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International Journal of Electrical and Electronics Engineering Research (IJEEER) ISSN 2250-155X Vol. 2 Issue 4 Dec - 2012 41-50 Š TJPRC Pvt. Ltd.,



Electrical Engineering Department, PDA College of Engineering Gulbarga, Karnataka, India

Electrical Engineering Department, Dr. Ambedkar Institute of Technology, Bengaluru, Karnataka, India

ABSTRACT Severe electricity shortages in India has necessitated the electric utilities, of Indian states, to adopt demand side management (DSM) to ease the situation. The DSM implementation in different sectors requires load study of that sector. The load study of residential sector of Karnataka, India is presented in this paper. The study is based on the appliance survey conducted in Gulbarga, a district head quarter of Karnataka. The sample consists of data of 252 households, comprising customers of low income, medium income groups. The study has revealed the patterns of consumption of electricity in LT-1 (5 A connections) and LT-2 (15 A connections) category houses, the stock of electrical appliances used by the households, and the degree of penetration of energy efficient appliances/devices. Based on these revelations, the utility can devise suitable DSM measures.

KEYWORDS: Conservation; Demand Side Management; Electricity; End Use; Residential INTRODUCTION Power sector operations in most of the countries all over the world have changed; this change has brought several challenges before these utilities. This is largely due to the uncertainties existing in the load growth, higher capital investments required in capacity addition, declining fuel sources and it’s associated environmental, effects and costs. Tariff changes due to the changing regulatory stands also affect the ability of utilities to service its customer base. The concept of demand-side management was developed in response to the potential problems of global warming and the need for sustainable development, and the recognition that improved energy efficiency represents the most cost-effective option to reduce the impacts of these problems. Demand-side management (DSM) refers to cooperative activities between the utility and its customers (sometimes with the assistance of third parties such as energy services companies and various trade allies) to implement options for increasing the efficiency of energy utilization, with resulting benefits to the customer, utility, and society as a whole [1][2]. The growth of power sector in India is manifold. The installed capacity has increased from 30,000 MW in 1981 to over 156783.98 MW on 31-01-2010. Despite this growth in supply, its power systems are struggling to overcome chronic power shortages and poor power quality. With demand exceeding supply, severe peak (around 15%) and energy (around 11%) shortages continue to annoy the sector [3]. Shortages are primarily aggravated by inefficiencies in power generation, distribution and end-use systems. The end-use systems are inefficient because of the irrational tariffs, technological obsolescence of industrial processes and equipment, lack of awareness and yet to be developed energy services (ESCO) industry in India. There are many problems being faced by the power sector in India; and the basic one is the poor financial conditions of the state utilities. Over the years, these utilities have been causing an increasingly larger drain on the State


Sangamesh G Sakri & G V Jayaramaiah

Government budgets. This is to the tune of 10-15% of the state fiscal deficits adversely impacting much needed investments in the other social sectors. The power sector is operating with very low or no returns on the equity and no contribution to future investments from internal resource. This results in inadequate investment in additional generation capacity which is likely to further exacerbate the existing gap between power supply and demand. With captive market growth of 9.5% in terms of capacity addition would not add much; hence it would be difficult to meet the demand in the coming years. The direct and indirect economic impact of outages resulting from the capacity shortages is enormous. Some of the visible impacts range from crores of Rupees of losses in the industrial sector to over sizing of pumping systems resulting in falling groundwater levels at an alarming rate in the agricultural sector. The Indian power sector is facing two basic and interdependent issues. They are inferior operational performance leading to poor revenue cash flow, and as a consequence, inadequate capital mobilization for sector expansion. Present approaches that are employed do not completely tackle these issues. Power sector plans focus exclusively on new supply and lately, to an extent, on improving supply efficiency and reducing T&D losses (for instance, the latter through the Accelerated Power Development Program). There is a major omission of neglect of demand-side management (DSM) opportunities in India [4, 5, 6]. There is a clear role and potential for utility driven DSM programs in India. It is estimated that the end-use efficiency improvement potential in industry and building sector alone is of the order of Rs. 14,000 Crores and 54 billion units of electricity saving. In view of this, to capture some of this potential, the Government of India has targeted 15% improvement in energy efficiency by 2007-08[7]. Also, the new Energy Conservation legislation seeks to implement energy efficiency policies that lead to widespread market development though better standards for appliances and equipment, energy efficiency labeling, rational cost-of-service based tariffs, mandatory energy audits, awareness and training, financial and fiscal incentives. The regular energy consumption patterns can be viewed as exogenous and known, but as alterable through interventions. The identification of the nature of the interventions to influence these patterns needs to be addressed. Obviously, the interventions must depend upon the determinants of energy consumption. In the case of residential electrical consumption, the main determinant would be the income if one goes by the conventional thinking. If this is considered, it is politically incorrect to think of income-reduction policies to reduce electricity consumption. A study shows that income is a weak predictor of residential electricity consumption explaining only 38% of electricity consumption [8]. It also reveals that, the appliance stock could explain as much as 93% of the dependent variable. Appliance stock, therefore, is a much better predictor of electricity consumption than income. The demand side management requires an understanding of the appliances that spell out electricity consumption. With this objective, a study of household electricity consumption in the state of Karnataka was carried out in 2010-11 by the author and Electrical Engineering students of PDA College of Engineering, Gulbarga by conducting surveys in the Gulbarga district of Karnataka. The number of consumers and their consumption details for the month of March 2011, in the surveyed categories of Gulbarga and GESCOM is given in table I. The LT-2 category refers to AEH or 15 ampere category corresponding to a 3.5 to 5 kVA connected load, and the LT-1 refers to non-AEH or 5 ampere limit category corresponding to a 1.15 kVA load. In India, LT-1 category consumers mainly include the Bhagya Jyothi/ Kutira Jyothi (BJ/KJ) scheme beneficiaries. BJ/KJ scheme refers to the low income group consumers to whom the government provides electricity at subsidised or zero rates [7]. Three different approaches have been used to study the residential electricity demand in the Metropolis of Bangalore, Karnataka [8]. They are the engineering approach, the appliance stock approach and the appliance census

Enduse Analysis of Electricity Appliances Used in Residential Sector of Karnataka to Identify Energy Conservation Opportunities for DSM


approach. They have established that the appliance stock and appliance census approaches have explained the end-use consumption of electricity much better than the engineering approach. The present study is based on a survey of the appliance stock in selected (sample) households. Another study [9] analyses the end-uses of various categories of appliances in the different electricity consuming sectors and has shown that in the domestic sector the electricity consumption varies between the urban and rural households as the appliance stocks are significantly different. Also across different slabs of electricity usage (9 slabs were considered based on the quantum of annual usage of electricity) there exists a growth trend in the appliance stock possessed by these households. The present study has benefited from these studies in getting better results from the pilot study and survey. The paper is arranged as follows. Section II deals with the implementation stages of DSM. In section III, the electricity scenario of the Karnataka state is given. Section IV, gives the methodology and sample selection details and in section V, the end use analysis of electricity in the residential sector of Gulbarga, Karnataka is discussed. Paper is concluded in section VI.

DSM IMPLEMENTATION Karnataka is one of the few states in India, which had the power reforms initiated, in 70s only, in the form of separation of generation and T&D operations. The Karnataka Electricity Board (KEB) for the transmission and distribution affairs and Karnataka Power Corporation (KPC), The major share of the generation of the state comes from the hydel power (around 3700 MW), which is mainly monsoon dependent. The major thermal power station, Raichur Thermal Power Station (RTPS), has an installed capacity of 1470 MW, is around 20 years old and has started facing problems. It has an average plant load factor of 81.68 in the year 2008-09, a reduction from 84.22 in 2007-08 [7, 13]. The central share is around 1600 MW and IPP contribution is 3050 MW. The objective of any DSM activity is to produce a loadshape change. Therefore, the successful implementation and the success of the program depend on the balancing of the needs of both the utility and customer [1]. The implementation of DSM program involves various steps, they are as following [11, 12]: Step 1: Carrying Load Research: This step involves the evaluation of the customer base, load profile on an hourly basis and will identify the sectors contributing to the load shape. It will also identify the tariff classes in the utility, current recovery from different sectors and current subsidy offered to different sectors. Step 2: Defining load-shape objectives: Here, DSM engineers will define the load shape objectives for the current situation, based on the results of the load research in the utility. Various load-shape objectives are mainly Peak Clipping (reduction in the peak demand), Valley Filling (increased demand at off-peak), Load Shifting (demand shifting to non-peak period), and Load Building (increased demand) are possible. The different load shape objectives are shown in Fig. 1. Step 3: Assessing program implementation strategies: This step identifies the end-use applications that can be potentially targeted to reduce peak demand, specifically in sectors with higher subsidies. This step also carries out a detailed benefitcost analysis for the end-users and the utilities, including analysis on societal as well as environmental benefits. Step 4: Implementation: Implementation stage involves designing the program for specific end-use applications; also promote the program to the target audience through marketing approaches such as advertising, bills and inserts. Step 5: Monitoring and Evaluation: This step entails tracking the program design and implementation. It also compares the same with proposed DSM goal set by the utility. A detailed benefit-cost analysis in this case will include identifying the


Sangamesh G Sakri & G V Jayaramaiah

avoided supply cost for the utility in relation to the total program cost for the utilities and benefits to the participants including the reduced bills or incentives to the end-users. This paper takes up the initial step, paving way for the DSM program designs in GESCOM region.

ELECTRICITY SCENARIO OF KARNATAKA On the demand side, the situation is same as the rest of the country. The energy shortage is a continuing affair. Fig. 2 shows the increasing trend of energy shortage; even though there was capacity addition during this period. To fulfill the required shortage of power, the state government purchases the peak power at the highest price or overdraws from the grid. The overdrawal from the grid results in paying for the energy in UI rates. This is a heavy burden on the exchequer. The amount of money paid towards UI charges in 2008-09 by the state government is given in table 2. The motivation for the DSM initiatives in Karnataka comes from this

Figure 1: Load Shape Objectives of DSM [11]

Figure 2: Energy Shortage In Karnataka from 2005 to 2009 [7]

Situation of the power in the state.To come out of the prevailing situation in power system the immediate solution available is DSM. Table 2: The UI Charges Paid by Karnataka in 2008-09[3] UI Energy UI Amount In MU Rs. in Crores April 23.979752 7.16 May 83.309754 50.42 June 126.880688 84.27 July 126.597982 112.69 August 82.377925 68.78 September 122.288561 92.04 October 142.83255 106.46 November 156.007659 134.68 December 159.152124 113.98 January 269.181047 195.61 February 150.337689 118.22 March 68.540441 45.74 * 1 crore = 10 million, US $ 1= Rs 55 Month

METHODOLOGY AND SAMPLE SELECTION For this work a survey research method was adopted. Here, for the study a questionnaire was used which consisted five sections with open-ended questions. This was tested initially with a pilot study and then it was selected based on the comments, suggestions and responses obtained from the pilot study.

Enduse Analysis of Electricity Appliances Used in Residential Sector of Karnataka to Identify Energy Conservation Opportunities for DSM


The sample taken represents different economic levels of Gulbarga. It is consisted of 126 households, of which 95 had LT-2 connections and 31 had LT-1 connections. For the purpose of analysis, the statistical techniques of multiple regression analysis, step-wise regression analysis, ANOVA, ratios and proportions, descriptive statistics are used [14]. In this work three different approaches are considered to study the electricity consumption of endues devices in a household, they are, the engineering approach, the appliance stock approach and the appliance census approach [9]. Engineering Approach: It is based on sample surveys of variables such as number of appliances, rated power of these appliances and number of hours of usage of these appliances. An engineering estimate of electricity consumption of the end-uses mainly depends on the number of hours of usage of the appliances owned by the households. It may not be a correct estimate as far as statistical significance is concerned. Because this number is obtained from users, which depends mainly on the reliability of a user in recollecting the usage hours. Appliance Stock Approach: Here the total electricity consumed by a household will depend on the total load (wattage of the appliances owned by the household) accounted by a household. Appliance Census Approach: It uses regression analysis to determine the contribution of various categories of appliances to the total electricity consumption. This is a more reliable approach as the regression coefficients indicate the marginal change in electricity consumption per unit change in the number of appliances of that particular category. And, if the change is linear, then the regression coefficient is also the average electrical energy consumed through that category of appliances.

END USE ANALYSIS LT-1 and LT-2 Connections The responses are obtained from 252 households. The data used for the analysis consists 190 (75.40%) LT-2 and 62 (24.60%) LT-1households. The average electricity consumption in LT-2 households is 4746.13 kWh/year with a standard deviation of 1230.42 kWh/year, which is more than 6 times that of LT-1 households. This has an average consumption of 203.46 kWh/year with a standard deviation 63.65 kWh/year. Appliance Stock The number of appliances of various types owned by LT-1 and LT-2 households differ in each category of usage such as water heating, lighting, cooking, etc. The LT-2 households mostly own appliances such as immersion rods, geysers, refrigerators, computers and water pumps. On the other hand none of the LT-1 households own geysers, pumps and refrigerators. Figure 3 gives the difference in proportion of households using appliances such as immersion

Figure 3: Houses Using Appliances Common to LT-1 and LT-2 Categories


Sangamesh G Sakri & G V Jayaramaiah

Rods (IR), Fans, television (TV), 40 W fluorescent lamp (FL), incandescent lamps (IL), Compact Fluorescent Lamps (CFL), which are common to both LT-1 and LT-2 categories. In the LT-2 category, 100% of the households have televisions whereas in the LT-1 households, the penetration of TVs is about 70.97%. Air coolers are more prominent along with fans among the LT-2 households (100%), the share of fans in LT-1 households is 77.42%. A larger proportion of households in the LT-1 category use 100 W and 60 W incandescent lamps but a larger proportion of LT-2 homes use fluorescent tubes and 20 W CFLs as indicated in the figures 4 and 5. The indication from the results shows that even though incandescent lamps are a common feature in both LT-1 and LT-2 households, the number of bulbs per 100 households varies from 200 bulbs in LT-1 to 162 bulbs in the LT-2 category. In the case of fluorescent tubes, the average number per 100 households is 142 for LT-1 whereas it is 200 for LT2. Engineering Approach [8] The engineering approach is based on the number of appliances, Nij, the wattage, Pij, and hours of usage, Tij of the appliance j reported by the sample household i during the survey. The total electricity consumption in a household

Ei = ∑ Eij j

i can be related thus to the appliance-wise consumption: where Eij is the electricity consumed by the jth appliance

E ij = N ij * C ij in the ith household. Also, in the ith household, where Cij is the electricity consumption of the jth appliance category and Nij is the number of electrical appliances in the jth category. Cij can be written as

C ij = Pij * Tij Where Pij the wattage of the jth appliance category and Tij is the number of hours per month for which the jth appliance is being used. The Cij, referred to as the engineering estimate of the appliance consumption depends upon the accuracy of determination of the hours of usage.

Figure 4: Share of Appliances in LT-1 Households Households

Figure 5: Share of Appliances in LT-2

Substituting Cij in the previous equation, the following expression is obtained for the electricity consumption of the ith household:


Enduse Analysis of Electricity Appliances Used in Residential Sector of Karnataka to Identify Energy Conservation Opportunities for DSM

Eij = ∑ N ij * Pij * Tij The survey data on appliance stock, rating in watts and hours of usage, gives the engineering estimates of the household consumption. These estimates were calculated for each of the appliance categories in both LT-1 and LT-2 households and the results are shown in figures 6 and 7. Lamps (incandescent and fluorescent), fans, fridge, geyser, immersion rod and ovens are

Figure 6: Share of Appliances in LT-1 Housholds

Figure 7: Share of Appliances in LT-2 Housholds

estimated to consume 78.89% of the average electricity consumption per LT-2 house. In contrast, lamps, fans, television, water heating and electric iron are estimated to consume 95.4% of the average electricity consumption per household in the LT-1 category. For the end-use of lighting, LT-1 households consume a high percentage (18.14%) of the average electricity consumption per household when compared to LT-2 households (12.79%). Appliance Stock Approach

Wij = ∑ ( N ji * Pij ) j

The electricity consumption in a household is determined by its stock of electrical appliances. If Nij and Pij are the number and wattage of electric appliances of the jth category in the ith household, then Nij*Pij would be the electrical load in watts due to this category of appliances. It follows that must be the total electrical load in watts due to this category of appliances. It is reasonable to expect the electrical energy consumption Ei (in kWh) of households is correlated with their loads Wi (in kW). When the yearly electricity consumption was regressed on the household load, the result was Ei = 3515 + 25.64 Wi and R2 = 0.326. From this we can conclude that a unit increase in load results in an increase of 25.64 kWh per year in electricity consumption. The actual increase would be different if the coincidence factor of using the appliances at the same time is taken into account. Also, the household load due to its appliance stock explains only 32.6% of the variation. Appliance Census Approach In the appliance census approach the relationship used to estimate the monthly electricity consumption of the jth appliance category is given by

Ei = ∑ N ij * Ai j

Here, Aj is the consumption per appliance of the jth category, which can be estimated by regressing the electricity consumption Ei on the number of appliances Nij of the jth category in the ith household. Here, the coefficient Aj is the marginal consumption of the jth appliance category, that is, the increase in consumption resulting from the addition of one


Sangamesh G Sakri & G V Jayaramaiah

appliance of the jth category. The marginal and the average consumption of the jth appliance category can be considered as identical, as a linear relation between the consumption and the number of appliances of a particular category is expected. Using the above relationship, regression analysis was carried out on the samples of LT-1 and LT-2 households separately. The result of the stepwise regression analysis on the LT-2 households is given in table 3. The result indicates a R2 value of 0.62, meaning that 62% of the variation in electricity consumption among the LT-2 houses is explained by the 12 categories of appliances included in the model. Table 4: Appliance wise Consumption in LT-1 and LT-2 Category Houses LT-1 Category Houses

LT-2 Category Houses

Appliance Av. Cons/ App. (kWh)

Std. Dev.

Av. Cons/ App. (kWh)

Std. Dev.

998.71 -------

147.47 --------






Electric Oven
















126.17 -------

25.49 --------






Motor Pump





Borewell Motor












Immersion rod

Computer IL















Iron Box



Similarly, a step-wise regression analysis was performed on the LT-1 sample households. The result in table 3 indicates a R2 value of 0.31 explaining 31% of the variation in the electricity consumption by the 7 categories of appliances in the LT-1 houses. The results show that immersion rods, TV and ILs are highest electricity consuming appliances in LT-1 households, consuming 998.71, 348.39 and 949.94 kWh per year respectively. In LT-2 households, as shown in table 4, refrigerators and water heaters (geysers and immersion rods) and fans consume the most electricity with 1163, 663.15 and 574.5 kWh per year respectively. The implied usage hours thus obtained from the regression analysis makes much better sense when compared to the user reported hours.

END-USE ANALYSIS IN THE RESIDENTIAL SECTOR The knowledge of consumption of different appliances is helpful in estimating the electricity consumption for different end-uses in the households. Using the appliance-wise consumption figures, electrical energy used for different end-uses can be calculated using the formula

Eim = ∑ N ijm * Aijm j


Enduse Analysis of Electricity Appliances Used in Residential Sector of Karnataka to Identify Energy Conservation Opportunities for DSM

Where Eim is the quantity of electricity for the mth end-use and Nijm is the number of appliances of type j in the ith household. Using the above equation, the electrical energy for the different end-uses was obtained for both LT-1 and LT-2 houses. The result shows that (refer figures 8 and 9) for LT-1 and LT-2 categories respectively) in the LT-1 houses, water heating (52.01%) consume most of the electricity and next lighting devices and entertainment appliances both consume (18.14%). Electricity consumed by the air circulation appliances (6.57%) and others (5.14%) follow them. In the LT-2 houses, it is the refrigeration appliances (24.5%) that consume the most followed by appliances used for air circulation (20.44%) and then the water heating devices (19.60%) and lighting devices (12.79%) follow

Figure 8: End Use Analysis of LT-1 Houses

Figure 9: End Use Analysis of LT-2 Houses

Considering both LT-1 and LT-2 consumers, the end-use estimates for residential electricity consumption in Gulbarga are obtained. The result (Fig. 10) shows that in the residential sector, water heating is the major end-use accounting for 23.38 per cent of the total consumption, followed by refrigeration with 21.65%, air circulation with 18.82% and lighting 13.42%. Entertainment, water lifting and grinding uses account for 9.54%, 7.88% and 2.33% of the total electricity consumption respectively. The remainder is accounted for by cooking, ironing and others. Electricity consumption in the residential sector consists of heating and non-heating uses. Heating uses mainly include cooking, water heating and ironing, while non-heating uses are lighting, refrigeration, water lifting, air circulation, etc. In the total consumption of electricity, heating and non-heating uses account for 28.60 and 71.40 per cent respectively.

Figure 10: End Use Analysis of Residential Sector

CONCLUSIONS The household survey of electricity consumption in Gulbarga city in GESCOM region has exhibited the pattern of consumption of electricity among the LT-1 and LT-2 category houses, the appliance stock used by the households. The end use analysis in the residential sector of Gulbarga has shown that electricity is mainly used for water heating, air circulation, entertainment, lighting and cooking. These results are essential in devising the DSM measures by


Sangamesh G Sakri & G V Jayaramaiah

the utility. The utility can come out with programs that can motivate the consumers to use energy efficient devices in most of the end uses like lighting, cooking, and entertainment. Along with this it should come out with a policy to mandate the use the renewable energy source like solar or other forms of primary energy sources for the water heating. This will help in replacing most of the existing electric water heating devices. This brings in reduction in the morning peaks of the utilities. These measures will certainly bring in the much needed relief to the utilities, which are reeling under the shortage crisis.


C. W Gellings., “The Concept of Demand-Side Management for Electric Utilities”, The IEEE Proceedings, October 1985, 73(10), 1468-1470.


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S. Kumar, “Demand Side Management Road Map in India”, IEEE Power & Energy Society General Meeting, 2009. PES ’09, 2009, 1-8.


S. G. Sakri, G.V. Jayaramaiah, “A Case Study of GESCOM Load Data For Demand Side Management”, National Conference on Recent Advances in Electrical Power and Energy System Management (RAEPESM2011), 25-26 March 2011, 132-136.


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K. V. Narasimha Murthy, G.D. Sumithra, and A.K.N. Reddy, “End Uses of Electricity in Karnataka Households”, International Energy Initiative Draft paper, Bangalore, 1996.

10. Annual Report October 2010, GESCOM, Karnataka. 11. DSM Best Parctices Guide Book for Pacific Island Power Utilities, International Institute for Energy Conservation (IIEC), July, 2006. 12. H. Nilsson, “The many faces of demand-side management”, Power Engineering Journal, October 1994, 207-210. 13. KPTCL, Karnataka website: 14. R.Ramanathan,IntroductoryEconometricswithApplications,TheDrydenPress, Harcourt Brace College Publishers, Florida, 1992.

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