Power Scenario of Punjab with special reference to Forecasting and Management

Electricity demand and supply forecasts assume special significance vis-Ii-vis ever-increasing demand. Different agencies and authors, to forecast demand, have used various methodologies but on the other hand, no concerted effort is visible in predicting the supply. Moreover none of the methodologies used can be considered perfect. The present study attempts to forecast the two and tends to reach the conclusion that in not too distant a future, there is likely to be a comfortable balance between them; A shift in policy has been observed as earlier, trends of demand growth were observed and it was assumed that they will continue to behave in the same manner and no effort was made to manage demand. However, demand has assumed a relatively significant role of late whereas supply was emphasised upon earlier. As a sequel to the above, it may be mentioned that demand measures are more effective than supply measures as they benefit not only the consumers and power utilities but also the environment by reducing generation requirements. This is especially beneficial in the state of Punjab where conventional sources of generating electricity have already been exhausted and capacity can be increased only by resorting to non-conventional and renewable sources. Different agencies and research institutions undertake forecasting as a routine exercise. Forecasting means making estimates about the future behaviour of a variable on the basis of its past trend. Forecasting demand and supply of electricity assumes special significance on account of its ever-growing demand. The forecasts can be made on the basis of relations between electricity demandsupply and their determining variables. A variety of methodologies have been used for this purpose. The World Energy Outlook (2000), projects electricity demand in India to increase by 5.4 per cent per year from 1997 to 2020, faster than the assumed GDP growth rate of 4.9 per cent. Generally a high positive correlation has been observed between the growth of GDP of a country and its electricity consumption. It is a good sign that with improving efficiency of electricity use; this correlation has come down in India. This is indicated by the elasticity of electricity consumption with respect to GDP, which has declined over the years. While consumption of electricity went up by 3.14 per cent for every one per cent growth in GDP in the first five-year plan period, it went up by only 0.97 per cent in the eighth plan period. NCAER (1960) made demand forecasts for energy for various years both at all-India level and regional level. The total energy as well as electricity consumption was projected by relating them to the hypothesis of economic development. Coefficients for elasticity of demand for energy were obtained at the aggregate level and used for forecasting demand for future periods. The Energy Survey Committee (1965) made demand forecasts at macro level, by using relation between national income and consumption and at sector level by estimating energy demand by various types e.g. coal, oil and electricity by assuming them to grow at given rates. Dhar and Sastri (1967) related observed input output coefficients with tlte desired level of production in different sectors for forecasting demand. The Report of the Fuel Policy Committee (1974) considered three methods for forecasting energy demand and found that trend method provided a reliable means of forecasting energy demand only in developed market economies. The committeepreferred regressionmodel to trend method. Parikh (1976) modified slightly the approach used by the Fuel Policy Committee and projected demand for energy under two scenariosone optimistic and the other pessimistic for the period 1991200 I. He also developed a simulation model based on Cross-country regression for energy demand to forecast commercial, non-commercial and electrical energy demand in developing countries. Pachauri (1977) too developed a simulation model for projecting the demand for electricity in the state of Andhra Pradesh. Reddy and Prasad (1977) selected a number of countries, both developed and underdeveloped to fmd out the relationship between consumption of energy and economic growth and they found a strong correlation between the two. The Central Electricity Authorityl (CEA) uses three methods for longterm projections of demand for electricity. These are trend method, the end use method and Scheer's formula. The Task Forc~ Report on Electricity (Rao 2004) suggests that demand forecasts made by CEA must take into account the elasticities of demand since the next few years are likely to see rebalancing of tariffs and reduction of thefts, resulting in variations in demand therefore demand forecasts will require much better information base on T&0 losses and other matters than is presently available. Prayas (2004) also points out a drawback in CEA's methodology of forecasting. The authority does not make any attempt to influence power consumption and reduce power requirement. Trends are studied and it is assumed that they will continue. It has been found that the peak load projections have always been very high in many states. A review of demand forecasts and actual demand on an all-India level, from 1995-2002 shows that the demand projections have been 15-20 per cent higher than actual demand. There is a valid reason for inflated demand projections. The state budgets are allocated on the basis of demand projections therefore the state electricity boards find it profitable to exaggerate demand projections. For example, the Eighth Five-Year Plan document estimated 20 per cent shortage of electricity at the end of plan period despite an additional generating capacity of 30,500 mw during the period. However, in reality, only 16,500 mw was added. The actual shortage still remained at 18 per cent. Ranganathan (2004) also supports this fact when he says that until recently, there was multilevel forecasting, done by the state electricity boards, by regional electricity boards, by CEA and fmally by Planning Commission and the demand forecasts were invariably exaggerated. Another example from MSEB shows that demand estimates made by the board at the time of Enron agreement in 1992 were inflated by about 2000 mw, which is almost exactly equal to the total capacity of the project. Thus, sometimes over projections are made in order to justify the mega power projects like Enron which further results in high costs, high subsidies, inefficient use, high demand and again high demand forecasts, thus creating a vicious circle of exaggerated forecasts. Over projections are also made due to the fact that optimum plant generation is not estimated and price elasticities are not taken into consideration. Projections generally ignore the scope of supply side efficiency enhancement. An unrealistically high demand forecast results in shortage psychosis. It might be argued that it is safe to have excess generating capacity in view of the fast growing demand for electricity but the need for excess capacity is fast becoming redundant because of the possibility of inter-regional exchanges of power. However, this is not true for Punjab where it has been observed that despite high forecasts, adequate capacity additions were not made and this has led to far reaching economic, political and social consequences in the state. The electricity demand forecasts are made at macro level whereas regional forecasts are very few. Moreover, energy demand forecasts are more abundantly available as compared to electricity demand and supply forecasts. Therefore, it is felt that state level forecasting exercises are required to have a realistic picture at the micro level. Keeping this fact in view, the state of Punjab was selected for the present study. Due to numerous factors, forecasting demand for electricity in the state has remained a problem. As previously mentioned, a variety of methodologies have been used for this purpose. However, none of them can be considered perfect due to the reasons outlined below : • Various policy changes continue to be introduced not only in Punjab but also in the neighbouring states and at central level which influences shifting and growth of industry. For example, incentives announced by Himachal Pradesh have attracted industry from Punjab and the shift of pharmaceutical industry from Jalandhar is particularly notable. On the other hand, free power has been reintroduced in Punjab, which is likely to increase demand for tubewells. Again, the decreasing water table in the state is already causing demand for electricity to increase. It is also possible that the number of tubewells reach a saturation point after some time. Moreover, if crop diversification takes place and the state comes out of wheat paddy rotation, agricultural power demand may decline. • With the constitution of Punjab State Electricity Regulatory Commission and enactment of Electricity Act (2003), consumers now have the option to set up their own captive power plants and will be able to obtain power through open access to the grid system. This has also caused industrial demand for electricity in the state to come down. • If the efficiency of power distribution system is enhanced through installation of capacitors, the demand for power will certainly decrease. • The extent of power cuts varies from time to time and the annual data is only an average of demand during different periods. Therefore this data may not depict the actual demand. The major objectives of this study are : • To review the present status of demand and supply of electricity in the state of Punjab, • To forecast demand and supply of electricity in the state, • To suggest some Demand Side Management (DSM) and Supply Side Management (SSM) measures to regulate demand. Annual data for the period 1989-2004 (i.e. for a period of fifteen years) is used for the study. The advantage of using annual data is that they are available at a much more detailed level and they sidestep the problem of seasonality. The data for electricity consumption and its price was collected from various issues of the Energy Statistics of Punjab. Data regarding Net State Domestic Product was obtained from various issues of the Statistical Abstract of Punjab. Dividing the net state domestic product at current price by the wholesale price index of respective years deflated the net state domestic product. For this purpose, the year 1993-94 was taken as base Year. The extent, to which demand will respond to price variations, is generally modelled using own price elasticity demand coefficients. It helps us in measuring and forecasting the demand for electricity, which occurs due to changes in its price. In the present study, projections are made using four techniques:(a) multiple regression technique, (b) Box-Jenkins method, (c) Trend method, (d) on the basis of consumption-income relationship as a high correlation has been observed between the two in the past though recently this relationship has weakened. Econometric analyses of electricity are normally based on log-linear specifications (Pesaran and Smith, 1995). In this model, economy-wise electricity consumption is assumed to be a function of various variables such as average price of electricity, real income of the state and electricity Consumption during the previous time period. Such a model is also called autoregressive model because the independent variable is expressed as a function of its own lagged values. The electricity demand equation takes the following form : Log 'It = at + ~ log Pt + a3 log Yt + a410gqt_l + et (1) 'It = desired demand for electricity in period t, P t = Ayerage price of electricity in period t, Yt = Net domestic product of the state at constant prices in period t, qt-t = Consumption of electricity (in mw) during period t-I, et= Error term, at = intercept a2, a3 and a4.= parameters. The electricity supply equation takes the following form : Log qt = a1 + a2 log Pt + a3 log C, + a4 log Gt + as log ICt + e, (2) qt = supply of electricity in period t, Pt = Average price of electricity in period t, Ct = Average cost of producing per unit of electricity in period t, Gt = Generation of electricity in period t, ICt = Installed capacity of electricity in period t, et= Error term, al = intercept a2, ~, 34 and as = parameters. In multiple regression method, we estimate the relationship between independent and dependent variables for the period for which data are available. Then we forecast the values of independent variables for future time periods. When such an exercise is carried out, one has to make future projections for each of the independent variables separately. Though numerous functional relationships exist, the following three equations were used to make projections for independent variables as they are most widely used in statistical analysis : Y = a + bt (Linear function) (3) logy = loga + (10gb) t (Log linear function) (4) logy = logk + (loga) bl ••••••(Growth curve) (5) After making projections for independent variables with the help of th~se equations, Karl Pearson's coefficient of correlation between each group of the projected and actual values was calculated. This was done in order to fmd out the best fit. The values obtained by the best fitting equation in each case were used to obtain final forecasts with the help of multiple regression equations. In Box-Jenkins method, we regress the dependent variable on its own past values : Ct = a + 13Ct_1 (6) Where : Ct = Consumption of electricity in period t, C,_1= consumption of electricity in period t-l a, 13= Constants. This auto regressive formulation assumes that a variable will continue to behave in the same way as in the past unless some major upheavals occur. Another form of the auto regressive equation was also used for making forecasts: log C, = loga + log13Ct-I (7) Here we have -taken the log values of consumption instead of actual values. Various studies have revealed a strong relationship between national income and consumption of electricity. Therefore, this relationship was explored for the Punjab economy through the following equation: logCt= loga + logbYt (8) Where : Ct = consumption of electricity during period t, Yt= net state domestic product of Punjab at constant prices during period t. a. b = constants Forecasting was also done using the growth rate equation of the following form : logYt = loga + (10gb) t + u (9) Where : Yt denotes : consumption of electricity in period t, t = time, U = error term, a, b = constants. Further, in order to find out which forecast gave the best results, Karl Pearson's coefficient of correlation was again calculated between actual values and forecasts obtained with the help of various equations and the highest coefficient of correlation was found for the forecasts made by multiple regression equations. Thus it was inferred that multiple regression gave the best demand forecasts.