The gap between energy demand and its generation is constantly widening. People have started giving more emphasis on renewable sources of energy. This paper presents the estimation of potential for wind energy generation maps based on fixed wind turbine capacity. Although wind energy has developed substantially in recent years, we have only wind speed and wind potential density maps. Our attempt here is to generate wind energy generation potential maps. Major step in achieving this goal is modeling of wind energy conversion system using TRNSYS software. The model consists of three main components namely the weather, the turbines and energy conversion parameters. The weather data are provided from the meteorological database, namely Meteonorm. The simulated output is compared with actual wind generation of wind farms. After comparing our model results with the existing wind energy generation data, we have extended to compute the wind energy generation for all locations in India. For simulation, 4691 locations are identified considering 0.25 ° × 0.25 ° interval. The energy generation simulated data are compiled and developed into maps that are useful to all wind energy developers. The data generated and presented in the form of maps are for all the 30 states of India.
The exponential increase in utilization of electrical energy and the constant decrease in conventional sources of energy have led to huge gap between demand and supply of electrical energy. This has led the people to switch over to renewable sources of energy such as solar, wind, biomass, geothermal, etc. According to International Energy Agency, India and China are likely to consume more than 28 percent of the world total energy by 2030. Renewable energy sources must contribute to a significant amount to protect our environment as they are least pollutants [
Among the various renewable sources of energy, wind is one of the most important sources and has widely gained attention in recent years. Although people harnessed energy from wind since ancient times, it was in different forms. Wind turbines were previously used for pumping water, grinding grains, etc. in some parts of the world before they are used for power generation [
Wind is considered as a promising alternative for power generation because of its environmental and economic benefits such as reduced greenhouse gas emission, reduced fuel cost and provides clean and cost effective energy [
The factors that influence the energy produced by wind energy generators (WTG) over a particular location include: 1) power curve of the wind turbine for different wind speed, 2) good distribution of wind velocity within a location and 3) strength of prevailing wind speed in the area. The total energy generated by WTG over a period can be calculated by summation of energies corresponding to all operational wind speed [
Our study presents an approach to develop wind energy generation map based on a typical wind turbine size and also presents a method of wind resource assessment in India. The selection of a particular wind turbine size is chosen in our study is based on
State | Total Cumulative Capacity (MW) up to March 2016 |
---|---|
Tamil Nadu | 7652.6 |
Maharashtra | 4671.4 |
Gujarat | 4031.0 |
Rajasthan | 3995.1 |
Karnataka | 2878.7 |
Madhya Pradesh | 2171.6 |
Andhra Pradesh | 1393.9 |
Kerala | 43.5 |
Telangana | 77.7 |
Others | 4.3 |
Total | 26,919.8 |
the majority usage of a wind turbine in the country. Based on this criterion, 0.8 MW turbine is preferred in our study.
Three software’s are used in the development of wind energy generation map. They are: a) Meteonorm, b) TRNSYS and c) Surfer. Meteonorm is a meteorological database that gives access to meteorological data for every location in the world that can be used in a variety of applications [
The methodology used in this study is to evaluate the wind energy potential conducted by a series of steps. First, the wind data is collected from a weather database and then a reference turbine model is selected followed by development of wind power conversion in TRNSYS software.
As discussed before and above the wind speed data for our study are taken from a meteorological database-Meteonorm. It gives weather information at a universally accepted reference data collection from a height of 10 meters. The database provides an average value collected over a period of 10 years. The data is retrieved in a standard TMY2 format that is also the format used by TRNSYS.
The annual average wind speed of India varies from 6 to 7 m/s. For this reason mostly class II and III wind turbines are used in our country. For assessment, a wind turbine from Enercon model of E-53 of 800 kW is chosen. This turbine size is selected because of the following reasons: 1) it represents the size that is most often used in the nation 2) it comes from a manufacturer that is known for its high quality 3) this turbine has a standard hub height of 75 meters that is widely being used [
Parameter | Value |
---|---|
Rated power | 800 kW |
Rotor diameter | 52.9 m |
Hub height | 75 m |
No. of blades | 3 |
Swept area | 2198 sq.m. |
Cut-in speed | 3 m/s |
Rated wind speed | 13 m/s |
Cut-off speed | 28 - 34 m/s |
Wind is not constant but varies with time. The variation of wind speed with height is called wind shear. It necessitates the need to convert the recorded wind speed to the height of the turbine used. This conversion is achieved using the standard wind profile power law. This power law is widely used for wind resource assessment where wind speed for various heights is retrieved from the standard recorded wind data. The wind profile power law relationship can be expressed as
where,
z is the desired height (m),
The exponent is an empirically derived coefficient that varies depending upon the stability of the atmosphere. Generally, the coefficient is taken as 1/7 or 0.143 for wind resource assessment. Thus, this value of coefficient is chosen for our study [
where, P is the power extracted from wind in Watts, ρ represents the air density, generally taken as 1.225 kg/cubic m. The swept area of the rotor is represented by A in sq.m. and V is the wind velocity in m/s. The parameter
The model used for converting kinetic energy of wind to electrical energy in TRNSYS is shown in
In the model presented the input is meteorological data in TMY2 format from Meteonorm. This information is then fed to the wind turbine component named Type-90 in TRNSYS. The turbine has the same technical specification as that of the Enercon E-53 model. The output of turbine gives generated power in the units of Watts read using Printegrator or Type-46b component. In addition to these components, an equation that converts the atmospheric pressure from the units of atm to Pa is used. It is inserted between the weather data and the wind turbine component.
For mapping the wind energy generation, the essential step is to collect the wind data of the desired location. For this purpose, data is collected for the entire country at 4691 locations. They are chosen in a grid manner of 0.25˚ × 0.25˚ station interval. This data is then contoured using Surfer for developing the wind energy generation maps.
In the system, the simulation control card is adjusted for one year (8760 hours) with a time interval of 0.125 hour.
The developed model has been validated with actual data of wind energy at a few locations. This is clearly depicted as shown in
After validation, the next step is to create maps of energy generation for different lo-
cations. Surfer 10 software is used for this purpose [
From the maps, it is visible that some states like Gujarat, Rajasthan, Tamil Nadu, Karnataka, Kerala, Maharashtra, Madhya Pradesh and Uttar Pradesh have higher energy generation than other states. Also it is observed that the wind speed is higher during May to August. Accordingly, there is higher energy generation in these months. However, the other months have relatively lower energy generation due to low wind speed. In general, the overall annual generation in India ranges till 1600 MWh.
It is well known that as we go higher from the ground level, wind velocity increases with increasing altitudes. As a result, some elevated areas have higher energy generation due to greater wind speed. For example, Deccan Plateau is elevated at 600 m and inclined towards south western part of India. This is the reason for higher generation in southern states. It is also clearly depicted that the western part of Madhya Pradesh has higher energy generation due to the presence of Satpura range of hills. There is a sudden change in energy generation near this region in the form of a straight line because of the presence of lower elevation surrounding the range of hills. Wind power density map of India is shown in
Considering 0.8 MW (Enercon E-53) wind turbine as a reference model, wind energy conversion system is designed and simulated in TRNSYS. The system’s simulated generation output is compared with the actual data for a few wind farms. The deviation
No. | State | District | ||
---|---|---|---|---|
Higher Generation (1100 - 1600 MWh) | Medium Generation (600 - 1100 MWh) | Lower Generation (0 - 600 MWh) | ||
1. | Andhra Pradesh | --- | Anantapur (600 - 900) | Nellore, Kurnool, Prakasam, Guntur, Krishna, West Godavari, East Godavari, Vishakhapatnam, Vizianagaram, Srikakulam (0 - 350), Chittoor, Cuddapah (400 - 600) |
2. | Arunachal Pradesh | --- | --- | Itanagar, East Kameng, Lower Subansiri, Southern part of Upper Subansiri, Lower Dibang Valley, Longding, Tirap, Changlang, Namsai, Lohit, West Siang, Upper Siang, Southern part of KurungKumey (0 - 200), Western part of Tawang, West Kameng, Dibang Valley, Anjaw (200 - 350), Eastern part of Tawang, Northern part of Upper Subansiri, Northern part of KurungKumey (400 - 500) |
3. | Assam | --- | --- | Dhubri, Kokrajhar, Goalpara, Bongaigaon, Chirang, Barpeta, Nalbari, Baksa, Udalguri, Darrang, Dispur, Morigaon, Sonitpur, Kamrup Metropolitan, Nagaon, KarbiAnglong, Golaghat, Dima Hasao, Cachar, Karimganj, Hailakandi, Jorhat (0 - 30), Lakhimpur (40 - 55), Sivasagar, Dhemaji, Dibrugarh, Tinsukia (60 - 80) |
4. | Bihar | --- | --- | PaschimChamparan, Gopalganj, Kishanganj, Rohtas, Gaya, Aurangabad (0 - 100) PurbaChamparan, Sitamarhi, Shivhar, Siwan, Araria, Bhagalpur, Nalanda, Munger, Bhojpur, Buxar, Kaimur, Arwal, Jehanabad, Newada, Jamui, Banka, Sheikhpura (100 - 200), Saran, Muzaffarpur, Madhubani, Darbhanga, Supaul, Vaishali, Samastipur, Medhupura, Saharsa, Purnia, Katihar, Patna, Begusarai, Khagaria (200 - 250) |
5. | Chhattisgarh | --- | --- | Surguja, Surajpur, Koriya, Korba, Mungeli, Bilaspur, Janjgir-Champa (0 - 50), Jashpur, Raigarh, Kawardha, Bemetara, Baloda Bazar, Durg Raipur, Mahasumand, Balod, Dhamtari, Gariaband, Uttar Kanker (50 - 100), Balrampur, Narayanpur, Kondagaon, Bastar, Dantewada, Bijapur, Sukma (100 - 150) |
6. | Delhi | --- | --- | Entire district (200 - 300) |
---|---|---|---|---|
7. | Goa | --- | --- | All districts (0 - 500) |
8. | Gujarat | --- | Surendranagar, Botad, Dahod, Rajkot, Jamnagar, Amreli, Junagadh, GirSomnath, Porbandar, DevbhumiDwarka, Chhota Udaipur (900 - 1200) | Banaskantha, Patan, Aravalli, Mahisagar, Sabarkantha, Mehsana, Ahmedbad, Gandhinagar, Kheda, Anand, Vadodara, Narmada, Bharuch, Surat, Tapi, Navsari, Valsad, The Dangs, Bhavnagar, PanchMahal, Kutch(0 - 600) |
9. | Haryana | --- | --- | Panchkula, Ambala, Yamunanagar, Kurukshetra, Karnal, Kaithal, Jind, Fatehabad, Sirsa, Hisar, Bhiwani, Panipat (0 - 300), Jhajjar, Gurgaon, Faridabad, Mewat (300 - 400), Sonipat, Rohtak, Mahendranagar, Palwal (175 - 300) |
10. | Himachal Pradesh | --- | --- | Una, Bilaspur, Hamirpur, Solan, Kangra, Shimla, Mandi, Central and south of Chamba, Western border of Kullu (0 - 200), Lahul and Spiti, Kinnaur, North eastern part of Chamba (400 - 600), Remaining all parts of Kullu (200-400) |
11. | Jammu & Kashmir | --- | Rajouri (750 - 900) | Bandipora, Kupwara, Ganderbal, Baramulla, Badgam, Pulwama, Anantnag, Doda, Ramban, Udhampur, Samba, Kathua, Jammu (0 - 400), Leh (Ladakh), Kargil, Srinagar, Kishtwar, Reasi, Punch (400 - 700) |
12. | Jharkhand | --- | --- | Dumka, Jamtara, Dhanbad, Bokaro, East Singhbhum, West Singhbhum, Jamshedpur, SeraikelaKharswan, Chaibasa (0 - 75), Hazaribag, Ramgarh, Ranchi, Khunti, Lohardaga, Latehar (125 - 200), Sahibganj, Godda, Pakaur, Deoghar, Giridh, Koderma, Chatra, Palamu, Garhwa, Simdega (75 - 125) |
13. | Karnataka | --- | --- | Bidar, Gulbarga, Remaining parts of Bijapur, Yadgir, Eastern part of Raichur (0 - 250), Karwar, Haveri, Chitradurga, Devangere, Shimoga, Udupi, Dakshin Kannada, Chikmagalur, Hassan, Kodagu, Chamarajanagar, Upper part of Tumkur (500 - 700), Southern part of Bijapur, Western part of Raichur, Bagalkot, Belgaum, Gadag, Dharwad, Kopal, Bellary, Lower part of Tumkur, Bangalore, Mysore, Kolar, Chikkaballapura, Mandya, Channapatna (250 - 500) |
14. | Kerala | --- | Eastern part of Palakkad, eastern part of Idukki (700 - 1000) | Kannur, Kozhikode, Remaining Malappuram, Western Palakkad, Thrissur, Ernakulam, Kottayam, Alappuzha, Pathanamthitta, Kollam, Thiruvananthapuram (0 - 300), Kasaragod, Wayanad, eastern Malappuram, Western part of Idukki (300 - 550) |
15. | Madhya Pradesh | Ratlam, Jhabua, Alirajpur, Dhar, Indore, Ujjain, Dewas, Khargone, Khandwa, Burhanpur (1100 - 1500) | Southern part of Mandsaur, Agar-Malwa, southern part of Rajgarh, Sehore, Bhopal, Lower Raisen, Harda, Betul, Western Chhindwara, Shajapur (700 - 1100) | Remaining all districts (0 - 500) |
16. | Maharashtra | --- | Palghar, Thane, Upper Raigarh, Upper Nandurbar (600 - 1000), Upper Amravati (1000 - 1200) | Remaining all districts (0 - 600) |
17. | Manipur | --- | --- | Lower Ukhrul, Lower Senapati, Remaining all districts, Upper Ukhrul, Upper Senapati, North part of Tamenglong, Upper Senapati |
18. | Meghalaya | --- | --- | Ri-Bhoi, West Jaintia Hills (0 - 10), Central part of West Garo Hills (20 - 25), Remaining all districts (10 - 20) |
19. | Mizoram | --- | --- | Lawngtlai, Lower Saiha (550 - 700), Remaining Saiha, Lower Lunglei (300 - 550), Remaining all districts (0 - 300) |
---|---|---|---|---|
20. | Nagaland | --- | --- | Eastern part of Tuensang, Eastern part of KiphireSadar, Eastern part of Phek (100 - 225), Remaining all districts (0 - 100) |
21. | Orissa | --- | Khorda, Bhubneswar, Nayagarh, Puri, Jagatsinghpur, Dhenkanal, Cuttack (650 - 800) | Western part of Ganjam, Kendrapara, Jajpur, Eastern Kandhamal, Lower Angul (300 - 600), Remaining Ganjam, Remaining all districts (0 - 300) |
22. | Punjab | --- | --- | North-western Pathankot, West TaranTaran, West Firozpur (175 - 250), Hoshiarpur, Kapurthala, Upper Fazilka, Moga, Upper Faridkot, Jalandhar (100 - 175), Remaining all districts (0 - 175) |
23. | Rajasthan | --- | Upper Jodhpur, Upper Banswara, Upper Pratapgarh, Lower Jhalawar (700 - 900), Jaiselmer, Western border of Barmer, Lower Banswara, Lower Pratapgarh (900 - 1200) | Remaining all districts (0 - 500) |
24. | Sikkim | Eastern Gangtok, Eastern Mangan(500 - 700) | Western Gangtok, Remaining Mangan, Upper Geizing (300 - 500) | Remaining districts (0 - 300) |
25. | Tamil Nadu | Karur, Tiruchirappalli, Upper Sivaganga, Thanjavur, Ariyalur, Pudukkottai, Thiruvarur, Lower Cuddalore, Perambalur (1200 - 1600) | Coimbatore, Erode, Namakkal, Upper Dindigul, Upper Madurai, Nagapattinam (700 - 1200) | Remaining all districts (0 - 700) |
26. | Telangana | --- | --- | Upper Mahbubnagar, Eastern Warangal (200 - 350), Medak, Hyderabad, Upper Nalgonda, Lower Nizamabad (350 - 500), Remaining all districts (0 - 200) |
27. | Tripura | --- | --- | South Tripura (50 - 100), Remaining all districts (0 - 50) |
28. | Uttar Pradesh | --- | --- | Lower Muzaffarnagar, Lower Bijnor, Bahraich, Lower Mathura, Hathras, Etah, Farrukhabad, Upper Mainpuri, Kannauj, Shravasti, Gonda, Faizabad, Sultanpur, Rae bareli, Fatehpur, Banda, Chitrakoot, Kaushambi, Pratapgarh, Jaunpur, Allahabad, Sultanpur, Upper Azamgarh,Upper Mau, Varanasi, Mirzapur, Sonbhadra, SantKabir Das Nagar, Chandauli, Ghazipur, Ballia (125 - 225), Bagpat, Meerut, Ghaziabad, JyotibaPhulenagar, Rampur, Moradabad, Gautam Buddha Nagar, Bulandshahr, Badaun, Bareilly,Pilibhit, Shahjahanpur, LakhimpurKheri, Sitapur, Hardoi, Bara Banki, Lucknow, Unnao, Kanpur, Aligarh, Upper Mathura (225 - 300), Remaining all districts (0 - 125) |
29. | Uttarakhand | --- | --- | Lower Uttarkashi, Central Uttarkashi, Upper border of RudraPrayag, Lower Udham Singh Nagar (200 - 350), Eastern Uttarkashi, Upper half of Chamoli, Upper half of Pithoragarh (350 - 500), Remaining all districts (0 - 200) |
30. | West Bengal | --- | --- | Maldah, Western part of DakshinDinajpur (100-175), Lower part of Uttar Dinajpur (175 - 250), Remaining all districts (0 - 100) |
observed between the two is small and validates our system. It can thus be utilized for estimating the energy generation at any location. Accordingly energy generation is computed for entire country and the values are contoured for map generation. Small deviation may be because the database Meteonorm gives approximate weather average data instead of actual causing a deviation of around 5 - 10 percent. The developed energy generation maps are different and more useful from the existing wind power density maps in two ways. First, energy generation maps provide exact generation potential at a particular location. Whereas the power density maps only provide with information about the wind potential based on wind speed. Also in some places the potential density maps show no potential but there is such a possibility in energy generation maps with proper advanced technology. The common similarity between the two is that there is high energy generation where there is high potential density. We have many wind power density maps at different heights but no one has so for computed for the energy generation maps for a fixed capacity and hub height of wind turbine.
Authors would like to thank Sri J. N. Singh, VCMT of Gujarat Energy Research & Management Institute (GERMI), Gandhinagar, for being constant source of inspiration and encouraging us to share our knowledge and experience in the form of publication for greater benefit of the solar industry in India and abroad. We would also like to thank Mr. Prashant Gopiyani, the Coordinator of Summer Internship 2016 for all his help and would also like to thank GERMI management for lending the required software’s for this research.
Tank, V., Bhutka, J. and Harinarayana, T. (2016) Wind Energy Generation and Assessment of Resources in India. Journal of Power and Energy Engineering, 4, 25-38. http://dx.doi.org/10.4236/jpee.2016.410002