# Assessment of Price Risk of Power under Indian Electricity Market

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3 5. CALCULATION OF RISK 5.1 VaR Calculation of Actual Data using Historical Method Value at risk (VaR) & Conditional value at risk (CVaR) methods are used at 95% confidence level & 99% confidence level. Two cases are studied: Case I: 10 MW power is purchased from market for whole day. Case II: 285 MW power is purchased from market for whole day. (Actually hourly MW power purchased from market will not be constant but for the sake of simplicity, the constant power transaction is assumed as 10 MW & 285 MW). Column 5 represents the hourly magnitude of risk at 95% confidence level. Column 6 gives the percentage magnitude of risk with reference to expected cost at 95% confidence level. Column 7 and column 8 represents the hourly magnitude of risk and the percentage magnitude of risk with reference to expected cost at 99% confidence level respectively. Table 1 VaR for 10 MW Purchase from Exchange for 1st April Algorithm for Value at Risk (Var) calculation for one hour is given in next paragraph. Algorithm 1. Compose an array of prices representing the payment made by utility company for purchase of power for each of the 100 days each hour. 2. Calculate an array of the changes in amount of payment made by utility from the previous day s payment. 3. Order this difference values from largest positive to largest negative. 4. To compute the 99% confidence level VaR, using the historical data, a value is chosen which is greater than 99% of all losses experienced in the historical sample period. 5. The definition of 99% confidence level VaR is the VaR (the value) such that a given loss will exceed this value only 1% of the time. So, the second ordered value is selected as the VaR at 99% confidence level. 6. The definition of 95% confidence level VaR is the VaR (the value) such that a given loss will exceed this value only 5% of the time. So, the sixth ordered value is selected as the VaR at 95% confidence level. Case I: 10 MW power is purchased from market for whole day. Following above algorithm value at risk is calculated for 95% and 99% confidence level. Table 1 shows the risk at 95% confidence level and at 99% confidence level for 10 MW purchase of power from Power Exchange for 1st April 2010 for 24 hours of day. In Table 1, Column 1 represents 24 hours for whole day. Column 2 shows the MW power purchased from power exchange for every hour. Column 3 shows the hourly spot price (collected from Indian Energy Exchange) on 1st April Column 4 shows the calculated expected cost in Rs. for purchasing 10 MW for whole day. Expected Cost = Hourly Spot Price x (1) Fig. 2: VaR for 10 MW Purchase from Exchange for 1st April

4 It can be observed from Table 1 & Fig. 2 that minimum spot price and maximum spot price for trading day (1st April 2010) are Rs and Rs respectively. The minimum value of Value at Risk at 95% and 99% confidence level are observed as Rs and Rs respectively. The maximum value of Value at Risk at 95% and 99% confidence level are observed as Rs and Rs respectively. Daily average percentage VaR at 95% confidence level are 38.6% and at 99% confidence level is 61.6%. If utility company blindly goes for trading, company is imposing to a huge amount of risk. Case II: 285 MW power is purchased from market for whole day. Following the same algorithm, the magnitude of risk is calculated for purchase of 285 MW power from power exchange on 1st April 2010 for whole day. Table 2 shows the result. It can be observe from Table 2 & Fig. 3 that minimum spot price and maximum spot price for trading day (1st April 2010) are Rs and Rs respectively. The minimum value of Value at Risk at 95% and 99% confidence level are observed as Rs and Rs respectively. Table 2 VaR for 285 MW Purchase from Exchange for 1st April Fig. 3: Var for 285 MW Purchase from Exchange for 1st April 2010 The maximum value of Value at Risk at 95% and 99% confidence level are observed as Rs and Rs respectively. Daily average percentage VaR at 95% confidence level are 29.40% and at 99% confidence level is 52.88%. If utility company blindly goes for trading, company is imposing to a huge amount of risk. Calculated VaR does not provide a measure of the potential losses exceeding the VaR value. Also, it may cause a stretch in the loss distribution tail which creates potential for even larger losses that exceed VaR value. CVar method gives more accurate result and overcome limitations of VaR. 5.2 Calculation of Risk using Conditional Value at Risk The average of the six largest ordered loss values evaluated following the algorithm of VaR gives the CVaR at 95% confidence level. As per definition of CVaR, the CVaR at 99% confidence level is simply the average of the two largest ordered loss values (i.e. first two ordered value). Same two cases are studied and CVaR is evaluated for both cases at two confidence level. Case I: 10 MW power is purchased from market for whole day. Following same algorithm of value at risk, conditional value at risk is calculated for 95% and 99% confidence level. Table 3 & Fig. 4 shows the risk at 95% confidence level and at 99% confidence level for 10 MW purchase of power from Power Exchange for 1st April 2010 for 24 hours of day. In Table 3, Column 1 represents 24 hours for whole day. Column 2 shows the MW power purchased from power exchange for every hour. Column 3 shows the hourly spot price (collected from Indian Energy Exchange) on 1st April Column 4 shows the calculated expected cost in Rs. for purchasing 10 MW for whole day. Expected Cost is given by equation 1. 15

5 Column 5 represents the hourly magnitude of CVaR at 95% confidence level. Column 6 gives the percentage magnitude of risk (CVaR) with reference to expected cost at 95% confidence level. Column 7 and column 8 represents the hourly magnitude of CVaR and the percentage magnitude of CVaR with reference to expected cost at 99% confidence level respectively. Table 3 CVaR for 10 MW Purchase from Exchange for 1st April Case II: 285 MW power is purchased from market for whole day. Following the same steps of calculation of 10 MW CVaR, evaluation of CVaR of 285 MW power on 1st April for same two confidence level is done. Table 4 shows the result. From Table 4 and Fig. 5, the minimum value of Conditional Value at Risk at 95% and 99% confidence level are observed as Rs and Rs respectively. The maximum value of Conditional Value at Risk at 95% and 99% confidence level are observed as Rs and Rs respectively. Daily average percentage CVaR at 95% confidence level are 43.29% and at 99% confidence level is 56.93%. Utility company may remove risk by adopting proper risk management strategies. Table 4 CVaR for 285 MW Purchase from Exchange for 1st April Fig. 4: CVaR for 10 MW Purchase from Exchange for 1 st April 16

6 Fig. 5: CVaR for 10 MW Purchase from Exchange for 1 st 6. CONCLUSION April Power trading is an integral aspect of competitive power markets. By efficiently managing risk, energy trading helps to ensure reliable electricity at stable prices, and promotes transparent energy markets. In this paper risk due to price volatility is discussed with reference to Indian Electricity Market. Using the data of market clearing prices (MCP) & market clearing volume (MCV) available on Indian Energy Exchange (IEX) and Power Exchange India Limited (PXIL), the risk is evaluated at each hour using Value at Risk (VaR) & Conditional Value at Risk (CVaR) method. The assessment of risk is important task of risk management. The VaR & CVaR method gives monetary value of risk enabling utilities to hedge risk. 7. REFERENCES [1] Min Liu, Felix F. Wu, Fellow, A Survey on Risk Management in Electricity Markets presented in the Proc. IEEE Power Eng. Soc. General Meeting Montreal, QC, Canada, [2] R. Dahlgren, C. C. Liu, and J. Lawarree, Risk assessment in energy trading, IEEE Trans. Power Systems, vol. 18, Issue 2, pp , May [3] Mario V.F. Pereira, M. F. McCoy, and H. M. Merrill, Managing risk in the new power business, IEEE Computer Applications in Power, vol. 13, Issue 2, pp , April [4] J. L. Higle, and S. W. Wallace, Managing risk in the new power business: a sequel, IEEE Computer Applications in Power, vol. 15, Issue 2, April [5] Prabodh Bajpai and S. N. Singh, Electricity Trading In Competitive Power Market: An Overview And Key Issues, International Conference on Power Systems in Kathmandu, Nepal, pp , Nov. 3-5, [6] S. J. Deng And S. S. Oren, Electricity derivatives and risk management, Energy 31 (2006) (www.elsevier.com/locate/energy) 8. AUTHOR S PROFILE Sandeep Chawda has completed his B.E. in Electrical Engineering from Rajive Gandhi Technical University, Bhopal, in He has completed M.E. in Power System in 2010 from University of Pune. Currently he is working as Assistant Professor and Head, Electrical Engineering Department in JSPM s Bhivarabai Sawant Institute of Technology & Research (For Women), Wagholi, Pune. His area of interest includes Power Market, Renewable Energies, Power Electronics. Dr. (Mrs.) S. R. Deshmukh has completed her B.E. in Electrical Engineering from Kolhapur University, in She has completed M.E. in Power System in 2002 from University of Pune. She has been awarded to Doctorate Degree in 2012 by University of Pune. Currently she is working as Assistant Professor in Electrical Engineering Department at PVG s College of Engineering & Technology, Pune. Her area of interest includes Power System Operation & Control, Application of Artificial Neural Network (ANN) to Power System. 17

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