Essay: Maximising the overall profit of coal-fired plant

Due to limited reserve potential of petroleum and natural gas, eco-conservation restriction on hydro-electric projects followed by geo-political perception of nuclear power; coal can be considered as backbone for present energy production scenario where transition period between current and cleaner energy mix coal technology would pass decades before the current power plants could be replaced. Thus, current thermal power plants can play a crucial role during this transition period. From purely economic point of view, the maximization of overall profit of coal-fired thermal power plant is a very complex activity. It depends on thermal efficiency of conversion, plant total cost and ‘up-time’ of plant (i.e., duration of failure free operation out of total duration). The enhancement of conversion efficiency of coal-fired power plant efficiency is restricted due to thermodynamic constraints. In the era of high computing facilities; modeling, simulation and optimization has become very pertinent tool for design and development. In the present work, the focus is given to identify the redundancy level on various components or subsystems of the coal fired power plant using modeling, simulation and optimization tools, so and so, that the overall profit of coal-fired plant could be maximized. Alternatively, the electricity generation facility could be cost effective. A simulated annealing technique for optimization has been used for maximization of profit in terms of net present value (NPV) of complex coal-fired power plant. At maximum NPV, the optimum numbers of redundancies on boiler sub-system, condensate extraction pump and boiler feed water pump is found to be 1, 3 and 1 respectively. These optimum redundancy levels do not change for wide load range of 126 to 210 MW on coal fired power plant.
Keywords: Net present value; Optimization, Simulated annealing, Economic analysis; Thermal power plant
Introduction
Energy plays a vital role in the socio-economic development and human welfare of a country (Energy Statics, 2013). Rapid industrialization, a fast pace of urbanization and the associated consumerism are indicative of India’s fast growing economy. Electric power is one of the most important sectors influencing the national economy. In 1947, coal based thermal power plants were the main sources of electric power production. Power stations of sizes 10-15 MW or even less were catering for local consumers only in a few urban areas, whereas the electricity access to rural areas was almost nil. There has been steady growth of power demand in India since independence; and thus the power sector has undergone with a significant progress on both fronts – installed generating capacity, and transmission and distribution of electricity. According to a report on the state of basic amenities available in India (2013), 20% of rural households had not accessed to electricity connections in 2012. While the per capita electricity consumption of India was around 0.5 MWhr per year in 2012, far behind the figure of 11.9 MWhr per year for USA and a world average of around 3.3 MWhr per year (CIA World Factbook, 2012). As per a survey, the total energy generating capacity (which include thermal, hydro, nuclear and renewable energy) has been reported to be 147,965 MW as on 31 March 2009 (refer Figure 1).
Figure 1 Contribution of thermal, hydro, nuclear and renewable in total generating capacity of India (CEA, 2009).
In which thermal energy (i.e., thermal power plants) contributes to highest figure quantum with 63% out of total energy generation in India. The contribution of coal, gas and diesel in thermal generation capacity of India is shown in figure 2, where coal contributes a major share with 83% (CEA, 2009). In our vibrant economy, the growth in demand for power has exceeded higher than supply of power. Concerted efforts are, therefore, needed to bridge this gap.
Figure 2 Contribution of coal, gas, diesel in thermal power generating capacity of India (CEA, 2009).
Considering the limited reserve potential of petroleum and natural gas, eco-conservation restriction on hydro-electric projects followed by geo-political perception of nuclear power; coal can be considered as backbone for present energy production scenario where transition period between current and cleaner energy mix coal technology would pass decades before the current power plants are replaced. Thus, current thermal power plants can play a crucial role during this transition period.
The plant size grows exponentially from ~200 MW to 1100 MW from 1960s to 1970s (Leyzerovich, 2008). Now-a-days, the thermal power plants are based on subcritical and supercritical operating conditions. In subcritical condition, the boiler generates the steam below critical point (i.e., 221.5 bar) consuming considerable latent heat of vaporization to convert the water into steam. While in supercritical conditions, the water and steam behaves alike. Supercritical steam conditions improve the turbine cycle heat rate significantly over subcritical steam conditions. The conventional coal-fired power plants have efficiency of about 32%, while efficiency of supercritical and ultra-supercritical power plants can be as high as 45 % with lower emissions (Greenfacts, 2013). However such an increment is gained only at the expense of increased cost and complexity of the plant. Moreover, the supercritical technology has some other limitations including metallurgical limit, high level erosion, increased supervision and maintenance costs, and limited scope for retrofitting opportunities.
In order to maintain failure free operation of given coal-fired power plant for a longer duration, it is necessary to maintain all components of the plant at higher operational availability level. Second approach, on the other hand, is based on implementation of stand-by units or redundancy. This approach includes the implementation of large number of stand-by units or redundancy units in parallel to active unit (Kumar et al. 2015). Also, numerous studies have been reported by researchers on system reliability and availability in thermal power plant (Shayan 1986; Kaushik and Singh 1994; Arora and Kumar 1997; Nakamura et al. 2001; Valdma et al. 2007; Sharma and Tewari 2009; Adhikary et al. 2012; Lisnianski 2012). Shayan (1986) considered a coal burning power plant and developed a model which generates a) the reliability of components, modules, and the unit, and b) the average amount of power generated by the unit at any given time and over a period of time. The plant components were grouped into seven modules in series. Result agrees with a simulation model and past data. The model also provided some useful tools in planning resources, maintenance and designing new similar systems. Kaushik and Singh (1994) evaluated the reliability of feed water system in a thermal power plant. The results obtained were useful for the proper planning and design of the feed water system in the plant. Arora and Kumar (1997) presented an interesting model for the evaluation of steady state availability and mean time between failure of steam and power generation system in the thermal power plant situated in North India.
From purely economic point of view, the maximization of the overall profit of thermal power plant is a very complex activity. It demands the use of large number of standby components/units in the plant. Since higher redundancy level in system also leads to high equipment cost, while low redundancy level leads to decrease in up-time resulting in decrease in revenue earned from sale of electricity. Thus, there is trade-off between use of redundancy and revenue earned in the plant (Kumar et al. 2015). At the advent of high computing facilities; modeling, simulation and optimization has become very pertinent tool for design and development of modern thermal power plants. Researchers reported considerable work on economic analysis using Net Present Worth method in the various process industries. Remer and Nieto (1995) presented twenty five various techniques implemented to make an assessment of the economic desirability of projects. They categorized them into five types viz., net present value methods, rate of return methods, ratio methods, payback methods, and accounting methods. They provided insight into the advantages and limitations of these project evaluation methods by comparing and contrasting them. Manninen and Zhu (1998) presented a new methodology for new design of power plants, which combines the benefits of thermodynamics, economics and mathematical optimization. Caputo and Pelagagge (2002) evaluated economic feasibility and financial risk of refuse derived fuel production plants on the basis of the net present value index over a capacity range of 25—200 ton/hr comparing either single refuse derived fuel production plants or facilities integrating also compost production and/or electricity generation. Poullikkas (2004) carried out a parametric study concerning the use of combined cycle technologies for power generation and cost-benefit analysis using the Independent Power Producers optimization algorithm in which the electricity unit cost was calculated by independent power producers in Cyprus. Giri and Dohi (2004) implemented the net present value approach to determine the economic manufacturing quantities for an unreliable production system over an infinite planning horizon. From numerical experiments, it was observed that the decision based on the average cost can be 10% worse than the decision based on net present value depending upon the machine failure rate. Caputo et al. (2005) investigated and evaluated the feasibility of using biomass to provide electricity in combustion and gasification plants. Moreover, in order to evaluate the impact of logistics on the bio-energy plants profitability, the effects of main logistic variables such as specific vehicle transport costs, vehicles capacity, specific purchased biomass costs and distribution density have been examined. Davison (2007) presented performance, cost and emissions data for coal and natural gas-fired power plants, based on information from studies carried out recently for the IEA Greenhouse Gas research and development program by major engineering contractors and process licensors. Boonnasa and Namprakai (2008) studied the sensitivity analysis concerning economic performance for the capacity improvement of a combined cycle power plant (100—600 MW). They concluded that for economical analysis, the payback period found to be in the range of 0.68—0.94 years, internal rate of return 29—176%, and net present value 116.5—154.63 MUS$. Madlener and Stoverink (2012) discussed the economic feasibility of constructing a 560 MW coal-fired power plant in Turkey, using real options theory. They developed a sequential investment model based on the binomial tree model. They found that the real options analysis can be very useful, especially for the strategic planning of projects. The ever-increasing demand to lower production costs to withstand competition has prompted engineers to look for rigorous methods of decision making, such as optimization methods, to design and produce products both economically and efficiently. Scientific literature is prolific both on exact and on heuristic solution methods developed to solve optimization problems. Although the former methods have an indisputable theoretical value when it comes to solve large realistic combinatorial optimization problems they are usually associated with large and even prohibitive running times. Heuristic methods do not guarantee to determine a global optimal solution for a problem but are usually able to find a good solution rapidly, perhaps a local optimum and require less computational resources. Several researchers employed the non-traditional optimization techniques to predict the operational behaviour of various sub-systems in process industries. Aggarwal et al. (1975) developed an algorithm for the heuristic solution of redundancy optimization problems. Gopal et al. (1980) presented a simple and computationally attractive method for solving constrained reliability optimization problem. Chern and Jan (1986) presented a class of reliability optimization problems with multiple-choice constraints. They used a combinatorial tree which always satisfies the multiple-choice constraints. Shetty et al. (1988) discussed an ‘M’ component series system with ‘M’ repair facilities in which the lifetime of each component and the repair time of the failed components obey exponential distributions with different parameters. System availability has been analyzed using Markov process. Campbell and Painton (1995) presented an optimization model to maximize one or more performance measures (e.g., system reliability or availability) subject to constraints (e.g., cost) in the presence of uncertainty about the component failure rates. Coit and Smith (1996) developed a problem specific Genetic Algorithm (GA ) to analyze series-parallel systems and to determine the optimal design configuration when there are multiple components choices available for each of several K-out-of-N:G subsystems. Ravi et al. (1997) applied an improved non-equilibrium simulated-annealing technique to find the global optimum of system cost of two kinds of complex systems subjected to constraints and optimum number of redundancies which maximize the system reliability. Cantoni et al. (2000) presented an approach to the optimal plant design under conflicting safety and economic constraints, based upon Monte Carlo evaluation with a genetic algorithms-maximization procedure. Barata et al. (2002) modeled a continuously monitored deteriorating systems using Monte Carlo simulation technique. The resulting model was embedded within an ‘on condition’ maintenance optimization scheme that aims at minimizing the expected total system cost over a given mission time. Elegbede and Abjallah (2003) described a methodology based on GA and experimented a plan to optimize the availability and cost of reparable parallel-series systems. A numerical example was used to assess the method. Alaya et al. (2004) solve a multidimensional knapsack problem using Ant Colony optimization, where a decision on a subset of objects, satisfying a few resource constraints, has to be made in order to maximize the total profit. Coit (2004) addressed system reliability optimization when component reliability estimates are treated as random variables with estimation uncertainty. Pareto optimality solutions were discussed for multi objective concepts. Castro and Cavalca (2006) presented an availability optimization of an engineering system assembled in a series configuration, with redundancy of units and corrective maintenance resources as optimization parameters. Sharma (2006) reported an interesting model for optimization of redundancy in a thermal power plant using Genetic Algorithm technique. He also reported that high reliability figures are required for most critical components such as boiler, turbine and condenser unit. Azadeh et al. (2007) presented an integrated Data Envelopment Analysis (DEA) methodology using Banker Charmes Cooper(BCC) input oriented model for assessment and optimization of conventional thermal power plants. Principal component anal
ysis and numerical taxonomy together with spearman correlation technique were used to verify and validate the findings of DEA-BCC approach. Li et al. (2008) presented a multi-objective constraint-handling method with the Particle Swarm Optimization (PSO) algorithm for tackling the power generation loading optimization problem. The proposed approach adopts the concept of Pareto dominance from multi-objective optimization and uses several selection rules to guide the search direction. A four-unit loading optimization for a local power plant has been simulated. The result revealed the capability, effectiveness and efficiency of applying the proposed approach in the power industry. Amir (2012) optimized the Rankine cycle of a thermal power plant. The effect of condenser pressure and boiler pressure to optimize power plant efficiency has been also analyzed. The results revealed that increase in boiler pressure and decrease in condenser pressure increase thermal efficiency. Twum et al. (2012) presented a multi-criteria optimization model and methodology for the Pareto optimal assignment of reliability to the components of a series-parallel system in order to maximize its reliability. Biegler-Konig (2013) modeled power plant blocks with fast Neural Networks and discussed a concept for online optimization of multi-block power plants over a period of time using an Artificial Bee Colony and Simulated Annealing algorithm. Limmeechokchai and Promjiraprawat (2013) developed a multi-objective and multicriteria optimization model for power generation expansion planning. Three objective functions were formulated and the hybrid approach of multi-objective evolutionary algorithm, deterministic optimization and decomposition technique were incorporated in order to improve the optimization performance for handling large-scale problem. The results indicated that natural gas will be the resource among three objectives with the highest compromise. The work on the economic analysis and redundancy optimization of coal-fired power plants based on optimization tools is available in literature. Now-a-days, for complex engineering optimization problems, non-traditional techniques for optimization such as Genetic Algorithm, Simulated Annealing technique are getting popular over traditional optimization techniques (i.e., Lagrange multiplier, exhaustive search, elimination methods etc). Non-traditional techniques for optimization of large and complex system result in considerable saving in computation time as compared to conventional methods. In order to maximize the net present value of coal-fired power plant a well tested simulated annealing optimization tool is chosen for economic optimization.
Nomenclature
MW Plant power output capacity PWFk Present worth factor at ‘kth’ year
ai,bi Values of constant obtained through curve fit K Escalation rate of fuel cost
pl plant life J Escalation rate of labour salary
fMW Percentage of net electric energy power plant output assumed as 91% of MW available for sale S Escalation rate of current price of electricity
Q Escalation rate of maintenance cost
NPVlifetime Plant net present value O Escalation rate of insurance cost
Ci Component cost k Kth year
Co Total operating cost mj Mass flow rate through ‘jth’ pump
Ran Revenue earned Rlifetime Lifetime revenue
Cs Labour average annual salary
nL Number of labour employed
Ccc Coal cost per tone
I Interest rate
Cep Current price of electricity
Cdirect Direct plant cost
Cindirect Indirect plant cost
CEep Cost of equipment
Cpiping Cost of piping
Cpumping Cost of pumping
Ccoal hadling Cost of coal handling
Cash handling Cost of ash handling
Cother Cost due to direct installation, auxiliary services,
instrumentation and controls and site preparation
fconversion A conversion factor
Ctci Total capital investment
Economic analysis module
The cost analysis of the plant has been carried out on the basis of total capital investment, operating cost and revenue. The total capital investment includes the total direct plant cost and total indirect plant cost. Total direct plant cost involves the cost of equipments (i.e., boiler, steam turbine, condenser, generator and auxiliary equipments including condensate extraction pump, feed water pump etc) and other costs associated with piping, electrical, civil works, direct installation cost, auxiliary services, instrumentation and controls, and site preparation. Total indirect plant cost includes the cost of engineering and set-up. In order to have a realistic estimate of primary components or equipments, it is necessary to include the latest cost of these components. Thus, the data for costs for each equipment/component in terms of power output capacity has been obtained from literature search (Caputo et al. 2005, Hasler et al 2009, Pauschert 2009, and NETL 2012); and the cost of each components are fitted using power law in terms of installed capacity of plant. The cost of ith component ( ) is defined as
(1)
Where i = Steam boiler, Steam turbine & generator, Boiler feed pump, Condensate extraction pump and Condenser, respectively. The curve fit of component cost has been obtained from data collected from literature; the values of empirical constants ai and bi are also listed in table 1. The comparison of curve fit against data for cost of boiler, turbine-generator, condenser, civil works, electrical works and piping works are shown in figures 1-6.
Table 1 Constants in eqn. (1) due to evaluation of component cost (in INR)
S.N. Equipments a b Reference
1 Steam boiler 76132335.1 0.7785 Caputo et al. (2005) & Hasler et al. (2009)
2 Steam-turbine and generator 3501170.7 1.17495 Caputo et al. (2005) & Pauschert (2009)
3 Feed pumps 35000 0.6107 Caputo et al. (2005)
4 Cond. extraction pump 9000 0.4425 Caputo et al. (2005)
5 Condenser 17,306,123.8 0.46913 Caputo et al. (2005) & NETL (2012)
6. Civil works 57,418,809.77 0.555 Caputo et al. (2005) & Pauschert, 2009
7. Electrical works 59,663,488.144 0.596 Caputo et al. (2005) & Pauschert, 2009
Figure 1 Comparing curve fit against cost data of boiler in terms of installed capacity
Figure 2 Comparing curve fit against cost data of turbine – generator in terms of installed capacity
Figure 3 Comparing curve fit against cost data of condenser in terms of installed capacity
Figure 4 Comparing curve fit against cost data of civil works in terms of installed capacity
Figure 5 Comparing curve fit against cost data of electrical works in terms of installed capacity
Figure 6 Comparing curve fit against cost data of piping works in terms of installed capacity
Total direct plant cost involves the cost of equipments and other costs can be written as
(2)
The cost of equipment, CEqp, can be written in terms of redundancies of respective components (Ni) as
(3)
The other costs associated with piping, civil works and electrical works can be obtained in form of eqn. (1) followed by table 1. However, the costs of piping works (in INR) can be obtained using polynomial form as
Cpiping =-895.91ï,´MW2+3674897.1ï,´MW-145238239.34 (4)
In the absence of suitable cost data of coal handling and ash handling system, the data of 300 MW plant for coal handling and ash handling system (Pauschert 2009) is adopted.
Ccoal handling = fconversion ï,´96ï,´105 (5)
Cash handling =fconversionï,´ 17ï,´106 (6)
The cost due to direct installation, auxiliary services, instrumentation and controls and site preparation can be group in other category as
(7)
Here is a factor to account for direct installation, auxiliary, instrumentation and control. is fixed at 0.65.
The total indirect plant cost has been associated with engineering and plant start-up, which can be obtained in terms of total equipment cost following Caputo et al. (2005) as
(8)
Here  is a factor to account for engineering and plant start-up, which is fixed at 0.22 in the present calculations.
Thus, total capital investment can be defined as the sum of total direct and total indirect plant cost as
(9)
The operating cost (includes the purchasing cost of coal feedstock, maintenance and labour, insurance and cost of power associated with boiler feedwater pumps and condensate extraction pumps for running the thermal power plant) is considered to be paid annually over the lifespan of the coal-fired power plant. It is likely to be change with economic climate (i.e. due to current interest rate and escalation rate in the prices of coal, maintenance, labour, insurance and pumping power). Therefore, in order to account the influence of interest rate and escalation rate on total operating cost over plant life, the present worth factor (PWF) can be defined as
(10)
Thus, the lifetime cost of coal or fuel, maintenance, labour, insurance and pumping power can be obtained in terms of PWF and escalation rate following Li and Priddy (1984) as
Coal or fuel cost
(11)
Maintenance cost
(12)
Labour cost
(13)
Insurance cost
(14)
Pumping cost
(15)
In equation (14) escalation rate on insurance (O) is assumed to be zero.
Here Avoverall, N, m, pump and P are the overall availability, number of pumps, pump efficiency, mass flow rate and pressure drop through jth pump, respectively. ‘nl’ is number of personnel employed. where ‘C’ is cost in (INR), while subscripts maint, ins, ep, cc and lab corresponds to maintenance, insurance, price of electricity (INR/MWhr), coal cost and average annual labour cost(individual), respectively.
The maintenance and insurance costs are taken to be 1.5 % and 1 % of the total capital investment obtained from the literature. The coal-storage and fumes treatment costs are neglected in the present work. The taxes and financial charges have been neglected in this work.
The lifetime cost of purchasing cost of coal feedstock, maintenance and labour, insurance and cost of power associated with boiler feedwater pumps and condensate extraction pumps for running the thermal power plant can be calculated as
(16)
Likewise, revenue over life span can be obtained from the sale of electricity in terms of PWF as
(17)
Caputo et al (2005) reported that 10% of total revenue is consumed in internal affair of the plant, which includes the pumping cost itself. Since, we have included the pumping cost in operating cost itself, the necessary adjustment in the net electric output would be required. From a baseline run it was observed that pumping cost is hardly 1% of total revenue. Therefore, the value of fMW has been fixed at 91%.
For assessment of the economic effectiveness of the investments, NPV method is most frequently used. In the present work, therefore, NPV method has been employed. The expression of net present value of plant on lifetime basis can be written as
(18)
The equipment cost of plant can be obtained from equation (1) followed by eqn. (2). The total operating cost which include fuel cost can be evaluated from eqns. (11)-(15) followed by equation (16) for coal consumption. The net present value of the investment on life cycle basis can be evaluated in terms of revenue earned on sale of electricity (eqn. 17), fixed and operating cost using eqn. (16).
The average personnel salary on annual basis is deduced from 210 MW plant (see table 2). The concise detail of plant personnel for a 210 MW plant is also shown in table 2.
Table 2 Data of 210MW plant for average salary of plant personnel as deduced from plant records
Total personnel deployed in 210 MW unit Total monthly salary of plant personnel (INR) Average monthly salary (INR)
233 6025000 25858.36
Current interest rate is fixed on 9%. The average electricity price at present is taken to be 4500 INR/MWhr. Plant life is assumed to be 35 year. Other input information including fuel cost, factor to account for expenses due to installation, instrumentation, engineering and plant start-up, Factor to account for power consumption within plant, interest rate and escalation rate has been represented in table 3.
Table 3 Input Data Collected From Literature and Plant Records
Parameter Notation Value Reference
Number of labour employed nL 233 *
Average labour cost on annual basis Cs 310300.3(INR/yr) *
Fuel cost Ccc 2.69681(INR /kg) *
Current price of electricity Cep 4500(INR/ MWhr) HERC (2013)
Factor to account for power consumption within plant fMW 0.91 Caputo et al. (2005)
Interest rate ‘i’ 9% *
* data deduced from plant records
Table 4 Escalation rates on various costs
Parameter Notation Escalation rate Reference
Current price of electricity S 5% Ranganathan (2005)
Labour salary J 10% ï,§
Fuel cost K 6.62% Saxena (2013)
Maintenance cost Q 7.43% HERC (2013)
ï,§ data deduced from plant records for salary
Using above input information, the effect of various variables is investigated on plant operating cost (i.e., fuel cost, pumping cost, insurance and maintenance cost), total capital investment, revenue and net present value.
Results and discussion
Plant life
The effect of variation in total plant life has been highlighted on lifetime cost associated with plant operation (i.e. coal feedstock, maintenance and labour, insurance and cost of pumping power due to feedwater pumps and boiler extraction pumps), total capital investment, revenue and net present value of plant as shown in figures 7-8. In figure 7, the effect on lifetime cost components such as fuel, maintenance, insurance, labour and pumping has been plotted against plant life, which varies from 0 to 35 years with a interval of 5 years. The total fuel cost, maintenance, insurance, labour cost and pumping cost over the plant life increases with plant life, as expected. The fuel cost is observed to be highly sensitive, while pumping cost is observed to be least sensitive for above range of plant life.
Figure 7: Effect of plant lifetime on fuel cost, maintenance cost, insurance cost, pumping cost and labour cost, plant load=210MW
Figure 8 highlights the effect on total operating cost, total capital investment, revenue earned and net present value with plant life up to 35 years. Total operating cost, revenue and net present value of plant also improves with plant life. Lifetime plant operating cost increases up to 5397.6 INR Crore, while total revenue increases up to 9421.7 INR Crore by increasing plant life from 5 to 35 years and plant net present value improves from -1965.4 INR Crore to 2058.8 INR Crore for encountered variation in plant life. At present time frame the total capital investment remains constant. It is observed from predictions that payback (or gestation period) of plant is nearly 10 year. Beyond this period, the plant starts showing profit.
Figure 8: Effect of plant life on total capital investment, total operating cost, total revenue and net present value, plant load=210MW
Plant Load
The effect of variation in plant load from 168MW to 221 MW on a 210 MW capacity coal fired power plant was investigated on total fuel cost, pumping cost, insurance and maintenance cost, labour cost, total capital investment, revenue and net present value of plant on lifetime basis has been highlighted in figure 9-10. Figure 9 show that except labour cost, all cost improves with plant load. Fuel cost, maintenance cost, insurance cost and pumping cost increases from 3262.4 to 4261.9 INR Crore, 641.11 to 775.19INR Crore, 178.08 to 215.32 INR Crore and 91.23 to 126.89 INR Crore respectively. The fuel cost is observed to be highly sensitive, while pumping cost has been found to be least sensitive.
Figure 9 Effect of plant load on lifetime total fuel cost, total maintenance cost and total labour cost, plant load=210MW
Figure 10 Effect of plant load on total capital investment, total operating cost, total revenue and net present value, Load=210MW
Figure 10 represents the effect of plant load on total operating cost, equipment, revenue and net present value of plant. As plant life increases, the total operating cost, total capital investment, revenue and plant net present value observed to be improved, as expected. Typical increase in total operating cost, revenue, total capital investment and plant net present value of plant has been increased from 4445.1 to 5651.6 INR Crore, 7537.3 to 9915.2 INR Crore, 1685.3 to 2037.7 and 1406.9 to 2225.9 INR Crore, respectively by increasing the plant load from 168 to 221 MW.
Interest rate
The effect of variation in interest rate has been highlighted on accumulated lifetime total fuel cost, pumping cost, insurance and maintenance cost, labour cost, total capital investment, revenue and net present value as represented in figure 11-12. In figure 11, the effect on fuel, maintenance, insurance, labour and pumping costs have been plotted against interest rate range from 9% to 15% on annual basis. As expected, the total fuel cost, insurance, pumping, labour and maintenance charges decreases over the interest rate increases.
Figure 11: Effect of interest rate on plant lifetime total fuel cost, total maintenance cost, total insurance, total pumping and total labour cost, plant load=210MW
Figure 12 highlights the effect on total operating cost, total capital investment, revenue earned and net present value on lifetime basis against interest rate. Total operating cost, revenue and net present value of plant decreases as the interest rate increases. Plant net present value decreases from 2058.8 to 326.36 INR Crore by increasing interest rate from 9% to 15% on annual basis.
Figure 12: Effect of interest rate on plant lifetime total capital investment, total operating cost, total revenue and net present value, plant load=210MW
Escalation rate
In power plant economic factors vary from year to year. In order to handle the fluctuations in economic climate, it is usual practice to include constant escalation rate for each module as listed in table 4. Using information of escalation rates and without using escalation rate, the various costs on lifetime basis (total fuel cost, pumping cost, insurance and maintenance cost, labour cost, total capital investment, revenue and net present value of plant) has been plotted on bar chart as shown in figure 13-14. The effect of escalation rates on labour cost has not been considered in the present work. In figure 13, the effect on accumulated operating cost components such as fuel, maintenance, insurance cost, labour and pumping cost, with and without escalation rate has been plotted at 210 MW plant load. With escalation, costs of fuel, maintenance, insurance, labour and pumping have been observed to be higher than the case of without escalation. The fuel cost is find be highly sensitive as it is the major share of operating cost.
Figure 13 Comparing the effect of escalation on plant lifetime total fuel cost, total maintenance cost, total insurance cost, total pumping cost and total labour cost, plant load=210MW
Figure 14 highlights the effect escalation rate on total operating cost, revenue earned, total capital investment and net present value of plant. At present timeframe, the total capital investment is invariant, as expected. The total operating cost, revenue earned and net present value of plant with escalation rates has been observed to be higher as compared to the case without any escalation.
Figure 14 Comparing the effect of escalation on plant lifetime total operating cost, total capital investment, total revenue and net present value of plant, Load=210MW
The various accumulated costs (total fuel cost, pumping cost, insurance and maintenance cost, labour cost, total capital investment, revenue and net present value of plant ) with and without escalation rates with plant life of 35 years has been shown in figure 13-14.The effect of escalation rates on pumping and labour cost has not been considered in the present work. In figure 13, the effect on accumulated operating cost components such as fuel, maintenance, insurance cost, labour and pumping cost, with and without escalation rates has been plotted at 210 MW plant load. Fuel cost, maintenance, insurance, labour and pumping cost with escalation rates has been observed to be 4050.8, 747.68, 207.68, 272.3 and 119.06 INR Crore respectively. On the other hand, these cost without escalation rates observed to be 1892.8, 311.52, 207.68, 272.3 and 119.06 INR Crore respectively. Figure 14 highlights the effect on total operating cost, revenue earned, total capital investment and net present value of plant. Total operating cost, revenue earned and net present value of plant with escalation rates has been observed to be 5397.6, 9421.7, 2058.8 respectively. If no escalation rates are introduced, total operating cost, revenue earned and net present value of plant observed to be 2607.4, 5456.7 and 883.95 INR Crore respectively.
Labour
The effect of variation in number of labour employed has been highlighted on accumulated total fuel cost, pumping cost, insurance and maintenance cost, labour cost, fixed cost, operating cost, revenue and net present value of plant as shown in figure 15-16. Since the contribution of labour cost in operating cost is very small, the variation in all trends for operating, revenue and net present value is marginal.
Figure 15 Effect of number of labour employed on plant lifetime total fuel cost, total maintenance cost, total insurance cost, total pumping cost and total labour cost, plant load=210MW
Figure 16 Effect of number of labour employed on plant lifetime total capital investment, total operating cost, total revenue and net present value, Load=210MW
Conclusions
The effect of various parameters on plant economics has been observed in this study and following conclusions have been achieved. In case of plant life ranging from 5 to 35 years, the fuel cost is observed to be highly sensitive, while pumping cost is observed to be least sensitive. It is also concluded that payback (or gestation period) of plant is nearly 10 year. Increase in interest rate, decreases the value of plant various cost. The total operating cost, revenue earned, net present value of plant, costs of fuel, maintenance, insurance, labour and pumping with escalation rates have been observed to be higher than the case of without escalation. Since the contribution of labour cost in operating cost is very small, the variation in all trends for operating, revenue and net present value is marginal. Results predict that plant life, interest rate and escalation rate observed to be very sensitive on plant economics in comparison to other factors under study.
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