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Efficient System Design and 

Sustainable Finance for China’s 

Village Electrification Program 

Preprint 

S. Ma and H. Yin 

Institute of Electrical Engineering 

Chinese Academy of Sciences 

D.M. Kline 

National Renewable Energy Laboratory

To be presented at Solar 2006 Conference and Exhibition 

Denver, Colorado 

July 8–13, 2006 

Conference Paper 

NREL/CP-710-39588 

July 2006 
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Efficient System Design and Sustainable Finance for China’s Village Electrification Program 

Ma Shenghong 

Institute of Electrical Engineering 

Chinese Academy of Sciences 

Beijing, China 

msh@mail.iee.ac.cn

Yin Huanying 

Institute of Electrical Engineering 

Chinese Academy of Sciences 

Beijing, China 

hyyin@mail.iee.ac.cn

David M. Kline 

National Renewable Energy Laboratory 

1617 Cole Blvd 

Golden, CO 80401 

david_kline@nrel.gov

ABSTRACT

This paper describes a joint effort of the 

Institute for Electrical Engineering of the 

Chinese Academy of Sciences (IEE), and the 

US National Renewable Energy Laboratory 

(NREL) to support China’s rural 

electrification program. This project 

developed a design tool that provides 

guidelines both for off-grid renewable energy 

system designs and for cost-based tariff and 

finance schemes to support them. This tool 

was developed to capitalize on lessons learned 

from the Township Electrification Program 

that preceded the Village Electrification Program. We 

describe the methods used to develop the analysis, some 

indicative results, and the planned use of the tool in the 

Village Electrification Program. 

政府投资与电网补贴

 Grant from Government & Subsidy from Public Grid

当地农电价格(Yuan/kWh) 0.39

Local electricity rate

Solar irradiation and wind speed 政府投资 (元)

太阳辐射量 (kWh/m^2/day) 3.4 Government Grant (CNY)

年平均风速 (m/s) 5

电网补贴 (元)

同一村落中 户用系统总数 0 Grid Subsidy (CNY)/year

Quantity of home systems

村落系统中总户数 50

 Quantity of households in the village

输入Input 输出Output

¥1,873,467.72

¥4,426.75

1. INTRODUCTION

China is engaged in a rural electrification program that will 

provide electricity to 30 million citizens who currently lack 

access, and has committed several hundred million dollars 

to this effort. Because of the program’s size, and China’s 

standing within the community of developing nations, this 

program could provide an influential model of best technical 

and financial practices in rural power systems. 

To build on the experience of the Township Electrification 

Program that came before, IEE and NREL set out to develop 

a tool that could provide reliable, easy-to-use guidance for 

those implementing the Village Electrification Program. 

Fig. 1. Initial version of the tool interface 

Such a tool needs to provide two key kinds of information: 

system design guidance, and estimates of the costs that can 

be used as the basis for financing for the systems. Figure 1 

shows the user interface of the initial version of the tool. 

The interface is bilingual and costs are expressed in Yuan. 

2. OVERVIEW OF THE ANALYSIS EFFORT

This work began with an analysis of several different sized 

villages under a range of renewable resource regimes. The 

results have been incorporated into a simple software 

system (the “tariff tool” or “tool”), which provides design 

and finance guidance for the situation chosen by the user. 

The first version of the software was developed as an Excel 

1
spreadsheet. At the time of this writing, the same algorithm 

was being rewritten in Visual Basic. 

The tool requires three kinds of inputs: the electricity tariff 

that customers will pay, renewable resource data, and the 

configuration of the village. From those user inputs, the 

tool calculates the grant required from the government to 

cover the initial capital cost, and the cross-subsidy required 

to cover the ongoing costs not covered by the specified user 

tariff. The tool also reports a summary of the design of the 

system used to develop the cost estimates. 

3. ANALYSIS METHODOLOGY

As described above, the tool provides design and finance 

guidance based on results for a range of conditions that have 

been developed off-line and incorporated into the tool as 

static tables. This section describes the analysis used to 

develop those tables. 

3.1 Range of Conditions Considered

Over the range of conditions summarized in Table 1, the 

analysis developed a least-cost design and estimates of the 

cost components of that design. There are 189 cases: (9 

village sizes) X (3 solar radiation regimes) X (7 wind speed 

regimes). The “village” with 1 home is a stand-alone single 

home system; all the others are based on a mini-grid design. 

TABLE 1: RANGE OF CASES CONSIDERED

VARIABLE VALUES 

CONSIDERED 

NOTES 

Village size 1, 20, 25, 30,35, 

40, 45,50,100,200 

Number of 

households 

Average annual 

solar radiation 

3.4, 4.18, 4.95 

kWh/m2

-day 

Average annual 

wind speed 

2, 3, 4, 5, 6, 7, 8 

m/s 

See section 3.4 on 

the development 

of time profiles 

for wind and solar 

The analysis for each case begins with three types of inputs: 

load curves, cost and performance data for system 

components, and the solar and wind regimes. These data 

are then used by NREL’s HOMER micro-power 

optimization model to find the least-cost system design that 

will meet the given load within specified tolerances. The 

analysis assumes that the systems will be off-grid and 

assumes that policy will not allow for the use of diesel 

generators. The elements of the design and cost analysis are 

described next. 

3.2 Data Sources

The data used were developed based on IEE’s field surveys 

and experience, Chinese manufacturer specifications, and in 

some cases NREL component test data. The data were then 

adapted to the form required as input to the HOMER model. 

3.3 Load Profiles

IEE and NREL developed the load curves based on surveys 

of some existing village power systems in Qinghai and 

Gansu provinces in Western China. In those surveys, IEE 

identified the kinds of end uses included in village homes, 

schools, and clinics. The end use categories are lights, TVs, 

VCRs, refrigerators, and other. IEE then estimated the 

power requirements and usage time profile for each device 

type. The profile is expressed as the probability that the 

device will be on during each hour of the day for two 

seasons. Summer season includes June, July, and August, 

and the second season includes all other months. The load 

curves are the result of hourly simulation for a 

representative year, based on the number of devices of each 

type connected to the system and their probability of being 

in use during that hour.1

 

The 8,760-hour annual load curve is part of the data used in 

the off-line HOMER analysis. In the user interface, the 

mini-grid size is specified by the number of residences 

included. Additional loads for nonresidential facilities are 

included as a function of the number of households. In 

addition, the village can include remote households that will 

be served by single-home systems, and the number of those 

is also specified. In many cases, all the households in the 

village may be served by single-home systems. The 

analysis does not explicitly consider the potential expansion 

of village systems with load growth from added devices or 

growing population. 

3.4 System Components

The system includes generators, batteries, an inverter/ 

rectifier, and if it is a mini-grid system, distribution lines 

and a central control system. Generators can be PV, wind 

turbines, or both. Several different sizes of wind turbines 

are used depending on the load being served. The analysis 

did not consider other technologies such as micro-hydro or 

biomass. Further work could consider those technologies, 

since the HOMER model can evaluate them as well. 

The design and cost results depend strongly on the cost and 

performance of the system components and considerable 

effort went into the development of these data. Each type of 

component is described in HOMER by a different set of cost 

and performance parameters [2]. 

2
3.5 Solar and Wind Resource Data

As seen above, a single annual average value is used to 

describe the solar and wind resource levels in kWh/m2

-day 

and m/s, respectively. Those average values are used as 

scale factors that multiply 8,760-hour time profiles, 

developed as described below. 

The solar radiation profile is for a single geographical 

location, relatively central to the area of Western China 

where most of the Village Electrification Program will be 

conducted. That location is used to develop the time profile 

of insolation by applying the Graham algorithm [1] to 

twelve monthly average values supplied by the NASA webbased data for the representative location [3]. 

Hourly wind-speed values are developed similarly. A table 

of monthly average wind speeds for the village of 

Zhoujiajing, Gansu was used as the basis for seasonal 

variations. In each month, hourly wind speeds were 

estimated by sampling from a Weibull distribution with the 

corresponding average value. 

3.6 System Design and Cost Estimation 

NREL’s HOMER model provides a method for finding the 

least-cost system design given the load, component, and 

renewable resource data described in Sections 3.1-3.4. Its 

methods are reviewed in broad outline here. For additional 

details, see [2]. 

HOMER proceeds by simulating each of the 8,760 hours of 

the representative year. The model performs this annual 

simulation for each of a number of candidate designs. After 

examining the entire set of designs, the model selects the 

one that can meet the load within the specified tolerance at 

least cost. The model can also perform this optimization 

across a range of exogenous variables, such as renewable 

resource levels and system component costs. 

The information available from a HOMER analysis includes 

cost data broken into capital and operation/maintenance 

costs for each component, as well as tools for comparing the 

least-cost systems with other potential options. The 

software also facilitates comparing across ranges of 

exogenous variables. When results are compared between 

two different renewable resource regimes, for example, the 

comparison uses the least-cost system for each of the two 

resource regimes. 

3.7 Tariff Policy

The policies of China’s Village Electrification Program 

combine with the system cost information to determine the 

tariff structure and the requirements for government 

support. The results included in the tariff tool incorporate a 

number of consensus recommendations from two 

workshops organized by IEE to solicit input on their initial 

recommendations. (The analysis described here was 

developed to support IEE’s initial recommendations and 

illustrate their implications for system financing.) The 

workshop recommendations were as follows: 

Basic loads are defined at a level of 200kWh/year per 

household. In mini-grid systems, loads for public facilities 

are added, for an average of 247.5 kWh/year per household. 

The tariff tool estimates finance parameters for systems that 

will cover basic loads. Users who want higher levels of 

service will have to make arrangements with their local 

service provider. 

Grants from government will be used to cover the initial 

capital cost of the system. Costs incurred after that will be 

covered from two sources: User tariffs equal to the locally 

prevailing rates for users connected to the grid, and a crosssubsidy collected from ratepayers connected to the national 

grid. Accordingly, the output of the tool gives the total 

grant required for the initial capital cost and the recurring 

annual cost required from the cross subsidy. 

The tariff policy recommendations make the calculation of 

the tariff information straightforward. The spreadsheet 

contains a static set of tables that are taken from HOMER 

results across the range of conditions listed in Table 1. The 

model selects system for the mini-grid, if any, based on the 

number of connected households and the renewable 

resource regime specified. For in-between values, the 

model selects the point from the table with the next largest 

village size and the next smallest solar and wind resource. 

This will ensure that the selected design will adequately 

meet the load for the specified number of homes. The 

model uses the cost components of the selected system 

design to compute the elements of the grant and subsidy 

components of the financing for the system. The model 

also reports the least-cost system design that is used as the 

basis for the costs. 

4. KEY RESULTS

The analysis described above supports several observations 

that will apply to the Village Electrification Program 

generally. 

4.1 Cost of Electricity

As with typical off-grid systems, electricity prices are 

relatively high. For the conditions considered here, 

levelized cost of electricity varies between $0.57 and 

$1.04/kWh for individual home systems. For a village 

3
mini-grid with 100 homes, levelized cost is between $0.87 

and $1.09. 

Comparing the results for home systems and mini-grids 

suggests that the minimum economic scale for mini-grids is 

at least 100 households, and probably higher. The cost of 

distribution lines and central control facilities are quite high 

compared to the generating equipment, and they do not 

increase as fast as village size. So for mini-grid systems, the 

levelized cost of energy is close to inversely proportional to 

village size, as shown in Figure 2. 

0

0.5

1

1.5

2

2.5

3

0 50 100 150 200

Village Size, no. of households

Cost of Electricity, $/kWh

2 m/s

8 m/s

Fig. 2: Cost of energy as a function of village size 

4.2 System Design 

Table 2 illustrates how the cost of electricity depends on the 

system design for a single-home system. 

TABLE 2: ALTERNATIVE SYSTEM DESIGNS

PV 

(kW) 

Wind 

(kW) 

Number 

of 

Batteries 

Cost of 

Energy 

($/kWh) 

0 1 3 $0.63 

.05 1 2 $0.71 

.2 0 5 $0.85 

The addition of a small wind turbine reduces the cost by 

26% compared to the system using PV only. These 

estimates are based on performance tests of 50 peak watt 

 wind turbines available in China, suggest that those 

turbines could be useful in remote applications in many 

areas of rural China. However, the increased complexity of 

systems that include wind turbines will also have to be 

considered in the design decision. The results in Table 2 are 

for wind speed 6 m/s and insolation 3.33 kWh/m2

-day 

Table 2 illustrates how the three components complement 

each other. Adding 50 Watts of PV eliminates 1 battery, but 

the PV panel costs more. Adding 200 Watts of PV and 2 

batteries to the least-cost system shown in the first row 

eliminates the need for the wind turbine, but increases total 

cost. 

4.3 Renewable Resource Availability 

Figure 3 shows how the least-cost single-home system 

configuration changes as a function of the renewable 

resource availability, showing the transition from PVbattery to wind-PV-battery to wind-battery as wind speeds 

increase. For mini-grid systems, wind turbines begin to be 

economic at higher wind speeds, because the larger wind 

turbines used in those systems have higher cut-in speeds. Global Solar (kWh/m2/d)) 

Fig. 3: Influence of renewable resource regime on system 

configuration 

Figure 4 shows how the wind and solar regimes affect the 

levelized cost of energy for single-home systems. Starting 

from nominal values of 4 m/s annual average wind speed 

and 4.18 kWh/m2

-day average insolation, the wind speed 

has a much greater potential impact on the levelized cost. 

Wind Speed (m/s) 

PV/Battery 

Wind/PV/Battery 

Wind/Battery

4
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.5 1 1.5 2 2.5

Value / Nominal Value

Cost of energy, $/kWh

Wind

Speed

Global

Solar

Fig. 4: Influence of renewable resource regime on cost 

4.4 Component Cost

Figure 5 shows how the initial capital cost of the PV panels, 

wind turbines, and batteries influences the levelized cost of 

energy for home systems. The influence of PV cost 

decreases markedly at higher values. 

0.72

0.74

0.76

0.78

0.8

0.82

0.84

0.86

0.88

0.7511.25

Value / Nominal Value

 LCOE, $ / kWh

PV Cost

Wind turbine cost

Battery Cost

Fig. 5: Relative influence of component costs 

4.5 Allocation of Total Cost

Figure 6 illustrates how the system costs of a home system 

are financed, using the recommendations of the workshop 

study group discussed in Section 3.6. The grant and crosssubsidy pay most of the costs because the cost of electricity 

from the small home system is an order of magnitude larger 

than the local grid-connected rate. 

$0.00

$0.20

$0.40

$0.60

$0.80

$1.00

$1.20

$1.40

2345678

Wind Speed, m/s

Costs, $/kWh

Tariff - Users

Tariff - Subsidy

Capital - Grant

Fig. 6: System Finance Components 

Figure 6 also illustrates how the allocations shift with 

changing system designs. Here the system design changes 

with the average annual wind speed, shown on the x-axis. 

Customer tariffs remain constant at $0.06/kWh (0.5 

Yuan/kWh), the nominal value for the local grid electricity 

rate. As the wind speed increases, the total cost decreases. 

Customer tariffs cover a slightly larger fraction of total 

costs. Between 2 and 3 meters per second, the crosssubsidy component actually increases slightly in absolute 

terms, even though total cost is decreasing. This happens 

because as wind turbines enter the system, the overhead and 

maintenance component increases while the initial capital 

cost decreases more than enough to compensate. Since the 

operating and maintenance costs are part of the subsidy, that 

component increases. 

5. USE OF THIS WORK IN THE VILLAGE 

ELECTRIFICATION PROGRAM

One of the most important and potentially influential 

features of this work is the way that it is proposed to be 

integrated into the Village Electrification Program in China. 

The workshop organized by IEE proposed that the tool 

based on this analysis be used as a mandatory standard for 

system design and finance throughout the program. At the 

time of this writing, that recommendation is under 

consideration by the National Reform and Development 

Commission. The simple interface and conceptual 

framework of the tool make it useable by system integrators 

and provincial utility staff independent of outside technical 

5
assistance. This ease of use will promote efficient system 

designs and sustainable finance through a decentralized 

organizational framework that can meet China’s ambitious 

timetable for rural electrification. 

6. ACKNOWLEDGMENTS

The authors would like to acknowledge the sponsorship of 

the US Department of Energy and the UN Department of 

Economic and Social Affairs in underwriting the work 

reported here. We would also like to thank Jean Ku at the 

National Renewable Energy Laboratory for invaluable 

advice and suggestions during all phases of this work. Any 

errors contained in this report are entirely the responsibility 

of the authors. 

7. REFERENCES

(1) V.A. Graham, K.G.T. Hollands, “A method to generate 

synthetic hourly solar radiation globally,” Solar Energy, 

44 (6), 333-341, 1990 

(2) T. Lambert, P. Gilman, P. Lilienthal, “Micropower 

System Modeling with HOMER,” in F. Farret, M. Simoes, 

Integration of Alternative Energy Sources of Energy, John 

Wiley and Sons, 2006. Available online at 

http://www.mistaya.ca/products/homer.htm 

(3) NASA, online solar radiation database, 

http://eosweb.larc.nasa.gov/sse/

 

1

 For any given hour, the assumption that all the devices of a 

given type have the same probability of being on implies 

that the number of devices on has a binomial distribution. 

In each hour, simulations are made by sampling from this 

distribution for each device type and adding the loads across 

device types. 

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14. ABSTRACT (Maximum 200 Words)

This paper describes a joint effort of the Institute for Electrical Engineering of the Chinese Academy of Sciences 

(IEE), and the U.S. National Renewable Energy Laboratory (NREL) to support China's rural electrification program. 

This project developed a design tool that provides guidelines both for off-grid renewable energy system designs and 

for cost-based tariff and finance schemes to support them. This tool was developed to capitalize on lessons learned 

from the Township Electrification Program that preceded the Village Electrification Program. We describe the 

methods used to develop the analysis, some indicative results, and the planned use of the tool in the Village 

Electrification Program. 

15. SUBJECT TERMS

China; Institute for Electrical Engineering of the Chinese Academy of Sciences; rural electrification; off-grid 

renewable energy systems; Township Electrification Program; Village Electrification Program 

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