Supplement: Investigating the Necessity of Demand Characterization andStimulation for Geospatial Electrification Planning in Developing Countries

Stephen J. Lee1, Eduardo Sanchez2, Andres Gonzalez-Garcıa1,3, Pedro Ciller3, Pablo Duenas1, JayTaneja4, Fernando de Cuadra Garcıa3, Julio Lumbreras2,5, Hannah Daly6,7, Robert Stoner1, Ignacio J.

Perez-Arriaga1,3

1MIT Energy Initiative, Massachusetts Institute of Technology2Departamento de Ingenierıa Quımica Industrial y del Medio Ambiente, Universidad Politecnica de Madrid

3Instituto de Investigacion Tecnologica, Universidad Pontificia Comillas4Department of Electrical and Computer Engineering, University of Massachusetts at Amherst

5Kennedy School of Government, Harvard University6MaREI Centre, Environmental Research Institute, University College Cork

7School of Engineering, University College Cork

1. Introduction

This supplement has several sections describing the assumptions and parameters used in the correspond-

ing working paper. Section 2 describes the Reference Electrification Model (REM) Uganda South Service

Territory (SST) base case. This section includes detailed descriptions of basic REM parameters and the

component and network catalogs used. Section 3 describes assumptions around characterizing demand in

the Uganda SST case, including the work that went into producing the buildings dataset. Finally, Section 4

describes assumptions that went into the experiments concerning demand stimulation with electric cooking

loads.

2. Reference Electrification Model Assumptions and Parameters

The studies presented in this working paper employ REM as described in [4, 5]. More information

on the specific methods in REM relevant to these studies can be found in these documents. Section 2.1

describes general assumptions and parameters used in the REM case, Section 2.2 defines the network catalog

components used by REM, and Section 2.3 defines the generation catalog components used. The values

presented in this section are held constant across the experiments presented in the paper and were originally

agreed upon through past exchanges with local experts. While changes to the specific details described here

IThis paper documents spotlights presented in IEA’s World Energy Outlook 2018 [1]. Descriptions have also appeared in [2, 3].Email address: leesj@mit.edu (Stephen J. Lee)

A MIT Center for Energy and Environmental Policy Research Working Paper September 6, 2019

are unlikely to change the general trends shown in the demand-related experiments, specific REM results

are sensitive to these parameter settings.

2.1. General Assumptions and Parameters

Several modeling parameters are delineated in Table 1, which are critical to the techno-economic opti-

mization steps carried out by REM.

Attribute Value

Distribution-level energy cost(

$kWh

)0.072

Grid reliability 95%

Minimum reliability threshold 0.80

Cost of non-served energy, regular demand ( $kWh) 0.20

Cost of non-served energy, critical demand ( $kWh) 4.00

Discount rate on grid extensions (%) 10%

Discount rate on mini-grids (%) 10%

Discount rate on stand-alone-systems (%) 15%

High voltage level (kV) 132.00

Medium voltage level (kV) 33.00

Low voltage (kV) 0.40

Distribution system losses 5%

Years of useful life for distribution network (years) 40

Years of useful life for mini-grids (years) 20

O&M labor cost(

$hr

)1.50

Per customer connection cost, SHSs ($) 65.00

Per customer connection cost, mini-grid ($) 65.00

Per customer connection cost, grid extension ($) 65.00

Per customer investment lifetime (years) 25.00

Table 1: Key parameters for the Uganda SST base case in REM. The parameters shown are held constant across all

of the experiments presented in the paper.

2.2. Network Catalog

The network catalog is a critical piece of the REM model, which requires both technical and economic

information of network components in order to produce cost-optimal designs. Low-voltage network com-

ponents are provided in Section 2.2.1, medium-voltage network components are provided in Section 2.2.2,

and medium-voltage to low-voltage transformer components are provided in Section 2.2.3.

2

2.2.1. Low voltage lines

Low-voltage line components are provided in Table 2.

LV line

component

index

Resistance

( Ω

km)

Reactance

( Ω

km)

Rated

current

(A)

Avg.

failure rate(failuresyear·km

) Overnight

cost ($)

Predictive

maintenance

cost($

year·km

)Corrective

maintenance

cost($

failure

)1 6.99 1.38 43 0.133 10,000 3 427

2 0.67 0.20 185 0.133 14,000 3 427

3 0.35 0.18 317 0.133 15,100 3 427

4 0.23 0.18 346 0.133 16,608 3 427

5 0.20 0.17 384 0.133 18,355 3 427

6 0.18 0.17 411 0.133 19,399 3 427

7 0.17 0.17 420 0.133 19,740 3 427

8 0.09 0.15 636 0.133 29,256 3 427

9 0.06 0.13 1272 0.133 57,876 3 427

10 0.03 0.13 2544 0.133 115,752 3 427

Table 2: Low-voltage line catalog data for the Uganda SST base case in REM.

2.2.2. Medium voltage lines

Medium-voltage line components are provided in Table 3.

2.2.3. Medium-to-low voltage (MV/LV) transformers

Medium-to-low voltage (MV/LV) transformer components are provided in Table 4.

2.3. Generation Catalog

Similarly to the network catalog discussed in Section 2.2, the generation catalog is critical to running

REM, which requires both technical and economic information of generation components to produce cost-

optimal mini-grid and stand-alone system designs. Solar panels are described in Section 2.3.1, batteries in

Section 2.3.2, charge controllers in Section 2.3.3, and inverters in Section 2.3.4. Only solar mini-grids have

been considered for this case study; no diesel generation is employed.

2.3.1. Solar panels

Solar panel component details are provided in Table 5.

3

MV line

component

index

Resistance

( Ω

km)

Reactance

( Ω

km)

Rated

current

(A)

Avg.

failure rate(failuresyear·km

) Overnight

cost ($)

Predictive

maintenance

cost($

year·km

)Corrective

maintenance

cost($

failure

)1 0.67 0.25 185 0.133 26,000 700 900

2 0.29 0.23 317 0.133 28,000 700 900

3 0.23 0.18 346 0.133 30,725 700 900

4 0.20 0.17 384 0.133 33,957 700 900

5 0.18 0.17 411 0.133 35,888 700 900

6 0.17 0.17 420 0.133 36,519 700 900

7 0.09 0.15 636 0.133 54,124 700 900

8 0.06 0.13 1272 0.133 107,071 700 900

9 0.03 0.13 2544 0.133 214,141 700 900

Table 3: Medium-voltage line catalog data for the Uganda SST base case in REM.

MV-LV

Transformer

Index

Installed

power

capacity

(kVA)

MV

Voltage

(V)

No-load

losses

(kW)

Short-circuit

resistance

on the LV

side (Ω)

Avg. failure

rate(failuresyear·km

) Overnight

cost ($)

Predictive

maintenance

cost(

$year)

Corrective

maintenance

cost(

$failure

)1 25 33 0.09 0.06 1.27 4,799.00 80.20 25.80

2 50 33 0.14 0.03 1.87 5,899.00 80.20 25.80

3 100 33 0.30 0.02 2.13 7,246.00 80.20 25.80

4 160 33 0.38 0.01 2.45 11,593.60 80.20 25.80

5 250 33 0.63 0.01 3.50 18,115.00 80.20 25.80

6 315 33 0.72 0.01 4.10 22,824.90 80.20 25.80

7 400 33 0.84 0.01 4.80 28,984.00 80.20 25.80

8 1000 33 1.55 0.00 10.00 72,460.00 80.20 25.80

9 2000 33 1.56 0.00 10.00 144,920.00 80.20 25.80

Table 4: Medium-to-low voltage (MV/LV) transformer catalog data for the Uganda SST base case in REM.

4

Size

(kW)

Cost

($)

Life

(years)

Installation cost

(as fraction of

panel cost)

Annual O&M cost

(as a fraction of

panel cost)

Annual O&M

(person-hour)

Annual

capacity

loss (fraction

of initial)

0.02 20 25 0.1 0.01 5 0.007

0.25 125 25 0.1 0.01 5 0.007

Table 5: Attributes of the solar panel components modeled.

2.3.2. Batteries

Battery

Name

Cost

($)

Capacity

(kWh)

Lifetime

throughput

(kWh)

Minimum

State of

Charge

(fraction)

Capacity at

end of life

(fraction of

initial

capacity)

Installation

costs as a

fraction of

battery

cost ($)

Annual O&M

as a fraction

of battery cost

($)

Annual

O&M

(person-

hours)

TROJ T105 150 1.38 1656 0.5 0.8 0.1 0.01 5

VIS CP12240D 60 0.2844 426 0.4 0.8 0.1 0.01 5

Table 6: Attributes of the battery components modeled.

Battery components details are provided in Table 6.

2.3.3. Charge controllers

Sizes

(kW)

Equipment

cost(

$kW

) Life

(years)

Efficiency

(fraction)

Installation

cost (fraction

of charge

controller

cost)

Annual

O&M cost

(fraction of

charge

controller

cost)

Annual

O&M

(person-

hours)

0.054 481 15 0.95 0.1 0.01 2

0.120 375 15 0.95 0.1 0.01 2

0.240 283 15 0.95 0.1 0.01 2

1.440 215 15 0.95 0.1 0.01 2

3.840 133 15 0.95 0.1 0.01 2

4.128 131 15 0.95 0.1 0.01 2

Table 7: Attributes of the charge controller components modeled.

Charge controller component details are provided in Table 7.

5

2.3.4. Inverters

Sizes

(kW)

Equipment

cost(

$kW

) Life

(years)

Inverter

efficiency

(fraction)

Rectifier

efficiency

(fraction)

Rectifier

Capacity/

Inverter

Capacity

Ratio

(fraction)

Installation

cost

(fraction of

equipment

cost)

Annual

O&M cost

(fraction of

equipment

cost)

Annual

O&M

cost

(person-

hours)

0.15 927 15 0.95 0.9 0.8 0.1 0.01 2

0.20 740 15 0.95 0.9 0.8 0.1 0.01 2

0.25 600 15 0.95 0.9 0.8 0.1 0.01 2

0.30 543 15 0.95 0.9 0.8 0.1 0.01 2

1.00 364 15 0.95 0.9 0.8 0.1 0.01 2

1.50 319 15 0.95 0.9 0.8 0.1 0.01 2

5.00 260 15 0.95 0.9 0.8 0.1 0.01 2

6.00 220 15 0.95 0.9 0.8 0.1 0.01 2

10.00 190 15 0.95 0.9 0.8 0.1 0.01 2

11.40 190 15 0.95 0.9 0.8 0.1 0.01 2

Table 8: Attributes of the inverter components modeled.

Inverter component details are provided in Table 8.

3. Demand characterization assumptions

Most of the demand characterization assumptions pertaining to demand profiles, customer type multipli-

ers, aggregate levels of demand, and the relationship between residential and commercial & industrial (C&I)

demand are described in the main text. In this supplemental section, building identification procedures are

discussed, describing how the buildings data set was compiled.

3.1. Building identification using satellite imagery

The building identification methodology used in this study consists of (1) automatic building identifica-

tion based on Google Maps satellite imagery for the Uganda South Service Territory (SST) and (2) manual

corrections for a specified sub-area. Automatic software-enabled techniques enable a rough approxima-

tion of building locations with some areas absent corresponding to missing Google Maps imagery. These

building locations form the basis for subsequent manual identification processes led by partners at GIZ by

6

(a) (b) (c) (d)

Figure 1: Building extraction examples in the Uganda SST. (a) Training images and (b) manually-labeled ground

truth data are used to inform a computer vision system to produce (d) pixel-based building classifications using (c)

new satellite imagery.

using both Google Maps and Bing Maps imagery. The hybrid approach produced planning-quality building

location information for the sub-area in addition to rough building location information for areas still within

the SST but outside of the sub-area.

3.1.1. Automatic building extraction and localization

A computer vision system based on convolutional neural networks for semantic segmentation [6] to ex-

tract building footprint information from Google Maps satellite imagery [7] was used. Because this system

represents a form of supervised machine learning, training data in the form of manual area-based building

annotations was required. Once trained, the computer vision system classified georeferenced building foot-

print areas from Google Maps imagery for nearly the whole SST. Ground truth data used for training is

shown in Figs. 1a and 1b. Samples of the system’s building footprint pixel-level classifications are shown

in part 1d, given previously unseen imagery as shown in part 1c. Finally, connection points to each building

were determined by the building localization algorithm described in [7]. In regions where Google Maps im-

agery was missing, pixel-level classifications were not generated. Connection points from the localization

process are displayed as yellow points in Fig. 2, and large areas with visibly missing Google Maps imagery

are outlined with green boxes.

3.1.2. Manual corrections

Because the automatic approach only provided approximate building location information and had miss-

ing areas due to limited Google Maps data, a manual correction effort was implemented for a specified

sub-area. Annotators used both Google Maps and Bing Maps imagery to add buildings that were missed

and remove buildings that were wrongly added by the automatic building identification effort. Switching

7

Figure 2: Buildings identified in the Uganda South Service Territory. An image showing a basemap with the South

Service Territory border (white outline), building locations from deep learning-based building extraction (yellow

points), the sub-area with manual corrections (orange outline), and building locations from GIZ-led manual building

identification efforts (blue points). Note that building points are fully missing in regions where we had no or poor

quality satellite imagery (green outlines).

between Google Maps and Bing Maps allowed for increased robustness against image quality issues. Fig.

2 shows the sub-area as an orange square, and additional building locations overlaid as blue points.

3.2. Additional REM visualizations

An additional visualization is provided showing generation dispatch results for a single mini-grid in

“central case with heterogeneous demand” in Fig. 3. Note that diesel generation is omitted from the

designs. This reflects the specification to avoid using diesel in the base case.

4. Cooking Study Assumptions

The general methodology used for the cooking studies is presented in the main paper. REM is employed

to assess the cost of electricity provision for each household modeled given different demand assumptions.

The costs of electric- and LPG-powered cooking can be compared for every residential household modeled

provided REM-determined electricity costs, LPG costs, and thermal efficiency values for both electric and

8

Figure 3: Four selected days out of the year simulated showing generation dispatch results for a single mini-grid in

the “central case with heterogeneous demand.”

9

LPG stoves. The share of residential households for which electric cooking is economically viable is calcu-

lated as the number of households for which cooking a meal with electric cookstoves is less expensive than

doing so with LPG stoves, divided by the total number of residential households. Key metrics used in the

calculations are provided in Table 9.

Electric cookstove thermal efficiency (%) 72

LPG price, July 2018(

$kg

)2.50

LPG specific calorific value(kWh

kg

)12.80

LPG cookstove thermal efficiency (%) 51

Meal energy requirement(

kWhmeal·HH

)1.16

Table 9: Key figures and metrics used to compare electric and LPG cooking.

The “electric cookstove thermal efficiency” assumption is derived from a U.S. DOE study that compares

efficiencies of both large and small induction and electric resistance cookstoves; it shows that they have

mean hybrid efficiencies between 66.22% and 73.59% [8]. The value of 72% is chosen as most coil-based

electric resistance and induction cookstoves achieve efficiencies around this value.

Assumptions around the “LPG price” of $2.5/kg were obtained from a number of sources. Dignited

provides data for six companies supplying LPG in Uganda in 2015, and shows that the average price of a

refill on a 12-15kg LPG cylinder is 9.260 UGX/kg [9]. Given that the exchange rate in 2015 was 3.226

UGX/$ [10], LPG prices can be assumed to be $2.87/kg. $2.5/kg was chosen as a conservative estimate that

could potentially also account for economies of scale in the provision of LPG.

The specific calorific value of LPG is 46.1 MJ/kg, which is equivalent to 12.8 kWh/kg. This information

is easily verifiable through a number of sources [11]. Additionally, the LPG cookstove thermal efficiency

value of 51% was reported by Shen et al. in the literature [12].

Finally, a meal energy requirement figure is calculated through a number of steps. The National Char-

coal Survey of Uganda 2015 shows that charcoal consumption in Uganda for charcoal cookstoves may be

assumed to be 2.2 kg/(household·day) [13]. Assuming charcoal has an energy density of 29.7 MJ/kg, and

the efficiency of charcoal cookstoves is 16%, the effective energy (heat) for cooking per household per day

is estimated to be 10.45 MJ/(HH·day). The charcoal cookstove efficiency assumption is supported by stud-

ies describing how traditional metal stoves in Uganda have a cooking efficiency of 15-17%, and that these

traditional stoves are by far the most common cooking technology used in the country [14, 13]. Assuming

that it is common for households to cook two and a half meals per day, the effective energy consumption

per meal would be 4.18 MJ/(meal·HH), which is equivalent to 1.16 kWh/(meal·HH).

10

Given the assumptions that electric cookstove thermal efficiencies are 72% and meal energy require-

ments are 1.16 kWh/(meal·HH), it is straightforward to show that 1.61 kWh/(meal·HH) of electricity is

required to cook a single meal, and 4.03 kWh/(HH·day) of total electricity is required to cook 2.5 meals per

household per day. While we could have modeled these loads directly in REM, doing so without account-

ing for coincident factors would lead to undesirably exaggerated effects on cost-optimal supply technology

designs. This is especially the case considering how “peaked” demand profiles incorporating electric cook-

ing options can be with otherwise low levels of demand. To account for this, only 2.04 kWh/(HH·day) of

total electricity are modeled, reflecting a conservative 51% coincident factor for each of the five hours of

the day electric cooking is modeled. Future work is intended to improve the assumptions provided in this

section, with specific aims to base the coincident factor analysis on empirical data and develop capabilities

for incorporating these factors into REM.

1. References

References

[1] International Energy Agency, World Energy Outlook 2018. Organisation for Economic Co-operation and Development,

OECD, 2018.

[2] S. J. Lee, “Empowered planning with models, satellites, & machine learning,” Energy for Growth Hub, 2018.

[3] S. J. Lee, “The virtuous cycle of clean cooking and electricity costs,” Energy for Growth Hub, 2019.

[4] P. Ciller, D. Ellman, C. Vergara, A. Gonzalez-Garcıa, S. J. Lee, C. Drouin, M. Brusnahan, Y. Borofsky, C. Mateo, R. Amatya,

R. Palacios, R. Stoner, F. de Cuadra, and I. J. Perez-Arriaga, “Optimal Electrification Planning Incorporating On- and Off-

Grid Technologies: The Reference Electrification Model (REM),” Proceedings of the IEEE, 2019.

[5] R. Amatya, M. Barbar, Y. Borofsky, M. Brusnahan, P. Ciller, T. Cotterman, F. de Cuadra, C. Drouin, P. Duenas, D. Ell-

man, A. Gonzalez-Garcıa, S. J. Lee, V. Li, C. Mateo, O. Oladeji, R. Palacios, I. Perez-Arriaga, R. Stoner, and C. Vergara,

“Computer-aided electrification planning in developing countries: The Reference Electrification Model (REM),” IIT Working

Paper, 2018.

[6] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE

Conference on Computer Vision and Pattern Recognition, pp. 3431–3440, 2015.

[7] S. Lee, Adaptive Electricity Access Planning. Massachusetts Institute of Technology, 2018.

[8] United States Department of Energy, “Energy conservation program: Test procedures for conventional cooking products with

induction heating technology; notice of proposed rulemaking,” United States Department of Energy, no. 10 CFR 430. Docket

No. EERE-2012-BT-TP-0013, 2013.

[9] Dignited, “Cooking Gas In Uganda: Brands, prices, refilling and where to buy,” 2015. http://www.dignited.com/

12873/cooking-gas-in-uganda-brands-prices-refilling-and-where-to-buy/.

[10] Uganda Bureau of Statistics, “Statistical abstract 2018,” Uganda Bureau of Statistics, 201.

11

[11] Wikipedia, “Liquefied petroleum gas — Wikipedia, the free encyclopedia.” http://en.wikipedia.org/w/index.php?

title=Liquefied%20petroleum%20gas&oldid=904480477, 2019. [Online; accessed 16-July-2019].

[12] G. Shen, M. D. Hays, K. R. Smith, C. Williams, J. W. Faircloth, and J. J. Jetter, “Evaluating the performance of household

liquefied petroleum gas cookstoves,” Environmental science & technology, vol. 52, no. 2, pp. 904–915, 2018.

[13] Uganda Ministry of Energy and Mineral Development, “National charcoal survey for uganda 2015: Final report,” 2016.

[14] Uganda Ministry of Energy and Mineral Development, “Biomass energy strategy (best): Uganda,” 2013.

12