450 jobs created

3,000 t CO2,equivalent/yr avoided Fostered economic development

Rio Tuba,

Philippines [32] -

2005 and

2016 -

893 kW; CAPEX = 514,000 USD Generation Cost = 0.686 USD/kWh 17 jobs directly created

Fostered economic development

Kundur Island, Indonesia [30] Fluidized Bed 2016 Palm Kernel Shells, Rubber Trees, Coconut Shells, Aiming for Calliandra

Feedstock change due to unforeseen price increase Interesting dynamics with local community and PLN

18k USD/month for biomass purchase Sumba Island, Indonesia [30] - 2015 Calliandra 1 MW using 30 t/d

Plantation license issue with local community

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3 Methodology & Data Sources

This section summarizes the methodologies used to answer the stated research questions and illustrates key data sources. Through surveys and data collection from involved stakeholders and partners, two scenarios will be created and evaluated thanks to the three previously mentioned KPIs. Additionally, the scenarios will be finalized thanks to various optimization tools and to a tight collaboration based on feedback sessions and valuable meetings with the stakeholders. The methodology approach is illustrated in Figure 8 and is further explained in the following sections of this chapter.

Figure 8 - Methodology Approach

3.1 Research & Data Analysis

The first steps of the thesis work is organized in a cycle of research, brainstorming and feedback session with the aim of modelling a demand curve and of analyzing energy supply alternatives. The second phase is characterized by selecting which energy conversion technology and biomass feedstock to implement in the integrated renewable-based scenario through different MCAs. Consequently, the two different scenarios are modelled thanks to different energy modelling tools and finally compared on the aforementioned KPIs to determine which alternative represents the most sustainable solution for the selected challenge.

3.1.1 Demand Analysis Approach

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assumptions to be considered with local community leaders and involved stakeholders. Required data to model both the household and the service building demand was acquired during the initial phase of the thesis work, thanks to literature review and local collaborators. For what concerns the households’ electricity demand, the data sourcing was carried out in the form of surveys posed to local communities. The questionnaire focused on assessing the type of home appliances used by the villagers and its abundance among the population, together with their common usage pattern. Once the usage pattern was modeled (see Section 3.5) and the average electricity consumption per appliance had been determined, it was possible to produce an average HHs’ demand curve. A similar approach was followed for the various service buildings.

3.1.2 Supply Analysis Approach

Considering the stability and economic benefits associated in microgrids with a large share of biomass based electricity production [8] and the biomass abundance characterizing the focus area, research into different biopower conversion technologies with their relative prime movers available for rural applications was the first step in order to gain an initial understanding of how these technologies performed or would perform (both technically and economically) in NIZs of South-East Asia. The choice of technologies to be evaluated was inspired by an initial brainstorming session with the Host Company contact and with the local collaborator in Indonesia. For each technology, information was collected according to available literature review and personal experience provided by EIFER field studies and local collaborators and preliminary values for a series of relevant parameters were identified in order to evaluate them through a MultiCriteria Analysis (MCA).

3.2 MultiCriteria Analysis

A MCA structures a decision problem in terms of several possible alternatives and assesses each of them under various criteria at the same time [33]. There are a number of MCA methods to rank, compare and/or select the most suitable options according to the chosen criteria. Depending on the method, each criterion can also be measured in different ways, either qualitatively or quantitatively [34]. The main advantage of MCA methods is their capability to integrate a diversity of criteria in a multidimensional way, and to be adapted to a large variety of contexts. The procedures and results obtained from MCA can be improved with the interaction of stakeholders, and in this regard MCA methods are particularly suitable to be used in combination with participatory methods [35].

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with involved stakeholders, however, when some of the parameters were not available in literature or not clearly defined, a redefining and further selection phase was required to obtain the final evaluation criteria.

3.2.1 Biopower Conversion Technology MCA

A series of different parameters that are deemed to be relevant to assess which technology constitutes the best fit for the analysed project have been chosen to compare and evaluate them:

Fuel Flexibility

Fuel flexibility is a parameter that evaluates the flexibility of the proposed technology regarding fuel, in the measure of fuel diversity and particle size tolerance. The parameter will be evaluated on a scale from 1 to 10, where 1 represents the lowest fuel flexibility and 10 stands for the highest fuel flexibility.

Moisture Content Flexibility

Similarly to fuel flexibility, moisture content flexibility measures the technology flexibility associated with the moisture content of the feedstock. The parameter varies between 1 and 10, where 1 represents the lowest moisture content flexibility associated with a low moisture content allowed, while 10 is associated with the highest moisture content flexibility and thus, with the highest moisture content allowed in the technology.

Size

Size is a relevant parameter considering the remote nature of the project and its need for an easy to scale up solution to fulfil further grid growth. The parameter will be evaluated on a scale from 1 to 10, where 1 represents the worst fit for the project size, while 10 identifies the highest possible fit for the project scale. Given the fact that the energy demand is expected to vary between 800-2,000 kWe, a value of 10 will be assigned at solutions of size ranges between 400-1,000 kWe, while a low value would be representative of generally too oversized or too downsized solutions.

Bankability

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Suitability for Microgrid

Given the remote nature of the project and the need for the bioenergy conversion technology to operate in a microgrid environment, the suitability for microgrid applications is an important parameter that considers experience with the proposed technology in similar projects and its adaptability to operate in an island mode. A value from 1 to 10 is assigned according to the evaluation of such suitability, where 1 represents a less suitable microgrid application, while 10 stands for a perfect fit for the island mode operation.

Cost Factor

Cost factor represents the economic parameter of the MCA analysis and is a weighted average of the total upfront investment or capital expenditure (CAPEX) cost and of the operation of maintenance cost, presented in Table 4. A value from 1 to 10 will be assigned to both quantities, where 10 represents the cheapest option and 1 represents the most expensive option. Since the goal is to have a sustainable system in the long-run, both from a business and from an environmental perspective, then the weight factors associated with CAPEX and OPEX will be respectively 0.2 and 0.6.

Table 4 - CAPEX and OPEX values for different Bioenergy Conversion Technologies

Technology Downdraft Gasifier Stoker Boiler Fluidized Bed Gasifier

CAPEX [$/kWe] [19] 2,100 1,900 - 4,200 3,500 - 5,300 OPEX F [CAPEX %] V [$/kWhe] [19], [36] 3-6% 4.0 3.2% 4.0-4.93 3-6% 4.0 Demand Flexibility

Demand flexibility represents the flexibility associated with a specific technology to load fluctuations and, given the microgrid operation, it represents a relevant factor. A value from 1 to 10 will be assigned to each technology according to available literature review, with 10 representing the technology that best copes with load fluctuations and 1 the worst.

Bioenergy Conversion Technology Index

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3.2.2 Biomass Resource MCA

Once the biomass power conversion technology to be implemented was selected, the research was moved to identify the best fitting biomass resource or mix for the bioelectricity-based part of the microgrid, the research spaced from different woody sources, fast growing species and Municipal Solid Waste (MSW). In a similar way to the analysis methods applied to the biomass power conversion technologies, biomass resources were evaluated on a series of different parameters through a MCA, such as:

Productivity

Productivity is a parameter that evaluates the yearly productivity of the proposed biomass resource in terms of fuelwood and, eventually, forage. The parameter will be evaluated on a scale from 1 to 10, where 1 represents the lowest productivity and 10 stands for the highest productivity.

Higher Heating Value (HHV)

The HHV is a fundamental parameter for the operation of the biomass-based part of the power plant, since it directly influences the amount of biomass required on a daily basis to supply the electricity demand. The parameter will be evaluated on a scale from 1 to 10, where 1 represents the lowest productivity and 10 stands for the highest productivity.

Moisture Content

Moisture content is another key parameter for the gasifier operation, since a higher moisture content implies a lower efficiency of the gasification process. This parameter measures the moisture content of the biomass resource as the weight percentage of moisture of the fuelwood fed to the gasifier, thus previously sun dried or oven dried and it varies between 1 and 10, with 1 presenting the biomass resource with the highest moisture content and 10 representing the biomass resource with the lowest moisture content.

Ash Content

Ash content, similarly to the moisture content, is a measure of the weight percentage of the biomass resource ash content after the gasification process is ended. A value between 1 and 10 is assigned to the parameter, with 10 being associated with the lowest ash content and 1 with the highest ash content.

Growth Time

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and 10 will be associated with the parameter, where 10 represent the fastest growing tree, while 1 the slowest.

Table 5 - Energy Plantation Growth Time

Biomass Resource Gliricidia Sepium Calliandra

Nursery [months] 2-3

3-4

ready when the plant is 25 cm tall Coppicing Start [months] 6-8 9-12 or when 2 m tall Global Index

The global index is obtained as the average of the proposed parameters and has the scope of determining the best solution. No specific parameter is prioritized, since all the proposed are believed to be equally important.

3.3 Scenario Development

In this section, the methodology approach used to develop the two proposed scenarios is described together with key parameters and assumptions for the creation of the above mentioned ones.

3.3.1 Business-As-Usual Scenario

In order to compare the obtained model to the current situation, a state-of-the-art scenario was modeled assuming that a Business As Usual (BAU) microgrid will supply the forecasted electricity demand, which means that a diesel generator or several diesel generators will supply the entire electricity demand. The tool utilized to model the BAU scenario is HOMER Pro version 3.13.8, a modelling and optimization tool for grid connected or isolated systems, assuming the following parameters:

● The current diesel-based microgrid will be scaled up to supply the entire foreseen demand growth of two Sulawesi islands

● Average diesel price of 0.681 USD/l for Indonesia [37] ● A project lifetime of 20 years

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● PLN electricity purchase price: 0.15 USD/kWh

The envisioned BAU system is portrayed in Figure 9 and foresees the implementation of two big scale diesel generators to supply the entire electricity demand for the project lifetime.

Figure 9 - BAU System Schematic

3.3.2 Renewable-based Scenario

Once all the technologies and resources to supply the demand are selected, the renewable-based microgrid will be modeled through HOMER Pro version 3.13.8, a modelling and optimization tool for grid connected or isolated systems, assuming the following parameters:

● 100% renewable-based electricity generation ● A project lifetime of 20 years

● An inflation rate of 3.5% [38] ● A nominal discount rate of 8% ● Load Following as control strategy

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Figure 10 presents a simplified version of the renewable-based (RB) scenario system schematic,

as a matter of fact, the battery storage and the inverters are omitted in the system configuration. The RB scenario envisions the establishment of an energy crop that supplies the total feedstock demand of the biopower conversion technology.

Figure 10 - RB Scenario System Schematic

3.3.3 HOMER modeling

HOMER is an optimization tool designed by National Renewable Energy Laboratory (NREL) for designing renewable hybrid systems and for assisting in the comparison of different generation sources in terms of cost and technical parameters in a polygeneration system [39]. This objective function of such an optimization tool is the minimization of the cost of the whole system, thus, for a given modelled scenario, the optimization tool provides the user with a series of feasible architectures [40]. The objective function is expressed by the following equation:

Where:

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R0 is the initial investment N is the project lifetime in years

Rt is the net cash flow for each component d is the discount rate

i indicates the individual system component

And it is subjected to the following constraints:

Where:

Pshedding is the load not served by the designed system

Pload is the system load

rload,t is the input operating reserve as a percentage of the load in the timestep t

Pload,t is the load in the timestep t

rpeak load is the input operating reserve as a percentage of annual peak load

Afterwards, the obtained configurations are compared on a series of different parameters, such as the total Net Present Cost (NPC) of the system and the Levelized Cost of Electricity (LCOE). The NPC represents the cost of the whole system measured over its entire lifetime, thus it accounts for capital costs, operation and maintenance costs (O&Ms), fuel and replacement cost [41] and it is calculated through the following equation:

Where:

Cann,tot is the total annualized cost of the system

CRF(d,N) is the Capital Recovery Factor

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Where:

Cann_cap is the annualized capital cost

Cann_rep is the annualized replacement cost

Cann_0andM is the annualized O&M cost

Cann_fuel is the annualised cost of the fuel used for generating energy

Rann_salv is the annualised total salvage value, which represents the value that a component retains

at the end of the project lifetime [41] and it is calculated as:

Where:

Ncomp is the component lifetime in years

Nrem is the remaining lifetime of the component in years at the end of the project, calculated with the following equation:

Where:

Nrep is the replacement duration in years, obtained as:

Where INT represents a function that returns the integer of a real number [39].

On the other hand, the CRF is what permits the conversion of a present value into an uniform annual cash flow over the project lifetime at a specific discount rate and it is expressed as:

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Where n is the project life, r is the discount rate, Et is the electricity generation in the year t, PE is the price of electricity, I t is the investment cost in the year t, OMt is the operation and

maintenance cost in the year t and Ft is the fuel cost in the year t.

3.4 Evaluation

The technical, economic and environmental performances and the social impact of the proposed renewable-based microgrid will be compared with the diesel-based one. In order to equally evaluate both scenarios, three fundamental Key Performance Indicators (KPIs) have been selected: Carbon Footprint (CF), Levelized Cost Of Electricity (LCOE), and number of jobs directly created. These parameters have been selected for their easiness to be calculated and for their relevance to sustainable development, as a matter of fact, within the Sustainable Development Goal (SDG) number 7 lies the scope to ensure clean and affordable energy for all, which means that new realized energy projects should focus on their solution’s CF, to guarantee clean energy, and LCOE, to guarantee the economic viability of such a solution from the consumers’ perspective. Furthermore, they should empower the local community by means of life quality improvement, thus the job creation parameter.

The carbon footprint is a measure of greenhouse gas emissions per unit of energy output (kg CO2-eq/kWh). The carbon footprint of the Indonesian electricity system is 0.755 kg CO2-eq/kWh according to data from 2017 [42]. For each microgrid configuration, the carbon footprint is evaluated considering reported industry standards for the technologies responsible for the greenhouse gas emissions, as it can be seen in Section 4.3 and Section 4.4.

To evaluate the social impact, the direct job creation from the power plant installation and the foreseen job creation from business growth and new business opportunities was estimated.

3.5 Data Sources

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3.5.1 Current Electricity Demand

Literature review and EIFER expertise on Indonesian rural electrification challenges supplied necessary knowledge to model a demand curve for Sulawesi islands, such as type of appliances, their abundance among HHs, their average electricity consumption and the inhabitants’ common usage pattern.

As of today, a diesel-based microgrid supplies electricity for 6 h/d to 63.7% of the local population of 2,200 households, from 6.00-12.00 pm. The main appliances are identified to be rice cookers, refrigerators, lamps, television, mobile charges and so on. Table 6 shows the averagely spread home appliances together with their average capacity in W and their average consumption in kWh/h.

Table 6 - Home Appliances [Source: Literature Review & EIFER Data]

Category Appliance Capacity [W]

Consumption [kWh/h]

Kitchen Rice Cooker 400 0.400

Refrigerator 150 0.150

Lighting Lamp (Indoor) 16 0.048

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Probability of occurrence represents the probability that a certain HHs appliance has to be in use at a specific time of the day and was determined thanks to literature review project feedback sessions.

Given the fact that currently electricity is made available from the existing diesel-powered microgrid just for 6 h/d, from 6.00-12.00 pm, the community usage pattern of widely spread appliances is presented in Figure 11, where RC stands for Rice Cooker, EH for Electricity Hob, R for Refrigerator, Li for indoor lamp, Lo for outdoor lamp and so on. It can be observed that the majority of the available electricity is used for lighting, charging mobile phones, operating the fan, while just a minority is expected to be used for refrigerators, since a minority of the population owns them. Since there is currently no possibility of benefitting from an electricity connection earlier than 6.00 pm, the community relies on inefficient and polluting fuelwood burning for cooking purposes.

Figure 11 - Use Pattern Probability of Occurrence with 6 h/d Diesel Microgrid

[Source: Literature Review & EIFER Data]

However, if a microgrid able to supply electricity for 24 h/d is envisioned, the usage pattern will evolve accordingly as Figure 12 and Figure 13 show.

Figure 12 shows the usage pattern that village workers will have when electricity supply is made

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On the other hand, Figure 13 presents the usage pattern of unemployed or non-working inhabitants when electricity supply is made available for 24 h/d. In this case, the graph of the probability of occurrence of use of a certain appliance reflects a more complicated usage in which electricity is used also during working hours.

Figure 12 - Use Pattern Probability of Occurrence of Workers with 24 h/d Electricity Access

Figure 13 - Use Pattern Probability of Occurrence of Non-Workers with 24 h/d Electricity Access

In addition to the households’ demand, other service buildings are considered in the electricity demand. The current existing service buildings are:

● A clinic with consultation rooms

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● An office

● A fish enterprise that produces roughly 30 t/d and that requires a cold storage ● A telecom tower

The generated load curve for the two small islands with the share of household and service buildings consumption foresees the large part of the electricity demand being associated with HHs and with the cold storage of the fishing business.

The forecasted daily demand for the two islands with the installed 24 h/d renewable-based microgrid and with the implementation of a cold storage for fishermen of 500 kW [Source: Literature Review & EIFER Data] is presented in Figure 14. Figure 14 was obtained as a result of

Figures 4-7 and represents the daily electricity demand for the first year when the cold storage

necessary to boost the local economy is introduced and when the connection rate of the village is 63.9%, representing 1100 HHs. As of 2016, the Indonesian national average of HHs electricity consumption per capita was 348.3 kWh/capita [43], while it would be just 232.8 kWh/capita in the modelled case.

Figure 14 - Daily Electricity Demand - Year 1 [Source: EIFER Data]

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Figure 15 - Seasonal Electric Demand Variation - Year 1 [Source: EIFER Data]

3.5.2 Forecasted Demand Growth

Table 7 represents the forecasted development of the islands as a reflection of the connection

rate growth and of the construction and expansion of new and existing service buildings.

Table 7 - Forecasted Development of the Islands’ Electricity Demand and Employment Rate

[Source: Literature Review and EIFER Data]

Quantity Year 2 Year 3 Year 4 Year 5

Electricity

Demand Growth 36% 15% 15% 17%

Connection Rate 70% 75% 80% 85%

Employment Rate 16% 19% 22% 25%

The demand is forecasted to grow up to 36%, reflecting the expected population growth and increased connection rate to the microgrid In the second year of its operation, the newly installed microgrid is indeed expected to serve the 70% of the population or 1,428 households. The demand is foreseen to grow significantly up to the fifth year after installation, in which the microgrid will serve up to 85% of the island's population or 2076 households.

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enterprises, this percentage is expected to increase to 25% in the fifth year. [Source: Literature Review and EIFER Data].

Reasoning for the electricity consumption increase lies in the increased connection rate and in the diversification and number growth of service buildings. which envision the following service building additions presented in Table 8. Table 8 represents the forecasted service building construction and expansion in the first five years of the project.

Table 8 - Service building evolution over the first five years of the project [Source: Literature

Review & EIFER Data]

Service Building Expansion/Construction Year 2 Expansion/Construction Year 5 Hospital 1 - Clinic 1 6 beds 3 14 beds Consultation Room 4 4 Analysis Lab 1 1 School Classroom 20 30 Mosque 2 3 Carpenter Enterprise 2 3 Office 1 3 Restaurants 2 3 Fish Facility (Production) 10 t/d 20 t/d Telecom Towers - 4

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Figure 16 - Daily Electricity Demand - First 5 Years of the Project [Source: Literature Review &

EIFER Data]

After the fifth year, the demand is expected to experience an annual constant growth of 1% throughout the entire lifetime of the project of 20 years (Figure 17).

Figure 17 - Total Yearly Electricity Demand Growth [Source: Literature Review & EIFER Data]

3.5.3 Renewable Resource Potential

Solar Resources

Indonesian Rural Electrification