The Transmission System Operator as Service Provider – The ... · • In Austria the ElWOG law...

The Transmission System Operator as Service Provider – The Example of the Procurement of Grid Losses in Austria

Dr. Florian Leuthold, UMM / Market Management

AUSTRIAN POWER GRID AG 15.04.2011 22

Framework

AUSTRIAN POWER GRID AG 15.04.2011 315.04.2011 3

The Regulated Business• In Austria the ElWOG law stipulates the role of the transmission

system operator

• Amongst these tasks are regulated activities that requires market-based 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


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!


Portfolio Management


0

50

100

150

200

250

300

350

400

450

500

0 1000 2000 3000 4000 5000 6000 7000 8000

Zeit T

Last

[MW

]Lo

ad [M

W]

Time T

Grid Losses in 2008 (APG)


T

MW

peak productbase product

MW

residual position

standard products

� Standard products are marketable liquidly at derivatives market; residual position will be counterbalanced at the spot market

� In a derivatives market netposition will be separated in a marketable part as well as in a spot market part

Decomposition of net position (“Lastgangzerlegung”)

Compare Borchert et al. (2006)


Objective: Minimizing the open spot quantity position � Min: t(Load_forecastt-Hedget)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

Load Decomposition: Minimizing Spot Market Quantities


Load Decomposition: Minimizing Spot Market ValuesObjective: Minimizing the market value of the open spot quantity position �

Min: t((HPFCt *(Load_forecastt-Hedget))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


Load Decomposition: Minimizing Risk MeasuresObjective: 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


Load Decomposition: CVaR Definition

0

VaR

CVaR

Probability1-ß

Maximum loss

Risk definition (CVaR) for the purchasing cost taking into account volatilities

Probability

Portfolio loss


Load Decomposition: CVaR MinimizationBased on Borchert and Schemm (2007)

CVaR � Variable for Conditional Value-at-Risk (objective value)VaR � Variable for Value-at-RiskVaR � Variable for values above Value-at-RiskP_Hp � Variable for hedge position per product pP_St � Variable for spot position in time increment t

p_fot � Parameter for load forecast in time increment tpmt,p � Parameter for product matrix describing which product p covers time increment tfpp � Parameter for futures (forward) price for product pspot_simut,s � Parameter for spot price in simulation s for time increment t

n � Scalar for number of simulations s � Scalar for confidence level (e.g. 0.95 for 95%)

s � Index for simulationt � Index for time increment

sVaRpmfpHPSPsimuspotVaR

tSPpmHPfopts

VaRn

VaRCVaRMin

t p ptpptsts

tp ptpt

s s

)**_(_*_

_)*_(_..

)()1(*

1

,,

,


Parameter Preparation

1. Correction for spikes

2. Impact of holidays and weekends

3. Local regression for seasonal corrections

4. Principal Component Analysis (PCA) to explain remaining variances


Spot Price Simulations • Stochastic processes

– First five factors of PCA explain 95% of the variance• 1st factor loading: modeled by two processes

(� Kalman filter)• 2nd to 5th factor loading: modeled by one process each

– Jump diffusion process to model price spikes• HPFC generation

– 1st step: Mean values of the processes– 2nd step: Fitting to the observed forward prices (� arbitrage-free)

• Spot price simulations– Monte-Carlo simulations of the factor loadings yield different HPFCs

(“scenarios”)� Spot price simulations not (yet!?) carried out by APG (external

provider priceIT)


Minimal Spot Quantities

Minimal Spot Market Value

-2000

0

2000

4000

6000

8000

10000

23500 24000 24500 25000 25500 26000 26500 27000

Load Decomposition: Efficient Frontier

� For a given risk attitude, the optimal expected purchasing cost can be realized

Additional purchasing potential

Risk CapitalCVaR

Pur

chas

ing

pote

ntia

l

MinimumCVaR


Risk Management


Procurement Path

T - 2 year T

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

� 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

� S-strategy

Procurement Path


T - 2 years

Quantity Limits: Example

Upper limit path

Lower limit path

Order pathLimit paths

Limit for open long position

[MWh]

Limit for open short position [MWh]

Actual order

Procurement path


Usually the energy business is subject to the following risk groups:Usually the energy business is subject to the following risk groups:

Market Risk Credit Risk OperationalRisk

�The spread risk of generation as well as the volumetric and weather risk associated with asset generation are borne by the generation companies.

How to group that risks?How to group that risks?


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

Market RiskMarket Risk


• 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).

Measure: Value at risk (VaR)Measure: Value at risk (VaR)

1

0

1***0

t

iit MV

tTTaVaR

1

0

1*1

**)(0

t

ii

t MVt

Tp

TaCVaR

• The Conditional Value-at-risk (CVaR) is the expected value of the losses that is greater than the VaR.


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, Mark-to-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

Credit RiskCredit Risk


Free credit limits per trade partnerExposure und Limit der Handelspartner

0 €

2 €

4 €

6 €

8 €

10 €

12 €

14 €

16 €

18 €

20 €

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Kre

dite

in M

io €

0 €

2 €

4 €

6 €

8 €

10 €

12 €

14 €

16 €

18 €

20 €

LimitExposure

Cre

dit l

ine

in M

io €

exposure and limits of counterparties


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

Operational RiskOperational Risk


� 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)

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)

Operational risk has some unfavourable characteristics:Operational risk has some unfavourable characteristics:

Operational risk: “A permanent beast”Operational risk: “A permanent beast”


Organizational Issues


Board of Directors (group level)

Executive orders e.g. organisation, authorisation, tasks

Managing Directors(inside trading company)

Company policies e.g. risk management, documentation, archiving

Board or Responsible Board Member/Holding (competence area)

Group policies e.g. rulebook, credit policy

Departments(inside trading company)

Working orders e.g. deal captureCollected in operations manual

Public BodiesLaws, regulations, market rules etc.

Regulations and hierarchy of rulesRegulations and hierarchy of rules


� 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

Group directive – “Company Rulebook”Group directive – “Company Rulebook”

Covers all activities of power trading and wholesale including energy related activities like emission trading, fuel hedging, etc.


Conclusions


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


Further Developments / Challenges

• Building-up standard procedures regarding market data and analyzes

• Building-up knowledge in simulating market price developments– HPFC– Spot price simulations� “This is where the (research) action is!”


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.

• Weigt, Hannes (2009): Modeling Competition and Investment in Liberalized Electricity Markets.

Dissertation, Dresden University of Technology. Available: http://nbn-

resolving.de/urn:nbn:de:bsz:14-qucosa-24711

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