Energy and emission analysis for industrial motors in Malaysia

ARTICLE IN PRESS Energy Policy 37 (2009) 3650–3658 Contents lists available at ScienceDirect Energy Policy journal homepage: www.elsevier.com/locate/enpol Energy and emission analysis for industrial motors in Malaysia R. Saidur a,, N.A. Rahim b, H.W. Ping b, M.I. Jahirul a, S. Mekhilef b, H.H. Masjuki a a b Department of Mechanical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia Department of Electrical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia a r t i c l e in f o a b s t r a c t Article history: Received 9 February 2009 Accepted 16 April 2009 Available online 19 May 2009 The industrial sector is the largest user of energy in Malaysia. Industrial motors account for a major segment of total industrial energy use. Since motors are the principle energy users, different energy savings strategies have been applied to reduce their energy consumption and associated emissions released into the atmosphere. These strategies include using highly efficient motors, variable speed drive (VSD), and capacitor banks to improve the power factor. It has been estimated that there can be a total energy savings of 1765, 2703 and 3605 MWh by utilizing energy-efficient motors for 50%, 75% and 100% loads, respectively. It was also found that for different motor loads, an estimated US$115,936 US$173,019 and US$230,693 can be saved in anticipated energy costs. Similarly, it is hypothesized that a significant amount of energy can be saved using VSD and capacitor banks to reduce speed and improve the power factor, thus cutting energy costs. Moreover, a substantial reduction in the amount of emissions can be effected together with the associated energy savings for different energy savings strategies. In addition, the payback period for different energy savings strategies has been found to be reasonable in some cases. & 2009 Elsevier Ltd. All rights reserved. Keywords: Industrial motors Energy savings Emission reduction 1. Introduction There has been a growing concern recently about energy use and its adverse impact on the environment. Most of the developing countries shifted from agriculture to industrialization and urbanization within a process of economic growth and development over the last few decades. Energy losses in a large number of industries exist, and potential for energy efficiency improvements is evident (Mohsen and Akash, 1998). Among the various sectors contributing to greenhouse gas (GHG) emissions, the contribution of the industrial sector is significant. Thus, lowering GHG emissions from the industrial sector offers the means of reducing overall GHG emissions. Energy conservation means less reliance on energy imports and, thus, less GHG emissions. Previous studies have reported that implementing a few select options at little or no cost in the industrial sector could reduce GHG emissions by 10–30% of GHG emissions (Ghaddar and Mezher, 1999; IPCC, 1996). In Malaysia, the industrial sector was found to be major user of energy. It accounted for some 48% of total energy use in 2007, as shown in Fig. 1 (EC, 2007). The increased use of energy raised serious concerns in the Malaysian government about the need to overcome heightened energy expenditure by promoting the end-use energy efficiency, which means using less energy while  Corresponding author. Tel.: +60 3 79674462; fax: +60 3 79675317. E-mail address: [email protected] (R. Saidur). 0301-4215/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2009.04.033 maintaining the same level of service. It can be achieved either by reducing total energy use or by increasing the production rate per unit of energy used. On the other hand, improving energy efficiency is the key to reduce greenhouse gas emissions. Therefore, energy research organizations and governments are actively engaged in developing methods of assessing energy efficiency. This assessment can provide a basis for establishing energy policy and can help in reducing GHG emissions. One way to achieve more efficient use of final energy in an industry is to determine the precise amount of energy used and identifiable energy losses. Various types of equipment and devices that use energy at varying levels of efficiency depend on the characteristics and working conditions (Fromme, 1996; Ibrik and Mahmoud, 2005; Priambodo and Kumar, 2001; Thollander et al., 2005; Chan et al., 2007). Energy use performances and energy efficiency in industry have also been studied in various countries (Ozturk, 2005; Christoffersen et al., 2006; Subrahamanya, 2006). Comprehensive literature on electrical motor energy savings, policy and technology can be found in a handbook written by Nadel et al. (2002). In Slovenia, the industrial sector consumes about 52% of total electrical energy (Al-Mansour et al., 2003). In Turkey, about 35% of total energy is used in the industrial sector (Onut and Soner, 2007). Approximately half of the total generated electricity in the UK is used to drive electrical motors. This means that efficiency improvements to electrical machines can have a very large impact on energy consumption (Mecrow and Jack, 2008). Motor-driven systems account for approximately 65% of the electricity used by industry in the European Union (Anon, ARTICLE IN PRESS 3651 R. Saidur et al. / Energy Policy 37 (2009) 3650–3658 Hr ISIC L 0.746 n PF P Savings SSR Nomenclature AES annual energy savings AFD adjustable frequency drive c average energy cost (US$/kWh) energy-efficient motor efficiency rating (%) Eee EF emission factor (kg/kWh) ER emission reduction in kg standard motor efficiency rating (%) Estd energy savings with the application of VSD ESVSD GDP gross domestic products GHG greenhouse gas Havg_usage annual average usage hours HEM high efficiency motor HP motor rated horsepower TNB VAV VFD VSD 2004). In Jordan, the industrial sector consumes about 31% of total energy (Al-Ghandoor et al., 2008). In Malaysia, about 48% of total energy is used to drive industrial motors, as shown in Fig. 2 (Saidur et al., 2009). In many industrialized countries, more than 70% of the total energy is consumed by electric motors. Therefore, the cost of energy to operate motors has become a real concern for industry (Akbaba, annual operating hours international standard industrial classification load factor (percentage of full load) conversion factor from horsepower to kW number of motors power factor motor power (kW) expected annual bill savings (US$) percentage energy savings associated certain percentage of speed reduction tenaga nasional Berhad variable air volume variable frequency drive variable frequency drive 1999). The energy consumed to drive electric motors used in industrial plants is about 65% of the total energy consumption in Turkey. Therefore, it is important to ensure placement of ‘‘high efficiency’’ motors in industrial plants wherever possible (Kaya et al., 2008). APEC-ESIS (2003) carried out some works on motor standards in APEC region. There are a number of different terms used to describe the AC drive. AFD, variable speed drive (VSD), VFD and inverters all are employed, but have the same meaning. The main purpose for all AC drives is to control the operation of the AC motor with regard to speed and torque. A drive is a technology that controls a motor’s speed to correspond with its load requirements. VSD’s have been used to provide significant savings in a number of applications. By introducing variable speed to the driven load, it is possible to optimize the efficiency of the entire system, and it is in this area that the greatest efficiency gains are possible (Mecrow and Jack, 2008). Power factor (PF) correction equipment that can be applied at the motor level will not only decrease energy use but also will significantly extend the life of the equipment (Bayindir et al., 2009; Colak et al., 2004). Capacitors today are smaller and can be applied more easily at the motor level than a few years ago. They have also come down in price to a level where the return on investment (ROI) is usually Others, 1% Residential, 19% Industrial, 48% Commercial, 32% Fig. 1. Statistics of energy uses in Malaysia. Others Electric furnaces Workshop machines Cranes ( Overhead & gantry) Conveyor systems Lifts/elevators/escalators Air cleaning equipments Ventilation & exhaust systems pumps Reciprocating air-compressor Refrigeration systems Air conditioning systems Steam/hot water boilers Electric motors Electric lights 0 5 10 15 20 25 30 Percentage (%) 35 Fig. 2. End use energy breakdowns in Malaysian industrial sector. 40 45 50 ARTICLE IN PRESS 3652 R. Saidur et al. / Energy Policy 37 (2009) 3650–3658 less than 1 year making them a fast return on the money spent. By combining several of the solutions, aggregate energy savings can easily approach 30–35% (Nakahoma, 2008). In the existing literature, no study has quantified the details of motor energy savings in the ASEAN industrial sector. This study presents the energy savings, bill savings and associated emission reductions by industrial motors for different energy savings strategies. It is hoped this study will be useful for formulating policy measures for industrial motors energy use in ASEAN and other countries. Furthermore, the results can furnish important guidelines and insights for future research and development allocations and energy projects to reduce motor energy use. Table 2 Number of audited industrial sector with ISIC code. Sectors Sector/ISIC code Number of audited factories Food products Wood and wood products Paper and paper products Chemicals Petroleum refineries Rubber and rubber products Plastic and plastic products Glass and glass products Iron and steel Fabricated metal products Cement Total 311 331 341 352 353 355 356 362 371 381 390 9 8 13 4 5 13 7 4 5 12 6 91 2. Methodology This section explains the targeted factories, walkthrough audit, data collected, and approaches used to estimate energy savings and emission reductions by variable speed drive, high efficiency motor and power factor improvement. The payback period and economics of energy savings for different strategies have been shown as well. These are elaborated below. 2.1. Targeted manufacturing factories The targeted industries in the present study are electricity users of TNB (a national utility company) from the industrial sector in various regions within Peninsular Malaysia. More than 2500 questionnaires were distributed by mail to various industrial firms, amounting to 10% of the total Malaysian industrial sector. Based on the response received, 125 industries (5% of the 2500 industries) were selected to perform the walkthrough audit. However, audit team managed to visit and collect complete data for 91 industries. The selection of these industries is based on the information provided during the mail survey, their willingness to accept our audit team and the amount of energy used. The locations of industrial regions along with number of audited factories in each region are shown in Table 1. The audited factories were divided into 11 sectors according to the product they manufactured. Table 2 shows the sectors with three digit International Standard Industrial Classification (ISIC) code and the number of factories audited from each sector. 2.2. Energy audit data Energy audit data have been collected using a walkthrough energy audit. Details of walkthrough energy audit can be found in Saidur et al. (2009). During the walkthrough audit in a factory, the audit team counted all the equipment on the production floor, and took notes on rated power from technical specifications on the equipment and operating hours per working day. The audit team also estimated total working days in a year in consultation with a responsible person associated with the production process. Table 1 Locations of audited factories. Location Number of audited factories Central (Selangor, Kuala Lumpur) North (Perak, Penang, Kedah, and Perlis) South (Johor, Melaka and Negeri Sembilan) East (Pahang and Terengganu) Total (East-coast of Malaysia) 41 25 14 11 91 The most important data that have been collected during the walkthrough audit are power rating and operation time for equipments/machineries using energy, fossil fuel and other sources of energy use, production figure, peak and off-peak tariff usage behavior, and power factor. From this study and published work (Saidur et al., 2009), it was found that the industrial motor consumes a major share of total industrial energy. Consequently, the electrical motor has been targeted to estimate energy savings and emission reduction by applying various energy savings strategies. 2.3. Estimating electric motor energy savings, its payback period and emission reductions Energy can be saved in different ways for different types of machinery using industrial energy, working with the different energy savings strategies. However, the focus of the present study is to identify major energy-using equipment and to apply energysaving options for this major energy-using equipment. Since motors consume a substantial share of total industrial energy (see Fig. 2), energy savings through the use of energy-efficient motors, VSDs and capacitors is considered. 2.4. Energy savings by using a high efficiency motor A high efficiency motor (HEM) uses low-loss materials to reduce core and copper losses. Therefore, it generates less heat and requires a smaller and, more energy-efficient cooling fan. The most popular approach is demand-side management, one aspect of which is to improve the efficiency to offset load growth. These facts led electric motor manufacturers to seek methods for improving the motor efficiency, which resulted in a new generation of electric motors that are known as energy-efficient electric motors. Several leading electric motor manufacturers, mainly in the US and Europe, have developed product lines of energy-efficient electric motors (Akbaba, 1999). Switching to energy-efficient motor-driven systems can save Europe up to 202 billion kWh in electricity use, equivalent to a reduction of h10 billion per year in operating costs for industry. It was reported that a reduction of 79 million tons of CO2 emissions (EU-15), or approximately a quarter of the EU’s Kyoto target, is achievable using energy-efficient motors. This is the annual amount of CO2 that a forest the size of Finland transforms into oxygen. If industries are allowed to trade these emission reductions based on energy saved, this would generate a revenue stream of h2 billion per year. For EU-25, the reduction potential is 100 million tons (Anon, 2004). ARTICLE IN PRESS 3653 R. Saidur et al. / Energy Policy 37 (2009) 3650–3658 2.4.1. Mathematical formulations to estimate energy savings using HEMs Annual energy savings (AES) by replacing a standard efficient motor with a high energy-efficient motor can be estimated using methodology described in Garcia et al. (2007):   1 1  100 (1)  AES ¼ hp  L  0:746  h  Estd Eee Annual bill savings associated with the above energy savings can be calculated as Savings ¼ AES c (2) 2.5. Motor energy savings using variable speed drive Many building systems are designed to operate at maximum load conditions. However, most building systems operate at their full load only for short periods of time. This often results in many systems operating inefficiently over long periods of time. Most such inefficient operations in buildings are encountered in airconditioning systems that are normally sized to meet peak load conditions. These occur only for short periods during the normal day. The efficiency of such systems can be improved by varying their capacity to match actual load requirements. As all these are variable torque applications, the power required (to drive the pumps or fans) varies to the cube of the speed and, therefore, large power reductions result from small reductions in speed, as shown in Fig. 3. The most common method is to modulate the speed of the motors of pumps and fans to vary their capacity using VSDs (Beggs, 2002). Variable frequency drives provide continuous control, matching motor speed to the specific demands of the work being performed. Variable frequency drives are an excellent choice for adjustable-speed drive users, because they allow operators to fine-tune processes while reducing costs for energy and equipment maintenance in heating, ventilating and air-conditioning of buildings (Jayamaha, 2006; Teitel et al., 2008). VSD installations can increase energy efficiency (in some cases energy savings can exceed 50%), improve power factor and process precision, and afford other performance benefits such as soft starting and overspeed capability. They also can eliminate the 100 Power consumption (%) 90 80 70 need for expensive and energy-wasting throttling mechanisms such as control valves and outlet dampers (Beggs, 2002). Electric motors are over 90% efficient when running at their rated loads. However, they are very inefficient at load following, or at part loads running. Conventional electric motors typically use 60–80% of their rated input energy, even when running at less than 50% load (Bouzidi, 2007). It is very important to select an electric motor of suitable power to work efficiently. In general, motors are chosen in big capacities to meet extra load demands. Big capacities cause motors to work inefficiently at low load. Normally, motors are operated more efficiently at 75% of rated load and above. Motors operated at less than 50% of rated load because they were chosen based on large capacity, perform inefficiently and, due to the reactive current increase, power factors are also decreased. These kinds of motors do not use the energy efficiently because they have been chosen for large motor power, not according to the needs. These motors should be replaced with new suitable-capacity motors, and when purchasing new motors, energy-saving motors should be preferred (Kaya et al., 2008). VSDs yield sizable energy savings (15–40% in many cases) and extend equipment life by allowing for gentle start-up and shutdown (Nadel et al., 2002). 2.5.1. Mathematical formulations to estimate energy savings using VSD There are many ways to estimate the energy savings associated with the use of VSD for industrial motors for various applications. This paper employed the methods found in Anon (2008). Energy use of fans and pumps varies according to the speed raised to the third power. So small changes in speed can result in huge changes in energy use. A motor energy savings using VSD can be estimated by (3) ESVSD ¼ nPHavg_usage SSR Table 3 shows the potential energy savings associated with the speed reduction using VSD for industrial motors (Anon, 2002). These data have been used to estimate motor energy savings using VSD. 2.6. Motor loss reductions using capacitor bank A power factor is the ratio of the real power to the apparent power and represents how much real power a piece of electrical equipment utilizes. It is a measure of how effectively electrical power is being used. Induction motors convert some 80–90% of the delivered apparent power into useful work. The remaining power is used to establish an electromagnetic field in the motor. The field is alternately expanding and collapsing (once each cycle). So the power drawn into the field in one instant is returned to the electrical supply system in the next instant. Therefore, the average power drawn by the field is zero and a reactive power does not 60 50 Table 3 Potential savings from VSD (Anon, 2008). 40 30 20 10 0 10 20 30 40 50 60 70 Rated speed (%) 80 90 Fig. 3. Relationship between motor power reduction and rated speed. 100 Average speed reduction (%) Potential energy savings (%) 10 20 20 40 50 60 89 22 44 61 73 83 ARTICLE IN PRESS 3654 R. Saidur et al. / Energy Policy 37 (2009) 3650–3658 register on a kilowatt-hour meter. Although it does no useful work, it circulates between the generator and the load and places a heavier drain on the power source as well as the transmission and distribution system (Kwiatkowski, 2009). Adding capacitors is generally the most economical way to improve a facility’s power factor, as shown in Fig. 4. While the current through an inductive load lags behind the voltage, current to a capacitor leads the voltage. Thus, capacitors serve as a leading reactive power to counter the lagging current in a system. The choice of the optimum type, size, number and strategic locations for capacitors in the plant is very important. There are three methods of improving a power factor using capacitors: 100 90 Power factor (%) 80 70 60 50 40 30 20 Power factor with capacitor Power factor without capacitor 10 0 0 10 20 30 40 50 60 Motor load (%) 70 80 90 Fig. 4. Power factor improvements by using capacitor. 100 (a) individual motor compensation (static capacitors) (b) centralized compensation located at the incoming power source (automatic capacitor banks) and (c) use of synchronous motor (in overexcited mode) as synchronous capacitors. Table 4 Multipliers to determine capacitor kilovars required for power factor correction (Gilbert and John, 2002). Existing PF (Cos +) before applying capacitors Target power factor required (Cos +) 0.80 0.85 0.90 0.92 0.95 0.98 1.0 0.40 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58 0.60 0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.70 0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 0.80 0.81 0.82 0.83 0.84 0.85 0.86 0.87 0.88 0.89 0.90 0.91 0.92 0.93 0.94 0.95 1.54 1.41 1.29 1.18 1.08 0.98 0.89 0.81 0.73 0.65 0.58 0.55 0.52 0.48 0.45 0.42 0.39 0.36 0.33 0.30 0.27 0.24 0.21 0.19 0.16 0.13 0.11 0.08 0.05 0.03 1.67 1.54 1.42 1.31 1.21 1.11 1.02 0.94 0.86 0.78 0.71 0.68 0.65 0.61 0.58 0.55 0.52 0.49 0.46 0.43 0.40 0.37 0.34 0.32 0.29 0.26 0.24 0.21 0.18 0.16 0.13 0.10 0.08 0.05 0.03 1.81 1.68 1.56 1.45 1.34 1.25 1.16 1.07 1.00 0.92 0.85 0.81 0.78 0.75 0.72 0.68 0.65 0.63 0.59 0.56 0.54 0.51 0.48 0.45 0.42 0.40 0.37 0.34 0.32 0.29 0.27 0.24 0.21 0.19 0.16 0.14 0.11 0.08 0.06 0.03 1.87 1.73 1.61 1.50 1.40 1.31 1.22 1.13 1.05 0.98 0.91 0.87 0.84 0.81 0.77 0.74 0.71 0.68 0.65 0.62 0.59 0.57 0.54 0.51 0.48 0.46 0.43 0.40 0.38 0.35 0.32 0.30 0.27 0.25 0.22 0.19 0.17 0.14 0.11 0.09 0.06 0.03 1.96 1.83 1.71 1.60 1.50 1.40 1.31 1.23 1.15 1.08 1.00 0.97 0.94 0.90 0.87 0.84 0.81 0.78 0.75 0.72 0.69 0.66 0.64 0.61 0.58 0.55 0.53 0.50 0.47 0.45 0.42 0.40 0.37 0.34 0.32 0.29 0.26 0.24 0.21 0.18 0.16 0.13 0.10 0.07 0.03 2.09 1.96 1.84 1.73 1.60 1.53 1.44 1.36 1.28 1.20 1.13 1.10 1.06 1.03 1.00 0.97 0.94 0.90 0.88 0.85 0.82 0.79 0.76 0.73 0.71 0.68 0.65 0.63 0.60 0.57 0.55 0.52 0.49 0.47 0.44 0.42 0.39 0.36 0.34 0.31 0.28 0.25 0.22 0.19 0.16 0.13 2.29 2.16 2.04 1.93 1.83 1.73 1.64 1.56 1.48 1.40 1.33 1.30 1.27 1.23 1.20 1.17 1.14 1.11 1.08 1.05 1.02 0.99 0.96 0.94 0.91 0.88 0.86 0.83 0.80 0.78 0.75 0.72 0.70 0.67 0.65 0.62 0.59 0.57 0.54 0.51 0.48 0.46 0.43 0.40 0.36 0.33 ARTICLE IN PRESS 3655 R. Saidur et al. / Energy Policy 37 (2009) 3650–3658 If the plant contains many small motors (in the 12 to 10 hp size range), it may be more economical to group the motors and place single capacitors or banks of capacitors at or near the motor control centers. In Malaysia, the capacitors are generally placed at a central location (at the incoming substation) and switched into the system automatically when the motors are started. It is important not to overcorrect, as overcorrection may result in greater problems such as overvoltage and insulation breakdown. It is recommended that the power factor be kept above 90% and below unity (100%) for optimal performance of the electrical system. 2.6.1. Calculating size of capacitor banks In the case of a centralized compensation, it is recommended that the first capacitor step be equal to half the value of the following steps, to allow a smooth overall linear correction system. Table 4 has been used in calculating capacitor values for a specific application. The correct capacitor size can be calculated by multiplying the factor when crossing the horizontal and vertical columns in the table by kW. The average installed cost of capacitors per kVAR at higher voltage levels (2400 V and up) are generally about US$11.4/kVAR (Yang, 2006). The payback period for power factor correction can be calculated using Eq. (4). Input data needed to estimate payback period are shown in Table 6. 2.6.2. Mathematical formulations of the payback period A simple payback period for different energy savings strategies can be calculated by Simple payback period ðyearsÞ ¼ Incremental cost Annual dollar savings (4) Input data needed to estimate energy savings and the payback period for these strategies are shown in Tables 5–7. Average usage Table 5 Input data for motor energy savings. Parameters Value Average usage hours Average electricity cost (US$/kWh) 6000 0.064 Table 7 Incremental price for VSD (Anon, 2002). HP Incremental price (US$) 3 5 7.5 10 15 20 25 30 2216 2461 3376 3349 4176 5316 6123 6853 Table 8 Emission factors of fossil fuels for electricity generation (Source: Mahlia, 2002). Fuels Coal Petroleum Gas Hydro Others Emission factor (kg/kWh) CO2 SO2 NOx CO 1.18 0.85 0.53 0.00 0.00 0.0139 0.0164 0.0005 0.000 0.000 0.0052 0.0025 0.0009 0.0000 0.0000 0.0002 0.0002 0.0005 0.0000 0.0000 hours have been collected from the energy audit survey data. Efficiency of standard and high efficiency motors has been collected from Garcia et al. (2007). Incremental costs associated with the usage of high efficient motor, VSD and power factor improvement capacitor have been taken from Garcia et al. (2007), Anon (2002) and Yang (2006). Since there is no comprehensive work on motors in Malaysia, these data have been used at least to obtain some modicum of insight into how much energy and cost can be saved along with emission reductions. Moreover, motors are manufactured, sold and used around the world. So data from variety of countries have been used in this estimation. 2.6.3. Estimation of emission reduction The energy savings is likely to reduce the electricity generation from power plants. As a consequence, the reduction of CO2 and other emissions from the fuels used by the power sector can be estimated. The amount of emission that can be reduced associated with the energy savings can be estimated using (Mahlia, 2002) ER ¼ AES EF Table 6 Efficiency of standard and high efficiency motors at different loads (Garcia et al., 2007). Motor HP 1 1.5 2 3 4 5.5 7.5 15 20 25 30 40 50 60 75 Load (50%) Load (75%) (5) Emission factor for per unit energy is shown in Table 8 and is used to estimate the amount of emission that can be reduced. Load (100%) Estd Eee Estd Eee Estd Eee 70.05 76.04 77.20 77.78 81.07 81.15 84.07 84.92 86.03 87.61 88.43 88.15 89.63 87.89 88.77 75.28 80.06 80.02 82.44 83.69 84.35 85.51 88.32 88.51 90.26 90.89 90.39 91.16 90.07 90.86 74.43 78.03 79.29 79.87 82.39 84.73 86.23 86.45 87.58 88.39 89.32 90.54 89.86 91.31 90.19 79.49 81.28 83.07 84.55 85.24 86.50 87.58 89.85 91.05 91.66 91.73 91.91 92.58 92.09 92.72 77.00 78.50 81.00 81.50 82.90 85.30 86.61 87.94 88.95 89.50 90.70 90.36 92.06 91.78 92.44 80.97 82.55 83.55 85.01 85.96 87.75 89.50 90.44 91.64 91.80 91.83 92.85 93.28 93.00 93.02 3. Results and discussion of electrical motor energy savings, payback period and associated emission reductions Using Eqs. (1), (2) and (4) and input data in Table 5, energy savings, bill savings and the payback period associated with energy savings as a result of using a high efficiency motor have been estimated and presented in Table 9 for different motor sizes and loads. Based on this table and by analyzing data, it was determined that 1765, 2703 and 3605 MWh of total energy can be saved by using energy-efficient motors for 50%, 75% and 100% motor loading, respectively. Similarly, associated bill savings for the estimated amount of energy savings are US$115,936, US$173,019 and US$230,693, respectively. It also has been found that the payback period for using energy-efficient motors ranges from 0.53 ARTICLE IN PRESS 3656 R. Saidur et al. / Energy Policy 37 (2009) 3650–3658 Table 9 Energy savings and payback period for high efficient motor. HP Quantity (no.) 1 3968 1.5 331 2 1653 3 2976 4 13,556 5.5 331 7.5 661 15 165 20 3306 25 992 30 331 40 661 50 331 60 827 75 165 Incremental price (US$) Load (50%) 24 21 25 27 60 65 91 147 197 246 257 231 281 574 518 Load (75%) Load (100%) Energy savings (MWh) Bill savings (US$/year) Payback Energy savings (yr) (MWh) Bill savings (US$/year) Payback Energy savings (yr) (MWh) Bill savings (US$/year) Payback (yr) 74 6 28 122 393 16 19 21 404 156 11 140 58 257 60 4730 394 1814 7798 25,169 1022 1194 1351 25,888 9989 682 8938 3721 16,417 3862 2.05 1.80 2.25 1.02 3.22 2.10 5.05 1.80 2.52 2.44 2.33 1.71 2.50 2.89 2.22 6118 458 3421 11,155 39,675 792 1598 1957 51,883 18,046 5261 7852 9746 8295 6763 1.59 1.55 1.19 0.71 2.04 2.71 3.77 1.24 1.26 1.35 1.62 1.95 0.95 5.72 1.27 8158 611 4562 14,873 52,900 1056 2131 2609 69,177 24,061 7014 10,469 12,994 11,060 9018 1.19 1.16 0.89 0.53 1.53 2.04 2.83 0.93 0.94 1.01 1.21 1.46 0.71 4.29 0.95 96 7 53 174 620 12 25 31 811 282 82 123 152 130 106 127 10 71 232 827 16 33 41 1081 376 110 164 203 173 141 Table 10 Motor energy savings with VSD for different % of speed reduction. Motor power (HP) 0.25 0.5 0.75 1 1.5 2 3 4 5.5 7.5 15 20 25 30 40 50 60 75 Energy savings (MWh) 10% speed reduction 20% speed reduction 30% speed reduction 40% speed reduction 50% speed reduction 60% speed reduction 114 57 97 391 49 325 880 5341 179 487 251 6519 2437 975 2600 1625 4904 1256 228 114 195 782 97 650 1761 10,682 357 975 502 13,038 4874 1950 5199 3250 9808 2511 316 158 270 1084 135 901 2441 14,809 496 1352 696 18,075 6758 2703 7208 4505 13,597 3481 378 190 323 1297 162 1078 2921 17,723 593 1617 833 21,631 8087 3235 8626 5391 16,272 4166 430 215 368 1475 184 1226 3321 20,151 674 1839 947 24,594 9,195 3678 9808 6130 18,501 4737 461 231 394 1582 197 1315 3561 21,607 723 1972 1016 26,372 9860 3944 10,517 6573 19,839 5079 Table 11 Bill (US$) savings for VSD. Motor power (HP) Speed reduction 10% 0.25 0.5 0.75 1 1.5 2 3 4 5.5 7.5 15 20 25 30 40 50 60 75 20% 30% 40% 50% 7295 14,590 20,227 24,205 27,521 3655 7311 10,135 12,129 13,790 6239 12,478 17,300 20,703 23,539 25,020 50,040 69,373 83,020 94,393 3120 6239 8650 10,351 11,769 20,797 41,595 57,665 69,009 78,462 56,342 112,683 156,220 186,952 212,562 341,832 683,664 947,806 1,134,260 1,289,638 11,439 22,877 31,716 37,955 43,154 31,196 62,392 86,498 103,514 117,694 16,071 32,141 44,559 53,325 60,630 417,206 834,412 1,156,799 1,384,366 1,574,005 155,980 311,959 432,489 517,569 588,469 62,392 124,784 172,996 207,028 235,387 166,378 332,757 461,322 552,073 627,700 103,986 207,973 288,326 345,046 392,312 313,850 627,700 870,220 1,041,411 1,184,070 773,970 1,547,940 2,146,008 2,568,173 2,919,978 60% 29,511 14,787 25,240 101,216 12,620 84,134 227,928 1,382,865 46,274 126,202 65,013 1,687,789 631,009 252,403 673,076 420,672 1,269,666 3,131,060 to 5.05 years for different percentages of motor loading. These payback periods indicate the introduction/implementation of energy-efficient motors would seem cost effective, as their payback periods are less than one-third of the motor life (if average motor life 20 years is considered) in some cases. Using Eqs. (3) and (4) and data from Tables 3, 5 and 7, the energy savings, bill savings and payback period for speed reduction of motors using VSD have been estimated and are shown in Tables 10–12. From Table 10, it is evident that a huge amount of energy can be saved for different percentages of speed reductions. More energy can be saved for higher speed reductions. Along with energy savings, a substantial amount in expense can be saved and associated emission reductions can be achieved using VSD for industrial motors in Malaysia as can be found in Table 11. From Table 12, it can be seen that the payback period for larger motors is economically very viable, since the payback period is very short. However, VSD is not economically viable for smaller motors, since the payback period is significantly higher as reported by other researchers as well (Tolvanen, 2008a, b). Abbott (2006) reported that the payback period of using VSDs for ARTICLE IN PRESS 3657 R. Saidur et al. / Energy Policy 37 (2009) 3650–3658 different sizes and categories of motors ranges from 0.4 to 1.5 years. Using data in Table 4 and considering cost of kVAR as US$11.4/ kVAR based on the study of Yang (2006), the required kVArs, to improve the power factor for 0.85–0.9, 0.95 and 0.98 have been estimated and are shown in Table 13. It should be noted that the cost effectiveness of power factor correction depends on several factors: utility power factor penalties, the need for additional system capacity, energy and demand cost, hours of facility operation, distribution system wire sizes, and the distance between the motor and the electrical meter (Gilbert and John, 2000). Using the data shown in Table 8, and Eq. (5), the amount of emissions that can be reduced as a result of introducing energyefficient motors has been estimated and is presented in Table 14. Table 12 Payback period for speed reduction with the application of VSD. Motor power (HP) 0.25 0.5 0.75 1 1.5 2 3 4 5.5 7.5 15 20 25 30 40 50 60 75 Payback period (yr) for speed reduction 10% 20% 30% 40% 50% 60% 113.79 58.28 39.77 30.52 21.27 16.64 12.02 9.70 7.81 6.47 4.62 4.15 3.88 3.69 3.46 3.32 3.23 3.14 56.89 29.14 19.89 15.26 10.63 8.32 6.01 4.85 3.91 3.23 2.31 2.08 1.94 1.85 1.73 1.66 1.61 1.57 41.04 21.02 14.34 11.01 7.67 6.00 4.33 3.50 2.82 2.33 1.66 1.50 1.40 1.33 1.25 1.20 1.16 1.13 34.29 17.56 11.99 9.20 6.41 5.02 3.62 2.92 2.35 1.95 1.39 1.25 1.17 1.11 1.04 1.00 0.97 0.95 30.16 15.45 10.54 8.09 5.64 4.41 3.19 2.57 2.07 1.71 1.22 1.10 1.03 0.98 0.92 0.88 0.86 0.83 28.13 14.41 9.83 7.54 5.26 4.11 2.97 2.40 1.93 1.60 1.14 1.03 0.96 0.91 0.86 0.82 0.80 0.78 Table 15 shows the emission reduction associated with the energy savings by motors using VSD. It should be pointed out that the amount of energy, money savings and emission reductions has been estimated for only 91 industries in Malaysia. Thus, there is a tremendous potential for saving of energy and lowering electricity bills for the total number of industries in Malaysia. Along with energy savings, it will reduce emission of pollutants released into the atmosphere as well. 4. Conclusion It can be concluded that (a) The study found that a substantial amount of energy and utility bills can be saved if high efficiency motors, VSD and capacitor banks are used for industrial motors. (b) It has been found that the payback period for using energyefficiency strategies for larger motors for VSD is reasonable (i.e. within 1–3 years). (c) The study also estimated that emissions can be substantially reduced by applying energy savings strategies to industrial motor. (d) It was also found that more energy can be saved at levels of higher speed reduction (i.e. speed reduction above 40%). (e) The required kVAr and cost of kVAr to improve the power factor that can reduce resistance (I2R) losses are estimated in this paper. Table 14 Emission reductions (ton) associated with energy savings for energy efficient motor. CO2 SO2 NOx CO 27,140 40,707 39,562 162 244 311 77 115 128 17 25 19 Table 13 cost of kVAr period for adding capacitor to improve power factor of industrial motors. HP 0.25 0.5 0.75 1 1.5 2 3 4 5.5 7.5 15 20 25 30 40 50 60 75 Quantity (no.) 4629 1157 1323 3968 331 1653 2976 13,556 331 661 165 3306 992 331 661 331 827 165 PF ¼ 0.90 PF ¼ 0.95 PF ¼ 0.98 kVAr required (kVAr) Cost of kVAr (US$) kVAr required (kVAr) Cost of kVAr (US$) kVAr required (kVAr) Cost of kVAr (US$) 121 60 104 414 52 345 932 5663 190 518 258 6906 2590 1037 2761 1728 5182 1292 1378 689 1181 4724 591 3936 10,630 64,560 2168 5902 2947 78,724 29,527 11,823 31,480 19,705 59,078 14,734 250 125 215 858 107 715 1931 11,731 394 1073 535 14,304 5365 2148 5720 3580 10,735 2677 2854 1427 2447 9786 1225 8154 22,019 133,731 4490 12227 6104 163,070 61,164 24,490 65,208 40,817 122,377 30,520 363 181 311 1243 156 1036 2797 16,989 570 1553 775 20,717 7770 3111 8284 5185 15,547 3877 4134 2066 3544 14,173 1773 11,809 31,889 193,680 6503 17,707 8840 236,171 88,582 35,468 94,440 59,114 177,235 44,202 ARTICLE IN PRESS 3658 R. Saidur et al. / Energy Policy 37 (2009) 3650–3658 Table 15 Emission reductions associated with energy savings by VSD. Motor power (HP) 0.25 0.5 0.75 1 1.5 2 3 4 5.5 7.5 15 20 25 30 40 50 60 75 Emission reductions (kg) for 20% speed reduction Emission reductions (kg) for 40% speed reduction Emission reductions (kg) for 60% speed reduction CO2 SO2 NOx CO CO2 SO2 NOx CO CO2 SO2 NOx CO 1,140,634 570,194 978,003 3,911,026 489,371 3,258,531 8,799,809 53,445,435 1,794,361 4,886,319 2,439,463 65,170,629 24,443,914 9,787,422 26,060,366 16,312,370 48,907,541 12,197,316 6828 3413 5854 23,411 2929 19,506 52,676 319,924 10,741 29,249 14,603 390,111 146,321 58,587 155,997 97,646 292,760 73,013 3217 1608 2758 11,029 1380 9189 24,815 150,716 5060 13,779 6879 183,781 68,932 27,601 73,490 46,001 137,919 34,396 694 347 595 2379 298 1982 5352 32,507 1091 2972 1484 39,639 14,868 5953 15,851 9922 29,747 7419 1892,415 946,003 1622,596 6,488,748 811,911 5,406,200 14,599,683 88,670,836 2,977,008 8,106,847 4,047,291 108,123,998 40,554,676 16,238,223 43,236,517 27,063,705 81,142,057 20,236,456 11,328 5663 9713 38,842 4860 32,361 87,394 530,783 17,820 48,528 24,227 647,230 242,760 97,202 258,814 162,003 485,716 121,135 5337 2668 4576 18,298 2290 15245 41171 250,052 8395 22,861 11413 304,910 114,364 45,792 121,927 76,320 228,821 57,067 1151 575 987 3947 494 3288 8880 53,932 1811 4931 2462 65,764 24,667 9877 26,298 16,461 49,353 12,308 2,307,191 1,153,346 1,978,233 7,910,939 989,864 6,591,120 17,799,614 108,105,540 3,629,502 9,883,690 4,934,369 131,822,409 49,443,372 19,797,286 52,713,014 32,995,476 98,926,617 24,671,844 13,811 6904 11,842 47,355 5925 39,454 106,548 647,119 21,726 59,164 29,537 789,089 295,968 118,506 315,540 197,511 592,174 147,686 6506 3252 5579 22,309 2791 18,587 50195 304,858 10,235 27,872 13,915 371,739 139,430 55,828 148,651 93,047 278,973 69,575 1403 702 1203 4812 602 4009 10,826 65,753 2208 6012 3001 80,178 30,073 12,041 32,062 20,069 60,170 15,006 Acknowledgement Authors would like to acknowledge the service provided by the University of Malaya ISI (UPISI) Journal centre for polishing the language. 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