Wind/diesel village-scale electric power systems: The performance and economic analysis of a simulated village system

Solar & Wind Tethnoloyv Vol. 7, No. 4, pp. 423~l.39, 1990 0741-983X/90 $3.00+.00 Printed in Great Britain. Pergamon Press pie

W I N D / D I E S E L V I L L A G E - S C A L E ELECTRIC P O W E R SYSTEMS" T H E P E R F O R M A N C E A N D E C O N O M I C A N A L Y S I S OF A S I M U L A T E D V I L L A G E SYSTEM

S. R. CONNORS The Energy Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A.

a n d

J. G. McGOWAN* and J. F. MANWELL Department of Mechanical Engineering, University of Massachusetts, Amherst, MA 01003, U.S.A.

(Receit,ed 21 April 1989 ; accepted 25 Auqust 1989)

Abst rae~This paper presents the results of an analytical study determining the energy and economic performance of battery storage type wind/diesel systems intended to supply the load of a village-scale system (grid isolated) in a developing country. It presents the development and details of the model for the simulation of the complete wind/diesel system including a generalized characterization of the local wind regime and the electrical load demands (including prioritization) of the village. The hour-by-hour simulation model is intended to simulate a typical one month period of system operation. Results from the energy flow simulation model are then input to a detailed economic model of the system. As summarized in the paper this benefit cost model is used to assess the relative cost and socio-economic impact associated with the electrical generating system's configuration and performance. Representative case studies for several wind/diesel system options using the combined performance/econonaic simulation are presented to demonstrate the usefulness of thc model.

I. INTRODUCTION

In less developed, rural regions of the world, the intro- duct ion of electric service can significantly increase the s tandard of living, part icularly in small popula t ion centers (villages). The existence of publicly available electric power helps to increase local economic activity by allowing technological improvements to enter small-scale industry, and the residential, commercial and public service sectors. However, in many places due to geographical , governmenta l and economic con- straints, people will have to wait long periods for the in t roduct ion of electric service, if such service must be provided th rough grid extension.

Barnes [1] and Pearce and Webb [2] have recently provided a good overview of the costs, benefits and consequences of rural electrification. M e n a n t e a u [3] describes the d isadvantages of providing electric service to rural areas th rough the extension of a cen- tralized grid. C o m m o n problems with supplying dec-

* ISES member.

tricity to areas of low popula t ion density th rough grid extension are the high capital outlays of in t roducing services, large t ransmission losses, and less reliable electric service because of overextended dis t r ibut ion networks. The high variabil i ty of rural electric loads, bo th daily and seasonally, and the people 's dependence on such supplies, are just two of many characteristics associated with rural electrification that must be factored into the design of the electricity supply network for the third world.

Brown [4] has pointed out tha t the lower energy demands of a developing countr ies ' rural or isolated regions may be bet ter served with local, small-scale power generators, p redominant ly diesel generator sets. However, diesel fuel costs can place a severe burden on the local economy. Variabil i ty in the delivery of fuel can often become a problem. One way to reduce this fuel dependence is to substi tute renewable energy sources for some of the diesel generat ing capacity. Wind energy represents a renewable energy source tha t has been predominate ly used to generate electric power in the past. Fur thermore , as described

423

424 S. R. CONNORS e l el/.

in recent reviews on the subject (McGowan el al. [5] and Lipman [6]), a significant number (and variety) of autonomous electrical generating systems based on the hybrid combination of wind and diesel systems have been constructed, or are currently under inves- tigation.

One of the more common configurations of wind/ diesel systems being considered is the battery storage type system. A recent application of this type of wind/ diesel system to an isolated grid (Cape Clear, Ireland) is summarized by Cramer el a/. [7]. In its most advanced configuration, with the use of micro- processor and power electronic components, this type of configuration has been defined as an Inte- grated wind/diesel system (Hansen el al. [8]). In this type of system, a combination of diesel engines and wind turbines are combined with a battery storage system. Local electric needs are met with power directly from wind turbines and/or diesels, and the battery storage system when necessary. The batteries are charged with excess windpower and occasionally by the diesel generator if the diesel is run at, or near, rated capacity to minimize wear. Advantages of battery storage type wind/diesel systems are discussed in Ref. [5]. Furthermore, by adding the battery storage component to the system, the number of diesel start- ups can be minimized, reducing the unit's wear, maintenance costs and increasing their service lifetimes.

Since existing, conventional components are used, operational simplicity and reliability of generation can be maintained. A drawback of these types of systems is the cost of the battery storage system. In addition, if the system is poorly configured, frequent deep cyc- ling of the batteries can lead to shorter battery lifetimes and therefore premature replacement of the batteries. Choice of system configuration is therefore a major determinant of the electric system's performance, cost and social impact. In this paper we exalnine the technical and economic performance of a series of integrated energy systems, supplying electricity to a representative village load for a variety of local wind characteristics.

The purpose of this paper is to demonstrate, through the use of a computer based analytical simulation, the relative characteristics of wind turbines, diesel generators and battery energy storage components in supplying electricity to a disaggregate village load. In order to demonstrate the trade-otis between the various components the following objectives had to be met :

(a) The development of an analytical model which simulated a complete wind/diesel electric system, including a general characterization of the wind

regime and the electricity demands of the village. To achieve this an hour-by-hour model was developed for a representative month-long period to assess system performance.

(b) In addition to the system-performance simulation model, an economic model was required to assess the relative cost and socio-economic impacts associated with the electric system's performance in meeting electricity needs. A social benefit~eost model was developed to aid in assessing the economic impacts of the delivery and disruption of electric service.

(c) Key design and input parameters had to be estimated and then varied to provide sufficient detail as to the relative merits of each component. While the village load was held constant, the wind regime was altered to identify the relative contribution of wind to system performance. Likewise, the number of wind turbines, diesel generators and battery storage units were varied. Wind/diesel/storage (WDS), wind only: storage (WS), and diesel only/storage (DS) configurations were evaluated. Finally, component costs and fuel costs were varied to identify the prices at which systems with similar performance characteristics become indifferent.

The body of this paper has been divided into three parts: first, a description of the analytical models (performance and economic): second, a description of the models inputs, specifically, a description of the village electric load, the wind regimes, their modelling, and the system configurations for the electric generation : and, third, for a typical case study, discussion of the simulation results and their implications.

2. OVERVIEW OF THE ANALYTICAL MODEL

The analytical model is broken down into two parts: A simulation-performance component and an economic-assessment component. As briett~ described by McGowan e t al. [5] the simulation is based on a quasi-steady-state model which, by incorporating relatively large amounts of storage and numerous wind turbines, allows for a static model (hourly energy balance) to be used instead of an instantaneous operational dynamic model. The simulation-performance model, embodied in a Fortran program, takes descriptions of the village load, the wind regime and the generating components (wind machines, diesels and battery storage), and performs an hour-by-hour energy balance simulation where energy flows from component to component and the village load are calculated and finally recorded in a spreadsheet readable file. This output file is then

Small-scale electric power systems 425

dropped into an existing spreadsheet model where performance characteristics are summarized, and economic measures calculated.

(a) The simulation-performance model Figure 1 shows the schematic for a generalized

wind/diesel/storage system with load control/black- out capabilities. A component switching algorithm attempts to meet hourly village loads on a prioritized basis while attempting to maximize wind energy util- ization and minimize use of the diesel generators.

The analytical model calculates the maximum amount of wind energy available for a given hour based on the characteristics of the wind machine, the hourly average windspeed, and the number of wind machines available in that hour. Then by comparing the available energy with the village's electric needs it will meet that need, charge the batteries, and in the event of excess capacity divert power to a series of dump loads followed by the furling of some of the wind machines. If the wind energy is insufficient, the model then chooses the combination of the diesel generators and battery storage which best meets the residual load given component availability in that hour. In choosing between diesel and battery power, the model selects battery storage whenever the available battery energy can meet the entire residual load. In the event there is not sufficient battery power, the best-fit mix of diesel generators are turned on with excess energy diverted to battery charging and dump loads. In the event that all three components, wind turbines, diesel generators and battery storage units are insufficient to meet village load, portions of the load go unserved on a prioritized basis embodied in the village load description.

The wind turbine modeled in this paper is the Acre- man 11/20 as described by Samarakou and Hennet

[9]. The diesels are assumed to run at their rated power whenever operating, with an average specific fuel consumption of 300 grams of fuel per kWh generated. The battery storage system was modeled using information on the performance of state-of-the-art lead- acid batteries as described by Paul [10]. Analytic expressions were developed to simulate both the charging and discharging effects of the batteries including performance penalties for rapid discharge.

(b) The economic-assessment model The performance model yields the generation and

electric distribution characteristics of the model such as total generation by wind turbines, and how much power went direct to the village load, to charge batteries and so forth. In addition, the amount of electricity delivered to each priority sector of the village is calculated. This information is then combined with the cost of system components, fuel and an estimation of the village's economic value for electricity to determine the unit cost of electricity, and economic impact associated with the system's ability to serve village electric needs.

Simple cost measures are calculated by combining the monthly capital costs of each component with the month's operation, maintenance and fuel costs. This sum is then divided by the quantity of electricity actu- ally delivered to the village, to calculate the unit cost of electricity (z/kWh). By dividing by the quantity of electricity delivered the costs of excess capacity and excessive use of battery storage and/or diesel generators can be factored directly into the cost of electricity.

In order to assess the economic impact that different electric generation systems have in meeting the village's electricity needs, a benefit-cost analysis of the economic impact of the supply-constrained electric

Primary Energy Power Generation. Control and L-ood Manoement

I Wind Turbines I

/ . ' , , . I cen''e';Lead 'V'~aQ ~ m eL[.~..t.te/[..st.°;ag :.. ~

\ / . . . . . . . . . . . I 1' ...... O:"ese, ':°e,)" I ,:,,ese, G,,r,o,ato,', I

m

- - V i l l a a e

- - L o a d

Fig. 1. Schematic of generalized wind/diesel/storage system.

426 S. R. CONNORS el al.

system was developed. The method compares the estimated value of electricity to the village and the costs associated with the generation system's ability to deliver (or not deliver) that electricity. In order to calculate the benefits and costs of electric service the electricity consumer's value for electric service must be estimated.

The consumer's value for electricity was broken down into five priority sectors which reflect the time- of-day and end-use values which the village has for electric power. For example, a kWh of electricity has a different economic value to the consumer at 2 in the morning than the same kWh at 3 in the afternoon. Furthermore, the consumer incurs costs associated with the use of electricity whether that service is delivered or not. To represent the changing economic value of electricity, various end-uses were assigned different priorities to reflect their time-of-day, and type-of-use value that the consumers had.

Figure 2 shows the graphical representation of a single priority sector's economic value for electric service. The value of the electricity delivered (and therefore its benefit) is the area under the demand curve shown, between the origin, and the quantity of electricity the system was able to deliver to that priority sector (Q~). The benefit-cost model assumes a dual- elasticity demand curve. The elastic region reflects the consumer's value for initial levels of service. Below a certain threshold of service (Qth,~), the consumer is very price sensitive. At or below this level of service the value of electricity to the consumer is assumed to be the consumer's upper reservation price (P ....... i), resulting in the flat portion of the curve. The value of

the reservation prices and the threshold level of service changes for each priority sector. Above the threshold level, the consumer's demand for electricity is more elastic, so the curve resembles a standard, downward sloping demand curve. At some point (Qa,~), the total demand for electricity by the priority sector is met, with an associated lower reservation price or value of Pmm,~ for the last kWh. Assuming that this shape curve approximates the priority sector's value for electric service, the total consumer value for electricity can be determined analytically from the quantities, P .........

Pmi,,.i, Qi, Qth.,, and Qa,i. If the system supplies Q~ electricity at a unit cost of

Costc, then the costs and benefits of electric service can be calculated. Table 1 shows the analytical expressions used to calculate benefit~zost ratios used in the analysis of the village electric power systems. Referring back to Fig. 2, at Q~, the consumer benefit from electric service is the hatched area to the left of Q~, while the consumer's dis-benefit of electricity (the cost to the consumer for unmet electric demand) is the crosshatched area to the right of Q.. For the sake of simplicity, the description of the costs of unserved electric demand was determined by this crosshatched area, with the demand curve modified for each priority to approximate th'e marginal and total costs of unserved demand. Andersson and Taylor [11] discuss in greater detail the different manifestations of unsupplied electricity, and their related economic costs.

The second type of cost is the cost of service, simply the unit cost of electric generation times priority sector's consumption of electricity over the time period. Therefore. the benefit--cost ratio, the measure by

P max,i

Price of Electrictiy

e . mln , i

Elastic Region Inelastic Region

Demand Curve

Q Q th , i i d,i

Quantv of Electrictv Desired and Delivered

Fig. 2. Consumer value of electric service.

Small-scale electric power systems

Table 1. Benefit cost equations

427

S y m b o l D e s c r l D t l o n U n I t s

Qi = Quantity of electricity del ivered [kWh]

Qd,i = Quantity of electricity desired [kWh]

Qth, i = Inelastic threshold quanti ty of electricity [kWh]

A = Threshold Ratio [A = Qth,i/Qd,i]

Pmax, i = Upper Reservation Price [S / kWh ]

Pmin , i = Lower Reservation Price IS / kWh ]

C V E i = Consumer Value of Electric Service [$ ]

C V M i = Consumer Value of Electricity Met/Served [$ ]

C V U i = Consumer Value of Electricity Unmet/Unserved [$ ]

C o s t e = Unit Cost of Electricity Delivered [S / kWh ]

COSte, i = Cost of Electricity of Delivered [$ ]

B / C i = Benefit-Cost Ratio for Priority Sector

B/Ctota I = Benefit-Cost Ratio for All Sectors

A n a l v t l c a l E x p r e s s i o n s

(1 - A) CVEi = Qd,i [ Pmax,i

2

- - ( Pm~,i - Pmin,i ) ]

CVMi = Pmax,i Qi (for Qi < Qth, i )

Pmax,i " Pmin,i CVMi = Pmax,i Qi - [

2 Qd,i(1-A)

C V U i = C V E i- C V M i

COSte, i = Cos t e x Qi

C V M i

B/C i = [ Coste, i + C V U i ]

:ECVM i B/Ctotal r ] t:ECOSte i + ]ECVUi

(Oi-Aadj)2] (for ai< ath,i)

which economic effectiveness of the system is determined, is simply the value of the electricity supplied divided by the sum of the cost of supplying that electricity and the value of the electricity the system was unable to serve.

Table 2 shows an example of the spreadsheet output summarizing the operational and economic performance of one of the systems evaluated. The top portion describes the system and the costs attached to the various components and operational charac-

428 S . R . (_~ONNORS ~'l ~//.

Table 2. Sample system simulation output

IWDS-10 6 m/a W I N D REGIME Wind Turbine Information

No. ofWECS 81 Capital Costs ($/kW) Rated Power (kW) 23.501 Fixed 0 & M (% Capital) Swept Area (m^2) 105.70 Var. O & M (S/kWh)

Avai labi l i ty 85.0% Lifetime (yrs.) Diesel Information

No. of Diesels 40.020 Capital Costs ($/kW) Combined Rating (kW) Fixed 0 & M (% Capital)

Avai labi l i ty 80.0% Var. O & M (S/kWh) Diesel Fuel Cost ($/kWh~ O. 110 Lifetime (yrs.)

Battery Information Installed Capacity (kWh) 480.01 Capital Costs (S/kWh)

I Fixed 0 & M (% Capital)

Var. O & M (S/kWh) Lifetime (yrs. /

Village Economic Parameters Discount Rate = Pr ior i ty One Two Three Four

Max Price ($/kWh)l 2.001 1.501 1.001 0.75 I Min Price (S/kWh) 0.15 0.15 0.15 0,15

Thresho d Ratio 0.50 0.50 0.50 0.50

System Performance Generation Total kWh Direct to to Charge to Energy Overcapacity

Generated Load Batteries Dumps Wind Turbines I 27297.891 13488.451 12641.041 663.981 504.46

100.05'o 49,4% 46.3% 2.4% 1.8% 82.1% 70.3% 98.1% 100.0% 100.0%

Diesel Generators I 5940.001 5697.241 242.761 0.00] 0 .00 100 .0% 95 .9% 4 .1% 0 .0% 0 .0%

17 .9% 29 ,7% 1.9% 0 ,0% 0 .0% Total I 33237.89[ 19185.691 12883.801 663,98] 504.46

lOO.O% 57.75 38.6~ =.o% 1.5%

I

1,250.00 I 2.00% I 0.020

2O

500.00 I 0,00% 0.020

100.00

12.0%

Five0,50

o.15 I o.5o I

supply Total kWh Wind Diesel Battery Percent of Delivered Generators Generators Storage Load Met

Energy Delivered I 26579.531 13488.45] 5697.241 7399.61} 90.33% 100.0% 50.7% 21.4% 27.8%

Supply By Priority Demand

Prior i ty Met (KWh) One 492.65 99.22% Two 9099,08 96.90%

Three 8529,98 87.96% Four 4004,22 79.57% Five 4453.60 92.64%

Percent Consumer Consumer Consumer Benefit/ Demand Met Value-Met Value-Unmet Cost of Elec. Cost Ratio

762.74 0.63 103.55 7.32 10860,07 55.81 1912.47 5.52

7342,18 294.61 1792.86 3.52 2739.19 280.31 841.62 2.44 1920.89 62.20 936.07 1.92

Total 26579.53 90.33% 23625.07 693.55 5586.57 3.76 (S) ($) (S)

Generation Costs (Monthly Basis) Capital O & M Fuel Costs Total Percent

Wind Turbines 2621.791 598"391 . . . . . I 3220.19 57.64% Diesel Generators I 462.351 118.80J 653.401 123455 22.10%

Batten/ Storaael 1109641 . . . . . 11R1 ,q.q ~ ~R% Totals 4193.781 ~ 653.401 5586.57 ($/mo.)

Percent 1 13.24%J 11.70% / 75.07%j Cost (C/kWh) = | 21.02 I


teristics. The capital and operating costs of the components associated with the various components of the wind/diesel system reflect recent experience in the U.S.A. windpower industry and future advances in inverter and power electronics development. We also note that the economic cost values used are reasonably close to figures recently published by the developers of the previously mentioned Cape Clear wind/diesel system [12].

The second portion of the table describes the system performance, tracing the contribution of each component in serving village load. Overcapacity for wind turbines is the amount of available wind energy that went unutilized due to blade furling. The breakdown of village electric supplies and benefit~zost calculations follows, trailed by the calculation and breakdown of generation costs. Tables of the primary results were then made so that trade-offs between components and system performance could be identified.

Simulation output, of course, is highly dependent on the characterization of the village load, the economic estimation of electricity's value, the wind regime under which the system works, the system itself, and the operational assumptions embodied in the simulation itself. For instance, the non-optimizing algorithm employed used a random number generator to determine wind turbine and diesel generator availability. Due to this, results can vary to a few percentage points due to the randomization effects. These effects can be minimized by increasing the duration of the hour-by-hour simulation.

3. TYPICAL SYSTEM SIMULATION DESCRIPTION

The "physical" system under evaluation is described by the village load, the wind regime, and the generation system. The system's dispatching algorithm and analysis methodology were described in the previous system. The three physical components are now described.

(a) The villa.qe load The village load is described by hourly power

requirements broken down by economic sector. Fig- ure 3 shows the village load profile by end-use sector. The predominant components are small-scale industry, combined commercial and public sectors, and the residential sector. Three small end-use sectors are also added, a local hospital and two village wells. The power requirements were built up assuming a number of shops, offices, electrified households and a set of appliances for each. Sector load profiles reflect the electric use characteristics of each. For example, the small-scale industry and commercial/public sectors demonstrate the bi-modal load profile characteristic of business activity in tropical areas. The residential sector's load profile is skewed to the late afternoon and early evening hours. The average load for the total system was 40.9 kW, with a daily maximum and minimum load of 72.7 and 13.5 kW respectively.

As discussed above, the consumer's value for electricity varies with the time of day. To incorporate this, each end-use sector's hourly power requirement was

A

"o

E o a

75 3

70

65

60

55

5O

45

40

35

30

25

20

15

10

5 0

1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4

• Well-Storage

[ ] Well-No Storage

[ ] Sm-Sc. Industry

• Residential

• Comm./Public

[ ] Small Hospital

H o u r of D a y Fig. 3. Aggregate demand for electricity by sector.

430 S.R. CONYORS et al.

e,,

E 0 a

80

60

40

20

[ ] Priority 5

• Priority 4

• Priority 3

• Priority 2

• Priority 1

1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4

Hour of Day Fig. 4. Aggregate demand for electricity by priority.

assigned a priority designation ranging from one to five, one being the highest priority. Combining the priority designations with the end-use power requirements yields an economic value load curve, pictured in Fig. 4. As mentioned, these priorities are used to calculate the benefit-cost ratios, and by the simulation's load management algorithm. For application to real villages, end-use sectors can be modified into geographical sectors using composite priorities to reflect more accurately the system's actual operation.

(b) Wind reyimes The simulation's hour-by-hour energy balance

calculations require hourly average windspeeds. The results presented used three computer generated wind regimes with average windspeeds of 5, 6 and 7 m/s. The wind regimes were constructed by randomly sam- pling from a Weibull distribution based on an average windspeed and windspeed standard deviation. Figure 5 shows the match between the ideal Weibull curve and the randomly sampled regime.

120

100 0

~ 8o Q.

w

~ 60 o

|

~ 40 o c

0" 20 0

0 0 1 2 3 4 5 6 7 8 9 10 11 12 t3 14 15

W i n d s p e e d (m/s) Fig. 5. Wind regime characterization.

16 17 18 19 20

Lower Wind Regime Program Input Values

Program Output Values Medium W l n d ~ e

Program Input Values Program Output Values

HI h Wind Re line Program Input Values

Program Output Values


Table 3, Wind regime program input/output

Average Windspeed Windspeed Standard

Deviation

5 . 0 0 3.00 5.01 2.85

Average Lu II Lull Standard

Duration Deviation

5.00 4.00 6.97 6.30

Maximum Lul l

Windspeed

3 . 0 0

6 . 0 0 3.60 5.00 4.00 3.00 5.92 3.58 7.41 7.06

7 . 0 0 4.20 5.00 4.00 3.00 7.11 4.28 7.00 6.15

(m/s) (m/s) (hrs) (hrs) (m/s)

431

Once the 720 hourly windspeeds were selected, the program reorders the hourly windspeeds, con- structing lulls in the randomly sampled array based on an average lull duration and lull standard deviation. To reflect an operational lull (relative to power generation), the maximum lull windspeed was selected to match the wind turbine's cut-in windspeed. Table 3 shows the input-output characteristics of the wind regime generating program.

(c) Generation systems Three groups of systems were evaluated using the

simulation. These are: Wind/Diesel/Storage systems (WDS); Wind/Storage systems (WS), and Diesel/ Storage systems (DS). The number of wind turbines, number and size of diesel generators, and size of the battery storage systems were altered within each group. The system configurations and their numerical designations are given in Table 4.

As can be seen, WDS systems have two 20 kW diesel generators, from 4 to 8 Aeroman 11/20 wind turbines, and storage capacities (maximum charge levels) from between 120 and 960 kWh. The WS systems have between 12 and 15 wind turbines and storage of either 720 or 960 kWh. The DS systems have different combinations of 20 and 40 kW diesel generators. The DS systems' battery storage is similar to the WDS system configurations.

4. SIMULATION RESULTS

The 40 systems (previously defined in Table 4) were modeled in three wind regimes, for a total of 96 performance simulations. Economic analysis was carried out with the aid of a spreadsheet program allowing for a greater number sensitivity analyses. The economic criteria used for base-case cost calculations can be found in the top portion of Table 2. Results will be presented in two steps, base-case descriptive results

followed by comparative results including sensitivity analysis.

(a) Descriptive results Base case results were tabulated for the 96 per-

formance simulations and are presented in Table 5. Presented are the three basic measures of system performance and economics; the per unit cost of electricity, the percentage of aggregate demand that the system was able to serve, and the aggregate benefiw cost ratio as a measure of the system's overall economic effectiveness. In addition, the percentages of electric generation (wind and diesel), and the per cent breakdown of costs are given. Cost percentages are broken down into wind, diesel and storage components. The additional fuel cost figure is a component of the diesel costs given.

Figure 6 shows the trade-offs between electricity costs and capability to meet the village electric load for all the system configurations and wind regimes evaluated. The "best" configurations for this par- ticular load are those closest to the lower right-hand corner. Approximate trade-off curves have been added to the graph to show the relative cost impacts of improving electric service. As expected, service and costs are improved as the average windspeed increases. What this figure demonstrates is that, using the assumed component costs, diesel/storage configurations are superior to almost every WDS and WS system when the average windspeed is lower than the nominal 7 m/s wind regimes. However, even some of the more poorly configured WDS systems dominate the DS systems for the higher wind regimes. With some cost reductions in wind turbines, the 6 m/s scen- arios may become cost competitive.

Another significant point illustrated by Fig. 6 is that, not surprisingly, WDS systems are clearly superior to WS systems for the analysis considered. How sensitive this relationship is with a more stable

432 S. R. CONNORS ('l d[.

Table 4. System configuration summary W i n d / D i e s e l

WD$ System Number

1

2

3

4

5

6 7

8 9 1 0

11 1 2

1 3 1 4

1 5

1 6 1 7

18 19 2O

Wind Diesel Turb ines Generators

N o . - P o w e r N o . - P o w e r 4 - 94 .0

5 - 1 1 7 , 5

6 - 1 4 1 . 0 7 - 1 6 4 . 5

8 - 1 8 8 . 0 - 94 .0

- 1 1 7 . 5 - 1 4 1 . 0 - 1 6 4 . 5 - 1 8 8 . 0 - 94.0 - 1 1 7 . 5 - 1 4 1 . 0 - 1 6 4 , 5

- 1 8 8 . 0 - 94.0 - 1 1 7 , 5 - 1 4 1 . 0 - 1 6 4 . 5

- 1 8 8 . 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

2 - 4 0

Bat te ry Storage Capacity

2 4 0

2 4 0

2 4 0

2 4 0

2 4 0

4 8 0

4 8 0

4 8 0

4 8 0

4 8 0

7 2 0

7 2 0

7 2 0

7 2 0

7 2 0

9 6 0 9 6 0 9 6 0 9 6 0 9 6 0

W i n d / S t o r a g e S y s t e m s WS W i n d Diesel

System Turb ines Generators Number N o . - P o w e r N o . - P o w e r

1 2 - 2 8 2 . 0 . . . .

2 3 - 3 0 5 , 5 . . . .

3 4 - 3 2 9 . 0 . . . .

4 15 - 3 5 2 , 5 . . . .

5 12 - 2 8 2 , 0 . . . . . .

6 13 - 3 0 5 . 5

7 14 - 3 2 9 . 0 . . . .

8 15 - 3 5 2 . 5 . . . . . .

Bat te ry Storage Capacity

7 2 0

7 2 0

7 2 O

7 2 0

9 6 0 9 6 0 9 6 0 9 6 0

D i e s e l / S t o r a g e S y s t e m s D S W i n d Diesel

System Turb ines Generators Number N o . - P o w e r N o . - P o w e r

1 3 - 60 2 3 - 80 3 . . . . . . 3 - 1 0 0 4 . . . . . . 3 - 60 5 3 - 80 6 . . . . 3 - 1 0 0 7 3 - 60 8 3 - 80 9 . . . . . . 3 - 1 0 0

1 0 3 - 60 11 . . . . 3 - 80 1 2 . . . . 3 - 1 0 0

Ba t te ry Storage Capacity

2 4 0

2 4 0

2 4 0

4 8 0 4 8 0 4 8 0

7 2 0

7 2 0

7 2 0

9 6 0 9 6 0 9 6 0

( N o . - Y . k W ) ( N o . - ~ k W ) ( k W h )

constrained system. To accomplish this, a benefit~:ost analysis must be carried out. Figure 7 shows how the per cent load met tracks with the benefit-cost ratio over the different system configurations. Since the bencllt cost ratio incorporates both the cost of dec- tricity and a level of service measure, when correctly calculated, it should be the primary measure for determining system performance. In order to do this actual survey data must be used to determine the true shape of the demand curves describing the consumer's value of electric service.

For Fig. 7, the "best" systems are closest to the upper right-hand corner, with more weight being attached to the benefit-cost ratio. In contrast with Fig. 6, several of the wind/storage systems now appear economically competitive with the diesel/storage systems. This is due in part to the better peak load capability of the WS systems. Also noticeable is the rapid decrease in benefit-cost ratios at higher per cent load mets. Here the increased capital costs of improving the level of service arc greater than the economic value of that marginal increase in service.

(h) ('omparatil,e resuhs (1) System expansion analysis. Another feature of

the simulation method, particularly when employed in evaluating the performance and economics for an actual situation is that the relative effects of incremen- tal capacity can be determined. For example, system WDS No. 1 can be expanded into system WDS No. 2 or system WDS No. 6 by adding either an additional wind turbine or 120 kWh of battery capacity. An example of this type of analysis is given in Fig. 8, showing the cost versus per cent load met for the 20 WDS systems, connected by lines of increasing wind turbines and battery storage.

As is apparent in the figure, initial gains in increasing service with the least cost-impact are obtained by adding wind turbines. However, the higher peaking capacity obtained by adding more wind turbines quickly saturates the village's peak demand lbr electricity and the battery's energy storage capacity. Fol- lowing the lower right-hand side of each wind regime's contour, the best expansion path for wind/diesel, storage systems is four wind turbines for the 5 m/s wind regime, two for the 6 m/s regime and a single wind turbine for the 7 m/s regime. Battery storage must then be added to maximize the use of available windpower.

wind regime has not yet been evaluated. For example, The backward bending nature of some of the as the average windspeed increases the trade-off increasing storage lines can be attributed to the large curves appear to converge. However, neither the per impact of forced outages on systems with relatively cent load met or the cost per kWh figures fully capture few generating components (4 or 5 wind turbines, two the economic impact of electric service in a supply- diesel generators) and variability in the results due to

Table 5. Summary of performance simulations 433

Wind~Diesel~Storage Systems 5 m/a Wind Regime

System Unit % Load I B/C Numbel Cost Met ~ Ratio

1 19,22 77.3 3.22 2 19,88 79,6 3.31 3 20.61 80.3 3.28 4 21 .12 83.3 3.41 5 22 .02 83,6 3.31 6 21 .32 78.3 3.08 7 21 .74 80,7 3,16 8 22 .27 81.2 3.14 9 22 .72 83.6 3.24

1 0 23,17 85.9 3 2 9 I 1 23 .73 77.8 2,85 I 2 23 .98 79,9 2,94 1 3 24 .09 82.8 3 .04 1 4 24 ,39 84.8 3.09 1 8 24 .84 86.2 3.09 1 6 26,01 77,9 2,66 I 7 26,01 80,9 2.78 1 8 26,12 83,1 2,86 1 9 26 .44 85.3 2,93 2 0 26 .83 86,7 2,92

% Generation Wind I Diesel

42.3 57.7 48,2 51 .8 55.0 45.0 59.9 40.1 64.4 35.6 42.6 57.4 49.7 50.3 57.5 42.5 63.4 36.6 68.0 32 .0 42.7 57.3 51.2 4 8 8 58.4 41.6 65,0 35.0 70.8 29.2 43.5 56.5 51.7 48,3 59,6 40,4 65.2 34.8 71.3 28.7

Percent Cost % Cost Wind IOiesetslStora9~ Fuel

35.2 51.8 12.9 34.9 41.2 46,7 12.2 31,1 47,2 41.2 11.6 26.8 51.7 37.3 10.9 24 .0 56.3 33.3 1 0 . 5 20.9 31.4 45.6 23.0 30,6 37.2 40.8 21.9 27.0 43.2 35.5 21.3 22.7 48.1 31.6 20,3 19.6 52.3 2 8 3 19,3 17.3 28.3 40.5 31.2 27.0 34.1 35.8 30. 23.3 39.3 31.7 28.9 20.2 44.2 27.9 27.9 17,1 48.8 24.3 26.9 14,3 25.8 36.2 38.0 24.1 31.1 32.4 38.6 21.1 36.2 28.4 35.4 17.9 40.6 25.3 34,1 15.5 44.9 22.0 33.1 12.£

8 m/e Wind Regime System Unit % Load B/C Number Cost Met Ratio

1 17.44 80.6 3.67 2 17.95 83.2 3.85 3 18.57 85.8 3.92 4 19.54 86.5 3.82 5 20 .48 87.3 3.72 6 19.31 81.8 3.53 7 19.42 84.7 3.73 8 19.66 87.5 3.83 9 20 .20 68.9 3.84

I 0 21 ,02 90.3 3.76 1 1 21 ,35 81.6 3,27 1 2 21 .22 86.0 3.51 1 3 21 .38 88.1 3.62 1 4 21.71 90.2 3.66 1 5 22.31 91.3 3,60 1 6 23 ,59 82.7 3,08 1 7 23.17 86,9 3.31 1 8 23 .40 88,4 3,35 1 9 23 .73 89.7 3.37 2 0 23,97 92.1 3.39


57.2 42.8 64.2 35.8 69.4 30.6 72.7 27.3 74.9 25.1 58.1 41 .9 87.1 32.9 74.3 25.7 79.7 20.3 82,1 17,9 60.2 39.8 68.2 31 .8 76,0 24.0 82.0 18.0 86.3 13.7 58.4 41.6 6 8 5 31.5 76.8 23.2 83.2 16.8 88.3 11 .7

7 m/e Wind Regime System Unit % Load B/C % Generation ~lumber Cost Met Ratio Wind I Diesel

1 15.55 85.3 4.45 2 15.79 90,5 4,82 3 16.46 91.4 4,72 4 17,35 91.9 4,54 5 18,20 92,9 4.40 6 16.86 89.1 4.46 7 16.75 92.0 4.68 8 17.16 93.3 4.65 9 17.96 94.4 4.50

t 0 19.08 94.3 4.25 11 18.71 89.3 4.10 12 18.35 93.3 4.36 1 3 18.41 95.1 4.42 1 4 19.08 96.8 4,31 18 20 .07 97.5 4.11 1 6 20 .95 87.9 3.65 1 7 20 .24 g2,8 3.97 1 8 20.11 95.7 4.07 1 9 20.51 98.2 4,03 2 0 21 .50 98,8 3,85

70.1 29.9 76.1 23.9 81.3 18.7 84.4 15.6 86.8 13.2 71.7 28.3 81.8 18.2 88.4 11,6 91.4 8.8 92,1 7.9 73.1 28,9 83.1 16.9 91.6 8.4 94.6 5.4 95.5 4.5 74.0 26.0 85.0 15.0 92.9 7.1 96.5 3.5 97.5 2.5i

Percent Cost % Cost Wind IDieeelelStora~e Fuel

39.6 48.7 1 3 7 30.1 46.4 40.7 12.9 25.6 51.9 36.0 1 2 1 22.1 56,4 32.2 11.4 19.4 60,1 29.1 10.8 17.2 35.3 40.4 24.4 25,8 42,3 34.3 23.4 21.0 48.5 29.1 22.4 16.9 53.9 24.7 21 .4 13.5 57.6 22.1 20.3 11.7 32.1 34,8 33,1 21.8 38.2 30,1 31.5 1 8 2 44.3 25.0 30.6 14.1 4 9 6 20.9 29,5 10,9 54.1 17.6 28.3 8.3 28.5 32.1 39.4 20.3

34.7 27.1 38.2 16,3 40.5 22,3 37.2 12.4 45.6 18.3 36.1 9.2 50.2 14.9 34.8 6.6

Percent Cost % Cost Wind tDieeelelStoraQe Fuel

44.7 40.8 1 4 5 24.5 51.5 35.0 13.5 20.3 57,9 29.3 12.8 16.0 62.7 25.3 12,1 13,0 88,5 22.0 11.4 10.8 39,7 34.7 25.6 20,5 48,3 26.7 25.0 1 4 0 55.3 20.7 24,0 9.2 60.0 17.3 22.7 6.8 63.0 15.7 21.4 5.9 35.7 29.8 34,5 17.2 43.5 22.8 33.7 11.5 50.9 16.1 33.0 6.C 55.6 13.2 31.2 4.0 58.8 11.8 29.5 3 2 32.3 25.9 41.8 14.7 39.7 19,3 40.9 9.3 48.4 13.8 40.0 4.e 51.2 10.6 38.2 2.3

54.4 9 4 38.2 1.7 (continued overle(l/)

434 S. R. CONNORS f l al.

Table 5. (continued) Wind~Storage Systems

5 m/s Wind Reltime System Unit % Load B/C % Generation Percent Cost % Cost qumber Cost Met Ratio Wind I Diesel Wind JDieselslStoragc Fuel

1 28 .09 75.9 2.4! 100.0 .0 72.9 0.0 27.1 0.0 2 29.11 77.2 2,41 100.0 .0 74.3 0.0 25.7 0.0 3 29.51 80.4 2 . 5 6 1 0 0 . 0 .0 75.7 0.0 24.3 0.0 4 29.97 83.4 2,60 100.0 :oOi 76.9 0.0 23.1 0.0 5 29 .37 79.3 2.54 100.0 67,0 0.0 33.0 0.0 6 29 .67 62,7 2.62 100.0 .0 68.6 0,0 31.4 0.0 7 30.44 84.6 2.61 100.0 .01 70.1 0.0 29.9 0.0 8 30 .87 87.4 2.63 100.0 .0 71 5 0.0 2 8 5 0.0

6 m/e Wind Regime System Unit % Load B/C % Generation Percent Cost % Cost ~um~e, Cost Met Ratio Wind I Diesel W*od IDieseIsIstora~s Fuel

1 25 .42 85.4 3.01 100.0 .0 73.4 0.0 26.6 0.0 2 26 .57 88.1 2.92 100,0 .0 74.8 0,0 25,2 0.0 3 27 .58 87.2 2.86 100.0 0 76.0 O0 24.0 0.0 4 26,83 87.4 2 7 6 IOQO .0 77,1 O0 22.9 0.0 5 26 .27 90.4 3.07 100.0 .0 67,6 0.0 32,4 0.0 6 27.17 91.8 3 0 0 100.0 .0 69.2 0.0 30.8 0.0 7 28.11 93.0 2.93 100.0 .0 70.6 0.0 29,4 0.0

i 8 29 .30 93.1 2.81 100.0 .0 71.8 0.0 28.2 0.0

7 m/s Wind Regime System Unit % Load B/C % Generation, Percent, ,C°st % Cost ~lumbe, Cost Met Ratio Wind I Diesel Wind IDieselslStorage. Fuel

1 23 .36 94,2 3.52 100,0 .0 73,8 0.0 26,2 0.0 2 24.57 94.2 3.35 100.0 .0 75.1 0.0 24.9 0,0 3 25 ,77 94.4 3.21, 1 0 0 0 .0 76.3 0.0 2 3 7 O 0 4 27 .00 94.3 3.06 100.0 .0 77.3 0.0 22.7 0 0 5 24 .72 97.1 3,35 100.0 .0 67,9 QO 32.1 0.0 6 25 .88 97.2 3.2(; 100.0 .0 69.4 0.0 3 0 6 0.0 7 27 .05 97.2 3.07 100.0 .0 7 0 7 0.0 29.3 O0 8 28 .26 97.2 2.94 100.0 .0 72.0 0.0 28.0 O0

Diesel~Storage Systems

System I Numbel

1 2 3 4 5 6 7 8 9

1 0 11 1 2

Unit % Load B/C Cost Met Ratio 18.43 82.3 3.72 18.85 90.4 4.16 19.67 92.2 4 .10


Percent Cost Wind [ DieselsJStorage

0.0 87.3 12.7 0.0 88.7 11.3 0~0 89~4 10~6

,0 100.0 .0 100.0 .0 100.0

20.41 64.4 3.59 .0 100.0 0.0 77.7 22.3 20 .72 91.6 3.87 0 100.( 0.0 7 9 7 20.3 21 .50 92.9 3.79 .0 100.0 0.0 80.7 1 9 3 22 .30 85.6 3.36 .0 100.0 0.0 69.8 30.2 22.58 91.6 3.58 ,0 100.0 0.0 72.1 27.9 23 .42 92.3 3,48 .0 100.0 0.0 73.3 26.7 24.03 86.0 3.18 .0 100.0 0.0 62.8 37.2 24.23 92.5 3.37 .0 100.0 0.0 65.7 34.3 24 .99 94.6 3.30 .0 100.0 O 0 67.5 3 2 5

% Cost Fuel 60.7 59,5 57,2 54,1 53.5 51.7 48.6 48.1 46,6 43., = 43.7 43.0

the relatively short (720 h) s imulat ion period incor- porat ing randomized availabili ty algorithms.

(2) Sensit ivi ty analysis. Using the basic economic model, the relative costs at which systems are equivalent can be determined. Systems WD S No. l0 and WS No. 5 bo th have percent load mets of slightly more than 90% for the 6 m/s wind regime. This is equivalent to the per cent load met for diesel/storage

system DS No. 2. For these three systems wind turbine and the diesel fuel costs were altered to determine the relative effects on the cost of electricity and the benefit~zost ratio.

Figure 9 shows the change is the cost of electricity and benefi t -cost rat io as the cost of wind turbines is reduced from the base case value of $1250/kW. With a 22% reduction of wind turbine costs (to $980/kW)


Wind Reaimes l ~ i"' ~'o"" "WS'i5 ) =='~'~'~' i=]

(~) ~ =/~ II + WDS,6~ l i " WS(6) :~1 (6) - 6 m/s (,) _ , IL r~ WDS(7).__J~r=,=~N,,,,,.WS(7],=,.,,.~,,,,I

I[ ; ]J

435

Cost of Electricity (c/kWh)

32 (5) Oj~:

• • " (6) 30 O O o O .... ~ ' ~ .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . * e j, (7) 28 e / N • =,,"

Oj' N N 26 [] B [] [] •• I ~."~ ....... / • ....................... N N ~

24 [] [] i+ [] • + + N .... :

2022 ~ ~ $ 1 8 ( [] [] :4. []~':~

(6) . 1 ~ [] a~ Q ~

16 (7) ~ -

14 i i i = i i

76 80 84 88 92 96 100

Percent Load Met

Fig. 6. Cost of electricity vs. percentage of load met.

BENEFIT/COST RATIO

Wind Reoimes (~ -5 m/s (~-6m/s (~-7m/s

i • WS(6)

• D S

5.0

4.5

4.0

3.5

3.0

2.5

2.0 75

[] []

(6) a - a a ~ D • Q []

(5) + 1" N .......... ,

[ ] - • w X I .............. )= N [] []'= ~ ' = , ~ \ • ; .......... . . '

[ ] [ ] [ ] m 0 [ ] ~ • * , N

[ ] • • ( 7 ) m .............................................. O ............ • O O (6) o o o (5)

8b 8~ 9~ 9; 1~0

PERCENT LOAD MET

Fig. 7. Percentage of load met vs. cost benefit ratio.

436 S.R. CONYORS et a/.

J =

U J

0

Cost of Electricity vs. Percent of Villaoe Load Met

28

26

24

2 2

20

18,

16,

14 i i i i i i i

76 79 82 85 88 91 94 97

(Dotted Lines - Increasing Wind Turbines, Solid Lines - Increasing Storage)

i ii J..il ....

5 / s . . . . . . . . . . . . . . . .

6 e 7 m/s White Dot

100

Percent Load Met

Fig. 8. Cost vs load met web graph.

WDS No. 10 becomes equivalent to DS No. 2 in the cost of electricity. To have the same economic impact for the village consumer (to offset distributional effects in the per cent load met) the wind turbine costs need t o d e c l i n e to $966/kW. The wind/storage system, WS No. 5, requires a 50% reduction in wind turbine costs (to $635/kW) to reach the 18.85 e/kWh benchmark. Its benefit/cost ratio equivalent, however, requires that wind turbine costs decrease to $622/kW.

The alternative question can be raised. For the same three systems, how much does the cost of diesel fuel have to rise for costs to equate? Holding the cost of wind turbines constant at $1250/kW, Fig. 10 shows that fuel cost need only rise 25%, from $1.20 to $1.50 per gallon for the WDS and DS systems to equate on a e/kWh basis. An equivalent increase is required for the systems to match benefit-cost ratios (refer to Fig. 10). Given the variability in world petroleum markets, such fuel price changes during system lifetime are certainly probable. For the wind/storage system, electric cost and benefit/cost equivalence is obtained with a 67% increase in diesel fuel costs.

The results from this sensitivity analysis show that, for the moderate 6 111/S wind regime, a modest increase

in fuel costs, with a slight reduction in wind turbine capital costs, wind/diesel/storage systems are economically competitive with diesel/storage systems. Wind/storage systems however must see significantly larger cost changes, and be better matched to village time-of-use load characteristics to become economically competitive.

5. CONCLUSIONS/RECOMMEN DATIONS

Although not intended as a definitive study, this work has demonstrated the typical performance and economic results for the modeling of small-scale, stand-alone, supply-constrained wind/diesel electric power systems using hour by hour energy balance methods. As demonstrated, such a model can be used to simulate the interaction between the generation system and the electric end-users. With accurate models of the system components, the overall model can determine the performance characteristics of the overall system as applied to a known village load. In addition, economic costs, including a benefit-cost analysis, can be estimated, determining the overall impact that such systems have upon the people they


Cost of Electricity vs. Wind Turbine Cost Sensitivity 26

25

24

~ 23

-~ 21

i 20 ,'n 19

DS#2 o 17 O

16

15 i

600 700 800 900 1000 11 O0 1200

Diesel Fuel ............. $1.20/gal Diesel Gensets ........ $500/kW Battery Storage,..$100/kWh

WECS Cos ts ($/kW)

Benefit-Cost Ratio vs. Wind Turbine Cost Sensitivity 5.00

4.75 ~ WDS#10

4.50 i DS#2

4.25

4.00

~ 3.75

m 3.50

3.25

Diesel Fuel ............. $1.20/gal Diesel Gensets ........ $500/kW Battery Storage...$100/kWh

3.00 = = = i i

600 700 800 900 1000 11 O0 1200

WECS Costs ($/kW)

Fig. 9. System economics as a function of wind turbine cost.

are designed to serve. Specifically, simulations that reflect the range of generating configurations, their cost, available wind energy resource, the demand for electric power, and its value to the end-user, allow a wide degree of sensitivity analysis to be performed. Such analyses are necessary to effectively evaluate the suitability of any given system given uncertain economic and electric use growth factors.

Future work on wind/diesel simulation models will focus on more detailed modeling of the system

components, and the operation of other types of wind/ diesel system architectures. For example, for battery storage systems, more accurate estimates of battery lifetimes in response to various types of charging/ discharging modes are required. For the diesel engine component, future modeling work should consider the diesel operation at less than rated power, and the costs in engine lifetime and maintenance that such off-design operation implies. Detailed studies should also include the input of wind and end-use

438 S . R . CONNORS et al.

Cost of Electricity vs. Fuel Price Sensitivity

46

~. 42

38

_~ 34

~= 30

26

22

DS #2

~ # 5

18 i i i i i

1 25 1.75 2.25 2.75 3.25 3.75 4.25

F u e l C o s t ($/USgal)

Wind Turbines ....... $1250/kW D i e s e l G e n s e t s ........ $500/kW Battery Storage..$100/kWh

o m

e r

O

¢ o

m

Benfit-Cost Ratio vs. Fuel Price Sensitivity 4,5

4.0 ~ ~ . ~ ,/ WDS #I0

3.0 ~

2.5 ~ ' WS#5 " ~ / DS#2

20: 1.5 i i i i i

1 25 1.75 2.25 2.75 3.25 375 4.25

Wind Turbines ....... $1250/kW D i e s e l G e n s e t s ........ $5001kW Battery Storage...$100/kWh

Fuel Cost ($/USgal)

Fig. 10. System economics as a function of fuel costs.

data on at least a yearly basis. Improved economic modeling of such systems should also be carried out, and should include improved estimates of a wind/ diesel system's capital costs as well as important economic factors in the overall benefit~zost analysis. For example, in the capital costs area, the economic effect of using power electronics system components in improved system's design must be quantified in terms of the system's operating costs. Future work should also include the more accurate modeling of the electric system and load management impacts using the con-

sumer's value of electricity, reliability and outage factors.

Another important focus could be future studies incorporating the basic simulation presented in this paper. The simulation results presented here indicate that a full range of parametric studies would be useful in evaluating the performance of a system under one set of conditions and in determining the effectiveness of different system components in various classes of operating environments. Important areas to consider are the performance of a given system with respect to


a growing electric demand, to changes in the energy demand mix where more base or peak power may be required. Impacts associated with overestimating available windpower or underestimating system costs can be evaluated from the point of the consumer and trial solutions evaluated. Also, the use of such a simulation can be a valuable analytical tool for effectively estimating the impact of other hybrid conventional and renewable energy sources to real human needs.

REFERENCES

1. D. F. Barnes, Electric Power Jor Rural Growth : How Electrieity ,4ff~cts Rural L([e in Developing Countries. Westview Press, Boulder (1988).

2. D. Pearce and M. Webb, Rural electrification in developing countries: A reappraisal. Energy Policy August, p. 329 (1987).

3. P. Menanteau, Rural electrification economics : grid and off-grid systems, in Solar Energy [or Developing Coun- tries ." Power ,/or Villages (Edited by B. McNelis and J. Morton). UK-ISES, London (1986).

4. D. T. Brown, Diesel Engines for the WorM, 1979 edn. Whitehall Press, London (1979).

5. J. G. McGowan, J. F. Manwell and S. R. Connors, Wind/diesel energy systems: review of design options and recent developments. Solar Energy 41,561 (1988).

6. N. Lipman, A review of autonomous wind/diesel stra- tegies. Proc. lOth BWEA Wind Con£ (1988).

7. G. Cramer, R. Grebe, B. Hanna and J. Sachau, Advanced autonomous electrical power supply for the small Irish Island of Cape Clear. Proc. European Wind Enerqy Association Con[~ (EWEC), Rome (1986).

8. J. C. Hansen, P. H. Madsen and P. Lundsager, Wind energy for electrification in developing countries. Proc. European Wind Energy Association Copl[~ (EWEC), Rome (1986).

9. M. T. Samarakou and J. C. Hennet, Simulation of a combined wind and solar power plant. Energy Res. 10, 1 (1986).

10. T. D. Paul, How to Design an Independent Power System. Best Energy Systems (1981).

11. R. Andersson and L. Taylor, The social cost of unsupplied electricity : a critical review. Enet:qy Economics July, p. 139 (1986).

12. G. Cramer and R. Rosendahl, Hoch auf dem Hugel. Umwelt Teenik Windenergie, September 1988.

Documents

Wind/diesel village-scale electric power systems: The performance and economic analysis of a simulated village system