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Modelling of hybrid energy system—Part III: Case study with simulation results
Renewable Energy 36 (2011) 474e481
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Renewable Energy journal homepage: www.elsevier.com/locate/renene
Modelling of hybrid energy systemdPart III: Case study with simulation results Ajai Gupta*, R.P. Saini, M.P. Sharma Alternate Hydro Energy Centre, Indian Institute of Technology, Roorkee, Uttarakhand 247667, India
a r t i c l e i n f o
a b s t r a c t
Article history: Received 28 September 2008 Accepted 23 April 2009 Available online 15 September 2010
This paper presents the results of the application of model (developed in part I) and simulation algorithm (developed in part II) for determining the techno-economics of battery storage type hybrid energy system intended to supply the load of a rural remote area having a cluster of nine villages (grid isolated). The hour-by-hour simulation model is intended to simulate a typical one month period of system operation. For simulation purpose, hourly solar insolation data and load data have been generated and used as an input data. Demand side management (DSM) is used in this study to smooth out the daily peaks and fill valleys in the load curve to make the most efficient use of energy sources. The economic analysis has resulted in the calculation of optimized hourly, daily, and monthly system unit cost of proposed hybrid energy system. The obtained results represent also a helpful reference for energy planners in Uttarakhand state and justify the consideration of hybrid energy systems more seriously. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Renewable energy Rural households Off-grid electrification System sizing Demand side management Economic analysis
1. Introduction
2. Case study
Since the sixth 5-year plan (1980e1985), India has been providing budgetary support for implementation of various renewable energy programmes for meeting ever-increasing energy demand in rural areas. The government of India has proposed to provide electricity by means of solar, biomass, small/micro hydro, wind energy and integrated renewable energy system (IRES)/ hybrid energy system (HES) to all the remote and inaccessible villages and their households therein in a phased manner by the year 2012 [1,2]. In accordance with the approved definition of village electrification, remote village/hamlets will be deemed to be electrified if a minimum of 10% of the households are provided with electricity and electricity is also made available for community facilities and for dalit bastis (habitations) of the village, if any [3]. The present installed generating capacity of the country is about 124,280 MW in which the share of hybrid energy system accounts for 608.62 kW only. Whereas about 20% of 593,732 villages are yet to be electrified (http://powermin.nic.in). This study aims at providing a detailed techno-economic evaluation of hybrid energy system to supply electricity to selected rural remote villages in India in decentralized mode.
Depending on a comprehensive assessment on non-electrified villages in the Uttarakhand state, Narendra Nagar block was found to be one of the most appropriate remote rural areas to be subject to a techno-economic study on rural electrification by proposed hybrid energy system [4e6].
* Corresponding author. E-mail addresses: [email protected], [email protected] (A. Gupta). 0960-1481/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.renene.2009.04.036
2.1. Study area The remote rural area for the study was Narendra Nagar block of district Tehri Garhwal of Uttarakhand state, India. The block consists of 15 un-electrified villages with 22 hamlets. There are 775 households with a population of 4755 according to the 1991 census [6]. The area comprises of major hilly and the fertile area under forest with scattered households. The area has been considered by Uttarakhand Renewable Energy Development Agency (UREDA) to be remote and not economically viable for electrification by grid extension. The total literacy rate of the Narendra Nagar block is 52%. The data are available in the published statistics [6]. Data regarding several aspects having an important bearing on rural energy planning are not readily available. Hence, survey was conducted for the household energy needs using multi-stage schedules for the present investigation. This survey was conducted during November 2006eApril 2007. During survey, it was found that six villages have already electrified by grid extension. The rest nine villages, which have been considered for the present study as the best candidate for
A. Gupta et al. / Renewable Energy 36 (2011) 474e481 Table 1 List of un-electrified villages & general details. S.N. 1. 2. 3. 4. 5. 6. 7. 8. 9. Total
Village Name Laga Mehra Saud Salem Khet Talai lambadi Bandhan Pungarh Bhangla Kakhoor Banskata
Latitude N & Longitude E
0
30 07 , 30 110 , 30 210 , 30 120 , 30 110 , 30 110 , 30 110 , 30 100 , 30 110 ,
0
78 23 78 240 78 210 78 230 78 190 78 180 78 270 78 270 78 290
Table 3 Daily and Monthly solar radiation. Population
Households
S. N.
700 345 111 200 70 62 300 340 1095 3223
60 65 17 30 12 11 55 62 190 502
1. January 2. February 3. March 4. April 5. May 6. June 7. July 8. August 9. September 10. October 11. November 12. December Annual Total (kW h/m2)
electrification by decentralized hybrid energy system consisting of micro-hydro, biogas, biomass, solar photovoltaic, diesel generator and battery. Table 1 shows the details of cluster of nine villages. 2.2. Assessment of energy potential The study area, though one of the most backward part of Narendra Nagar block, occupies a unique position as far as natural sources are concerned. The study area has adequate sunshine, low to moderate wind speeds, falling water is available 7e8 months in a year. Biomass potential is available in abundance and the animal population of this area is relatively much greater than in other parts of Narendra Nagar block. 2.2.1. Micro-hydro Based on the published statistics [6], it has found that out of nine villages, only three villages have micro hydro potential at these sites. The total potential at these sites has been estimated as 14.2 kW (w15 kW). In order to estimate the hydro electric generation that could be supplied, we only considered the MarcheOctober period by consulting senior citizens of villages. Table 2 shows the details of micro-hydro potential. 2.2.2. Solar energy Solar radiation data have been taken from the solar radiation data hand book at Latitude 30 320 N, Longitude 78 030 E [7]. Table 3 shows the daily and monthly global solar radiation. Total solar energy potential of study area is estimated about 1854.18 kW h/m2/ yr. 2.2.3. Biogas energy To assess the biogas potential, buffaloes, horse, goats, and cow/ ox have been considered. The data on number of cattle is estimated by consulting Sarpanch and senior citizens of the villages. Based upon the survey, it was found that there is 4564 cattle population at study area. The village wise distribution of livestock is shown in Table 4. The biogas production from the dung has been evaluated based on the assumption that 10 kg/day dung will be available from cow/ ox, 15 kg/day from buffaloes, 1 kg/day from goat, and 10 kg from horse. The cattle dung availability in the study area is about 25 560 kg/day. The biogas estimation is based on the cattle dung
Table 2 Estimated energy potential of micro-hydro potential. S.N.
Village Name
1. Talai lambadi 2. Pungarh 3. Banskata Total Power potential
475
Head (m)
Discharge (m3/sec)
Power (kW)
2 (7.00) 7.00 5.00
0.05 0.06 0.06
7 (3.5 þ 3.5) 4.20 3.0 14.2
Month
Daily Total (kW h/m2)
Monthly Total (kW h/m2)
3.58 4.40 5.47 6.35 6.95 6.06 5.25 4.80 5.32 5.13 4.22 3.53
110.98 123.20 169.57 190.50 215.45 181.80 162.75 148.80 159.60 159.03 126.60 105.90 1854.18
production from different types of animals and assuming that 0.036 m3 of biogas is generated per kg of cattle dung. Therefore, the biogas availability in the study area is about 644.112 m3/day out of which biogas 510.8796 m3/day, is used for cooking. The balance 133.2323 m3/day is available for generation of electricity. 2.2.4. Biomass (fuelwood) energy To assess the biomass potential, agricultural and forest waste (fuelwood) have been considered. On the basis of data published [6] from all the nine villages it is estimated that about 1083.35 Ton/yr fuelwood and 44.71 Ton/yr of crop residue is available as surplus by taking 2% sustainable yield of fuelwood. However, in this study only 1% sustainable yield of fuelwood is considered to avoid deforestation and crop residues are left to feed the livestock. The balance 147.757 Ton/yr is available for generation of electricity. 2.3. Demand side management Demand side management (DSM) can be obtained by using 11 W, and 20 W compact fluorescent lamps (CFL), and 55 W energy saver fans instead of 60 W, 100 W incandescent lamps and 65 W ordinary fans respectively. After the replacement, the total daily energy demand of cluster of nine villages drops to 1271.61 (32.30% decrease) and 608.97 kW h/day (44.40% decrease) in summer and winter respectively. Similarly peak load is reduced to 113.824 (37.65% decrease) and 68.774 kW h/day (41.70% decrease) in summer and winter respectively. Total annual energy savings comes out to be 206998.85 kW h (35%) [8,9]. 2.4. Demand assessment The data for load demand estimation has been collected as the basis of questionnaire. The energy demand was estimated by
Table 4 Details of Livestock and Biogas potential. S. N.
Village Name
Total No. of Cattle
Total Dung/day (kg)
Total Biogas Generated (m3/day)
1. 2. 3. 4. 5. 6. 7. 8. 9. Total
Laga Mehra Saud Salem Khet Talai lambadi Bandhan Pungarh Bhangla Kakhoor Banskata
546 591 153 273 109 99 501 564 1728 4564
3060 3310 850 1530 610 550 2810 3160 9680 25560
77.112 83.412 21.42 38.556 15.372 13.86 70.812 79.632 243.936 644.112
476
Table 5 Details of Total Load of nine villages. Time Segment (Hours)
Electrical Load (kW h) Household Load Lighting Load (11 W)
5.522 5.522 11.044 11.044
Radio/Music System (25 W)
Lights for Small Shops (20 W)
Floor Mill (5 kW)
Industrial Load
Community Load
Saw Mill/Paddy Huller (5 kW)
One Primary Health Centre (20 W)
45.18 45.18
11.044 11.044 11.044 11.044 5.522
45.18 45.18 45.18 45.18 45.18
82.83 30232.95
406.62 148416.3
12.55 12.55
55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 662.64/0 162346.8
5.0 5.0 5.0 5.0
School Lights (20 W)
5.0 5.0 5.0 5.0
0.040 0.040 0.040 0.040 0.040
0.72 0.72 0.72 0.72 0.72
12.55 12.55 0.34 0.34 0.34 0.34
50.20 18323.0
1.36 496.40
0.040 0.040 0.040 0.040
20.0 7300
20.0 7300 7300
7796.4
Contribution (%)
0.360 131.40
2.03
2.0 2.0 2.0 2.0 2.0 2.0 24.0 8760
3.60 1314.0
2.0/2.0 2.0/2.0 2.0/2.0 2.0/2.0 7.522/7.522 7.522/7.522 11.044/11.044 68.774/68.774 58.49/58.49 10.76/10.76 10.76/10.76 65.98/10.76 111.16/55.94 100.40/45.18 55.22/0.0 67.77/12.55 67.77/12.55 111.824/56.604 113.824/58.604 113.824/58.604 113.824/58.604 107.922/52.702 57.22/2.0 2.0/2.0 1271.61/608.97 384620.85 384620.85
10205.4 1.90
93.42
Street Lights (20 W) 2.0 2.0 2.0 2.0 2.0 2.0
45.18 45.18
359319.05
Commercial Load Fan (55 W) Summer/Winter
100 2.65
A. Gupta et al. / Renewable Energy 36 (2011) 474e481
0:00e1:00 1:00e2:00 2:00e3:00 3:00e4:00 4:00e5:00 5:00e6:00 6:00e7:00 7:00e8:00 8:00e9:00 9:00e10:00 10:00e11:00 11:00e12:00 12:00e13:00 13:00e14:00 14:00e15:00 15:00e16:00 16:00e17:00 17:00e18:00 18:00e19:00 19:00e20:00 20:00e21:00 21:00e22:00 22:00e23:00 23:00e24:00 Daily Load Annual Load Total Individual Load
T.V. (90 W)
Total Electrical Load/Hour (kW h) Summer/Winter
A. Gupta et al. / Renewable Energy 36 (2011) 474e481 Table 6 General details of cluster of villages.
Table 8 Operating periods for electric appliances.
S. N.
Parameters
Details
1. 2. 3. 4. 5. 6. 7. 8. 9.
Households (HH) Population Basic School (4 rooms) Junior Basic School Boys (8 rooms) Senior Secondary School Boys (8 rooms) Primary Health Centre (2 rooms) Floor Mill Saw Mill/Paddy huller Fair Price Shops/Control Rate Shops
502 3223 3 2 1 1 1 1 16/1
considering the household load (lighting, TV, fan, radio/music system), commercial load (lighting for small shops and floor mill), industrial load (saw mill or paddy huller) and community load (primary health centre, street lights, and school lighting) [10,11]. By summing up all appliance demands in each time interval, the overall load profile can be made. Table 5 shows the description of the different categories of load and the corresponding electrical energies computed on the basis of eq. (12.1e12.2) (see Part I). The total energy requirement of study area is estimated as 1271.61 and 608.97 k Wh/day in summer and winter respectively. The general details of villages and electric appliances used for villages are shown in Table 6 and Table 7. Operating periods for different electric appliances are given in Table 8.
The cost of energy generated by energy resources is obtained by adding the capital recovery cost and operation & maintenance cost per unit of energy [12]. Typical calculations are made on an annual basis and the cost of energy is calculated by the following expressions:
ALCCðRsÞ Total annual energy generatedðkWhÞ
ALCC ¼ C0 CRF þ ACF þ ACOM CRF ¼
dð1 þ dÞn ð1 þ dÞn 1
S. N.
Various Appliances
Operating periods/day (hr) Summer/Winter
Average Daily Duty Cycle (hr/day)
1. 2.
CFL for HH Lighting TV
9 9
3. 4. 5. 6. 7. 8. 9. 10.
Ceiling fan Radio/Music system CFL for small shops Floor Mill Saw Mill/Paddy Huller CFL for health centre CFL for street lights CFL for school lighting
4:00e8:00, 17:00e22:00 7:00e9:00, 12:00e14:00, 17:00 e22:00 11:00e23:00 7:00e9:00, 15:00e17:00 17:00e21:00 9:00e13:00 9:00e13:00 8:00e13:00, 17:00e21:00 0:00e6:00, 18:00e24:00 8:00e13:00
12* 4 4 4 4 9 12 5
* The average daily duty cycle value of 12 is chosen for summer season. While for the winter season a value 0 is chosen.
profile, the fuel consumption along with energy share and other economical & operational data are calculated. One set of 24 h data (site load, solar potential and scheduling of renewable generators) is gathered in each month from January to December. These 12 sets of data are used in simulation algorithm as an input data to simulate the output. Different values for parameters used for simulation purpose is shown in Table 9. 4.1. Simulation results for optimal sizing
3. Unit cost of resources
COE ¼
477
(1.1) (1.2)
(1.3)
where ALCC is the annualized capital cost (Rs), COE unit cost of energy (Rs/kW h), CRF capital recovery factor, C0 capital Cost Rs/ kW, ACF annual fuel cost (Rs), ACO&M annualized operation and maintenance cost, d interest rate, and n is the life time. The unit cost of different resources is shown in Table 9.
The validity of the model and proposed strategy is examined by applying it to a hybrid generation system having the data given in Table 9. Firstly, the generating capacity for micro-hydro generator, biogas generator, and biomass (fuelwood) generator are determined on the basis of respective potential assessment. Then the required generating capacity of PV array area is determined according to the amount of rest total daily electrical energy demand for design month. After this, the generating system is designed in different proportion (penetration) of PV array area: only 0% proportion, only 20% proportion, 40% proportion, 60% proportion, 80% proportion, and 100% proportion. Hourly solar radiation data for this site have been calculated at latitude 30 320 N, Longitude 78 030 E [7]. The model and operation strategy is applied to the hybrid generation system for the six cases given in Table 10. The simulation program will use above mentioned input and repeatedly simulates hourly system operation over the month. For each combination, the hourly, daily and monthly unit costs are evaluated. The feasible solutions are ranked by system optimized unit cost and are presented in Table 10, which clearly indicates that the economic optimal penetration level is 20% for photovoltaic array area. The
4. Results and analysis Based on the time series calculations, the developed model simulates the hybrid energy system, so that, for estimated load
Table 7 Electric Appliances used for the villages. S. N.
Appliances
Quantity/HH or room
Total
1. 2. 3. 4. 5. 6. 7. 8.
CFL for HH Lighting CFL for small shops CFL for health centre CFL for street lights CFL for school lighting Colour TV (36 cm) Ceiling fan Radio/Music system
1 or 2 Points 1 Points 1 Points [1 Pole @ Clusters of 5 HH] 1 Points 1 2 1
1004 17 2 100 36 502 1004 502
Table 9 Data used for simulation. S. N.
Type of Energy Resources
Cost of Energy (Rs/kW h)
Efficiency
Daily Operating Periods
1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Micro-hydro Generator Photovoltaic Generator Biogas Generator Biomass Generator Diesel Generator Battery Inverter/Rectifier PVG-Inverter Battery-Inverter Charge Controller
1.45 15.68 3.98 4.78 11.0 3.26 e 17.72 4.33 e
0.60 0.1154 1.0 1.0 1.0 0.90a, 1.0b 0.95 e e 0.90
22 e 10 12 10 e e e e e
a b
Charging efficiency. Discharging efficiency.
h h h h (max)
478
A. Gupta et al. / Renewable Energy 36 (2011) 474e481
Table 10 Optimized configurations as a function of the solar contribution. S. N.
Penetration Level PV (%)
PV Area (m2)
Battery Size (kW h)
DEG Unit (kW)
Unit cost (Rs/kW h)
Dump Energy (%)
Unmet Energy (%)
1. 2. 3. 4. 5. 6.
0 20 40 60 80 100
0 201.8507 403.7014 605.5521 807.4028 1009.2356
0 106 148 194 276 373
51 46 35 23 12 0
10.41 10.08 10.85 11.27 10.78 9.7619 (16.6993)
0 0.35 1.64 1.27 0.41 0
0 0 0.12 0 0 0
optimum unit sizing of different components, for the design month August is presented in Table 11. The hourly detailed simulation operation as a sample for case 2 (i.e. 20% penetration level) is given in Table 12.
4.2. Simulation results for cost optimization The results from the simulation for the optimal operation of the system during a typical summer day (i.e. 2nd day of design month August) are presented in tabular form in Table 12. The second column of Table 12 gives the total hourly load demand for 24 hour; it had a total load demand of 1271.61 kW h. The maximum hourly load demand is 113.824 kW h at 1800e2100 hour and the minimum is 2 kW h at 2300e2400 and 0000e0400. The scheduling of micro-hydro generator, biogas generator, and biomass generator is given in third, fourth and fifth columns respectively. The sixth column shows the photovoltaic generator power profile, it had a maximum energy of 14.9189 kW h at 1100e1300 hour and a minimum of 2.0979 kW h at 0600e0700 and 1700e1800 hour. Similarly a column seventh, shows photovoltaic generator power after inverter operation. The eighth and ninth columns give the total hourly net load (i.e. hourly deficit) and surplus energy for 24 hour respectively. Similarly, other columns have the same meaning as indicated by the heading of the particular column. All modelling and simulation is based on an hourly energy balance where the hybrid energy system supplies power to meet load demand (and possibly charge the batteries). For simplicity, the generator capacities were given as constant values, but the loads were varying. The initial battery energy level and the minimum allowable state of charge were set at 100% and 20% of maximum allowable state of charge respectively. At day 1, the battery was started with initial SOC 106 kW h (100%), it was reached the minimum SOC of 34.6761 at 2000e2100 hour, and at 2400, it had a SOC of 93.8183 kW h. At day 2, battery bank was discharged with SOC of 93.8183 kW h, supplying the net load demand during hour 0000e0100 (battery discharge strategy). The micro-hydro generator supplied the load demand completely during hour 0100e0600. The battery bank was charged
Table 11 System component sizing. S. N.
Type of Components
Installed Capacity (kW)
1. 2. 3. 4. 5. 6. 7. 8.
Micro-Hydro Generator Solar Photovoltaic Generator Biogas Generator Biomass Generator Diesel Engine Generator Battery Inverter Dump Load
15 202 m2 20 34 46 106 kW h 35 12
completely by the micro-hydro generator (battery charging strategy). During this period the excess available energy was diverted to the dump load, which in this study was assumed to be electric water heater. The load demand increased after 0600, at the same time, the solar photovoltaic contribution started to raise, the micro-hydro generator, photovoltaic generator and batteries were used to supply the load demand (battery discharge strategy). The load demand further increased after 0700, at the same time, biogas generator came into system, the micro-hydro generator, biogas generator, photovoltaic generator and batteries were used to supply the load (battery discharge strategy) during hours 0700e0900. The load demand decreased after 0900, biogas generator stopped, and the micro-hydro generator and photovoltaic generator took over till the next generator was brought back online, the excessive energy from the solar photovoltaic was used to charge the battery bank (battery charging strategy) during hours 0900e1100. The load demand increased after 1100, at the same time biomass generator came into system, biogas generator was brought back, and all the renewable generators and battery bank were used to supply the load. Approximately, 1300 hour, the solar power started to decrease; batteries were unable to supply the net load completely (battery control set point 1, Table 1, Part II). At this time, diesel generator was brought online to meet the net load demand. The diesel was at its rated power. The excessive power generated from the diesel generator was used to charge the battery (cycle charging strategy). The battery SOC was going up till the diesel generator stopped operating, the battery bank attained the SOC 80% (or more) at the end of 1500 hour, the diesel generator stopped, as specified in the set point (diesel generator stop set point 7), and the battery inverter took over till the diesel generator was brought back online. The diesel generator was brought back online at approximately hour 1700, when the net load demand exceeded 36.8 kW h (diesel generator starts set point 6, Table 1, Part II). The diesel generator supplied the net load and battery-inverter was used to shave the peak net load demand (peak shaving strategy). The net load demand decreased after 2100, the diesel generator was still operating due to set point constraints (diesel generator stop set point 7, Table 1, Part II). The net load was supplied by the diesel generator. The excessive power generated from the diesel generator was used to charge the battery. The battery SOC was going up till the diesel generator stopped operating. The battery bank attained the 80% or more SOC at the end of 2400 hour, finally, the diesel generator stopped, as specified in the set point 7 (diesel generator stop set point 7, Table 1, Part II). After this, the simulation profile was same for the rest days of the month, i.e., the battery was started with initial SOC of 93.8183 kW h, it was reached the minimum SOC of 34.6761 kW h and at the end of the day, it had a SOC of 93.8183 kW h. In this way, the program repeatedly simulates hourly system operation over the month. Once hourly energy output values of a stand-alone hybrid energy system are estimated, algorithm aggregates the hourly
Table 12 Hourly simulation results for design month August. ELOAD (kW h)
EMHG (kW h)
EBGG (kW h)
EBMG (kW h)
EPVG (kW h)
EPVG-INV (kW h)
ENETLOAD (kW h)
ESURPLUS (kW h)
EBATT-INV (kW h)
EBATT-LEFT (kW h)
EDEG (kW h)
DEG Status
EUNMET (kW h)
EDUMP (kW h)
Unit Cost Rs./kW h
0:0e1:0 1:0e2:0 2:0e3:0 3:0e4:0 4:0e5:0 5:0e6:0 6:0e7:0 7:0e8:0 8:0e9:0 9:0e10:0 10:0e11:0 11:0e12:0 12:0e13:0 13:0e14:0 14:0e15:0 15:0e16:0 16:0e17:0 17:0e18:0 18:0e19:0 19:0e20:0 20:0e21:0 21:0e22:0 22:0e23:0 23:0e24:0
2.0 2.0 2.0 2.0 7.522 7.522 11.044 68.774 58.49 10.76 10.76 65.98 111.16 100.40 55.22 67.77 67.77 111.824 113.824 113.824 113.824 107.922 57.22 2.0
e 2.0 2.0 2.0 7.522 7.522 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 e
e e e e e e e 20.0 20.0 e e 20.0 20.0 20.0 e e e 20.0 20.0 20.0 20.0 20.0 e e
e e e e e e e e e e e 34.0 34.0 34.0 34.0 34.0 34.0 34.0 34.0 34.0 34.0 34.0 34.0 e
0.0 0.0 0.0 0.0 0.0 0.0 2.0979 5.3615 8.8581 11.4223 13.2872 14.9189 14.9189 13.2872 11.4223 9.0912 5.1284 2.0979 0.0 0.0 0.0 0.0 0.0 0.0
e e e e e e 1.9930 5.0934 8.4151 1.7600 1.7600 2.9800 14.1730 12.6228 10.8512 8.6367 4.8720 1.9930 e e e e e e
2.0 0.0 0.0 0.0 0.0 0.0 0.0509 34.6805 21.0748 0.0 0.0 0.0 33.9870 24.7771 1.3688 16.1334 19.8980 46.8310 50.824 50.824 50.824 44.922 14.220 2.0
0.0 6.65 6.65 6.65 1.4041 1.4041 0.0 0.0 0.0 7.7514 9.2620 9.5435 0.0 20.1618 42.3996 0.0 0.0 0.0 0.0 0.0 0.0 1.0241 30.1910 41.80
2.0 0.0 0.0 0.0 0.0 0.0 0.0509 34.6805 21.0748 0.0 0.0 0.0 33.9870 0.0 0.0 16.1334 19.8980 0.8310 4.824 4.824 4.824 0.0 0.0 0.0
91.7130 97.0995 102.4860 106.0 106.0 106.0 105.9464 69.4406 47.2566 55.0080 64.2700 73.8135 38.0377 54.3687 88.7124 71.7298 50.7846 49.9098 44.8319 39.7540 34.6761 35.5056 59.9603 93.8183
e e e e e e e e e e e e e 24.7771 1.3688 e e 46.0 46.0 46.0 46.0 44.922 14.220 2.0
e e e e e e e e e e e e e On-Run Run-Off e e On-Run Run Run Run Run Run Run-Off
e e e e e e e e e e e e e e e e e e e e e e e e
e e e 2.0806 1.2637 1.2637 e e e e e e e e e e e e e e e e e e
4.33 1.45 1.45 1.45 1.45 1.45 4.40 4.84 5.69 4.11 4.11 4.67 5.88 7.48 6.93 5.88 5.14 7.16 6.87 6.87 6.87 6.94 5.80 11.0
A. Gupta et al. / Renewable Energy 36 (2011) 474e481
Time Segment
479
480
A. Gupta et al. / Renewable Energy 36 (2011) 474e481
Table 13 Details of Renewable Energy Generation. S. N.
Type of Renewable Energy Generators
Annual Total (kW h)
Contribution (%)
1. 2. 3. 4. 5.
MHG Output BGG Output BMG Output PVG Output Total REG Output
48510 72225 142620 43369.09 306724.09
15.82 23.55 46.50 14.14 100
Table 14 Details of Load Distribution. S. N.
Load Distribution between Units
Annual Total (kW h)
Contribution (%)
1. 2. 3. 4. 5. 6. 7.
Load Load Load Load Load Load Load
42640.76 70685.27 136298.23 27633.55 277257.61 66852.14 40510.77
11.0864 18.3779 35.4370 7.1846 72.0860 17.3810 10.5326
by by by by by by by
MHG BGG BMG PVG REG DEG BATT
Table 15 Details of Unmet Energy & Dump Energy. S. N.
Parameters
Annual Total (kW h)
Annual Average (daily)
Contribution (%) of Total Load
1. 2.
Unmet Energy Dump Energy
0.0 3182.1376
0.0 8.72
0.0 0.8273
Table 16 Diesel Generator Performance Results. S. N.
Parameters
Annual Total
Annual Average (daily)
1. 2. 3.
Diesel Fuel Consumption (L) Diesel Run Hours (hr) Diesel Start-Stops
25396.2573 2129 337
69.58 5.83 0.92
Table 17 Battery Performance Results. S. N.
Parameters
Annual Total (kW h)
1. 2.
Battery Charge Energy Battery Discharge Energy
32191.3033 40510.8066
values to compute daily and monthly values. Daily energy values are used to perform a resource-load matching analysis aimed at evaluating an energy system’s ability to meet the end-user’s daily energy needs on an economic basis, while monthly values are used to evaluate system’s performance. On the basis of monthly values, the annual value is employed to calculate a hybrid energy system’s average cost of energy production. Tables 13 and 14 gives a summary of the renewable energy generation and contribution of the various energy resources used in order to cover the load demand respectively, whereas Tables 15, 16 and 17 give a summary of unmet and dump energy, diesel engine generator performance and battery performance results respectively, and Table 18 gives detailed economic results. The results shown are for specified parameters, which can vary for individual customers, as well as, from area to area. It can be seen that the least economical system is the stand-alone micro-hydro generation system (1.45 Rs/kW h) as it has to be run almost all the time in order to meet the load demand constantly. On the other hand, the most expensive system is the stand-alone solar photovoltaic system (15.68 Rs/kW h). So the stand-alone solar photovoltaic system will cost more money than it is necessary. Regarding the biomass energy it is clear that potential of biogas is sufficient with second lowest cost of energy. In order to fully utilize the biogas resource, one is required to explore the possibility of generating electricity using biogas engine system in decentralized mode because the cost of generation from the individual resource is Rs. 3.98/kW h followed by biomass energy system (Rs. 4.78/kW h), diesel generator (Rs. 11.0/kW h). The optimized annual system unit cost is determined by taking average of annual optimized total cost of all months, which comes out to be Rs. 6.23/kW h. 5. Summary and conclusions An integrated techno-economic analysis of typical hybrid energy system is presented. In this context, the configuration and model details of the proposed hybrid energy system is described first, including the battery storage system and the electronic devices. Accordingly, a combined dispatch strategy based solution algorithm is presented, in order to determine the optimal operation, optimal sizing, and cost optimization for a hybrid energy system. A special feature of the proposed model is that a cost constant (cost/unit) for each of the proposed resource is introduced in the
Table 18 Economic Results. S. N.
Month
1. January 2. February 3. March 4. April 5. May 6. June 7. July 8. August 9. September 10. October 11. November 12. December Annual Total Optimum Cost (Rs) Annual Total Load (kW h) Annual Average daily Cost (Rs/kW h)
Parameters Minimum Hourly Unit Cost of HES (Rs/kW h)
Maximum Hourly Unit Cost of HES (Rs/kW h)
Optimum Daily Unit Cost of HES (Rs/kW h)
Optimum Monthly Unit Cost of HES (Rs/kW h)
3.98 3.98 1.45 1.45 1.45 1.45 1.45 1.45 1.45 1.45 3.98 3.98
17.72 17.72 11.0 7.64 11.0 9.31 11.0 11.0 11.0 11.0 17.72 17.72
5.65 5.77 6.35 6.44 6.45 6.43 6.40 6.24 6.28 6.22 5.75 5.65
5.65 5.77 6.35 6.44 6.45 6.43 6.40 6.24 6.28 6.22 5.75 5.65 2395115.327 384620.85 6.23
Rs is the Indian currency, i.e. Indian rupees (INR).
A. Gupta et al. / Renewable Energy 36 (2011) 474e481
cost objective function in such a way that resources with lesser unit cost share the greater of the total energy demand in an attempt to optimize the objective function. Finally, a quite extensive case study is carried out, in order to demonstrate the validity of the model and proposed strategy. By using DSM, the hybrid energy system components are reduced in size and the energy available is utilized in a proper way, which can lead to reduction in the capital cost of the system. Further, PV energy is also utilized effectively (in different proportion of a PV array area) to optimize the size of the diesel generator and battery storage reducing the total system cost. Taking into account the extensive results obtained, hybrid energy system may be cost-effective electrification solution for numerous isolated consumers. Our techno-economic feasibility study of hybrid energy system demonstrates that these systems can theoretically reduce generation costs and increase the reliability of energy supply. The local people will be employed to take off the operation and maintenance of the power system as well as to manage the collection of revenues from each household, which may be used for maintaining the sustainability of the system. On top of this, subsidy possibilities e granted by ministry of power e should greatly increase the economic attractiveness of similar electricity production applications.
481
References [1] Zia H, Devadas V. Energy management in Lucknow city. Energy Policy 2007;35:4847e68. [2] Rural electrification policy: No. 44/26/05-RE (Vol-II). India: Ministry of Power; 2006. [3] Remote village electrification programme: No. 15/6/2006-07-RVE. India: Ministry of New and Renewable Energy; 2006. [4] Malhotra KS, Chaurasia PBL. A plan for energy management in 110 villages of the Silora block in Rajasthan, India. Energy 1981;6(7):591e601. [5] Vera JG. Options for rural electrification in Mexico. IEEE Trans Energy Convers 1992;7(3):426e33. [6] Unelectrified villages e surveys, potential sources and electricity demand: 1ain project report-2005 (Vol-II e 4/12). Roorkee, India: A.H.E.C, I.I.T.; 2005. [7] Mani A, Rangarajan S. Solar radiation over India. 2nd ed. Allied Publishers Private Limited; 1982. pp. 302e303. [8] Al-Alawi A, Islam SM. Demand side management for remote area power supply systems incorporating solar irradiance model. Renew Energy 2004;29:2027e36. [9] Atikol U, Dagbasi M, Guven H. Identification of residential end-use loads for demand-side planning in northern Cyprus. Energy 1999;24:231e8. [10] Ahmed GE. Photovoltaic-powered rural zone family house in Egypt. Renew Energy 2002;26:379e90. [11] Adeoti O, Oyewole BA, Adegboyega TD. Solar photovoltaic based home electrification system for rural development in Nigeria: domestic load assessment. Renew Energy 2001;24:155e61. [12] Nelson DB, Nehrir MH, Wang C. Unit sizing and cost analysis of stand-alone hybrid wind/PV/fuel cell power generation systems. Renew Energy 2006;31:1641e56.
Contents lists available at ScienceDirect
Renewable Energy journal homepage: www.elsevier.com/locate/renene
Modelling of hybrid energy systemdPart III: Case study with simulation results Ajai Gupta*, R.P. Saini, M.P. Sharma Alternate Hydro Energy Centre, Indian Institute of Technology, Roorkee, Uttarakhand 247667, India
a r t i c l e i n f o
a b s t r a c t
Article history: Received 28 September 2008 Accepted 23 April 2009 Available online 15 September 2010
This paper presents the results of the application of model (developed in part I) and simulation algorithm (developed in part II) for determining the techno-economics of battery storage type hybrid energy system intended to supply the load of a rural remote area having a cluster of nine villages (grid isolated). The hour-by-hour simulation model is intended to simulate a typical one month period of system operation. For simulation purpose, hourly solar insolation data and load data have been generated and used as an input data. Demand side management (DSM) is used in this study to smooth out the daily peaks and fill valleys in the load curve to make the most efficient use of energy sources. The economic analysis has resulted in the calculation of optimized hourly, daily, and monthly system unit cost of proposed hybrid energy system. The obtained results represent also a helpful reference for energy planners in Uttarakhand state and justify the consideration of hybrid energy systems more seriously. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Renewable energy Rural households Off-grid electrification System sizing Demand side management Economic analysis
1. Introduction
2. Case study
Since the sixth 5-year plan (1980e1985), India has been providing budgetary support for implementation of various renewable energy programmes for meeting ever-increasing energy demand in rural areas. The government of India has proposed to provide electricity by means of solar, biomass, small/micro hydro, wind energy and integrated renewable energy system (IRES)/ hybrid energy system (HES) to all the remote and inaccessible villages and their households therein in a phased manner by the year 2012 [1,2]. In accordance with the approved definition of village electrification, remote village/hamlets will be deemed to be electrified if a minimum of 10% of the households are provided with electricity and electricity is also made available for community facilities and for dalit bastis (habitations) of the village, if any [3]. The present installed generating capacity of the country is about 124,280 MW in which the share of hybrid energy system accounts for 608.62 kW only. Whereas about 20% of 593,732 villages are yet to be electrified (http://powermin.nic.in). This study aims at providing a detailed techno-economic evaluation of hybrid energy system to supply electricity to selected rural remote villages in India in decentralized mode.
Depending on a comprehensive assessment on non-electrified villages in the Uttarakhand state, Narendra Nagar block was found to be one of the most appropriate remote rural areas to be subject to a techno-economic study on rural electrification by proposed hybrid energy system [4e6].
* Corresponding author. E-mail addresses: [email protected], [email protected] (A. Gupta). 0960-1481/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.renene.2009.04.036
2.1. Study area The remote rural area for the study was Narendra Nagar block of district Tehri Garhwal of Uttarakhand state, India. The block consists of 15 un-electrified villages with 22 hamlets. There are 775 households with a population of 4755 according to the 1991 census [6]. The area comprises of major hilly and the fertile area under forest with scattered households. The area has been considered by Uttarakhand Renewable Energy Development Agency (UREDA) to be remote and not economically viable for electrification by grid extension. The total literacy rate of the Narendra Nagar block is 52%. The data are available in the published statistics [6]. Data regarding several aspects having an important bearing on rural energy planning are not readily available. Hence, survey was conducted for the household energy needs using multi-stage schedules for the present investigation. This survey was conducted during November 2006eApril 2007. During survey, it was found that six villages have already electrified by grid extension. The rest nine villages, which have been considered for the present study as the best candidate for
A. Gupta et al. / Renewable Energy 36 (2011) 474e481 Table 1 List of un-electrified villages & general details. S.N. 1. 2. 3. 4. 5. 6. 7. 8. 9. Total
Village Name Laga Mehra Saud Salem Khet Talai lambadi Bandhan Pungarh Bhangla Kakhoor Banskata
Latitude N & Longitude E
0
30 07 , 30 110 , 30 210 , 30 120 , 30 110 , 30 110 , 30 110 , 30 100 , 30 110 ,
0
78 23 78 240 78 210 78 230 78 190 78 180 78 270 78 270 78 290
Table 3 Daily and Monthly solar radiation. Population
Households
S. N.
700 345 111 200 70 62 300 340 1095 3223
60 65 17 30 12 11 55 62 190 502
1. January 2. February 3. March 4. April 5. May 6. June 7. July 8. August 9. September 10. October 11. November 12. December Annual Total (kW h/m2)
electrification by decentralized hybrid energy system consisting of micro-hydro, biogas, biomass, solar photovoltaic, diesel generator and battery. Table 1 shows the details of cluster of nine villages. 2.2. Assessment of energy potential The study area, though one of the most backward part of Narendra Nagar block, occupies a unique position as far as natural sources are concerned. The study area has adequate sunshine, low to moderate wind speeds, falling water is available 7e8 months in a year. Biomass potential is available in abundance and the animal population of this area is relatively much greater than in other parts of Narendra Nagar block. 2.2.1. Micro-hydro Based on the published statistics [6], it has found that out of nine villages, only three villages have micro hydro potential at these sites. The total potential at these sites has been estimated as 14.2 kW (w15 kW). In order to estimate the hydro electric generation that could be supplied, we only considered the MarcheOctober period by consulting senior citizens of villages. Table 2 shows the details of micro-hydro potential. 2.2.2. Solar energy Solar radiation data have been taken from the solar radiation data hand book at Latitude 30 320 N, Longitude 78 030 E [7]. Table 3 shows the daily and monthly global solar radiation. Total solar energy potential of study area is estimated about 1854.18 kW h/m2/ yr. 2.2.3. Biogas energy To assess the biogas potential, buffaloes, horse, goats, and cow/ ox have been considered. The data on number of cattle is estimated by consulting Sarpanch and senior citizens of the villages. Based upon the survey, it was found that there is 4564 cattle population at study area. The village wise distribution of livestock is shown in Table 4. The biogas production from the dung has been evaluated based on the assumption that 10 kg/day dung will be available from cow/ ox, 15 kg/day from buffaloes, 1 kg/day from goat, and 10 kg from horse. The cattle dung availability in the study area is about 25 560 kg/day. The biogas estimation is based on the cattle dung
Table 2 Estimated energy potential of micro-hydro potential. S.N.
Village Name
1. Talai lambadi 2. Pungarh 3. Banskata Total Power potential
475
Head (m)
Discharge (m3/sec)
Power (kW)
2 (7.00) 7.00 5.00
0.05 0.06 0.06
7 (3.5 þ 3.5) 4.20 3.0 14.2
Month
Daily Total (kW h/m2)
Monthly Total (kW h/m2)
3.58 4.40 5.47 6.35 6.95 6.06 5.25 4.80 5.32 5.13 4.22 3.53
110.98 123.20 169.57 190.50 215.45 181.80 162.75 148.80 159.60 159.03 126.60 105.90 1854.18
production from different types of animals and assuming that 0.036 m3 of biogas is generated per kg of cattle dung. Therefore, the biogas availability in the study area is about 644.112 m3/day out of which biogas 510.8796 m3/day, is used for cooking. The balance 133.2323 m3/day is available for generation of electricity. 2.2.4. Biomass (fuelwood) energy To assess the biomass potential, agricultural and forest waste (fuelwood) have been considered. On the basis of data published [6] from all the nine villages it is estimated that about 1083.35 Ton/yr fuelwood and 44.71 Ton/yr of crop residue is available as surplus by taking 2% sustainable yield of fuelwood. However, in this study only 1% sustainable yield of fuelwood is considered to avoid deforestation and crop residues are left to feed the livestock. The balance 147.757 Ton/yr is available for generation of electricity. 2.3. Demand side management Demand side management (DSM) can be obtained by using 11 W, and 20 W compact fluorescent lamps (CFL), and 55 W energy saver fans instead of 60 W, 100 W incandescent lamps and 65 W ordinary fans respectively. After the replacement, the total daily energy demand of cluster of nine villages drops to 1271.61 (32.30% decrease) and 608.97 kW h/day (44.40% decrease) in summer and winter respectively. Similarly peak load is reduced to 113.824 (37.65% decrease) and 68.774 kW h/day (41.70% decrease) in summer and winter respectively. Total annual energy savings comes out to be 206998.85 kW h (35%) [8,9]. 2.4. Demand assessment The data for load demand estimation has been collected as the basis of questionnaire. The energy demand was estimated by
Table 4 Details of Livestock and Biogas potential. S. N.
Village Name
Total No. of Cattle
Total Dung/day (kg)
Total Biogas Generated (m3/day)
1. 2. 3. 4. 5. 6. 7. 8. 9. Total
Laga Mehra Saud Salem Khet Talai lambadi Bandhan Pungarh Bhangla Kakhoor Banskata
546 591 153 273 109 99 501 564 1728 4564
3060 3310 850 1530 610 550 2810 3160 9680 25560
77.112 83.412 21.42 38.556 15.372 13.86 70.812 79.632 243.936 644.112
476
Table 5 Details of Total Load of nine villages. Time Segment (Hours)
Electrical Load (kW h) Household Load Lighting Load (11 W)
5.522 5.522 11.044 11.044
Radio/Music System (25 W)
Lights for Small Shops (20 W)
Floor Mill (5 kW)
Industrial Load
Community Load
Saw Mill/Paddy Huller (5 kW)
One Primary Health Centre (20 W)
45.18 45.18
11.044 11.044 11.044 11.044 5.522
45.18 45.18 45.18 45.18 45.18
82.83 30232.95
406.62 148416.3
12.55 12.55
55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 55.22/0.0 662.64/0 162346.8
5.0 5.0 5.0 5.0
School Lights (20 W)
5.0 5.0 5.0 5.0
0.040 0.040 0.040 0.040 0.040
0.72 0.72 0.72 0.72 0.72
12.55 12.55 0.34 0.34 0.34 0.34
50.20 18323.0
1.36 496.40
0.040 0.040 0.040 0.040
20.0 7300
20.0 7300 7300
7796.4
Contribution (%)
0.360 131.40
2.03
2.0 2.0 2.0 2.0 2.0 2.0 24.0 8760
3.60 1314.0
2.0/2.0 2.0/2.0 2.0/2.0 2.0/2.0 7.522/7.522 7.522/7.522 11.044/11.044 68.774/68.774 58.49/58.49 10.76/10.76 10.76/10.76 65.98/10.76 111.16/55.94 100.40/45.18 55.22/0.0 67.77/12.55 67.77/12.55 111.824/56.604 113.824/58.604 113.824/58.604 113.824/58.604 107.922/52.702 57.22/2.0 2.0/2.0 1271.61/608.97 384620.85 384620.85
10205.4 1.90
93.42
Street Lights (20 W) 2.0 2.0 2.0 2.0 2.0 2.0
45.18 45.18
359319.05
Commercial Load Fan (55 W) Summer/Winter
100 2.65
A. Gupta et al. / Renewable Energy 36 (2011) 474e481
0:00e1:00 1:00e2:00 2:00e3:00 3:00e4:00 4:00e5:00 5:00e6:00 6:00e7:00 7:00e8:00 8:00e9:00 9:00e10:00 10:00e11:00 11:00e12:00 12:00e13:00 13:00e14:00 14:00e15:00 15:00e16:00 16:00e17:00 17:00e18:00 18:00e19:00 19:00e20:00 20:00e21:00 21:00e22:00 22:00e23:00 23:00e24:00 Daily Load Annual Load Total Individual Load
T.V. (90 W)
Total Electrical Load/Hour (kW h) Summer/Winter
A. Gupta et al. / Renewable Energy 36 (2011) 474e481 Table 6 General details of cluster of villages.
Table 8 Operating periods for electric appliances.
S. N.
Parameters
Details
1. 2. 3. 4. 5. 6. 7. 8. 9.
Households (HH) Population Basic School (4 rooms) Junior Basic School Boys (8 rooms) Senior Secondary School Boys (8 rooms) Primary Health Centre (2 rooms) Floor Mill Saw Mill/Paddy huller Fair Price Shops/Control Rate Shops
502 3223 3 2 1 1 1 1 16/1
considering the household load (lighting, TV, fan, radio/music system), commercial load (lighting for small shops and floor mill), industrial load (saw mill or paddy huller) and community load (primary health centre, street lights, and school lighting) [10,11]. By summing up all appliance demands in each time interval, the overall load profile can be made. Table 5 shows the description of the different categories of load and the corresponding electrical energies computed on the basis of eq. (12.1e12.2) (see Part I). The total energy requirement of study area is estimated as 1271.61 and 608.97 k Wh/day in summer and winter respectively. The general details of villages and electric appliances used for villages are shown in Table 6 and Table 7. Operating periods for different electric appliances are given in Table 8.
The cost of energy generated by energy resources is obtained by adding the capital recovery cost and operation & maintenance cost per unit of energy [12]. Typical calculations are made on an annual basis and the cost of energy is calculated by the following expressions:
ALCCðRsÞ Total annual energy generatedðkWhÞ
ALCC ¼ C0 CRF þ ACF þ ACOM CRF ¼
dð1 þ dÞn ð1 þ dÞn 1
S. N.
Various Appliances
Operating periods/day (hr) Summer/Winter
Average Daily Duty Cycle (hr/day)
1. 2.
CFL for HH Lighting TV
9 9
3. 4. 5. 6. 7. 8. 9. 10.
Ceiling fan Radio/Music system CFL for small shops Floor Mill Saw Mill/Paddy Huller CFL for health centre CFL for street lights CFL for school lighting
4:00e8:00, 17:00e22:00 7:00e9:00, 12:00e14:00, 17:00 e22:00 11:00e23:00 7:00e9:00, 15:00e17:00 17:00e21:00 9:00e13:00 9:00e13:00 8:00e13:00, 17:00e21:00 0:00e6:00, 18:00e24:00 8:00e13:00
12* 4 4 4 4 9 12 5
* The average daily duty cycle value of 12 is chosen for summer season. While for the winter season a value 0 is chosen.
profile, the fuel consumption along with energy share and other economical & operational data are calculated. One set of 24 h data (site load, solar potential and scheduling of renewable generators) is gathered in each month from January to December. These 12 sets of data are used in simulation algorithm as an input data to simulate the output. Different values for parameters used for simulation purpose is shown in Table 9. 4.1. Simulation results for optimal sizing
3. Unit cost of resources
COE ¼
477
(1.1) (1.2)
(1.3)
where ALCC is the annualized capital cost (Rs), COE unit cost of energy (Rs/kW h), CRF capital recovery factor, C0 capital Cost Rs/ kW, ACF annual fuel cost (Rs), ACO&M annualized operation and maintenance cost, d interest rate, and n is the life time. The unit cost of different resources is shown in Table 9.
The validity of the model and proposed strategy is examined by applying it to a hybrid generation system having the data given in Table 9. Firstly, the generating capacity for micro-hydro generator, biogas generator, and biomass (fuelwood) generator are determined on the basis of respective potential assessment. Then the required generating capacity of PV array area is determined according to the amount of rest total daily electrical energy demand for design month. After this, the generating system is designed in different proportion (penetration) of PV array area: only 0% proportion, only 20% proportion, 40% proportion, 60% proportion, 80% proportion, and 100% proportion. Hourly solar radiation data for this site have been calculated at latitude 30 320 N, Longitude 78 030 E [7]. The model and operation strategy is applied to the hybrid generation system for the six cases given in Table 10. The simulation program will use above mentioned input and repeatedly simulates hourly system operation over the month. For each combination, the hourly, daily and monthly unit costs are evaluated. The feasible solutions are ranked by system optimized unit cost and are presented in Table 10, which clearly indicates that the economic optimal penetration level is 20% for photovoltaic array area. The
4. Results and analysis Based on the time series calculations, the developed model simulates the hybrid energy system, so that, for estimated load
Table 7 Electric Appliances used for the villages. S. N.
Appliances
Quantity/HH or room
Total
1. 2. 3. 4. 5. 6. 7. 8.
CFL for HH Lighting CFL for small shops CFL for health centre CFL for street lights CFL for school lighting Colour TV (36 cm) Ceiling fan Radio/Music system
1 or 2 Points 1 Points 1 Points [1 Pole @ Clusters of 5 HH] 1 Points 1 2 1
1004 17 2 100 36 502 1004 502
Table 9 Data used for simulation. S. N.
Type of Energy Resources
Cost of Energy (Rs/kW h)
Efficiency
Daily Operating Periods
1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Micro-hydro Generator Photovoltaic Generator Biogas Generator Biomass Generator Diesel Generator Battery Inverter/Rectifier PVG-Inverter Battery-Inverter Charge Controller
1.45 15.68 3.98 4.78 11.0 3.26 e 17.72 4.33 e
0.60 0.1154 1.0 1.0 1.0 0.90a, 1.0b 0.95 e e 0.90
22 e 10 12 10 e e e e e
a b
Charging efficiency. Discharging efficiency.
h h h h (max)
478
A. Gupta et al. / Renewable Energy 36 (2011) 474e481
Table 10 Optimized configurations as a function of the solar contribution. S. N.
Penetration Level PV (%)
PV Area (m2)
Battery Size (kW h)
DEG Unit (kW)
Unit cost (Rs/kW h)
Dump Energy (%)
Unmet Energy (%)
1. 2. 3. 4. 5. 6.
0 20 40 60 80 100
0 201.8507 403.7014 605.5521 807.4028 1009.2356
0 106 148 194 276 373
51 46 35 23 12 0
10.41 10.08 10.85 11.27 10.78 9.7619 (16.6993)
0 0.35 1.64 1.27 0.41 0
0 0 0.12 0 0 0
optimum unit sizing of different components, for the design month August is presented in Table 11. The hourly detailed simulation operation as a sample for case 2 (i.e. 20% penetration level) is given in Table 12.
4.2. Simulation results for cost optimization The results from the simulation for the optimal operation of the system during a typical summer day (i.e. 2nd day of design month August) are presented in tabular form in Table 12. The second column of Table 12 gives the total hourly load demand for 24 hour; it had a total load demand of 1271.61 kW h. The maximum hourly load demand is 113.824 kW h at 1800e2100 hour and the minimum is 2 kW h at 2300e2400 and 0000e0400. The scheduling of micro-hydro generator, biogas generator, and biomass generator is given in third, fourth and fifth columns respectively. The sixth column shows the photovoltaic generator power profile, it had a maximum energy of 14.9189 kW h at 1100e1300 hour and a minimum of 2.0979 kW h at 0600e0700 and 1700e1800 hour. Similarly a column seventh, shows photovoltaic generator power after inverter operation. The eighth and ninth columns give the total hourly net load (i.e. hourly deficit) and surplus energy for 24 hour respectively. Similarly, other columns have the same meaning as indicated by the heading of the particular column. All modelling and simulation is based on an hourly energy balance where the hybrid energy system supplies power to meet load demand (and possibly charge the batteries). For simplicity, the generator capacities were given as constant values, but the loads were varying. The initial battery energy level and the minimum allowable state of charge were set at 100% and 20% of maximum allowable state of charge respectively. At day 1, the battery was started with initial SOC 106 kW h (100%), it was reached the minimum SOC of 34.6761 at 2000e2100 hour, and at 2400, it had a SOC of 93.8183 kW h. At day 2, battery bank was discharged with SOC of 93.8183 kW h, supplying the net load demand during hour 0000e0100 (battery discharge strategy). The micro-hydro generator supplied the load demand completely during hour 0100e0600. The battery bank was charged
Table 11 System component sizing. S. N.
Type of Components
Installed Capacity (kW)
1. 2. 3. 4. 5. 6. 7. 8.
Micro-Hydro Generator Solar Photovoltaic Generator Biogas Generator Biomass Generator Diesel Engine Generator Battery Inverter Dump Load
15 202 m2 20 34 46 106 kW h 35 12
completely by the micro-hydro generator (battery charging strategy). During this period the excess available energy was diverted to the dump load, which in this study was assumed to be electric water heater. The load demand increased after 0600, at the same time, the solar photovoltaic contribution started to raise, the micro-hydro generator, photovoltaic generator and batteries were used to supply the load demand (battery discharge strategy). The load demand further increased after 0700, at the same time, biogas generator came into system, the micro-hydro generator, biogas generator, photovoltaic generator and batteries were used to supply the load (battery discharge strategy) during hours 0700e0900. The load demand decreased after 0900, biogas generator stopped, and the micro-hydro generator and photovoltaic generator took over till the next generator was brought back online, the excessive energy from the solar photovoltaic was used to charge the battery bank (battery charging strategy) during hours 0900e1100. The load demand increased after 1100, at the same time biomass generator came into system, biogas generator was brought back, and all the renewable generators and battery bank were used to supply the load. Approximately, 1300 hour, the solar power started to decrease; batteries were unable to supply the net load completely (battery control set point 1, Table 1, Part II). At this time, diesel generator was brought online to meet the net load demand. The diesel was at its rated power. The excessive power generated from the diesel generator was used to charge the battery (cycle charging strategy). The battery SOC was going up till the diesel generator stopped operating, the battery bank attained the SOC 80% (or more) at the end of 1500 hour, the diesel generator stopped, as specified in the set point (diesel generator stop set point 7), and the battery inverter took over till the diesel generator was brought back online. The diesel generator was brought back online at approximately hour 1700, when the net load demand exceeded 36.8 kW h (diesel generator starts set point 6, Table 1, Part II). The diesel generator supplied the net load and battery-inverter was used to shave the peak net load demand (peak shaving strategy). The net load demand decreased after 2100, the diesel generator was still operating due to set point constraints (diesel generator stop set point 7, Table 1, Part II). The net load was supplied by the diesel generator. The excessive power generated from the diesel generator was used to charge the battery. The battery SOC was going up till the diesel generator stopped operating. The battery bank attained the 80% or more SOC at the end of 2400 hour, finally, the diesel generator stopped, as specified in the set point 7 (diesel generator stop set point 7, Table 1, Part II). After this, the simulation profile was same for the rest days of the month, i.e., the battery was started with initial SOC of 93.8183 kW h, it was reached the minimum SOC of 34.6761 kW h and at the end of the day, it had a SOC of 93.8183 kW h. In this way, the program repeatedly simulates hourly system operation over the month. Once hourly energy output values of a stand-alone hybrid energy system are estimated, algorithm aggregates the hourly
Table 12 Hourly simulation results for design month August. ELOAD (kW h)
EMHG (kW h)
EBGG (kW h)
EBMG (kW h)
EPVG (kW h)
EPVG-INV (kW h)
ENETLOAD (kW h)
ESURPLUS (kW h)
EBATT-INV (kW h)
EBATT-LEFT (kW h)
EDEG (kW h)
DEG Status
EUNMET (kW h)
EDUMP (kW h)
Unit Cost Rs./kW h
0:0e1:0 1:0e2:0 2:0e3:0 3:0e4:0 4:0e5:0 5:0e6:0 6:0e7:0 7:0e8:0 8:0e9:0 9:0e10:0 10:0e11:0 11:0e12:0 12:0e13:0 13:0e14:0 14:0e15:0 15:0e16:0 16:0e17:0 17:0e18:0 18:0e19:0 19:0e20:0 20:0e21:0 21:0e22:0 22:0e23:0 23:0e24:0
2.0 2.0 2.0 2.0 7.522 7.522 11.044 68.774 58.49 10.76 10.76 65.98 111.16 100.40 55.22 67.77 67.77 111.824 113.824 113.824 113.824 107.922 57.22 2.0
e 2.0 2.0 2.0 7.522 7.522 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 e
e e e e e e e 20.0 20.0 e e 20.0 20.0 20.0 e e e 20.0 20.0 20.0 20.0 20.0 e e
e e e e e e e e e e e 34.0 34.0 34.0 34.0 34.0 34.0 34.0 34.0 34.0 34.0 34.0 34.0 e
0.0 0.0 0.0 0.0 0.0 0.0 2.0979 5.3615 8.8581 11.4223 13.2872 14.9189 14.9189 13.2872 11.4223 9.0912 5.1284 2.0979 0.0 0.0 0.0 0.0 0.0 0.0
e e e e e e 1.9930 5.0934 8.4151 1.7600 1.7600 2.9800 14.1730 12.6228 10.8512 8.6367 4.8720 1.9930 e e e e e e
2.0 0.0 0.0 0.0 0.0 0.0 0.0509 34.6805 21.0748 0.0 0.0 0.0 33.9870 24.7771 1.3688 16.1334 19.8980 46.8310 50.824 50.824 50.824 44.922 14.220 2.0
0.0 6.65 6.65 6.65 1.4041 1.4041 0.0 0.0 0.0 7.7514 9.2620 9.5435 0.0 20.1618 42.3996 0.0 0.0 0.0 0.0 0.0 0.0 1.0241 30.1910 41.80
2.0 0.0 0.0 0.0 0.0 0.0 0.0509 34.6805 21.0748 0.0 0.0 0.0 33.9870 0.0 0.0 16.1334 19.8980 0.8310 4.824 4.824 4.824 0.0 0.0 0.0
91.7130 97.0995 102.4860 106.0 106.0 106.0 105.9464 69.4406 47.2566 55.0080 64.2700 73.8135 38.0377 54.3687 88.7124 71.7298 50.7846 49.9098 44.8319 39.7540 34.6761 35.5056 59.9603 93.8183
e e e e e e e e e e e e e 24.7771 1.3688 e e 46.0 46.0 46.0 46.0 44.922 14.220 2.0
e e e e e e e e e e e e e On-Run Run-Off e e On-Run Run Run Run Run Run Run-Off
e e e e e e e e e e e e e e e e e e e e e e e e
e e e 2.0806 1.2637 1.2637 e e e e e e e e e e e e e e e e e e
4.33 1.45 1.45 1.45 1.45 1.45 4.40 4.84 5.69 4.11 4.11 4.67 5.88 7.48 6.93 5.88 5.14 7.16 6.87 6.87 6.87 6.94 5.80 11.0
A. Gupta et al. / Renewable Energy 36 (2011) 474e481
Time Segment
479
480
A. Gupta et al. / Renewable Energy 36 (2011) 474e481
Table 13 Details of Renewable Energy Generation. S. N.
Type of Renewable Energy Generators
Annual Total (kW h)
Contribution (%)
1. 2. 3. 4. 5.
MHG Output BGG Output BMG Output PVG Output Total REG Output
48510 72225 142620 43369.09 306724.09
15.82 23.55 46.50 14.14 100
Table 14 Details of Load Distribution. S. N.
Load Distribution between Units
Annual Total (kW h)
Contribution (%)
1. 2. 3. 4. 5. 6. 7.
Load Load Load Load Load Load Load
42640.76 70685.27 136298.23 27633.55 277257.61 66852.14 40510.77
11.0864 18.3779 35.4370 7.1846 72.0860 17.3810 10.5326
by by by by by by by
MHG BGG BMG PVG REG DEG BATT
Table 15 Details of Unmet Energy & Dump Energy. S. N.
Parameters
Annual Total (kW h)
Annual Average (daily)
Contribution (%) of Total Load
1. 2.
Unmet Energy Dump Energy
0.0 3182.1376
0.0 8.72
0.0 0.8273
Table 16 Diesel Generator Performance Results. S. N.
Parameters
Annual Total
Annual Average (daily)
1. 2. 3.
Diesel Fuel Consumption (L) Diesel Run Hours (hr) Diesel Start-Stops
25396.2573 2129 337
69.58 5.83 0.92
Table 17 Battery Performance Results. S. N.
Parameters
Annual Total (kW h)
1. 2.
Battery Charge Energy Battery Discharge Energy
32191.3033 40510.8066
values to compute daily and monthly values. Daily energy values are used to perform a resource-load matching analysis aimed at evaluating an energy system’s ability to meet the end-user’s daily energy needs on an economic basis, while monthly values are used to evaluate system’s performance. On the basis of monthly values, the annual value is employed to calculate a hybrid energy system’s average cost of energy production. Tables 13 and 14 gives a summary of the renewable energy generation and contribution of the various energy resources used in order to cover the load demand respectively, whereas Tables 15, 16 and 17 give a summary of unmet and dump energy, diesel engine generator performance and battery performance results respectively, and Table 18 gives detailed economic results. The results shown are for specified parameters, which can vary for individual customers, as well as, from area to area. It can be seen that the least economical system is the stand-alone micro-hydro generation system (1.45 Rs/kW h) as it has to be run almost all the time in order to meet the load demand constantly. On the other hand, the most expensive system is the stand-alone solar photovoltaic system (15.68 Rs/kW h). So the stand-alone solar photovoltaic system will cost more money than it is necessary. Regarding the biomass energy it is clear that potential of biogas is sufficient with second lowest cost of energy. In order to fully utilize the biogas resource, one is required to explore the possibility of generating electricity using biogas engine system in decentralized mode because the cost of generation from the individual resource is Rs. 3.98/kW h followed by biomass energy system (Rs. 4.78/kW h), diesel generator (Rs. 11.0/kW h). The optimized annual system unit cost is determined by taking average of annual optimized total cost of all months, which comes out to be Rs. 6.23/kW h. 5. Summary and conclusions An integrated techno-economic analysis of typical hybrid energy system is presented. In this context, the configuration and model details of the proposed hybrid energy system is described first, including the battery storage system and the electronic devices. Accordingly, a combined dispatch strategy based solution algorithm is presented, in order to determine the optimal operation, optimal sizing, and cost optimization for a hybrid energy system. A special feature of the proposed model is that a cost constant (cost/unit) for each of the proposed resource is introduced in the
Table 18 Economic Results. S. N.
Month
1. January 2. February 3. March 4. April 5. May 6. June 7. July 8. August 9. September 10. October 11. November 12. December Annual Total Optimum Cost (Rs) Annual Total Load (kW h) Annual Average daily Cost (Rs/kW h)
Parameters Minimum Hourly Unit Cost of HES (Rs/kW h)
Maximum Hourly Unit Cost of HES (Rs/kW h)
Optimum Daily Unit Cost of HES (Rs/kW h)
Optimum Monthly Unit Cost of HES (Rs/kW h)
3.98 3.98 1.45 1.45 1.45 1.45 1.45 1.45 1.45 1.45 3.98 3.98
17.72 17.72 11.0 7.64 11.0 9.31 11.0 11.0 11.0 11.0 17.72 17.72
5.65 5.77 6.35 6.44 6.45 6.43 6.40 6.24 6.28 6.22 5.75 5.65
5.65 5.77 6.35 6.44 6.45 6.43 6.40 6.24 6.28 6.22 5.75 5.65 2395115.327 384620.85 6.23
Rs is the Indian currency, i.e. Indian rupees (INR).
A. Gupta et al. / Renewable Energy 36 (2011) 474e481
cost objective function in such a way that resources with lesser unit cost share the greater of the total energy demand in an attempt to optimize the objective function. Finally, a quite extensive case study is carried out, in order to demonstrate the validity of the model and proposed strategy. By using DSM, the hybrid energy system components are reduced in size and the energy available is utilized in a proper way, which can lead to reduction in the capital cost of the system. Further, PV energy is also utilized effectively (in different proportion of a PV array area) to optimize the size of the diesel generator and battery storage reducing the total system cost. Taking into account the extensive results obtained, hybrid energy system may be cost-effective electrification solution for numerous isolated consumers. Our techno-economic feasibility study of hybrid energy system demonstrates that these systems can theoretically reduce generation costs and increase the reliability of energy supply. The local people will be employed to take off the operation and maintenance of the power system as well as to manage the collection of revenues from each household, which may be used for maintaining the sustainability of the system. On top of this, subsidy possibilities e granted by ministry of power e should greatly increase the economic attractiveness of similar electricity production applications.
481
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