5th International Conference on Electrical and Computer Engineering ICECE 2008, 20-22 December 2008, Dhaka, Bangladesh
Computerized Modelling of Hybrid Energy System— Part III: Case Study with Simulation Results Ajai Gupta, R. P. Saini, and M. P. Sharma Alternate Hydro Energy Centre, Indian Institute of Technology, Roorkee Roorkee, Uttarakhand-247667, India E-mail:
[email protected] (Ajai Gupta)
renewable energy system/Hybrid energy system operating in decentralized mode. • Thirdly, five hundred million people are not very far from a power network, and can exercise both the above solution. The proper management of available energy resources is a must to meet the energy demand in a sustainable manner locally and globally. In many cases, utility grid extension is impractical because of dispersed population, rugged terrain, or both; thus small, stand-alone hybrid energy systems are likely to be the most viable option. The Hybrid Energy System (HES) has received much attention over the past decade. It is a viable alternative solution as compared to systems, which rely entirely on hydrocarbon fuel. Apart from the mobility of the system, it also has longer life cycle. In particular, the integrated approach [1-2] makes a hybrid system to be the most appropriate for isolated communities of a remote area.
Abstract - 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 demand 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 demand data have been generated and used as an input data. 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. Index terms-Renewable Energy, Rural households, Offgrid Electrification, System sizing, Economic analysis.
I.
Introduction
II. Hybrid Energy Systems
Energy is vital for sustaining life on earth expresses the economic stability of a nation. It is needed to improve the quality of life by exploiting the natural resources. The careless exploitation of these resources ultimately affects the environment on which such systems thrive. The energy problem is, thus, synonymous to ecological and economic problems. The efforts should, therefore, need to find an optimal solution for sustainable energy supply. The world energy demand has been increasing exponentially and conversely. The conventional energy resources are exhaustible and limited in supply. Therefore, there is an urgent need to conserve what we have in hand and explore the wider use of alternative energy resources. Currently, more than half of the world population lives in rural areas in developing countries. The majority of these population make use of fuels like fuel wood, cattle dung cakes, agricultural wastes etc. using inefficient technologies. Thus the basic energy needs consists of heating, lighting and electricity etc are hardly satisfied; all this contributes to maintain the cycle of poverty. Moreover, this problem is composed of three ways: • Firstly, one billion people are connected to the electricity network and need an inexpensive technique of grid connection and electric distribution. • Secondly, another one billion people, being away from utility network, may be energized through integrated
978-1-4244-2015-5/08/$25.00 (c)2008 IEEE
Hybrid energy systems generally integrate renewable energy sources with fossil fuel powered diesel/petrol generator to provide electric power where the electricity is either fed directly into the grid or to batteries for energy storage. The role of integrating renewable energy in a hybrid energy system is primarily to save diesel fuel. Examples of renewable energy sources commonly used in hybrid configurations are wind turbines, photovoltaic systems, micro-hydro, biomass, and fuel cells. A hybrid energy system consists of two or more energy systems, an energy storage system, power conditioning equipment and a controller. A hybrid energy system may or may not be connected to the grid. They are generally independent of large centralized electric grids and are used in rural remote areas. For systems employing totally clean renewable energy, high capital cost is an important barrier. However, we can produce green power by adding different renewable energy sources to diesel generator and battery, which is also called a hybrid system. This kind of system can compromise investment cost, diesel fuel usage cost and also operation and maintenance costs [3-5]. Hybrid systems for rural electrification can be configured in three different ways: grid connected off-grid with distribution system and off-grid for direct supply. The
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first configuration is able to rely on the grid if the hybrid system has problems. Similarly, feeding the power to the grid, thereby, boosting the voltage and minimizing power cuts strengthen the grid. For off-grid configurations, the hybrid can either be connected to many load centres, or can act as a source of supply for one or two loads, thus avoiding the need of a distribution system. An isolated offgrid system is usually used to charge batteries or supply power to small rural industry/households.
as the best candidate for 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. B.
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 7-8 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. • Micro hydro: Based on the published statistics [7], it has found that out of nine villages, only three villages have micro hydro potential. The total potential at these sites has been estimated as 14.2 kW (~15 kW). In order to estimate the hydroelectric generation that could be supplied, we only considered the March-October period by consulting senior citizens of villages. Table 2. shows the details of micro hydro potential.
III. Hybrid Energy Systems For Village Electrification To provide energy services to remote areas, three options are available [6]: i) Hybrid energy system (HES) can be used to augment power from centralized power plant. ii) Generate fossil fuel based power (e.g. diesel gensets); and iii) Generate renewable power using hybrid energy systems. One possible solution that helps to cancel out the drawbacks of diesel and renewable energy technologies is to employ both types in combination, for minimizing costs and maximizing availability.
Table 2 Estimated energy potential of micro hydro power Power S. Discharge Village Name Head (m) (kW) N. (m3/sec) 1. Talai lambadi 2 (7.00) 0.05 7 (3.5+3.5) 2. Pungarh 7.00 0.06 4.20 3. Banskata 5.00 0.06 3.0 Total Power potential 14.2
IV. Methodology A.
Assessment of Energy Potential
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 unelectrified villages [7] with 22 hamlets. There are 775 households with a population of 4755 according to the 1991 census. 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 [7]. 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 2006-April 2007.
• Solar Energy: Solar radiation data have been taken from the solar radiation data hand book [Mani et al. 1982] at latitude 30°32' N, Longitude 78°03' E [8]. Table 3. shows the daily and monthly global solar radiation. Total solar energy has been taken as 1854.18 kWh/m2/year. Table 3 Hourly and Daily Solar Radiation S. Daily Total Monthly Total Month (kWh/m2) N. (kWh/m2) 1. January 3.58 110.98 2. February 4.40 123.20 3. March 5.47 169.57 4. April 6.35 190.50 5. May 6.95 215.45 6. June 6.06 181.80 7. July 5.25 162.75 8. August 4.80 148.80 9. September 5.32 159.60 10. October 5.13 159.03 11. November 4.22 126.60 12. December 3.53 105.90 Annual Total (kWh/m2) 1854.18
Table 1 List of unelectrified villages & general details S. Latitude N & Village Name Population Households N. Longitude E 1. Laga Mehra 30°07', 78°23' 700 60 2. Saud 30°11', 78°24' 345 65 3. Salem Khet 30°21', 78°21' 111 17 4. Talai lambadi 30°12', 78°23' 200 30 5. Bandhan 30°11', 78°19' 70 12 6. Pungarh 30°11', 78°18' 62 11 7. Bhangla 30°11', 78°27' 300 55 8. Kakhoor 30°10', 78°27' 340 62 9. Banskata 30°11', 78°29' 1095 190 Total 3223 502
• Biogas Energy: To assess the biogas potential, cattle, 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 are 4564 cattle population at study area. The village wise distribution of livestocks 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
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
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ALCC = CO × CRF + ACF + ACO & M (2) Where, ALCC = Annualized capital cost (Rs) COE = Unit cost of energy (Rs/kWh) CRF = Capital recovery factor C0 = Capital cost Rs/kW ACF = Annual fuel cost (Rs) ACO & M = Annualized operation and maintenance cost
availability in the study area is about 25560 kg/day. The biogas estimation is based on the cattle dung 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 (Thermal load). The balance 133.2323 m3/day is available for generation of electricity.
S. N. 1. 2. 3. 4. 5. 6. 7. 8. 9.
⎡ d (1 + d) n ⎤ ⎥ CRF = ⎢ n ⎢⎣ (1 + d) − 1 ⎥⎦
Table 4 Details of Livestocks and Biogas Potential Total Total Total No. of Biogas Village Name Dung/ day Cattle Generated (kg) (m3/day) Laga Mehra 546 3060 77.112 Saud 591 3310 83.412 Salem Khet 153 850 21.42 Talai lambadi 273 1530 38.556 Bandhan 109 610 15.372 Pungarh 99 550 13.86 Bhangla 501 2810 70.812 Kakhoor 564 3160 79.632 Banskata 1728 9680 243.936 Total 4564 25560 644.112
d = interest rate n = life time The unit cost of different resources is shown in Table 8. Table 8 Sizing & Cost of Energy for different resources S. Cost of Energy Installed Type of Energy Resources N. (Rs/kWh) capacity 1. Micro Hydro Generator 1.45 15 kW 2. Solar Photovoltaic Generator 15.68 23.31 kW 3. Biogas Generator 3.98 20 kW 4. Biomass Generator 4.78 34 kW 5. Diesel Engine Generator 11.0 46 kW 6. Battery 3.26 106 kWh 7. Inverter 35 kW 8. Photovoltaic-inverter 17.72 9. Battery-inverter 4.33 -
• Biomass (fuelwood) Energy: To assess the biomass potential, agricultural and forest waste (fuelwood) have been considered. On the basis of data published [7] from all the nine villages it is estimated that about 1083.35 Tonnes/year fuelwood and 44.71 Tonnes/year 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 livestocks. The balance 147.757 Tonnes/year is available for generation of electricity. C.
E.
A computer program is developed to determine the optimal sizing of system components by minimising the system unit cost. Data input to the program are i) Hourly load demand data for the design month.
ii) Hourly solar radiation data for the design month. iii) Unit cost of different components and other required parameters. The program will use these input to determine optimum size of each system component. The program repeatedly simulates hourly system operation over the month for every component combinations. The feasible solutions are ranked by system unit cost. The optimum sizing of different component is given in Table 8.
Demand Assessment
The data for load demand estimation has been collected on the basis of questionnaire. The energy demand was estimated by considering the household load (lighting, T.V., 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). The total energy requirement is estimated as 1271.61 kWh/day in summer and 608.97 kWh/day in winter. A detail of total load of cluster of nine villages with demand side management is shown in Table 5. The general details of villages and electric appliances used for villages are shown in Table 6 and Table 7. D.
System sizing
V.
Optimized Simulations Results
The optimized simulation results for hybrid energy system for a design month August using developed computer algorithm are shown in Table 11, 12, 13 and 14 and monthly results in Table 15. Different values for parameters used for simulation purpose is shown in Table 9 & 10. 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/kWh) as it has to be run 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./kWh). So the stand-alone system will cost more money than it is necessary Regarding the biogas energy it is clear that potential of biogas is sufficient with second lowest cost of energy.
Unit cost of resources
The cost of energy generated by a renewable energy resource is obtained by adding the capital recovery cost and operation & maintenance cost per unit of energy. Typical calculations are made on an annual basis and the cost of energy in Rs/kWh is calculated by the following expressions: ALCC (Rs) COE = (1) Total annual energy generated (kWh)
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Table 5 Details of Total Load (in kWh) of Nine Villages Time Segment (Hours)
Winter
Summer
0:00-1:00 1:00-2:00 2:00-3:00 3:00-4:00 4:00-5:00 5:00-6:00 6:00-7:00 7:00-8:00 8:00-9:00 9:00-10:00 10:00-11:00 11:00-12:00 12:00-13:00 13:00-14:00 14:00-15:00 15:00-16:00 16:00-17:00 17:00-18:00 18:00-19:00 19:00-20:00 20:00-21:00 21:00-22:00 22:00-23:00 23:00-24:00 Daily Load March Load April Load May Load June Load July Load Aug. Load Sep. Load Oct. Load Nov. Load Dec. Load Jan. Load Feb. Load Yearly Load
Household Load Fan Lighting T.V. (90 W) (55 W) Load Summer/ (11 W) Winter
5.522 5.522 11.044 11.044
45.18 45.18
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 2567.73 2484.90 2567.73 2484.90 2567.73 2567.73 2484.90 2567.73 2484.90 2567.73 2567.73 2319.24 30232.95
406.62 12605.22 12198.60 12605.22 12198.60 12605.22 12605.22 12198.60 12605.22 12198.60 12605.22 12605.22 11385.36 148416.3
Radio/ Music System (25 W)
Electrical Load (kWh) Commercial Load Lights Floor Mill for Small (5 kW) Shops (20 W)
Industrial Community Load Load Saw Mill One Street School /Paddy Primary Lights Lights Huller Health (20 W) (20 W) (5 kW) Centre (20 W) 2.0 2.0 2.0 2.0 2.0 2.0
12.55 12.55
55.22 / 0.0 55.22 / 0.0 55.22 / 0.0 55.22 / 0.0 55.22 / 0.0 12.55 55.22 / 0.0 12.55 55.22 / 0.0 55.22 / 0.0 55.22 / 0.0 55.22 / 0.0 55.22 / 0.0 55.22 / 0.0 662.64 / 0 20541.84 19879.20 20541.84 19879.20 20541.84 20541.84 19879.20 20541.84 0.0 0.0 0.0 0.0 162346.8
50.20 1556.2 1506.0 1556.2 1506.0 1556.2 1556.2 1506.0 1556.2 1506.0 1556.2 1556.2 1405.6 18323.0
5.0 5.0 5.0 5.0
0.34 0.34 0.34 0.34
1.36 42.16 40.80 42.16 40.80 42.16 42.16 40.80 42.16 40.80 42.16 42.16 38.08 496.40
0.040 0.040 0.040 0.040
20.0 620 600 620 600 620 620 600 620 600 620 620 560 7300
Table 6 General Details of Clusters of Villages Parameters 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
20.0 620 600 620 600 620 620 600 620 600 620 620 560 7300
0.360 11.16 10.80 11.16 10.80 11.16 11.16 10.80 11.16 10.80 11.16 11.16 10.08 131.40
2.0 2.0 2.0 2.0 2.0 2.0 24.0 744 720 744 720 744 744 720 744 720 744 744 672 8760
2.0 2.0 2.0 2.0 7.522 7.522 11.044 68.774 0.72 58.49 0.72 10.76 0.72 10.76 0.72 65.98 / 10.76 0.72 111.16 / 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 3.60 1271.61 / 608.97 111.6 39419.91 108.0 38148.30 111.6 39419.91 108.0 38148.30 111.6 39419.91 111.6 39419.91 108.0 38148.30 111.6 39419.91 108.0 18269.10 111.6 18878.07 111.6 18878.07 100.8 17051.16 1314.0 384620.85
Thermal Load (kWh) Cooking Load
335.05
670.08
670.08
1675.21 51931.51 50256.30 51931.51 50256.30 51931.51 51931.51 50256.30 51931.51 50256.30 51931.51 51931.51 46905.88 611451.65
Table 9 Different parameters used for simulation
Details 502 3223 3 2 1 1 1 1 16 / 1
S. N. Various Generators Efficiency 1. Micro Hydro Generator (MHG) 0.60 2. Solar Photovoltaic Generator (PVG) 0.1154 3. Biogas Generator (BGG) 1.0 4. Biogas Energy System 0.27 5. Biomass Generator (BMG) 1.0 6. Biomass Energy System 0.21 7. Diesel Engine Generator (DEG) 1.0 8. Battery Charging efficiency 0.90 9. Battery Discharging efficiency 1.0 10. Rectifier / Inverter efficiency 0.95 11. Charge Controller efficiency 0.90
Table 7 Electric Appliances used for the Villages Appliances Quantity/HH or room 1 or 2 Points CFL for HH Lighting 1 Points CFL for small shops 1 Points CFL for health centre [1 Pole @ Clusters of 5 HH] CFL for street lights 1 Points CFL for school lighting 1 Colour T.V. (36 cm) 2 Ceiling fan 1 Radio/Music system
5.0 5.0 5.0 5.0
0.040 0.040 0.040 0.040 0.040
Total Electrical Load/Hour (kWh) Summer/ Winter
Total 1004 17 2 100 36 502 1004 502
Table 10 Operating period for different generators S. N. Various Generators Operating Period 22 hour/day 1. Micro Hydro Generator 2. Solar Photovoltaic Generator 3. 10 hour/day Biogas Generator 4. 12 hour/day Biomass Generator 5. 10 hour/day (max) Diesel Engine Generator
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Table 11 Hourly Simulation Results for Design Month August Time ELOAD EMHG EBGG EBMG EPVG EPVG-INV ENETLOAD ESURPLUS EBATT-INV EBATT-LEFT EDEG Segment (kWh) (kWh) (kWh) (kWh) (kWh) (kWh) (kWh) (kWh) (kWh) (kWh) (kWh) First Day Simulation 2.0 0.0 2.0 0.0 2.0 103.8947 0:0-1:0 2.0 2.0 0.0 0.0 5.3865 0.0 106.0 1:0-2:0 2.0 2.0 0.0 0.0 5.3865 0.0 106.0 2:0-3:0 2.0 2.0 0.0 0.0 5.3865 0.0 106.0 3:0-4:0 0.0 0.0 1.1373 0.0 106.0 4:0-5:0 7.522 7.522 0.0 0.0 1.1373 0.0 106.0 5:0-6:0 7.522 7.522 2.0979 1.9930 0.0509 0.0 0.0509 105.9464 6:0-7:0 11.044 9.0 5.3615 5.0934 34.6805 0.0 34.6805 69.4406 7:0-8:0 68.774 9.0 20.0 8.8581 8.4151 21.0748 0.0 21.0748 47.2566 8:0-9:0 58.49 9.0 20.0 - 11.4223 1.7600 0.0 7.7514 0.0 55.0080 9:0-10:0 10.76 9.0 - 13.2872 1.7600 0.0 9.2620 0.0 64.2700 10:0-11:0 10.76 9.0 9.5435 0.0 73.8135 11:0-12:0 65.98 9.0 20.0 34.0 14.9189 2.9800 0.0 12:0-13:0 111.16 9.0 20.0 34.0 14.918914.1730 33.9870 0.0 33.9870 38.0377 54.3687 24.7771 13:0-14:0 100.40 9.0 20.0 34.0 13.287212.6228 24.7771 16.3310 0.0 34.0 11.422310.8512 1.3688 34.3437 0.0 88.7124 1.3688 14:0-15:0 55.22 9.0 34.0 9.0912 8.6367 16.1334 0.0 16.1334 71.7298 15:0-16:0 67.77 9.0 34.0 4.8720 4.8720 19.8980 0.0 19.8980 50.7846 16:0-17:0 67.77 9.0 0.8310 49.9098 46.0 17:0-18:0 111.824 9.0 20.0 34.0 2.0979 1.9930 46.8310 0.0 0.0 50.824 0.0 4.824 44.8319 46.0 18:0-19:0 113.824 9.0 20.0 34.0 0.0 50.824 0.0 4.824 39.7540 46.0 19:0-20:0 113.824 9.0 20.0 34.0 0.0 50.824 0.0 4.824 34.6761 46.0 20:0-21:0 113.824 9.0 20.0 34.0 0.0 44.922 0.8295 0.0 35.5056 44.922 21:0-22:0 107.922 9.0 20.0 34.0 34.0 0.0 14.220 24.4547 0.0 59.9603 14.220 22:0-23:0 57.22 9.0 0.0 2.0 33.8580 0.0 93.8183 2.0 23:0-24:0 2.0 Second Day Simulation 0.0 2.0 0.0 2.0 91.7130 24:0-25:0 2.0 2.0 0.0 0.0 5.3865 0.0 97.0995 25:0-26:0 2.0 2.0 0.0 0.0 5.3865 0.0 102.4860 26:0-27:0 2.0 2.0 0.0 0.0 5.3865 0.0 106.0 27:0-28:0 2.0 0.0 0.0 1.1373 0.0 106.0 28:0-29:0 7.522 7.522 0.0 0.0 1.1373 0.0 106.0 29:0-30:0 7.522 7.522 2.0979 1.9930 0.0509 0.0 0.0509 105.9464 30:0-31:0 11.044 9.0 5.3615 5.0934 34.6805 0.0 34.6805 69.4406 31:0-32:0 68.774 9.0 20.0 8.8581 8.4151 21.0748 0.0 21.0748 47.2566 32:0-33:0 58.49 9.0 20.0 - 11.4223 1.7600 0.0 7.7514 0.0 55.0080 33:0-34:0 10.76 9.0 - 13.2872 1.7600 0.0 9.2620 0.0 64.2700 34:0-35:0 10.76 9.0 9.5435 0.0 73.8135 35:0-36:0 65.98 9.0 20.0 34.0 14.9189 2.9800 0.0 36:0-37:0 111.16 9.0 20.0 34.0 14.918914.1730 33.9870 0.0 33.9870 38.0377 54.3687 24.7771 37:0-38:0 100.40 9.0 20.0 34.0 13.287212.6228 24.7771 16.3310 0.0 34.0 11.422310.8512 1.3688 34.3437 0.0 88.7124 1.3688 38:0-39:0 55.22 9.0 34.0 9.0912 8.6367 16.1334 0.0 16.1334 71.7298 39:0-40:0 67.77 9.0 34.0 4.8720 4.8720 19.8980 0.0 19.8980 50.7846 40:0-41:0 67.77 9.0 0.8310 49.9098 46.0 41:0-42:0 111.824 9.0 20.0 34.0 2.0979 1.9930 46.8310 0.0 0.0 50.824 0.0 4.824 44.8319 46.0 42:0-43:0 113.824 9.0 20.0 34.0 0.0 50.824 0.0 4.824 39.7540 46.0 43:0-44:0 113.824 9.0 20.0 34.0 0.0 50.824 0.0 4.824 34.6761 46.0 44:0-45:0 113.824 9.0 20.0 34.0 0.0 44.922 0.8295 0.0 35.5056 44.922 45:0-46:0 107.922 9.0 20.0 34.0 34.0 0.0 14.220 24.4547 0.0 59.9603 14.220 46:0-47:0 57.22 9.0 0.0 2.0 33.8580 0.0 93.8183 2.0 47:0-48:0 2.0
Table 12 Simulation Results for Daily and Monthly Description of Parameters Total Load (kWh) Total MHG output (kWh) Total BGG output (kWh) Total BMG output (kWh) Total PVG output (kWh) Total REG output (kWh) Load by MHG (kWh) Load by BGG (kWh) Load by BMG (kWh) Load by PVG-INV (kWh) Load by REG (kWh) Load by DEG (kWh) Load by Battery (kWh) Total Unmet Energy (kWh) Total Dump Energy (kWh)
DEG Status
EUNMET EDUMP Unit Cost (kWh (kWh Rs./kWh
On-Run Run-Off On-Run Run Run Run Run Run Run-Off
-
3.2812 5.3865 5.3865 1.1373 1.1373 -
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
On-Run Run-Off On-Run Run Run Run Run Run Run-Off
-
1.8725 1.1373 1.1373 -
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
Table 13 Economic Results Parameters Rs. / kWh 11.0 Optimum Maximum Hourly Unit Cost of HES 1.45 Optimum Minimum Hourly Unit Cost of HES 6.24 Optimum Daily Unit Cost of HES 6.24 Optimum Monthly Unit Cost of HES
Daily* Monthly** Percentage 1271.61 39419.91 100.0 198 6138 200 6200 408 12648 111.8918 3468.6458 917.8918 28454.6458 174.044 5395.364 13.6869 200 6200 15.7281 408 12648 32.0853 75.1502 2329.6562 5.9098 857.1942 26573.0202 67.4101 271.2879 8409.9249 21.3342 143.1276 4436.9556 11.2557 0.0 0.0 0.0 4.1471 128.5601 0.3261
Table 14 Fuel Consumption & Battery Results Parameters DG Fuel Consumption (Liters) DG Run Hours (h) DG Start-Stops Battery Initial State (kWh) Battery Final State (kWh) * Hourly Simulation Results after 24 hours ** Monthly Simulation Results after 744 hours
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Daily* 70.60 9.0 2.0 93.8183 93.8183
Monthly** 2188.84 279.0 62.0 -
Table 15 Monthly results Description of Month 1 Parameters (kWh) 18878.07 Total Load 0.0 Total MHG output Total BGG output 6045.0 Total BMG output 11346.0 Total PVG output 2981.42 Total REG output 20372.42 0.0 Load by MHG 5302.36 Load by BGG 9896.81 Load by BMG Load by PVG-INV 1665.62 Load by REG 16864.79 0.0 Load by DEG Load by Battery-inv 2013.27 0.0 Unmet Energy (%) Dump Energy (%) 0.09 0.0 Diesel Fuel Consumption (L) 0.0 DG Run Hours (h) 0.0 DG Start-Stops 5.65 Daily Unit Cost Monthly Unit Cost 5.65
Month 2 Month 3 Month 4 Month 5 Month 6 Month 7 Month 8 Month 9 Month 10 Month 11 Month 12 17051.16 0.0 5600 9520 2793.57 17913.57 0.0 5349.23 7987.05 1706.49 15042.78 0.0 2008.37 0.0 0.39 0.0
39419.91 38148.3 39419.91 38148.3 39419.91 39419.91 6138 5940 6138 5940 6138 6138 6200 6000 6200 6000 6200 6200 12648 12240 12648 12240 12648 12648 3858.85 4349.80 4884.99 4139.98 4162.35 3468.64 28844.85 28529.80 29870.99 28319.98 29148.35 28454.64 5395.36 5221.32 5395.36 5221.32 5395.36 5395.36 6200 6000 6200 6000 6200 6200 12648 12240 12648 12240 12648 12648 2495.65 2768.952 3080.75 2702.34 2689.44 2329.65 26739.01 26230.27 27324.11 26163.66 26932.81 26573.02 8709.51 8275.551 8139.02 8303.901 8661.45 8409.92 3971.36 3642.636 3956.74 3680.715 3825.62 4436.95 0.0 0.0 0.0 0.0 0.0 0.0 1.43 0.02 0.64 0.96 2.12 0.33 2262.54 2072 2122.20 2158.89 2250.72 2188.84
0.0 0.0 5.77 5.77
279 31 6.35 6.35
240 30 6.44 6.44
248 31 6.45 6.45
255 30 6.43 6.43
Average Annual Unit Cost (Rs./kWh)
279 31 6.40 6.40
279 62 6.24 6.24
38148.30 5940 6000 12240 3678.42 27858.42 5221.32 6000 12240 2407.29 25868.61 8038.98 4240.67 0.0 1.02 2093.72 270 60 6.28 6.28
39419.91 18269.10 18878.07 6138 0.0 0.0 6200 6000 5580 12648 10200 11594 3627.60 2923.17 2500.30 28613.60 19123.17 19674.30 5395.36 0.0 0.0 6200 5731.32 5302.36 12648 8557.56 9896.81 2315.91 1801.81 1669.65 26559.27 16090.69 16868.83 8313.81 0.0 0.0 4546.80 2178.40 2009.23 0.0 0.0 0.0 0.58 0.05 0.03 2165.20 0.0 0.0 279 62 6.22 6.22
0.0 0.0 5.75 5.75
0.0 0.0 5.65 5.65
6.14
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/kWh followed by biomass energy system (Rs. 4.98/kWh), diesel generator (Rs. 11.0/kWh). The optimized annual system unit cost is determined by taking the average of optimized unit cost of all months, which comes out to be Rs. 6.14/kWh.
[3] K. Rajashekara, “Hybrid fuel cell strategies for clean power generation,” in Proc. 2004 of IEEE, pp. 2077-2083, March 1993.. [4] A. Rosenthal, S. Durand, M. Thomas and H. Post, “Economic analysis of PV hybrid power system: Pinnacles National Monument,” in Proc. of IEEE Photovoltaic Specialists Conf., pp. 1269-1272. [5] W.D. Kellog, M.H. Nehrir, G. Venkataramanan, and V. Gerez, “Generation unit sizing and cost analysis for standalone wind, photovoltaic, and hybrid wind/pv systems,” IEEE Transactions on Energy Conversion, vol. 13, No. 1, pp. 70-75, March 1998. [6] H. C. de. Coninck, K. J. Dinesh, A. Kets, S. Maithel, P. Mohanty, and H. J. de Vries, “Providing electricity to remote villages-Implementation models for sustainable of India’s rural power,” Energy research centers of Netherlands, ECN Rep. ECN-C-05-037, July 2005. [7] “Unelectrified villages: surveys, potential sources and electricity demand,” Alternate Hydro Energy Centre, I.I.T. Roorkee, Main Project Report, 2005, vol. II (4/12). [8] Solar Radiation over India, 3rd ed., Allied Publishers Private Limited, India, 1982, pp. 302–303.
VI. Conclusions Given the fact that a hybrid energy system consisting two or more energy system has the advantage of stability, supply the power on sustainable basis. The objective of the electrification at the study area can be achieved by making use of solar, micro hydro, biogas, biomass, battery with diesel generator based hybrid energy system. The system was modelled by making use of the computer program based on combined dispatch strategy, developed in C++. Depending upon the variation in discharge and availability of other resources and future increase in demand, the hybrid energy system as indicated above may be able to fulfil the demand of study area in the integrating manner. 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.
Biographies Ajai Gupta was born in Bareilly, India. He received the B. Tech in Electrical Engineering from I.E.T. Rohilkhand University, Bareilly in 2000 and M. Tech in Instrumentation and Control from Aligarh Muslim University, Aligarh in 2004 respectively. Currently he is a Research Scholar at Alternate Hydro Energy Centre, Indian Institute of Technology, Roorkee, India.
References [1] R. Ramakumar, I. Abouzahr and K. Asenyayi, “A Knowledge-Based approach to the Design of Integrated Renewable Energy Systems,” IEEE Trans. on Energy Conversion, vol. 7, No. 4, pp. 648-657, 1992.. [2] R. Ramakumar, I. Abouzahr, K. Krishnan and K. Ashenayi, "Design Scenario for integrated Renewable Energy Systems", IEEE Trans. on Energy Conversion, Vol. 10, No. 4, pp. 736746, December 1995.
Dr. R. P. Saini is serving as an Associate Professor at A.H.E.C, Indian Institute of Technology, Roorkee, India. Dr. M. P. Sharma has been working as an Associate Professor at A.H.E. C., Indian Institute of Technology, Roorkee, India since the last 20 years
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