Scale-up plan for Grid Connected Renewable Energy Technologies. New and Renewable Energy Department Government of Madhya Pradesh

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1 Scale-up plan for Grid Connected Renewable Energy Technologies Task 2 Report: Renewable Energy Potential in Madhya Pradesh Report Prepared For New and Renewable Energy Department Government of Madhya Pradesh Report Prepared By PricewaterhouseCoopers Private Limited February 11, 2014

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3 Table of contents 1. Introduction Context and objective Study approach Limitations of the study 8 2. Assessing solar energy potential Solar potential assessment methodology Estimating availability of land for installing solar projects Gross solar energy potential in the state of Madhya Pradesh Assessing wind energy potential Wind potential assessment methodology Estimating availability of land for installing wind projects Gross wind energy potential in the state of Madhya Pradesh Assessing biomass energy potential Estimating biomass potential from crop residues Estimating biomass power potential from Lantana Mexicana Gross Biomass potential in the state of Madhya Pradesh Assessing small hydro potential Types and schemes of Small Hydropower System Estimating potential from run of river schemes Estimating potential from canal fall based schemes Estimating potential from dam toe based schemes Self identified small hydro projects Gross small hydro potential in the state of Madhya Pradesh Renewable energy potential in the State of Madhya Pradesh 23 Appendix A. - Assessing Solar Energy Potential 24 A.1. Detailed description of assumptions used for estimating solar energy potential 24 Appendix B. - Assessing Wind Energy Potential 25 B.1. Monitoring stations data recorded by CWET in the state of Madhya Pradesh 25 B.2. Detailed description of assumptions used for estimating wind energy potential 26 Appendix C. - Assessing Biomass Energy Potential 27 C.1. Potential of biomass from agricultural residues 27 PwC 3

4 Appendix D. - Assessing Small Hydro Potential 29 D.1. Details of potential canal fall projects in the state of Madhya Pradesh 29 D.2. Details of potential dam outlet projects in the state of Madhya Pradesh 30 D.3. Details of potential run of river projects in the state of Madhya Pradesh 32 D.4. Details of self identified sites for SHP Projects in the state of Madhya Pradesh 34 Abbreviations 35 References 36 PwC 4

5 List of tables Table 1: Key assumptions for estimating solar potential Table 2: Capacity utilization factor in different district of the state Table 3: Wasteland area suitable for solar power plants (km 2 ) Table 4: Solar power potential in the state of Madhya Pradesh Table 5: Particulars used for assessment of capacity utilisation factor Table 6: Estimated capacity utilisation factor at different locations in the state of Madhya Pradesh Table 7: Comparing location advantage for different states Table 8: Wasteland area suitable for wind power plants Table 9: Wind power potential in the state of Madhya Pradesh Table 10: Biomass potential from crop residues Table 11: Biomass potential from crop residues across different districts in Madhya Pradesh Table 12: Biomass potential from Lantana Mexicana Table 13: Gross biomass potential in the state of Madhya Pradesh Table 14: Gross small hydro potential in the state of Madhya Pradesh Table 15: Renewable energy potential in the State of Madhya Pradesh PwC 5

6 List of figures Figure 1: Annual GHI and DNI map of India... 9 Figure 2: Monthly GHI variation in Madhya Pradesh...10 Figure 3: Monthly DNI variation in Madhya Pradesh...10 PwC 6

7 Renewable energy potential in the state of Madhya Pradesh 1. Introduction 1.1. Context and objective Renewable Energy is increasingly becoming an integral part of energy security initiative in Madhya Pradesh. The renewable energy policies formulated by New and Renewable Energy department of Madhya Pradesh, nodal agency for development of renewable energy in the state, are approved by the Government of Madhya Pradesh. All the renewable energy policies have been formulated to assist the developers in executing the projects in the timely manner while laying adequate emphasis on the other aspects such as environmental considerations. With relevant actors for up-scaling the renewable energy technology is place, it is now important to estimate the potential of each of the grid connected technology to provide the interested developers with the information on the potential sites and scale of the projects that could be developed in the state. Madhya Pradesh is the second largest state in India and is sparsely populated. Hence there are good amount of land banks that are available and could be used for developing renewable energy projects. This study is undertaken to estimate the renewable energy potential for the grid connected technologies in the state of Madhya Pradesh Solar, Wind, Biomass and Small Hydro Study approach With an objective to assess the renewable energy potential in the state, the project team deliberated on a pragmatic methodology wherein the available datasets in public domain, publications by the state and central departments and research papers are used along-with analytical tools. In order to estimate the solar potential in the state, firstly the wasteland area available across the state was aggregated and based on the reference studies available, only four different categories of land was selected which were considered to be suitable for solar installation. Thereafter, the RET screen analysis tool was used to assess potential districts with desirable CUF levels. Henceforth, taking a normative parameter of 48MW of solar project can be supported in one sq.km of area; the solar potential across the state was estimated. In order to estimate the wind power potential in the state, the relevant datasets pertaining to dynamics of wind power was aggregated from the CWET centres. Also, the wasteland area available across the state was aggregated and based on the reference studies available, only four different categories of land was selected which were considered to be suitable for wind installation. Thereafter, the RET screen analysis tool was used and likely CUF levels thus obtained was mapped against the districts of the state. Based on a widely accepted normative parameter of 9MW of wind project potential can be supported in one sq.km of area, the wind potential across the state was estimated. For estimating the potential of biomass power potential in the state, agricultural production data made available from Department of Agriculture, Madhya Pradesh was utilised and a normative crop to residue ratio was adopted to estimate the residue production in the state. Thereafter, the operational parameters as prescribed by MPERC with regards to biomass technology were considered and henceforth, the biomass power potential in the state was estimated. With regards to estimation of small hydro power potential in the state, the data aggregated by Alternate Hydro Energy Centre (AHEC). The estimation of the power potential by the above agencies has been adopted for small hydro projects individually for all the different schemes- run-of-river, canal based and dam toe projects. PwC 7

8 Renewable energy potential in the state of Madhya Pradesh 1.3. Limitations of the study The renewable power estimation has been done through building some assumptive premises. While estimating the power potential of all the available renewable energy technologies namely-wind, solar and biomass certain variables in the form of assumptions have been considered. This has brought certain limitations to the study. The wasteland considered for estimating the solar and wind energy potential are not mutually exclusive in nature. Hence, land assumed to be suitable for the stated technologies may overlap with each other. The wind speed or wind power density data collected from the monitoring stations as well as the solar insolation data collected for a particular district holds true only for 50km 2. For the present study, the wind and solar data thus collected has been assumed to hold constant for the entire district. Land required for setting the solar project should have slope less than 3%. However, for the present analysis regarding estimation of solar potential, 10%of the wasteland is assumed to be suitable for setting solar power projects. No physical survey has been conducted on the considered stretch of wasteland to gauge its feature. The data used for assessing solar and wind power is available for western and central regions of the state. Therefore estimated potential for the state may change on availability of data from western regions of the state. The competing use of the agricultural residue as biomass and bagasse has not been considered while estimating the biomass and bagasse power potential and therefore the estimated potential is the maximum potential that could be achieved if the entire biomass residue from the select crops is directed toward the power generation. PwC 8

9 Renewable energy potential in the state of Madhya Pradesh 2. Assessing solar energy potential India is endowed with good solar energy potential with most of the country having about 300 a days of sunshine per year with annual mean daily global solar radiation in the range of 5-7 b kwh/m 2 /day. The amount of solar power available per unit area is known as irradiance. Irradiance is a radiometric term for the power of electromagnetic radiation at a surface, per unit area. Solar radiation that is received as heat and light can be converted to electricity using solar photovoltaic or solar thermal technology. Availability of reliable solar radiation data is vital for the success of solar energy installations. Proper understanding of the solar data and its variations can address the important aspects of solar projects primarily technology selection, site selection, system configuration, performance and operations. Solar irradiation data could be gathered from various sources, primarily through ground observations, through atmospheric models or through satellite based observations. The most prevalent method for gathering solar data is through satellites. Mean data for solar irradiation is a critical parameter while deciding a site for setting up the plants which shall be considered along with solar time series 1. Other site specific parameters that are critical to the success of a solar power plant are ambient air temperature, wind speed and direction and relative humidity. Figure 1: Annual GHI and DNI map of India GHI and DNI in KWh/m 2 /day > Source: MNRE and NREL The radiation reaching the earth's surface can be represented in a number of different ways. Global Horizontal Irradiance 2 (GHI) is the total amount of shortwave radiation received from above by a surface horizontal to the ground. This value is of particular interest to photovoltaic installations and includes both Direct Normal Irradiance 3 (DNI) and Diffuse Horizontal Irradiance 4 (DIF), whereas for solar collectors which are concentrating in nature DNI data is required. 1 Solar time series is a tool used to predict solar irradiance 2 GHI is the total amount of shortwave radiation received from above by a surface horizontal to the ground. This value is of particular interest to photovoltaic installations and includes both Direct Normal Irradiance (DNI) and Diffuse Horizontal Irradiance (DIF). 3 DNI is the amount of solar radiation received per unit area by a surface that is always held perpendicular (or normal) to the rays that come in a straight line from the direction of the sun at its current position in the sky. The amount of irradiance annually received by a surface can easily be maximized by keeping it normal to incoming radiation. This quantity is of particular interest to concentrating solar thermal installations and installations that track the position of the sun. PwC 9

10 Renewable energy potential in the state of Madhya Pradesh For the solar resource assessment, the publicly available solar irradiation data provided by the National Renewable Energy Laboratory (NREL) and NASA is used. The data has been measured for more than 20 year period and is based on highly reliable modeling techniques using information on cloud cover, atmospheric water vapour and gases and the amount of colloids in the upper atmosphere. The irradiation information will be of immense help for providing solar planners, designers, engineers, renewable energy analysts in providing an initial assessment of a site and estimated returns from a solar project. As shown from the data and the resource maps, Madhya Pradesh shows peaking seasons with good solar potential in both summer and winter months. The Monthly GHI and DNI variation, at 10 km resolution based on hourly estimates of radiation over long term in Madhya Pradesh is used in the study conducted by MNRE and NREL. Figure 2: Monthly GHI variation in Madhya Pradesh Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Source: MNRE and NREL > All units in KWh/m 2 /day Figure 3: Monthly DNI variation in Madhya Pradesh Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Source: MNRE and NREL > Solar potential assessment methodology Solar irradiation data could either be collected through satellite imaging or through ground measurements. For the solar resource assessment, the existing data sources are analysed. The first source of analysis would be solar irradiation data provided by NASA that has been measured for more 4 DIF is solar radiation that does not arrive on a direct path from the sun, but has been scattered by molecules and particles in the atmosphere and comes equally from all directions. On a clear day, most of the solar radiation received by a horizontal surface will be DNI, while on a cloudy day most will be DIF. PwC 10

11 Renewable energy potential in the state of Madhya Pradesh than 20 year period and is based on highly reliable modelling techniques using information on cloud cover, atmospheric water vapour and gases, and the amount of colloids in the upper atmosphere. NASA data is represented by a very coarse spatial resolution data and simple empirical models, thus smoothing-out regional climate patterns. The RET-Screen Photovoltaic Project Model has been used to evaluate the energy production and estimate the likely capacity utilisation factor (CUF) that grid connected photovoltaic projects would exhibit when they are installed in the state. Table 1: Key assumptions for estimating solar potential 5 Particulars Key Assumptions Resource Assessment Solar Tracking (Yes/No) No Orientation Fixed Slope provided to the panel As per the latitude of the district/region under analysis Azimuth 0.0 PV Orientation Type Poly Silicon Power Capacity 1000kW Efficiency of the module 13.1% Normal Operating cell temperature 45 o Temperature Coefficient (%/ 0 C) 0.4 Solar Collector Area (m 2 ) 7,639 Miscellaneous Losses 5% Inverter Efficiency 98% Miscellaneous Losses 2% The algorithms for calculating the energy generated from the proposed photovoltaic system based on the six step methodology. c 1. Calculate solar radiation in the plane of solar array 2. Calculate energy produced by the solar array 3. Calculate miscellaneous losses such as plant availability, grid availability, cable and wire losses etc. 4. Estimate inverter efficiency and losses 5. Calculate energy delivered Table 2: Capacity utilization factor in different district of the state Districts Inclination for 0 azimuth DSR Horizontal (kwh/m 2 /day) DSR Tilted (kwh/m 2 /day) Estimated CUF (%) Bhopal Chattarpur Chindwara Dewas Guna Gwalior Hoshangabad Indore Jabalpur Mandla Mandsaur Morena Neemuch Rewa Refer to Appendix A1 PwC 11

12 Renewable energy potential in the state of Madhya Pradesh Districts Inclination for 0 azimuth DSR Horizontal (kwh/m 2 /day) DSR Tilted (kwh/m 2 /day) Estimated CUF (%) Sagar Satna Seoni Shahdol Shajapur Shivpuri Ujjain Estimating availability of land for installing solar projects Solar by virtue of its modular nature possesses the inherent capability to be installed across regions with diverse and distinct topography. The total geographical area of the state is 308,252 d km 2 while the waste land area stands at 40,042 e km 2 Wasteland area represents 13 % of the total geographical area of the state. It can be installed on different categories of wasteland. The wasteland categories used for estimating the potential of solar energy in the state land with dense scrub, land with open scrub, Under-utilised/degraded forest (scrub domin) and Under-utilised/degraded forest (Agriculture). In the wake of carrying out the analysis to estimate the availability of land for installing solar PV projects in the state, it is deliberated to source the relevant data from the Wasteland Atlas of India, 2006 f. All large PV systems require fairly flat land. There are no official records that are available that classifies the land based on slope. To count for topology, the assumption of 10% of the total wasteland area 6 is used to estimate the solar potential in the state. The districts which resulted in CUF of 18% or more are selected for carrying out the analysis for estimating the gross solar potential. Table 3: Wasteland area suitable for solar power plants (km 2 ) District WL-1 (km 2 ) WL-2 (km 2 ) WL-3 (km 2 ) WL-4 (km 2 ) Proportion of WL area used for estimating solar power Total Area used for estimating solar power (km 2 ) Bhopal % Chattarpur % Chindwara % Dewas % Guna % Gwalior % Hoshangabad % Indore % Jabalpur % Mandla % Mandsaur % Morena % Neemuch % Rewa % Sagar % Satna % Seoni % Shahdol % Shajapur % Shivpuri % Only 4 classifications of waste land area are used for estimating the potential of solar photovoltaic - land with dense scrub, land with open scrub, under-utilised/degraded forest (scrub domin) and under-utilised/degraded forest (Agriculture) PwC 12

13 Renewable energy potential in the state of Madhya Pradesh District WL-1 (km 2 ) WL-2 (km 2 ) WL-3 (km 2 ) WL-4 (km 2 ) Proportion of WL area used for estimating solar power Total Area used for estimating solar power (km 2 ) Ujjain % Total Gross solar energy potential in the state of Madhya Pradesh The technical potential of solar energy for given land availability and power density is 88,364 MW. g Table 4: Solar power potential in the state of Madhya Pradesh District Potential District Potential Bhopal Morena Chattarpur Neemuch Chindwara Rewa Dewas Sagar Guna Satna Gwalior Seoni Hoshangabad Shahdol Indore Shajapur Jabalpur Shivpuri Mandla Ujjain Mandsaur Total 88,364 PwC 13

14 Renewable energy potential in the state of Madhya Pradesh 3. Assessing wind energy potential The Wind Resource Assessment Programme is being implemented in the country through the Centre for Wind Energy Technology (C-WET), an autonomous institution of MNRE and associated Nodal Agencies. The data for Wind Resource Assessment is collected from about 1244 h wind monitoring stations installed in 31 i states and Union Territories. According to CWET, the wind power potential in India at 50 m hub-height is estimated to be 49,130 j MW and at 80 m hub-height is estimated to be 1,02,788 k MW. The assessment of wind potential is made by assuming that 2% l of the total land availability for wind power generation in the potential areas. However the state of Madhya Pradesh has very low population density and large proportion of wastelands that could be utilised by wind power projects. The land availability for wind power projects in the state of Madhya Pradesh is substantially higher than estimated by CWET on pan India basis. Therefore potential of wind power in the state is reassessed based on actual land availability in the state for wind power projects Wind potential assessment methodology The wind resource is a critical variable in determining the economic potential of the wind project. Wind speed varies with the season and time of day. The wind data is collected on average annual wind speed, frequency distribution of the wind at various speeds and wind shear 7. Wind speed and wind density data could either be collected through satellite imaging or through ground measurements. For the wind resource assessment in this study, the data recorded by wind monitoring stations 8m installed by CWET have been used. The data from CWET is used in the RETScreen model to produce the results. The assumptions used for producing the results on CUF are provided in Table 5. RETScreen provides two options to compute CUF for different regions. The first approach is based on wind velocity and second approach uses the wind power density to compute CUF. The second approach is conservative in comparison to the first approach i.e. RETScreen returns lower PLF when option 2 is considering the parameters provided in the Table 5. Table 5: Particulars used for assessment of capacity utilisation factor 9 Particulars Key Assumptions Wind Power Density-annual Referred to CWET data Height of measurement Referred to CWET data Wind shear component Referred to CWET data. In case data is not provided by CWET, 0.14 is used as a normative value Air temperature-annual Referred to NASA assumptions Air pressure-annual Referred to NASA assumptions Power Capacity (kw) 2100 Number of Turbines 1 Hub Height 100 m Array losses 5% Airfoil losses 2% Miscellaneous losses 4% Availability 96% RET-Screen calculates the unadjusted energy production from the wind turbines. It is the energy that one or more wind turbines will produce at standard conditions of temperature and atmospheric pressure. The calculation of gross energy generated energy is based on the energy production curve of the selected wind turbine at the average wind speed at a specific hub height for the proposed site. 7 Wind shear is Wind shear is quantified as the exponent in the Power Law equation that relates wind speeds at two different heights 8 For details refer to Appendix B1. 9 For details refer to Appendix B2 PwC 14

15 Renewable energy potential in the state of Madhya Pradesh After calculating gross energy production, the RET-Screen Wind Energy Project Model calculates the renewable energy delivered to the electricity grid, taking into account array losses, airfoil losses, downtime losses and the miscellaneous losses. Basis on the adopted approach, the likely CUF at 35 n locations of Madhya Pradesh has been evaluated. Table 6: Estimated capacity utilisation factor at different locations in the state of Madhya Pradesh Site District Estimated CUF (%) Site District Estimated CUF (%) A lot Ratlam 18.68% Kukru Betul 34.17% Banbir Kheri Guna 14.41% Lahori Shajapur 18.33% Barkheri Ujjain 10.64% Machla Indore 19.23% Barodiya Mandsaur 9.13% Mahuriya Shajapur 27.24% Baroli Indore 20.46% Mamatkheda Ratlam 18.68% Belma Indore 19.00% Mandwa Khargaone 12.01% Bodhina Ratlam 22.00% Mirzapur Sehore 26.45% Bori Barhanpur 14.91% Nachanbhor Dhar 11.81% Chorasia Ratlam 22.23% Nagda Dewas 33.43% Badhalia Garhidadar Shadol 10.86% Pahari Satna 5.83% Ghat Pipariya Betul 10.89% Purtala Chindwara 9.87% Jaithal Hill Ujjain 10.64% Sanawad Khargaone 17.54% Jamgodrani Dewas 29.45% Searmau Betul 11.17% Kalapahad Sehore 12.60% Sendhva Badwani 19.41% Kanchroota Dhar 11.71% Sodang Hill Ujjain 19.81% Kawasa Ratlam 15.71% Tanoriya Shajapur 20.02% Kheda Dhar 26.00% Ubhariya Betul 11.08% Vallyarpani Badwani 30.58% For a specific project developer, wind power projects are expected to have similar or minimal variation in capital cost, debt equity ratio, depreciation, operation and maintenance and tax structure across the locations. The propensity of the developer to select the site for wind project development will therefore depend on the tariff and the estimated CUF. In order to maximise the return, the product of CUF and the tariff (T) is an important Factor (F = CUF*T) in deciding the location for project implementation. For the higher value of Factor (F), ceteris paribus, the project returns for the wind project developer will be higher. In fact, according to the stated analysis, the state of Madhya Pradesh shall provide highest returns to the wind project developers. The average value of Factor (F) is 1.06 in the states other than Madhya Pradesh. At a tariff approved by MPERC for wind power projects in its order dated March 26, 2013, the CUF of 20% returns a value of 1.18 for Factor (F). Considering this, after making an assessment on CUF for different sites, the wind sites that are having CUF greater than 18% 10 are considered for assessment of gross technical potential. The stated restriction is imposed to assess the potential that is economically and financially feasible. Table 7: Comparing location advantage for different states State Regulatory Commission Region CUF (%) Tariff (INR/KWh) Factor (F) Madhya Pradesh MPERC All regions 20.00% Andhra Pradesh APERC All regions 24.50% CUF of 18% at tariff of 5.92 gives a return value of 1.06 for a factor (F) PwC 15

16 Renewable energy potential in the state of Madhya Pradesh State Regulatory Commission Region CUF (%) Tariff (INR/KWh) Factor (F) Rajasthan RERC Jaisalmer, Jodhpur and 21.00% Barmer Rajasthan RERC Other regions 20.00% Maharashtra MERC Zone % Maharashtra MERC Zone % Maharashtra MERC Zone % Maharashtra MERC Zone % Gujarat GERC All regions 24.00% Karnataka KERC All regions 26.50% Tamil Nadu TNERC All regions 27.15% Estimating availability of land for installing wind projects The total geographical area of the state of Madhya Pradesh is 308,252 o km 2 out of which waste land area is 40,042 p km 2. Wasteland area represents 13 % of the total geographical area of the state. Four wasteland categories are used for estimating the potential of wind energy land with dense scrub, land with open scrub, under-utilised/degraded forest (scrub domin) and under-utilised/degraded forest (Agriculture). Out of the total wasteland from the stated categories, only 45% of the land may be suitable for putting wind turbines. Therefore, only 45% of wasteland area has been used for the analysis. Table 8: Wasteland area suitable for wind power plants District Total land area (km 2 ) WL-1 (km 2 ) WL-2 (km 2 ) WL-3 (km 2 ) WL-4 (km 2 ) Total WL 11 area (km 2 ) Proportion WL of Total land area (%) Area used for estimating wind potential (%) Betul 10, % 45% Badwani 5, % 45% Dewas 7, % 45% Dhar 8, % 45% Indore 3, % 45% Ujjain 6, % 45% Ratlam 4, % 45% Sehore 6, % 45% Shajapur 6, % 45% 3.3. Gross wind energy potential in the state of Madhya Pradesh The technical potential of wind energy for given land availability and power density is 19,550 MW. q Table 9: Wind power potential in the state of Madhya Pradesh Site District Gross potential A lot Ratlam 501 Baroli Indore 582 Belma Indore 582 Bodhina Ratlam 501 Chorasia Badhalia Ratlam Total WL area represents the sum of WL-1, WL-2, WL-3 and WL-4. PwC 16

17 Renewable energy potential in the state of Madhya Pradesh Site District Gross potential Jamgodrani Dewas 1678 Kheda Dhar 1294 Kukru Betul 517 Lahori Shajapur 1120 Machla Indore 582 Mahuriya Shajapur 1120 Mamatkheda Ratlam 501 Mirzapur Sehore 843 Nagda Dewas 1678 Sendhva Badwani 2832 Sodang Hill Ujjain 764 Tanoriya Shajapur 1120 Vallyarpani Badwani 2832 Total 19,550 PwC 17

18 Renewable energy potential in the state of Madhya Pradesh 4. Assessing biomass energy potential Biomass includes agricultural and forest residues as well as organic household and industrial wastes. MNRE estimates that the surplus biomass that is potentially available for power generation could support roughly 33 r GW of installed capacity in India. In addition to this, MNRE estimates that there is a potential of about 15 s GW using cogeneration 12 in various industries including sugar mills, breweries, textile mills, distilleries, fertilizer plants, pulp and paper mill, and rice mills. Biomass resources are used for power generation through three general applications: grid-connected biomass power plants, off-grid distributed biomass power and cogeneration via sugar mill and other industries. Grid-connected biomass power plants Off-grid/distributed biomass power applications Cogeneration Bagasse and Non-bagasse Power generation through biomass 4.1. Estimating biomass potential from crop residues The state of Madhya Pradesh has witnessed a strong growth in crop productions in the last few years. Therefore the availability of biomass is increasing year on year. However, as a conservative estimate the potential for biomass is computed based on the most recent estimate without counting for increase in production in the coming years. The major crops t in the state of Madhya Pradesh are Wheat, Jowar, Bajra, Maize, Gram and Moong. The residue ratio of each of these crops is considered for estimating the biomass potential from crop residues. The data for crop residues was collected from the Department of Agriculture (DOA). One of the limitations of the study is that the competing use of the biomass is not considered while estimating the biomass potential. The calorific value for each biomass residue is taken from various public sources to estimate the energy available from crop residues. Further for assessing the potential, CUF is assumed to be 70% u and efficiency of the biomass projects is assumed to be 23% v. Table 10: Biomass potential from crop residues Crop Type of residue Total crop production w ('000 tonnes) Crop to Residue ratio x Collection Efficiency y Calorific value (kcal/kg) Moisture content z (%) Biomass potential 13 Bajra Bajra Stalks 384 1:2 60% 3,950 aa 20% Gram Gram Straw 3,812 1:1 60% 3,810 bb 20% Cogeneration plants are mainly found in the sugar industry where the heat and electricity is used for sugar production and the power surplus is typically sold to the grid. 13 Biomass potential is calculated at 70% PLF and efficiency of 23% approved by MPERC. PwC 18

19 Renewable energy potential in the state of Madhya Pradesh Crop Type of residue Total crop production w ('000 tonnes) Crop to Residue ratio x Collection Efficiency y Calorific value (kcal/kg) Moisture content z (%) Biomass potential 13 Jowar Jowar Stalk 543 1:4 60% 4,460 cc 20% Maize Maize Stalks 2,400 1:2 60% 3,850 dd 20% Moong Moong Husk 35 1:2 60% 4,100 ee 20% 1.90 Paddy Paddy Straw 3,022 1:2 60% 3,000 ff 10% Wheat Wheat Husk 16,125 2:3 60% 3,800 gg 20% Total 1, The power potential which can be supported through different feedstock across the various districts in the state has been provided below : Table 11: Biomass potential from crop residues across different districts in Madhya Pradesh Potential Districts >50 Chindwara and Hoshangabad Raisen and Dhar Rewa, Ujjain, Seoni, Dewas, Satna, Rajgarh, Sagar, Sehore and Badwani Vidisha, Betul, Indore, Harda, Morena, Gwalior, Shivpuri, Bhind, Jabalpur, Guna, Balaghat, Ashoknagar, Ratlam, Chattarpur, Khargone, Shajapur, Mandsaur, Sheopur, Khandwa, Tikamgarh and Datia Narsinghpur, Katni, Damoh, Singroli, Shadol, Sidhi, Panna, Neemuch, Bhopal, Jhabua and Dindori 0-10 Alirajpur, Anuppur, Mandla, Umaria and Burhanpur 4.2. Estimating biomass power potential from Lantana Mexicana The state of Madhya Pradesh houses over hh km 2 of forest cover which is 24.66% ii of its total geographical area. In terms of forestry biomass, the only biomass that is considered for assessing the biomass potential is Lantana Mexicana. The data on forest covered by LM was collected from Department of forest (DOF), GOMP. DOF revealed that there are no technical estimates on annual yield of LM. However, on rough basis, DOF informed that a yield of 4 jj tonnes per hectare on dry basis can be used to compute the biomass potential from LM. Also, the technical estimate of calorific value of LM is not known. Therefore the estimate of biomass potential from LM is based on requirement of 7,000 kk tonnes/annum of biomass for each MW of biomass potential. Table 12: Biomass potential from Lantana Mexicana District Area under Lantana ll (Hectares) Biomass Potential Alirajpur 1, Ashoknagar 7, Chindwara 100, Dewas 180, Dhar 1, Hoshangabad 13, Indore Jhabua 3, Khargone 1, Mandsaur 15, Raisen 2, Refer to Appendix C1 PwC 19

20 Renewable energy potential in the state of Madhya Pradesh District Area under Lantana ll (Hectares) Biomass Potential Rajgarh Ratlam 3, Sagar 4, Sehore 1, Shahdol 15, Shajapur 1, Shivpuri 7, Vidisha 2, Total 363, Gross Biomass potential in the state of Madhya Pradesh The total biomass potential in the state of Madhya Pradesh has been tabulated below: Table 13: Gross biomass potential in the state of Madhya Pradesh Biomass Feedstock Potential Biomass potential from crop residue 1,255 Biomass potential from Lantana Mexicana 207 Total 1,462 The potential from co-generation, minor crops and forestry residues other than LM has not been assessed in this report which may increase the total biomass potential of the state. PwC 20

21 Renewable energy potential in the state of Madhya Pradesh 5. Assessing small hydro potential Hydropower system is a well-developed technology which uses kinetic energy of moving water into useful electrical energy. Resettlement and Rehabilitation (R&R), risk of flood and damage to flora and fauna of the local area are some of the big problems to get clearance for developing reservoir for large hydropower projects. These points get minimised in development of small hydropower projects, which are of capacity less than 25 MW. India has as an estimated potential of about 19,749 mm MW from perennial flow rivers, streams and a large irrigation canal network with dams & barrages with available technical feasible sites of around 6474 nn in number. The identified small-hydro potential for the state of Madhya Pradesh as specified by MNRE is 820 oo MW Types and schemes of Small Hydropower System Mode of operation of Small Hydropower plant can be distinguished based on water flow Run-of the- river schemes Canal fall based scheme Dam-toe based scheme 5.2. Estimating potential from run of river schemes These schemes are normally on hilly streams comprising diversion structures (small dam/weir). These schemes have limitations of flows as small hilly streams are generally not perennial. The non perennial streams have very wide flow variations throughout the year. Substantial storage cannot be economically developed in such streams as a result of which availability of power during lean months may be relatively very small. As per the estimates of Alternate Hydro Energy Centre (AHEC), the potential of power generation by Run-of-River sites in Madhya Pradesh is 122 pp MW Estimating potential from canal fall based schemes These schemes utilise the water fall on irrigation canal to generate power. The head available for power generation in such schemes is in range of 3 to 8 qq meters. The powerhouse in such schemes is located in the bypass channel, which takes off from the main irrigation canal constructed adjacent to the fall structure in the upstream. The discharge from the powerhouse is fed back to the irrigation canal in the downstream. The irrigation canals have a number of fall structures along its lengths. In order to achieve the desired levels of the command area, its flow is diverted for irrigation. In some case, these falls could at sometimes be at close intervals, in such cases it might be economical to combine two or three falls and provide a single powerhouse to utilise the combined head. As per the estimates of AHEC, the potential for power generation on Run-of-River sites in Madhya Pradesh is 30 rr MW Estimating potential from dam toe based schemes These schemes utilise the head of an existing dam or barrage located in the dam. The water from the reservoir behind the dam can be drawn through the existing irrigation vents or by a separate intake. The choice regarding either a channel or tunnel for the water conductor depends on topographical and geotechnical considerations. PwC 21

22 Renewable energy potential in the state of Madhya Pradesh As per the estimates of AHEC, the potential for power generation on Run-of-River sites of Madhya Pradesh is 41 ss MW Self identified small hydro projects Self identified sites are unadvertised project sites for which a particular entity comes up with the proposal and the Nodal Agency proceeds to examine the feasibility of the site for setting up a Hydro Power Project while determining the optimum power potential that can be harnessed. Till date, there are 74 sites which have been identified by prospective developers to set up the hydro power project. The established potential of the above mentioned sites is 218MW Gross small hydro potential in the state of Madhya Pradesh The total small hydro potential in the state of Madhya Pradesh under various schemes of small hydro power has been tabulated below: Table 14: Gross small hydro potential in the state of Madhya Pradesh 16 Type of small hydro scheme Potential SHP potential for run-of-river scheme SHP potential for canal fall based schemes SHP potential for dam toe based schemes SHP potential for self identified sites Total 411 Madhya Pradesh state is rich in water resources. There are number of rivers that flow through the state of Madhya Pradesh - Narmada, Mahanadi, Godavari, Tapi, Mahi, Chambal, Betwa, Ken and Son. As part from these are number of tributaries of Ganga and Yamuna that originates or merge in the state of Madhya Pradesh. Limited number of studies has been undertaken to assess the hydro potential in the state. Given the number of rivers and the tributaries that flow through the state, there is a need to commission separate study to understand and unlock the technical potential of small hydro projects. 15 For details refer appendix D.4 16 For details refer appendix A.4 PwC 22

23 Renewable energy potential in the state of Madhya Pradesh 6. Renewable energy potential in the State of Madhya Pradesh The gross renewable energy potential in Madhya Pradesh has been tabulated below: Table 15: Renewable energy potential in the State of Madhya Pradesh Renewable Energy Technology Potential Solar 88,364 Wind 19,550 Biomass 1,462 Small Hydro 411 Total 109,787 PwC 23

24 Appendix Appendix A. - Assessing Solar Energy Potential A.1. Detailed description of assumptions used for estimating solar energy potential S.No Assumptions Description Resource Assessment 1 Normal Operating Cell Temperature (NOCT) The Nominal Operating Cell Temperature (NOCT) is defined as the temperature reached by open circuited cells in a module under the conditions as listed below: 1. Irradiance on cell surface = 800 W/m 2 2. Air Temperature = 20 C 3. Wind Velocity = 1 m/s The best module operated at a NOCT of 33 C, the worst at 58 C and the typical module at C respectively. 2. Temperature Coefficient Temperature coefficients provide the rate of change (derivative) with respect to temperature of different photovoltaic performance parameters. The derivatives can be determined for short-circuit current (Isc), maximum power current (Imp), open-circuit voltage (Voc), maximum power voltage (Vmp), and maximum power (Pm p), as well asfill factor (FF) and efficiency (η). Typical silicon solar panels have a temperature coefficient of about 0.4 to 0.5 percent. 3 Miscellaneous losses Typically it ranges from 0 to 20% and for this analysis it has been considered as 5% Inverter 4 Inverter Efficiency Typically it lies between 93% to 97%. Discussion with sector experts suggests that the inverter efficiency can be as high as 98% 5 Inverter Losses In most cases this value will be zero. Discussion with sector experts suggests that the inverter efficiency can be as high as 2% PwC 24

25 Appendix Appendix B. - Assessing Wind Energy Potential B.1. Monitoring stations data recorded by CWET in the state of Madhya Pradesh Site District Wind Speed (m/s) WPD (W/m 2 ) Wind Shear A lot Ratlam Banbir Kheri Guna Barkheri Ujjain Barodiya Mandsaur Baroli Indore Belma Indore Bodhina Ratlam Bori Barhanpur Chorasia Badhalia Ratlam Garhidadar Shadol Ghat Pipariya Betul Jaithal Hill Ujjain Jamgodrani Dewas Kalapahad Sehore Kanchroota Dhar Kawasa Ratlam Kheda Dhar Kukru Betul Lahori Shajapur Machla Indore Mahuriya Shajapur Mamatkheda Ratlam Mandwa Khargaone Mirzapur Sehore Nachanbhor Dhar Nagda Dewas Pahari Satna Purtala Chindwara Sanawad Khargaone Searmau Betul Sendhva Badwani Sodang Hill Ujjain Tanoriya Shajapur Ubhariya Betul Vallyarpani Badwani PwC 25

26 Appendix B.2. Detailed description of assumptions used for estimating wind energy potential S.No Assumptions Description 1 Wind Shear Coefficient Value of wind shear coefficient for different sites has been taken from the dataset provided by the CWET monitoring stations. For the sites where the information on shear coefficient is not provided, 0.14 has been considered. 2. Array Losses The array losses range between0.5% to 8%. For this analysis this has been considered as 5% 3. Airfoil Losses Based on the suggestive figures provided under RET screen modelling analysis, it has been considered as 5% 4. Miscellaneous losses Based on the suggestive figures provided under RET screen modelling analysis, it has been considered as 5% PwC 26

27 Appendix Appendix C. - Assessing Biomass Energy Potential C.1. Potential of biomass from agricultural residues Districts Paddy straw Wheat husk Jowar husk Bajra husk Maize husk Gram husk Moong Total Potential Alirajpur Anuppur Askhonagar Balaghat Barwani Betul Bhind Bhopal Burhanpur Chhatarpur Chhindwara Damoh Datia Dewas Dhar Dindori Guna Gwalior Harda Hoshangabad Indore Jabalpur Jhabua Katni Khandwa Khargone Mandla Mandsaur Morena Narsinghpur Neemuch Panna Raisen Rajgarh Ratlam Rewa Sagar Satna Sehore Seoni Shahdol Shajapur Sheopur Shivpuri Sidhi Singroli PwC 27

28 Appendix Districts Paddy straw Wheat husk Jowar husk Bajra husk Maize husk Gram husk Moong Total Potential Tikamgarh Ujjain Umaria Vidisha Total 1,255 PwC 28

29 Appendix Appendix D. - Assessing Small Hydro Potential D.1. Details of potential canal fall projects in the state of Madhya Pradesh District Site Scheme Capacity Dewas Chandrakesher Canal Fall 0.10 Dewas Gajakhedi Canal Fall 0.15 Dewas Malakhar Canal Fall 0.07 Dewas Loharda Canal Fall 0.07 Dewas Dani Canal Fall 0.07 Dhar Kunda Canal Fall 0.15 Dhar Jhedi Canal Fall 0.15 Dhar Lakhankot Canal Fall 0.23 Harda Ahalwari Canal Fall 0.13 Harda Sonkhedi Canal Fall 0.10 Harda Manjli Canal Fall 0.10 Harda Abgaonkhurd Canal Fall 0.10 Harda Roalgaon Canal Fall 0.05 Harda Kapasi Canal Fall 1.73 Hoshangabad Dharam Kundi Canal Fall 1.45 Hoshangabad Dobka Canal Fall 0.08 Indore Khodaria-I Canal Fall 0.15 Indore Khodaria-II Canal Fall 0.08 Jhabua Bhilatpura Canal Fall 0.10 Jhabua Amlal Canal Fall 0.13 Jhabua Padiyal Canal Fall 0.20 Katni Sleemanabad Canal Fall 4.15 Khandwa Pokhar Canal Fall 0.13 Khandwa Balrampur Canal Fall 0.10 Khandwa Rustampur Canal Fall 0.15 Khargone Bhagwanpura Canal Fall 0.28 Khargone Maddani Canal Fall 0.20 Khargone Behrupura Canal Fall 0.20 Khargone Kolapur Canal Fall 0.05 Mandla Madhopur Canal Fall 0.10 Mandla Bhapsa Canal Fall 0.20 Mandla Bichhiya Canal Fall 0.08 Mandla Chhatarpur Canal Fall 0.75 Mandla Gawara Canal Fall 1.25 Mandla Silgiti Canal Fall 0.40 Mandla Kamara Canal Fall 0.45 Mandla Banchhipani Canal Fall 0.10 Raisen Bari Canal Fall 0.10 Raisen Patni Canal Fall 0.10 Raisen Amrawad-I Canal Fall 0.15 Raisen Amrawad-II Canal Fall 0.15 Satna Patharhata Canal Fall 7.13 Satna Gunwara Canal Fall 5.40 Satna Birma Canal Fall 3.25 Total PwC 29

30 Appendix D.2. Details of potential dam outlet projects in the state of Madhya Pradesh District Site Scheme Capacity Barwani Ralawati Dam Outlet 0.05 Barwani Ralawati Dam Outlet 0.05 Barwani Lower Goi Dam Outlet 1.60 Betul Sampna Dam Outlet 0.15 Dewas Chandra-Kesher Dam Outlet 0.15 Dewas Chandra-Kesher Dam Outlet 0.05 Dewas Paras Dam Outlet 0.05 Dhar Man Dam Outlet 0.40 Dhar Man Dam Outlet 0.95 Dhar Sakelda Dam Outlet 0.05 Dindori Upper Narmada Dam Outlet 0.40 Dindori Upper Narmada Dam Outlet 0.30 Harda Ganjal Dam Outlet 0.55 Harda Ganjal Dam Outlet 0.10 Harda Imlidhana Dam Outlet 0.05 Hoshangabad Tawa Dam Outlet 2.35 Hoshangabad Dokarikheda Dam Outlet 0.10 Hoshangabad Morand Dam Outlet 1.55 Indore Choral Dam Outlet 0.15 Indore Nakheri Dam Outlet 0.10 Jabalpur Bargi Diversion Dam Outlet Jabalpur Barnoo Dam Outlet 0.20 Jabalpur Madai Dam Outlet 0.05 Jabalpur Atariya Dam Outlet 0.45 Jabalpur Bahoribund Dam Outlet 0.05 Jabalpur Bahoribund Dam Outlet 0.05 Jabalpur Pariat Dam Outlet 0.05 Jhabua Mod Sagar Dam Outlet 0.05 Jhabua Jobat Dam Outlet 0.40 Jhabua Dhamoi Dam Outlet 0.10 Khandwa Sukta Dam Outlet 0.35 Khandwa Chhota Tawa Dam Outlet 0.05 Khargone Gadigaltar Dam Outlet 0.05 Khargone Navalpura Dam Outlet 0.05 Khargone Dejla-Dewada Dam Outlet 0.35 Khargone Satak Dam Outlet 0.05 Khargone Jhirbhar Dam Outlet 0.05 Khargone Upper Beda Dam Outlet 0.35 Khargone Segwal Dam Outlet 0.05 Khargone Sipton Dam Outlet 0.05 Khargone Ambak Dam Outlet 0.05 Mandla Matiyari Dam Outlet 0.35 Mandla Matiyari Dam Outlet 0.30 Mandla Majhgaon Dam Outlet 0.05 Mandla Bichhiya Dam Outlet 0.10 Mandla Dhuandhar Dam Outlet 0.05 Mandla Upper Burhner Dam Outlet 0.25 Mandla Upper Burhner Dam Outlet 0.05 Mandla Halon Dam Outlet 0.55 PwC 30