ENERGY MODEL OF ELECTRIC SECTOR FOR TAMIL NADU

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1 ENERGY MODEL OF ELECTRIC SECTOR FOR TAMIL NADU B.R.Prabakar 1, K.Karunanithi 2, S.Kannan 3, C. Thangaraj 4 1 P.G.student, Department of EEE, Kalasalingam University, Tamil Nadu, India 2 Department of EEE, Kalasalingam University, Tamil Nadu, India 3 Department of EEE, Ramco Institute of Technology, Tamil Nadu, India 4 Vice chancellor, Vignan University, Andhra Pradesh, India 1 brprabakar@gmail.com, 2 k.karunanithi@klu.ac.in, 3 kannaneeeps@gmail.com, 4 thangaraj.vc@gmail.com Abstract This paper presents an energy model of electric sector for Tamil Nadu, an Indian state using Energy and Power Evaluation Program (ENPEP-BALANCE) package. It is an equilibrium model that attempt to establish supply and demand equilibrium across entire energy system. In the year 2013, the peak load for Tamil Nadu was approximately MW and peak shortage was about 3000 MW. Tamil Nadu is the only state in India, which has the highest renewable power penetration of about 49% of the total installed capacity at the end of the year The of generation, average derated capacity, ratio of supply and demand, Energy Not Served (ENS) is calculated for different price elasticity of energy demand. The pollutants such as CO 2, SO 2 and Particulate Matter (PM) emitted from the plants are also estimated. Key words: Emission; ENPEP; ENS; Equilibrium model; renewable energy sources, Tamil Nadu. 1. INTRODUCTION Electricity plays an important role for the development of any country. It was reported that southern region of India had the highest peak demand and energy shortage in Tamil Nadu, one of the states in southern region of India, had the i) minimum peak shortage of 1.4% in August and September 2013, ii) maximum peak demand shortage of 17.9% in January 2013, iii) minimum energy shortage of 1.5% in October 2013 and iv) maximum energy shortage of 21.7% in January 2013[1]. The average peak demand and energy shortage of the year 2013 had been calculated as 8.19% and 10.5% respectively. In the last few years Tamil Nadu is facing massive power shortage due to many reasons [2]. The problem of this power shortage is being felt mainly by the industries, leading to a loss in production efficiency and heavy loss of income. During the power cuts, the urbanized people are using uninterrupted power supply system (UPS). To overcome power shortage, the small scale and large scale industries are using diesel/kerosene generators [3]. In this paper, Energy and Power Evaluation Program (ENPEP-BALANCE) [4, 5] is used to design the energy model for electricity of Tamil Nadu for the period of 30 Years from 2013 to ENPEP balance was developed by Argonne National Laboratory (ANL) under the auspices of the U.S. Department of Energy (DOE) and the International Atomic Energy Agency (IAEA). Two general classes of Energy models are generally considered: optimization and equilibrium [6]. The ENPEP model is an example of an equilibrium model that has been used in developing country situations. Equilibrium models use a different approach to generate the solution for the energy system. However, there is no objective function to optimize. Instead, at each decision point in the energy system, an equilibrium model seeks to simulate the forces that determine which energy supply will be chosen to satisfy the demand. The equilibrium solution can deal with local optimization; that is, the various sectors can have different optimal conditions. The resulting solution, because it more closely depicts real conditions, is more readily implemented, whether in the form of government policy or energy system expansion plans. This paper is organized as follows: section 2 describes over view of Tamil Nadu power sector, section 3 describes implementation in ENPEP-BALANCE, section 4 describes results and discussions and section 5 concludes. 2. OVERVIEW OF TAMIL NADU POWER SECTOR Tamil Nadu is the eleventh largest state in India by area and the seventh most populous state with a population of about 74 million, nearly 6% of India's population. Tamil Nadu has the highest level of urbanization in India, which accounts for 9.6% of India s urban population. The State of Tamil Nadu is located in the southern region of India with electricity generation capacity of MW, at the end of the year Table 1 shows the existing installed capacity of the Tamil Nadu at the end of year 2013 [7]. 5681

2 S. No. Name of the plant Type of fuel Cap. (MW) FOR (Forced Outage Rate) (%) Table 1 Technical and economic data of existing plants Maint. Sche. (Days/ye ar) Capital ($/kw) Fixed O&M ($/kw-year) Variable O&M ($/MWh) Plant life time (year) Emission Factor, Kg/GJ [8] CO 2 SO 2 PM 1 Ennore Coal Tuticorin Coal Mettur (Stage I&II) Coal Mettur (Stage III) Coal Nr. Chennai (Stage I) Coal Nr. Chennai (Stage II) Coal Biomass Wind farm TANGEDCO Gas IPP-I Gas IPP-II Diesel IPP-III Coal Share from Central Lignite Kalpakkam Nuclear Koodankulam Nuclear Hydro Hydro Total At the end of the year 2013, the total installed capacity was MW. Out of MW installed capacity; coal 25%, lignite 16%, diesel 2%, gas 5%, nuclear 3% and renewable powers contribute 49% (Wind 34%, Biomass 4% and Hydro 11%). The generation mix in the year 2013 is shown in Figure 1. Table 2 Sector wise electricity consumption in 2013 [9] Sl. No. Sector % share 1 Agriculture 25 2 Commercial sectors 25 3 Domestic 20 4 Municipal waterworks and 20 street lighting 5 Industrial IMPLEMENTATION IN ENPEP ENPEP is one of the energy planning software, which is used to develop the energy model for the state Tamil Nadu. In this paper, ENPEP is used to analysis the ENS, average derated capacity, of generation and the ratio, supply/demand value for Tamil Nadu for the duration of thirty years ( ). Figure 1 Fuel mix ratio in 2013 The various energy consuming sectors in Tamil Nadu are enlisted below as shown in Table 2. The consumption of total generated energy by different sectors is mentioned below. 3.1 LOAD DATA The detailed load data of Tamil Nadu for the year 2013 is available in [10]. Table 3 shows the month wise peak demand (restricted) for the year The minimum and maximum demand occurs in the month of January and October respectively. The total energy requirement for the year 2013 has been estimated as GWh. 5682

3 Table 3 Load demand in 2013 Month Peak demand (MW) January 9942 February March April May June July August September October November December The load growth is assumed as 6.6% of previous year peak demand. In the base year (2013), the peak demand was MW and it is increased to MW at the end of study period (2042). Figure 2 shows that the forecasted demand of Tamil Nadu state. Figure 2 Forecasted peak demand 3.2 Network used in ENPEP - BALANCE Network used for Tamil Nadu power sector is shown in Figure 3. In this network, nine resources including hydro and five demand sectors are considered. Wind and Solar plants are considered as thermal plants with high FOR values. One decision/allocation node and one electricity dispatch node is used in this network. Decision/allocation node is among the most important nodes in defining the role competing fuels and energy technologies will play in a future energy system. Figure 3 Network used in ENPEP 3.3 Expansion candidates Six number of variable expansion (in addition to existing plants) candidates are considered in this analysis. The technical and economic data of expansion candidates are shown in Table 5. Sl. No. Name of the plant Type of fuel Table 5 Technical and economic data of expansion candidates Capacity FOR (MW) (%) Maint. Schedule (Days/year) Capital ($/kw) Fixed O&M ($/kwyear) Variable O&M ($/MWh) Plant life time (year) Emission Factor, Kg/GJ CO 2 SO 2 1 Uppur Coal Udangudi Coal MTMTEL Coal R-LNG based gas plant Gas Solar Cheyyur Coal PM 5683

4 Table 6 shows the addition of both existing and expansion candidates for every year of study. Since Tamil Nadu has Very good potential for both solar and wind energy, it is assumed that solar and wind penetration increased by 500 MW every year. In 2013, the installed capacity of wind plant was 7077 MW. Based on solar energy policy 2012 [11], Tamil Nadu government has decided to install 3000 MW solar power within the year But growth of solar penetration is less due to financial and some other practical difficulties. Based on plants addition given in Table 6, the total installed capacity will be MW at the end of the year The Ennore power plant of capacity 450 MW is to be retired by 2017 due to its poor plant load factor. The fuel mix ratio in 2042 is shown in Figure 4. Out of MW, renewable energy technology (RET) contributes MW, which accounts for 35.15% of total installed capacity. Table 6 Details of addition of plants for the planning horizon Figure 4 Fuel mix ratio in 2042 Year Ennore Tuticorin Mettur (Stage I&II) Mettur (Stage III) Nr. Chennai (Stage I) Nr. Chennai (Stage II) Biomass Wind farm TANGEDCO IPP-I IPP-II IPP-III Share from Central Kalpakkam Koodankulam Hydro 1 Hydro 2 Uppur Udangudi MTMTEL R-LNG based gas plant Cheyyur Solar TOTAL Total

$Generally it is used as a fractional value. This value indicates percentage change in demand for each percent change in price.$ Negative value means with increase in price, demand goes down and value of -0.5 means demand drops by 0.5% for each price increase of 1%.

Negative value means with increase in price, demand goes down and value of -0.5 means demand drops by 0.5% for each price increase of 1%.

5 4. Results and discussion In this section, the results obtained by ENPEP for two different elasticity values (0 and -0.5). Elasticity represents the price elasticity of energy demand. Generally it is used as a fractional value. This value indicates percentage change in demand for each percent change in price. Negative value means with increase in price, demand goes down and value of -0.5 means demand drops by 0.5% for each price increase of 1%. The sector wise energy demand for the planning period is shown in Figure 7(without price elasticity). In 2013, agriculture and commercial sectors each consumed GWh. Domestic and municipal sectors each consumed GWh and industry sector consumed 9229 GWh. There will be uniform growth in consumption of electricity for each sector. In 2042, the total energy consumption by all sectors will be GWh. 4.1 Forecasted peak demand Figure 5 shows that the variation of peak demands when price elasticity is -0.5 is considered. It has been observed that during the period and peak demand increases, when compared without elasticity. The reason is that during those periods, the of electricity generation will be less. Figure 7: Sector wise energy demand (without elasticity) Figure: 5 Forecasted peak demand 4.2 Average derated capacity The average derated capacity of the plants for every year shown in Figure 6. In the year 2013, the total installed capacity was MW. But derated capacity was only MW only. This is due to less capacity factor of intermittent resources like wind, bio mass, solar, etc. At the end of planning period, the total installed capacity will be MW. But the average derated capacity will be MW. The sector wise energy demand for the planning period is shown in Figure 8 (with price elasticity). In 2013, the total energy consumption by all sectors will be GWh when compared to Figure 8; consumption of electricity by each sector will be varying depending on the price. In 2042, the total energy consumption by all sectors will be GWh. It will be less compared to previous case. Figure 8: Sector wise energy demand (with elasticity) 4.4 Estimated electricity generation The estimated electricity generation of committed plants is shown in Figure 9 including. The energy generation is always higher when price elasticity is included, than that of without elasticity, except for the year Figure 6: Average derated capacity 4.3 Sector wise energy demand 5685

6 Figure 9: Estimated energy generation 4.5 Variation of ENS Figure 10 shows the variation of energy not served (ENS) for the whole study periods including price elasticity. In the year 2013, the ENS was 6264 GWh. When elasticity is included, there will be surplus of energy in the years and as shown in Figure 10. Figure 11 Ratio of supply and demand 4.7 Cost of generation The average of electricity for the planning period is shown in Figure 12. In 2013, the average of generation was 8.4 cents/kwh. The will be always higher, when price elasticity is included, when compared to without elasticity. Figure 10 Variation of energy not served (ENS) 4.6 Ratio of supply and demand The ratio of supply and demand indicates that whether the system is balanced or not. If this ratio is equal to one, system is balanced and if it is less than one, there will be deficiency in supplying the energy. If it is more than one, there will be surplus of electricity. Figure 11 shows the ratio of supply and demand for the study periods. It may be noted that, for without elasticity, there is always shortage of energy and this ratio is always less than one. When price elasticity of demand is included, this ratio will be more than one in the years and Figure 12 Cost of electricity generation 4.8 Estimated pollutants The pollutants such as CO 2, SO 2 and Particulate Matter (PM) emitted by plants for every year of planning study are shown in Table 8. In the base year, total CO 2 emitted will be tonne, total SO 2 emitted will be tonne and PM emitted will be 6208 tonne. It will be noted that estimated pollutants have been increased if elasticity in demand is considered; this is due to increase in supply of energy compared to without elasticity. 5686

7 Table 8 Pollutants emitted by plants in tonne Elasticity: 0 Elasticity: -0.5 Year CO 2 SO 2 PM CO 2 SO 2 PM Conclusion In this paper, an energy model of electricity for Tamil Nadu is proposed with consideration of different RETs for 30 years from 2013 to The various factors such as average derated capacity, ENS, energy consumption by demand sectors, ratio of supply and demand, average of energy generation, pollutants CO 2, SO 2 and PM emitted by thermal plants are analysed for with and without price elasticity in demand. For without elasticity in demand, there will be always shortage of energy and if elasticity in demand is considered it is possible to have surplus of energy in some years and this results show that if demand side management (DSM) is implemented in all demand sectors, the power and energy shortage of Tamil Nadu can be minimized. References [1] Central Electricity Authority/yearly report-2013 [Online].Available: (accessed online on ). [2] S.R.Rallapalli, S. Ghosh, Forecasting monthly peak demand of electricity in India-A critique, Energy Policy, vol. 45, pp , [3]Tamil Nadu Energy department Policy Note [Online]. Available: (accessed online on ) [4] Argonne National Laboratory (U.S. Department of energy) online available: [5] Center for Energy, Environmental, and Economic Systems Analysis (CEEESA) [6] Richard R. Cirillo, Charles M. Macal, and William A. Buehring, The use of nonlinear equilibrium models for energy planning in developing countries: ENPEP as an example, Environmental Assessment and Information Sciences Division Argonne National Laboratory, pp [7]Installed capacity (in MW) of power utilities in the states/uts located in southern region [Online]. Available: (accessed online on ). [8] P. Krittayakasem, S. Patumsawad, S. Garivait, Emission Inventory of Electricity Generation in Thailand Journal of Sustainable Energy & Environment, vol. 2, pp.65-69, [9] Chapter 9. Power and Renewable energy [Online]. Available: (accessed online on ). [10] Southern regional power committee Bangalore, progress report-2013 (from January to December). [Online].Available: (accessed online on ) [11]Tamil Nadu solar energy policy [Online]. Available: (accessed online on ) 5687

Analyzing Reliability of Tamil Nadu Power Grid for the Year 2015 using Wasp-IV

Indian Journal of Science and Technology, Vol 9(38), DOI: 10.17485/ijst/2016/v9i38/101963, October 2016 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 Analyzing Reliability of Tamil Nadu Power Grid