Tempora mutantur, nos et mutamur in illis : The Transmission System Operator as Service Provider Using the Example of the Procurement of Grid Losses

Tempora mutantur, nos et mutamur in illis : The Transmission System Operator as Service Provider Using the Example of the Procurement of Grid Losses Dr. Florian Leuthold, UMM / Market Management

Framework AUSTRIAN POWER GRID AG 21.02.2011 2

The Regulated Business In Austria the ElWOG law stipulates the role of the transmission system operator Amongst these tasks are regulated activities that requires marketbased methods Primary control Secondary control (Tertiary control) (Flow-based) Allocation of cross-border capacities If this creates rents, the transmission system operator is obliged to use the rents for price decrease or investments AUSTRIAN POWER GRID AG 21.02.2011 3

New Challenges and Opportunities Beginning with 2009, the Austrian regulator ECG evaluated whether the procurement of grid losses could be carried out centrally Coordinated procurement Procurement of standard products similar to those traded via exchanges or on the OTC market Daily clearing of residual positions via power exchanges Central clearing of balancing energy Loss forecast by the (distribution) system operators but monitoring by the APG Will be implemented if APG can gather 60% of Austrian s grid losses. Non-regulated activity! Incentive for APG to participate required! AUSTRIAN POWER GRID AG 21.02.2011 4

Portfolio Management AUSTRIAN POWER GRID AG 21.02.2011 5

Grid Losses in 2008 (APG) 500 450 400 350 300 Last [MW] Load [MW] 250 200 150 100 50 0 0 1000 2000 3000 4000 5000 6000 7000 8000 Zeit T AUSTRIAN POWER GRID AG Time T 21.02.2011 6

Decomposition of Net Position ( Lastgangzerlegung ) MW Compare Borchert et al. (2006) MW standard products T residual position base product peak product In a derivatives market netposition will be separated in a marketable part as well as in a spot market part Standard products are marketable liquidly at derivatives market; residual position will be counterbalanced at the spot market AUSTRIAN POWER GRID AG 21.02.2011 7

Typical Trading Structure in Electricity Load Short positions Long positions Portfolio Load forecast Week products Quarter products Calendar products 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 Time [Calendar weeks] AUSTRIAN POWER GRID AG 21.02.2011 8

Load Decomposition: Minimizing Spot Market Quantities Objective: Minimizing the open spot quantity position Min: Σ t (Load_forecast t -Hedge t ) 2 Pros: Easy to implement Market knowledge is not required Cons: Inter-temporal hedging options based on price-correlations are not taken into account Price gaps between futures and spot market cannot be exploited One-dimensional criterion Assessment: The open spot position itself is not a sufficient criterion in a liberalized electricity market Financial aspects and volatilities are more important than pure quantities AUSTRIAN POWER GRID AG 21.02.2011 9

Load Decomposition: Minimizing Spot Market Values Objective: Minimizing the market value of the open spot quantity position Min: Σ t ((HPFC t *(Load_forecast t -Hedge t )) 2 ) Pros: Market prices are included (price forecasts required) Easy to implement Cons: Risk is in price volatilities not in absolute price numbers Price gaps between futures and spot market cannot be exploited One-dimensional criterion Assessment: The market value is consider to be a deterministic parameter The optimization is based on deterministic future prices (HPFC, Hourly Price Forward Curve) This approach fulfills the minimal requirements regarding a liberalized electricity market AUSTRIAN POWER GRID AG 21.02.2011 10

Load Decomposition: Minimizing Risk Measures Objective: Minimizing the market risk of the open spot quantity position Min: CVaR(Open_Position) See next slides Pros: Risk of spot market purchases is adequately taken into account Constraints due to risk attitudes can be included Market prices are included (price forecasts required) Reliable methodology Cons: Price gaps between futures and spot market cannot be exploited Assessment: The market value of the open position is treated as stochastic parameter State-of-the-art methodology for a liberalized electricity market Compatible with standard risk measures AUSTRIAN POWER GRID AG 21.02.2011 11

Load Decomposition: CVaR Definition Risk definition (CVaR) for the purchasing cost taking into account volatilities Probability VaR CVaR Maximum loss 0 Probability 1-ß Portfolio loss Compare Schemm and Borchert (2010) AUSTRIAN POWER GRID AG 21.02.2011 12

Load Decomposition: CVaR Minimization Based on Borchert and Schemm (2007) Min CVaR s. t. p _ fo VaR s t = VaR = p + ( P _ 1 n * (1 β ) H p * pm t, p s ) + ( VaR P _ S t [ ] spot _ simu * P _ S + ( P _ H * fp * pm ) VaR s t t, s t p p p t, p s t ) CVaR VaR VaR P_H p P_St p_fo t pm t,p fp p spot_simu t,s Variable for Conditional Value-at-Risk (objective value) Variable for Value-at-Risk Variable for values above Value-at-Risk Variable for hedge position per product p Variable for spot position in time increment t Parameter for load forecast in time increment t Parameter for product matrix describing which product p covers time increment t Parameter for futures (forward) price for product p Parameter for spot price in simulation s for time increment t n Scalar for number of simulations s β Scalar for confidence level (e.g. 0.95 for 95%) s t Index for simulation Index for time increment AUSTRIAN POWER GRID AG 21.02.2011 13

Load Decomposition: Efficient Frontier 10000 8000 Purchasing potential 6000 4000 2000-2000 Minimum CVaR Additional purchasing potential Minimal Spot Market Value Minimal Spot 0 Quantities 23500 24000 24500 25000 25500 26000 26500 27000 Risk Capital CVaR For a given risk attitude, the optimal expected purchasing cost can be realized AUSTRIAN POWER GRID AG 21.02.2011 14

Risk Management AUSTRIAN POWER GRID AG 21.02.2011 15

Order Path Price volatilities and market liquidity for forward products vary, i.e.: Long-term range (~ > 2 years): Low volatility, low liquidity Mid-term range (~ > 6 month): Medium volatility, medium liquidity Short-term range (~ < 6 month): High volatility, high liquidity S-strategy Order Path T - 2 year T Order strategy defines how much and when energy should be bought In the S-strategy, most of the quantities are within the mid-term rang Compare Schemm and Borchert (2010) AUSTRIAN POWER GRID AG 21.02.2011 16

Quantity Limits: Example Order path Limit paths Upper limit path Actual order Order path T - 2 years Limit for open long position [MWh] Lower limit path Limit for open short position [MWh] Compare Schemm and Borchert (2010) AUSTRIAN POWER GRID AG 21.02.2011 17

How to Group Energy Risks? Usually the energy business is subject to the following risk groups: Market Risk Credit Risk Operational Risk The spread risk of generation as well as the volumetric and weather risk associated with asset generation are borne by the generation companies. AUSTRIAN POWER GRID AG 21.02.2011 18

Market Risk Market risk describes the risk resulting from changes in market parameters which affects all open positions at asset-, trading and wholesale transactions Drivers: Market prices, volatilities, exchange rates, spreads, interest rates, correlations, different basis Measures: Definition and monitoring of Value-at-Risk (VaR) limits, Stress Limits, stop loss limits, volumetric limits AUSTRIAN POWER GRID AG 21.02.2011 19

Price Risk of Open Positions purchased and sold energy is similiar net margin is certain (ignoring credit risk) no price risk! purchased sold open position purchased amount > sold amount price risk: is it possible to sell the open position with prices higher than the price for the purchased amounts? purchased open position sold purchased amount < sold amount price risk: what happens if market prices increase? purchased sold AUSTRIAN POWER GRID AG 21.02.2011 20 Compare Borchert et al. (2006)

Measure: Value at risk (VaR) The VaR decribes the maximum potential loss within a considered period (T) with a certain probability (confedence level) under the assumption of normally distibuted absolute daily market value changes ( MV). t 1 1 VaR = a * T * σ + T * MV t0 i t i= 0 The Conditional Value-at-risk (CVaR) is the expected value of the losses that is greater than the VaR. CVaR = ϕ ( a) * T 1 p * σ t 0 + T * 1 t t 1 i= 0 MV i AUSTRIAN POWER GRID AG 21.02.2011 21

Limiting the Value at risk VaR-Limit (1 day, 95%): T = 1 and p = 95% (a = 1,6449) VaR 1day LimitVaR1day DynVaR-Limit (dyn., 95%): dynamic T and p = 95% (a = 1,6449). DynVaR LimitDynVaR Backtesting AUSTRIAN POWER GRID AG 21.02.2011 22

Credit Risk Credit risk describes the risk resulting from business partners non-compliance with contractual obligations (e.g. not able or not willing to pay, insolvency) Drivers: Drivers are credit worthiness of business partners, delivery period, Markto-Market of the deal and contractual terms like payment conditions, withdrawal clause, netting agreement Measures: Definition and supervision of limits per Counterpart (Scoring) Counterpart and exposure monitoring Management of securities Netting AUSTRIAN POWER GRID AG 21.02.2011 23

Free Credit Limits per Trade Partner 20 18 16 exposure Exposure and und limits Limit der of counterparties Handelspartner Limit Exposure 20 18 16 14 12 10 8 6 4 2 0 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Credit Kredite line in Mio in Mio 28 29 30 31 14 12 10 8 6 4 2 0 AUSTRIAN POWER GRID AG 21.02.2011 24 Compare Borchert et al. (2006)

Operational Risk Operational risk describes the risk that results from financial damage due to lacks within the internal organisation (processes, systems, human activities) respectively financial damage because of external events (natural catastrophe, man-made disaster) Drivers: Drivers are change, complexity, complacency, as well as capacity, capability, availability Measures: Monitoring of error rates and documentation of faults (risk diary) Documentation of business processes Access rights in IT systems Measures for business continuity and business recovery in case of disaster AUSTRIAN POWER GRID AG 21.02.2011 25

Operational Risk: A Permanent Beast Operational risk has some unfavourable characteristics: It forms a bundle of highly diverse risks It is inherent to business and therefore often taken unconsciously It may hit directly and indirectly, i.e. via a credit risk or a market risk Mostly, there is no reward for taking it Low frequency-high impact events play a very prominent role It is in most cases the operational risk that forms the second largest exposure of a bank! (e.g. internal fraud at Société Générale a couple of years ago) AUSTRIAN POWER GRID AG 21.02.2011 26

Organizational Issues AUSTRIAN POWER GRID AG 21.02.2011 27

Group Directive Company Rulebook Covers all activities of power trading and wholesale including energy related activities like emission trading, fuel hedging, etc. Objectives & Principles of Risk Management Transaction Principles Basic Orders and Overall Mandates for Trading, Wholesale and Retail Roles and Responsibilities in Risk Management Approved Trading Counterparties, Markets and Products Market and Product Approval Process Risk Management Methodologies (VaR etc.) Compliance & Reporting & Escalation Procedure to Risk Management Committee Company Rulebook AUSTRIAN POWER GRID AG 21.02.2011 28

Conclusions AUSTRIAN POWER GRID AG 21.02.2011 29

Conclusions New tasks of the transmission system operator as service provider for other system operators Challenges Higher orientation towards established market methodologies to cope with and control new risk factors New know-how has to be brought into the company Opportunities Returns that do not result from the regulated business Has to be negotiated with regulator! AUSTRIAN POWER GRID AG 21.02.2011 30

References / Bibliography Borchert, Jörg and Ralf Schemm (2007). Einsatz der Portfoliotheorie im Asset Allokations-Prozess am Beispiel eines fiktiven Anlageraumes von Windkraftstandorten. Zeitschrift für Energiewirtschaft, 31(4), 311-322. Borchert, Jörg, Ralf Schemm, and Swen Korth (2006). Stromhandel eine quantitative Einführung in Institutionen, Marktmodelle, Pricing und Risikomanagement. Stuttgart: Schäffer-Poeschel Verlag. Müsgens, Felix and Burkhard Steinhausen (2010). Portfoliomanagement: Optimale Energiebeschaffung unter Berücksichtigung von Risiken. Zeitschrift für Energiewirtschaft, 34(2), 109-116. Schemm, Ralf and Jörg Borchert (2010). Konzept für die Verlustenergiebeschaffung. BET GmbH, Aachen, Germany. Unpublished Report. Weigt, Hannes (2009): Modeling Competition and Investment in Liberalized Electricity Markets. Dissertation, Dresden University of Technology. Available: http://nbnresolving.de/urn:nbn:de:bsz:14-qucosa-24711 AUSTRIAN POWER GRID AG 21.02.2011 31