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Energy for Sustainable Development 31 (2016) 14–23
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Energy for Sustainable Development
Global cost advantages of autonomous solar–battery–diesel systemscompared to diesel-only systems
C. Cader a,⁎, P. Bertheau a, P. Blechinger a,b, H. Huyskens a, Ch. Breyer c
a Reiner Lemoine Institut gGmbH, Ostendstr 25, 12459 Berlin, Germanyb Department of Engineering, Berlin Institute of Technology, Fasanenstraße 89, 10623 Berlin, Germanyc Laappenranta University of Technology, Skinnarilankatu 34, 53850 Lappeenranta, Finland
⁎ Corresponding author.E-mail address: [email protected] (C. Cade
http://dx.doi.org/10.1016/j.esd.2015.12.0070973-0826/© 2016 International Energy Initiative. Publish
a b s t r a c t
a r t i c l e i n f oArticle history:Received 31 May 2013Revised 31 July 2015Accepted 29 December 2015Available online xxxx
Isolated diesel systems are themain electricity generationmethod inmany rural areas nowadays and represent aviable option to supply un-electrified villages in theGlobal South. However, this generation scheme leads to a de-pendency on fossil fuels and their price volatility on a global market with a projected increase of costs in the fu-ture. At the same time, high carbon dioxide emissions increase environmental costs. Up to date, many hybridmini-grid pilot projects and case studies were performed globally to assess how the inclusion of renewable en-ergy in these systems can enhance technical and economic performance. This provides insights in local character-istics and challenges of that approach on a case by case basis. This study, on the other hand, takes a look at theoverall global potential for solar–battery–diesel mini-grids for rural electrification and derives a comparativeanalysis of the respective regions. The introduction of a GIS-based analysis in combination with a sophisticatedmini-grid simulation allows a highly automated approach to draw global conclusions with the option to down-scale to local regions. The results of the methodology show that in many regions substantial LCOE reductionsare achievable by introducing solar–battery–diesel systems compared to pure diesel systems. Furthermore, thecrucial role of spatial varying of diesel fuel prices over different regions and the impacts on the feasibility ofsolar–battery–diesel systems can be observed.
© 2016 International Energy Initiative. Published by Elsevier Inc. All rights reserved.
Keywords:Rural electrificationgeo-spatial analysisOff-gridSpatial cost modeling
Introduction
Access to electricity is still a huge challenge for more than 1.3 billionpeople globally. Highly affected regions are located in rural remote re-gions in Sub-Saharan Africa, Southeast, and South Asia (IEA, 2014a).Rural remote regions are facing the great challenge that the populationdensities are often low, the demand is difficult to assess and the reliabil-ity regarding the ability to pay for electricity is unclear(Zvoleff et al.,2009; Winkler et al., 2011). Low local demand for electricity makesthe installation of transmission lines expensive and less efficient andhence the hybrid renewable system more feasible. Centrally organizedpower supply systems are not designed for small settlements, andtheir costs are often underestimated (Cader, 2015; Chaurey et al.,2004). These are reasons for utilities and power companies to be carefulabout investing in that area and opening the space for decentralized en-ergy solutions (Narula et al., 2012).
Electricity is a key requirement for development: education, healthsector, and economic sectors benefit from it. A clear relation betweenthe gross national product (GDP) and the national energy consumptionscan be shown (Doll and Pachauri, 2010). In addition, the human
r).
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development index (HDI) is also positively influenced by having accessto electricity (Kanagawa and Nakata, 2008). Electricity access is notgiven because the national electricity transmission and distribution in-frastructure does not reach far enough, the grid is facing regularpower outages, or the electricity from the grid or alternatives such assmall fossil fuelled generators are not affordable for the respective in-habitants (Kaundinya et al., 2009). In particular, the latter option –small diesel generators – is a frequently usedmethod of electricity gen-eration for remote locations (Bertheau et al., 2014). With the interna-tional agenda of universal access to electricity by the UN accompaniedby other global institutions and emerging national development policiesand the new goals and efforts regarding low carbon development, re-newablemini-grids are becomingmore andmore important. The Inter-national Energy Agency publishes statistics regarding urban, rural, andtotal un-electrified population per country (IEA, 2014b). In addition, anewly developed multitier framework also includes the quality of elec-trification, such as the duration of availability of electricity per day andtheir affordability (Worldbank/ESMAP, 2014).
Solar–battery–diesel mini-grids are a possible solution for providinghigh quality access to electricity in different regions, such as Laos (Blumet al., 2015), Burkina Faso (Ouedraogo et al., 2015), or Nigeria (Dada,2014; Ohiare, 2015) and Cameroon (Nfah et al., 2010; Nfah, 2013).Hazelton et al. (2014) provide a comprehensive overview of benefits
d.
15C. Cader et al. / Energy for Sustainable Development 31 (2016) 14–23
and risks of hybrid mini-grids. Bhattacharyya (2013) discusses chal-lenges of decentralized options for rural electrification from a multi-disciplinary perspective. The expansion or so-called hybridization ofexisting diesel grids with renewable energy systems could significantlyreduce the electricity costs and the emission of air pollutants (Dekkeret al., 2012). IEA's New Policies Scenario suggests the generation of26 TWh for only Sub-Saharan Africa by mini-grids until the year 2040.The two most prominent technologies to achieve that are solar photo-voltaic (PV) and oil, followed by hydro power (Fig. 1).
Within this study, a comparable analysis is carried out to evaluatewhere mini-grids powered by solar PV and diesel will be the most eco-nomical solution. This paper presents a novel methodology on how toscale up a localmini-grid potential analysis at one specific site to a globalcomparative assessment for solar–battery–dieselmini-grids by combin-ing a GIS-based approach with an energy system simulation. The paperprovides an overview to the following discussions:
1. Where will solar–battery–diesel systems be more economical thanpure diesel systems under actual technology costs in the developingworld?
2. Which percentage of solar power should be installed in the opti-mized hybrid systems and which parameters influence that?
3. What is the optimum size of batteries in the systems and how is it re-lated to low carbon development?Decentralized systems allow electricity generation without the needfor a national grid. In this study, the economic performance of solar–battery–diesel systems is compared to diesel-only systems, as thelatter are frequently used in many regions (Bertheau et al., 2014).Advantages of diesel generators are low investment costs, simple op-eration, and a well-known technology. Recent calculations of ananalysis in Nigeria show that they are the optimum choice in somelocations if compared to grid extension or small-scale PV solutions(Ohiare, 2015). Furthermore, fossil fuelled generators can be runflexibly and therefore allow varying electricity demands. However,diesel generators contribute to local and global environmental pollu-tion (Ramanathan and Feng, 2009). In addition to pure financial ben-efits, also the aspects of CO2 savings and independency of fossil fuelsupply are reflected with an establishment of hybrid mini-grids.Solar–battery–diesel mini-grids are chosen as an alternative to purediesel powered generations as many areas with low electrificationrates are located in areas where a high photovoltaic potential can beexpected (Breyer et al., 2011). Solar irradiation as a local resourceholds the advantage of overall spatial availability,which is only limitedby climatic factors aswell as the day and night rhythm.With regard tothe steep learning curves and achieved grid parity of small renewable
Fig. 1. Projection of the role of mini-grids in the IEA New Policy scenario. Source (IEA,2014a, 2014b).
energy systems under certain scenarios (Schleicher-Tappeser, 2012;Breyer andGerlach, 2013), investigations in the potential of solar ener-gy (photovoltaic) are the key to achieve global electrification. Due tothe progress in the technical development, generation costs of renew-able energy drop, thus becoming more and more economically feasi-ble. In addition, battery costs are declining fast, which makes storageoptions much more viable today (Juelch et al., 2015; Nykvist andNilsson, 2015).In the past few years, more andmore pilot projects and detailedmini-grid case studies were developed, implemented, and analyzed. Thisprovides helpful insights in local environments and performance of in-dividual systems. However, to get an overviewof the overall potentialsof mini-grids, a broader research framework is necessary. This globalcomparison of potentials is especially helpful when it comes to busi-nessmodel development for different regions. The utilization of GIS al-lows an in-depth study of a large spatially disaggregated data set. Inparticular, when it comes to renewable energies, local resource varia-tion needs to be accounted for. As diesel prices vary on a national leveland are also subject to transport costs, these local characteristics needto be taken into consideration. GIS tools allow the processing of largedata sets to account for the preparation of automation to optimizeLCOE-based mini-grids for many different locations at once.
Methodology and input data
The work described in the paper is based on the approach describedin Fig. 2. Data sources are listed in Table 1. In the first step of the analysisa GIS-based approach is chosen to select all countries with rural electri-fication rates of 90% or belowbasedon 2013 data (IEA, 2014b). For thesecountries, a high spatial resolution raster is defined. The raster cell size(length of sides) is set to 1/6 degree. The pixel size corresponds toabout 18 km at the equator. This value is chosen to reflect the recom-mended grid resolution which compromises between the coarsest andfinest legible grid resolution (Hengl, 2006). It is adopted to correspondto the spatial properties of the input data and still allow a simulationin a conceivable time frame. The sample countries are classified into131,147 pixels in total. For each of these raster cells (pixel) the input pa-rameters for the simulation (hourly solar irradiation, travel time, na-tional diesel price, and transport costs) were extracted for the centrecoordinates from global data bases. This automation allows the bulkprocessing of the spatially dissolved simulations.
The calculations aim at the distinction of two different scenarios:
1. Areaswhere the use of renewable energies applying a solar–battery–diesel system ismore cost-effective than the use of conventional die-sel generators and which would produce less emissions.
2. Areas where diesel-generated electricity is the cheapest option be-cause here the investment in renewable energy technologies mightbe difficult due to a lower potential and existing cheap alternatives.Model formation to calculate electricity costs for the two scenarios,diesel stand-alone systems and hybrid solar–battery–diesel systems,is carried out using amodeling tool developed by the RLI (Fig. 3). Thistool simulates the techno-economic optimizationbased on LCOE. Thedesign of the approach is based on the modeling approach by Szaboet al. (2013, 2011). Their spatial concept is adopted and extendedfrom an African perspective to a global scale. Furthermore, input pa-rameters and model assumptions were changed and updated. Dieseltransport costs are calculated using a global travel time raster. Theglobal travel time raster (Nelson, 2008) is analyzed and validated re-garding the infrastructure data,which can be drawn from the VMap0data derived from the Digital Chart of theWorld compiled by the Na-tional Imagery andMappingAgency (NIMA)of theUnited States. Theglobal travel time raster is a product of spatial infrastructure assess-ments, land cover analysis, and a digital elevation model amongother input parameters. It therefore reflects the impact of missing in-frastructural elements, which is especially important for electricity
Fig. 2.Workflow diagram: combining GIS with mini-grid simulation.
16 C. Cader et al. / Energy for Sustainable Development 31 (2016) 14–23
generation and electricity access in remote regions. We assume thatdiesel is purchased at the national diesel price (Ebert, Metschies,Schmid, and Wagner, 2009) in the next major settlement.The accessibility of each region within the countries is considered toreflect the additional costs for transporting oil to the remote regionsfor the use in generators (Szabo et al., 2011). The transport costs arecalculated as a function of the respective national diesel price for thetransport vehicle, the amount of transported diesel, and travel time(Eq. (1)). The variables are as follows: Pt, transport cost; Pd, nationaldiesel price; c, diesel consumption of transport vehicle (l/h) = 12; t,travel time (h); V, volume of diesel transported (division to get thevalue per kilowatt hour); and rt, factor for the return travel of the
Table 1Data sets and respective sources.
Data set Source
Rural electrification rates IEA World Energy Outlook 2014: Electricity data basedatabase/(accessed 24/05/2014).
Country borders Global administrative areas www.gadm.org (accessedAccessibility Nelson, A., 2008. Estimated travel time to the nearest c
Research Centre of the European Commission, Ispra Ita24/05/2014).
National diesel prices World Bank, Pump price for diesel fuel. Available onlinSolar irradiation Stackhouse P.W., Whitlock C.H., (eds.), 2009. Surface m
Science Enterprise Program, National Aeronautic and S
transporter = 2, reducing the weight of the transport costs to thevalue (k) = 0.4:
Pt ¼ rt� Pd � c� tð Þ � Vð Þ � k ð1Þ
In the calculation, further costs like charges for the transport vehicleand labor costs for the service are not considered. Inmany cases, the fuelis transported by large companies that shift the incidental costs inter-nally to achieve affordable prices. However, these costs still occurfrom the macroeconomic perspective.
(http://www. Worldenergyoutlook.org/resources/energydevelopment/energyaccess
24/05/2014).ity of 50,000 or more people in year 2000. Global Environment Monitoring Unit—Jointly. 2008, http://forobs.jrc.ec.europa.eu/products/gam/index.php (accessed
e: http://data.worldbank.org/indicator/EP.PMP.DESL.CD (accessed 20/02/2015).eteorology and Solar Energy (SSE) release 6.0 Methodology, NASA SSE 6.0, Earthpace Administration (NASA), Langley, http://eosweb.larc.nasa.gov/sse/
Fig. 3. Schematic simulation approach.
17C. Cader et al. / Energy for Sustainable Development 31 (2016) 14–23
For the analysis, global solar irradiation values for a year on an hour-ly basis are prepared as these are required for calculating the PV yieldfrom the irradiation value. The potential PV yield is estimated fromthe site-specific global horizontal irradiation (GHI) for optimally fixedtilted mono-crystalline modules (Huld et al., 2008). An LCOE-based op-timization is carried out for both options: the simulation of diesel-onlymini-grids and solar–battery–diesel mini-grids (Fig. 3). The optimiza-tion yields at minimizing LCOE for both of the systems.
Global calculation of electricity costs for a solar–battery–diesel sys-tem and pure diesel system is carried out using a generic algorithm. Ini-tial PV costs are set to 1500 €/kWp, battery costs are assumed to be350 €/kWh. Weighted cost of capital (WACC) is set to 10% per annum.For the diesel component a genset efficiency of 0.33 l/kWhel is adopted,the operational expenditure is set to 0.01 l/kWhel (Table 2).
The calculation of LCOE is based on Eq. (2), which is drawn fromShort et al. (1995). Abbreviations are as follows: CAPEX, capital expen-ditures; CRF, capital recovery factor; WACC, weighted average cost ofcapital; N, project lifetime; OPEX, operation and maintenance expendi-tures per year; Costsfuel, cost of diesel per liter; Fuel, consumed dieselper year; and Elconsumed, consumed electricity per year. It accounts forCAPEX, OPEX, and the fuel price over the project life time and considersa capital recovery factor, which is represented as a function of weightedcost of capital and project life time (Eq. (3)). As a result, all occurringcosts are annualized for the project duration:
LCOE ¼ CAPEX� CRF WACC;Nð Þ þ OPEXþ Costsfuel � FuelElconsumed
ð2Þ
CRF WACC;Nð Þ ¼ WACC� 1þWACCð ÞN1þWACCð ÞN‐1
ð3Þ
An hourly load profile is necessary to account for electricity demandestimation. Zeyringer et al. (2015)) show that detailed input data can be
Table 2Technical parameters and cost assumptions.
Parameter Unit Value
Diesel generator efficiency % 30Annual fuel price increase % 3Battery round cycle efficiency % 90Battery max. depth of discharge % 50Battery life time Y 8Battery C-rate kW/kWh 1/3Capital expenditure diesel generator €/kW 0Operational expenditure diesel generator—variable €/kWh 0.01Capital expenditure PV €/kW 1500Operational expenditure PV—fixed % of CAPEX/y 2Capital expenditure battery €/kWh 350Operational expenditure battery—fixed €/kWh/y 10Project duration Y 20WACC % 10
utilized to develop load demand estimations. This study uses a simpli-fied approach of using one standardized hourly load profile for all loca-tions to achieve a better comparability of results (Fig. 4). The loadprofileis characterized by a strong evening peak, which is typical for rural re-gions with predominant agricultural activities. For the simulation, the24-h load profile is concatenated 365 times to account for the annualconsumed electricity in hourly resolution. Boundary conditions arethat the load demand and the stability criterion, the spinning reserve,have to be met in every hour of the year.
Results
Country selection
The country selection leads to a sample of 76 countries for themini-grid potential analysis. A summary of results for each country is at-tached in the Annex. Fig. 5 displays the relation between national andrural electrification rates as defined by IEA (2014a, 2014b) and indicatesthe absolute number of non-electrified population of each of the 76countries. The country sample shows a great variety regarding therural electrification rates of almost 0 up to 0.9 of the total population.Lowest values are mostly found in Sub-Sahara Africa, with some coun-tries having rural electrification rates less than 10%. Other countriesare reaching higher overall electrification values (N0.5), whereas theirhuge population size still leaves many people in darkness. In particular,this applies for India, withmore than 300million non-electrified people.Other countries with a high absolute number of people without accessto electricity are Nigeria, Ethiopia, Bangladesh, Indonesia, and theDem. Rep. Congo (Fig. 5). All sample countries account to 1245 millionun-electrified people. This number is close to the internationally statednumber of 1.3 billion, which may include more than the chosen 76countries.
Fig. 4. Standardized load profile to estimate the electricity consumption of rural areas.Characteristic for that is the prominent evening peak.
Fig. 5. Relation of rural and national electrification rate and accumulated number of un-electrified population per country.
18 C. Cader et al. / Energy for Sustainable Development 31 (2016) 14–23
Comparative assessment of solar–battery–diesel systems and pure dieselsystems
Significant potentials of solar–battery–diesel systems exist globally.The overall finding is that in most locations power supply with solar–battery–diesel mini-grids is economically feasible over pure diesel-based power generation. LCOEs are reduced by 14 €ct/kWh on globalaverage (Annex). Countries with the highest average LCOE reductionsare the Central African Republic, Niger, Chad, Mali, and Malawi with
Fig. 6. LCOE reduction of solar–battery–dieselmini-grids compared to pure diesel systems. GiveAsia. The histogram shows the respective frequency distribution.
more than 40 €ct/kWh. Fig. 6 displays local LCOE reductions, showingclearly national imbalances resulting from varying national dieselprices. Sub- national differences in LCOE reduction, such as in Columbia,Chad, or Indonesia are derived from varying diesel prices due to a highvariance in transport costs reflecting a varying accessibility within therespective country. Under the assumed national diesel prices and tech-nology costs, pure diesel systems only dominatewhere diesel fuel pricesincluding transport are below 50 €ct/kWh. Also increasing prices in re-mote areas due to higher transportation costs are observable (Fig. 6).
n are the results for the three geographical regions South America, Sub-Saharan Africa, and
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The latter is especially recognizable in large remote areas such asPeru or Mongolia. Local diesel prices (national diesel prices plus trans-port costs) are inmost locations between 1 and 2 €/l. Adding respectivetransport costs makes systems with high shares of PV and batteries at-tractive. Good solar resources in the case study countriesmake PV feasi-ble in most locations. All analyzed countries except North Korea andMongolia show higher solar yield values than 1500 kW/m/year. 24countries are showing values higher than 2000 kW/m/year (Annex).Generally, without batteries, about 30%–45% solar energy proportionin the hybrid systems are optimal (Fig. 7).When increasing the solar en-ergy share due to high diesel prices and good solar resources, a higherbattery capacity becomes necessary to shift the required energy fromdiesel to PV or to the battery, as PV is not available in the evening andat night. With the strong peak load in the evening (Fig. 4), batteriesare indispensable and become competitive if diesel prices are high. Asa consequence, the battery size is clearly correlated to the PV share inthe system.
Fig. 8 illustrates that large battery capacities are only installed inregions with high PV penetration. Regarding the PV share and batte-ries, a general rule of thumb is that batteries are getting importantwhen the PV share reaches more than 45% of the installed capacity.With the assumed load and battery costs, the transition pointwhere batteries become necessary to allow load shifting into theevening hours can also be drawn from Fig. 8. The majority of regionsdo not require the installation of large batteries to be competitive tothe pure diesel systems. The results of this study can be verified withthe finding from (Narula et al., 2012). Their research focus on theelectrification of Southern Asia and conclude that the cost of electric-ity is decreasing when more and more distributed electricity gener-ation is included in the overall system.
Fig. 7. Solar energy share of the produced energy. Given are the results for the three geographicafrequency distribution.
Discussion
Influence of national diesel prices
The global results show the impact of national diesel prices, whichare influenced by national taxes and subsidies. Oil producing countries,such as Colombia, Nigeria, and Angola, do not achieve high cost savingsbecause their local costs of renewable hybrid solutions do not competewith the low diesel prices. If the diesel costs are subsidized by the gov-ernment, the costs for the national economy are increasing. High subsi-dies are generally not sustainable over the long term and the operatinglife of a generation genset (Zerriffi, 2011). There can be differentiatedbetween subsidies on CAPEX or OPEX. When aiming at a possible self-sustainable system, subsidies for CAPEX should be favoured over subsi-dies for OPEX. The overall use of subsidies has to be carefully assessed,also considering the future development. Import tariffs on solar panelsin certain countries augment CAPEX. The strong dependency and signif-icance of the diesel price show that the system is connected to politicaldecisions and fossil fuel pricing. In particular, in regard to a future out-look with assumed raising diesel prices the cost advantages of thesolar–battery–diesel systems become apparent.
Impact of travel time
In very remote locations, the advantage of being independentfrom fossil through systems with almost 100% renewable share en-sures a stable energy supply, as these regions are difficult to accessand hence are prone to fuel shortages and delays on fuel deliveries.The transport costs, which are added to the national diesel prices,may be overestimated in the study, as often large companies sell
l regions South America, Sub-Saharan Africa, andAsia. The histogram shows the respective
Fig. 8. Installed battery capacity. Given are the results for the three geographical regions South America, Sub-Saharan Africa, and Asia. The histogram shows the respective frequency dis-tribution. Regions without installed battery capacity (pixel number 88,000) exceed diagram dimensions.
20 C. Cader et al. / Energy for Sustainable Development 31 (2016) 14–23
their diesel fuel for lower prices in these regions and compensate forthe higher costs internally.
Two concepts: “fuel saver” and renewable energy systems
Regarding the optimum share of photovoltaics in the system, twomain concepts can be differentiated: With low shares of PV (below40% of installed capacity), the design can be described as “fuel saver”(Davies, 2014). Here batteries are mainly included for stability reasonsand power fluctuations of photovoltaic, such as through a suddencloud cover. In addition, these systems reduce fuel consumption duringthe day as the demanded energy is directly produced by the photovolta-ic panels up to their installed capacity. If larger shares of solar energy inthe system are desired, larger batteries are required to allow the usageof PV generated energy in the evening and at night. Hereby largerCAPEX occur for large battery installations. OPEX on the other handwill decrease, as less diesel fuel is required.
Limitations
The study is limited by focusing only on solar–battery–diesel andpure diesel systems as technology. Smaller solutions, such as solarhome systems or pico hydro are not included just as the calculationand feasibility of grid extension. Furthermore, a generic comparisonfrom a bird's eye view does not consider local characteristics as muchas a local case study will do. That means the next steps for buildingmini-grids require in-depth feasibility studies. For example, detailedload estimations are required for exact load estimation at each location.Market potential analysis on a global scale allows the inclusion of otherglobal indices in the analysis. Thus, the attractiveness of amarket can beestimated for international investors or companies looking for regionswhere they can expand their services. Innovative business models for
the application of small distributed energy generation systems in ruralareas are required (Zerriffi, 2011). Possible challenges regarding the op-portunities of small power producers need to be addressed and policiesneed to be designed by energy regulations in the respective countries(Tenenbaum et al., 2014). Transmission grid development will be themain electrification pathway in total numbers, especially in urbanareas. Therefore, a next step will be a grid extension cost modeling—tocontrast grid extension with decentralized options like solar home sys-tems ormini-grids, comparable to thework of Zeyringer et al. (2015) forthe example of Kenya.With such an approach on a global scale andwithincluding detailed electricity infrastructure data, the calculation of theabsolute potential of solar–battery–diesel mini-grids is possible.
The strong pilot project nature ofmini-grid development necessarilyneeds up scaling; therefore, the development of business models is re-quired (Chaurey et al., 2012). By designing them, strong focus shouldbe set to the sustainability of the project. As an example, maintenancestructures are needed. Remote accessibility is also a problem for trans-port of technology for decentralized approaches. Many companies areafraid to consider these due to logistic risks. Future population growthrates will increase and aggravate the situation of many un-electrifiedareas.
Conclusion
The developedmethodology allows the depiction and comparison ofdifferent electrification pathways over a large geographical scale. GIS astool for the input parameter preparation and visualization of results onthe one hand and the simulation for comparing the costs of the two op-tions in a detailed way on the other hand allow this complimentary ap-proach. After a careful consideration of local resources, accessibility, andtechnology costs a large potential of hybrid solar–battery–diesel sys-tems compared to diesel-only systems is evaluated. In particular, the
21C. Cader et al. / Energy for Sustainable Development 31 (2016) 14–23
combination of about one third PV and two thirds diesel generation isthe LCOE-optimized system configuration. Only with very high dieselprices in highly remote areas a larger share of PV in combination withbatteries becomes attractive. Pure diesel systems are economic in coun-tries with low national diesel prices today. With the dynamic develop-ment in renewable energy and storage technology, these results maychange in future towards more renewable energy and less diesel fuelconsumption.
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Acknowledgment
The present study was only realizable with funding from cdwStiftungsverbund gGmbH (formerly SMA Stiftungsverbund gGmbH)and Reiner Lemoine-Stiftung. The authors would like to thank thesetwo organizations for supporting and enabling the current research.Also, the authors would like to thank Markus Hlusiak for the initializa-tion of the first version of the simulation tool.
Appendix A. Annex
Indicator
Population withoutelectricityNational electrificationrate
Rural electrificationrate
Local dieselprice
AverageLCOE
Average LCOEreduction
Solarirradiation
Unit
Million % % ¤ct/l ¤ct/kWh ¤ct/kWh kWh/m/ySource
IEA IEA IEA GIZ model model NASA/DLRngola
15.0 0.30 0.06 0.69 0.20 0.03 1992 rgentina 1.5 0.96 0.61 1.50 0.38 0.13 1658 angladesh 62.0 0.60 0.48 0.91 0.26 0.05 1665 enin 7.0 0.28 0.06 1.41 0.35 0.12 1905 olivia 1.2 0.88 0.66 0.63 0.19 0.02 1853 otswana 1.0 0.66 0.51 1.05 0.27 0.08 2034 urkina Faso 14.0 0.16 0.02 1.38 0.34 0.12 2017 urundi 9.0 0.10 0.07 1.89 0.44 0.19 1793 abo Verde 0.0 0.94 0.84 1.39 0.35 0.12 1781 ambodia 10.0 0.34 0.18 1.58 0.38 0.15 1833 ameroon 10.0 0.54 0.17 1.59 0.37 0.16 2130 en. Afr. Rep. 4.0 0.03 0.01 2.77 0.46 0.46 1964 had 12.0 0.04 0.00 2.30 0.40 0.37 2180 olombia 1.4 0.97 0.89 1.78 0.39 0.20 1704 omoros 0.0 0.45 0.35 1.77 0.39 0.20 2176 ongo 3.0 0.35 0.05 1.38 0.35 0.11 1746 em. Rep. Congo 60 0.09 0.01 1.25 0.33 0.09 1798 jibouti 0.0 0.50 0.14 1.51 0.36 0.15 2168 ominican Republic 0.4 0.96 0.90 1.35 0.34 0.11 1851 PR Korea 18.0 0.26 0.11 0.43 0.15 0.00 1663 cuador 0.9 0.94 0.84 1.18 0.30 0.10 2057 l Salvador 0.5 0.93 0.82 1.51 0.38 0.12 1609 quatorial Guinea 0.0 0.66 0.48 1.93 0.40 0.24 2106 ritrea 4.0 0.32 0.17 1.17 0.30 0.10 2058 thiopia 70.0 0.23 0.10 1.17 0.31 0.08 1612 abon 1.0 0.60 0.34 1.26 0.32 0.10 1982 ambia 1.0 0.35 0.02 0.97 0.27 0.06 1797 hana 7.0 0.72 0.52 1.15 0.3 0.09 1856 uatemala 2.2 0.86 0.75 1.29 0.33 0.10 1889 uinea 10.0 0.12 0.03 1.54 0.38 0.13 1929 uinea-Bissau 1.0 0.20 0.06 0.98 0.26 0.07 1960 aiti 7.3 0.28 0.08 1.27 0.32 0.10 1804 onduras 1.1 0.86 0.75 1.04 0.28 0.07 1784 dia 304.0 0.75 0.67 1.50 0.36 0.14 1740 donesia 60.0 0.76 0.59 1.13 0.29 0.09 2014 ory Coast 15.0 0.26 0.08 1.36 0.34 0.12 2043 maica 0.2 0.93 0.87 1.40 0.36 0.11 1578 enya 35.0 0.20 0.07 1.42 0.35 0.13 1858 os 1.0 0.78 0.70 1.38 0.36 0.10 1635 sotho 2.0 0.28 0.17 1.77 0.39 0.20 2014 beria 4.0 0.02 0.00 2.24 0.42 0.32 2020 adagascar 19.0 0.15 0.04 2.22 0.41 0.33 2078 alawi 15.0 0.09 0.05 1.98 0.40 0.26 2027 ali 11.0 0.27 0.12 1.70 0.41 0.16 1440 auritania 3.0 0.21 0.02 1.23 0.31 0.10 1942 ongolia 0.0 0.90 0.73 0.89 0.25 0.05 1661 ozambique 15.0 0.39 0.27 1.34 0.32 0.13 2115 yanmar 36.0 0.32 0.18 1.34 0.32 0.13 1810 amibia 2.0 0.30 0.17 1.32 0.33 0.11 1847 epal 7.0 0.76 0.72 2.28 0.38 0.37 2242 icaragua 1.6 0.74 0.50 1.05 0.28 0.08 1952 iger 15.0 0.14 0.04 1.40 0.37 0.11 1381 igeria 93.0 0.45 0.35 1.25 0.31 0.11 1866 akistan 56.0 0.69 0.57 1.14 0.31 0.08 1684 anama 0.4 0.89 0.63 1.72 0.39 0.19 1851 eru 2.7 0.91 0.65 1.08 0.30 0.07 1748 hilippines 29.0 0.70 0.52 0.25 0.09 0.00 2037 atar 0.0 1.00 0.69 2.13 0.46 0.25 1695(continued on next page)
(A
22 C. Cader et al. / Energy for Sustainable Development 31 (2016) 14–23
continued)ppendix A (continued)
Indicator
RRSaSeSiSoSoSoSrSuSwSyTTUYZZ
Population withoutelectricity
National electrificationrate
Rural electrificationrate
Local dieselprice
AverageLCOE
Average LCOEreduction
Solarirradiation
Unit
Million % % ¤ct/l ¤ct/kWh ¤ct/kWh kWh/m/ySource
IEA IEA IEA GIZ model model NASA/DLRéunion
0.0 0.99 0.87 1.86 0.38 0.24 2192 wanda 10.0 0.17 0.05 1.58 0.39 0.14 1773 o Tome &Principe 0.0 0.59 0.40 1.55 0.38 0.14 1865 negal 6.0 0.55 0.28 1.77 0.41 0.18 1981 erra Leone 6.0 0.05 0.01 1.27 0.34 0.09 1706 malia 9.0 0.15 0.04 1.29 0.32 0.11 2099 uth Africa 8.0 0.85 0.82 1.30 0.32 0.11 1921 uth Sudan 11.0 0.01 0.00 2.29 0.45 0.31 1957 i Lanka 2.0 0.89 0.88 0.98 0.27 0.06 1814 dan 24.0 0.35 0.21 0.63 0.18 0.03 2155 aziland 1.0 0.27 0.24 1.50 0.38 0.13 1720 ria 1.6 0.93 0.84 0.60 0.18 0.03 1818anzania
36.0 0.24 0.07 1.39 0.34 0.12 2008 ogo 5.0 0.27 0.21 1.60 0.39 0.14 1851 ganda 31.0 0.15 0.07 1.36 0.34 0.11 1953 emen 13.8 0.42 0.29 0.58 0.17 0.03 2231 ambia 10.0 0.26 0.14 1.78 0.40 0.19 2065 imbabwe 8.0 0.40 0.14 1.06 0.28 0.08 1986 ll countries ∑ 1,245 Ø0.49 Ø0.34 Ø1.39 Ø0.33 Ø0.14 Ø1891 AReferences
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