A presentation on Quantitative developments for the energy industry, comprising of two business cases in Renewable Energy and Power Asset Modelling and Optimization

1. Manuele Monti, Ph.D.
GDF Suez Energia Italia
Energy Management - Power & Risk Portfolio
Quantitative approaches and
venues for Energy Trading & Risk
Management
Presentation Title
Milano, 25 Marzo 2014
E' vietata la riproduzione, anche parziale, di immagini, testi o contenuti senza
autorizzazione dell’autore e di GDF Suez Energia Italia s.p.a.
Copyright all contenents reserved.

2. Group profile: Global
2

3. Group profile: Global
3

4. Outline presentation
4
Quantitative Analysis and Analytics in ETRM companies
An example of quantitative applications:
Asset modelling and portfolio management (VPP)
Strategy for QA & Analytics desk development

5. ETRM company structure: Financial optimization
5
Gas
Desk
Energy Management
Gas & Power Markets
(OTC markets, IPEX, IDEX, PSV, Cross Border, outborder, Green)
B2B B2C
Power Generation
Gas
Power
Sales Organization
Gas
Power
Green
Green
Power
Desk
Make-or-Buy
Asset-backed Trading Portfolio Optimization
Giants
VPP/
Tolling/
RHP
Risk Management
Spark Spread maximization

6. ETRM company structure: activities
6

7. Quantitative Analysis & Analytics
techniques:
o Hedging and asset allocation vanilla products, swap,
exotic
o Asset modeling and optimization with structured
energy products (VPP, Swing, storage)
o Asset Risk Management
«P»-side o Buy-side of the finance
Real probability: «model the future»
Typical Quant figures:
Quant Risk & Portfolio
Manager
Quant strategist
ERPM Quant developer
ETRM company structure: activities
Techniques:
o Stochastic processes and Montecarlo
simulation
o Stochastic – Dynamic programming
o Risk Metrics (VaR, EaR, CaR, ES)

8. «Q»-side or Sell-side of the finance
Risk neutral: «extrapolate the present»
Typical Quant figures:
Quant Trader (FO Quant)
Structured deal trader
Algorithmic trader e HFT in
Prop desks
ETRM Quant developer
Quantitative Analysis & Analytics
techniques:
o Pricing derivatives vanilla, exotics and structured
energy products (VPP e Swing Options, Storage)
o Trading fundamental, technical, algo trading and
HFT
o Asset Backed Trading
Techniques:
o Stochastic processes and Montecarlo
simulation
o Stochastic – Dynamic programming
o Algorithmic and meta trading strategies
o High Performance Computing, big data
& data mining
o DMA e latency monitoring
ETRM company structure: activities

9. Typical Quant figures:
Energy Market Analyst
Bidding strategist
ETRM Quant developer
Quantitative Analysis & Analytics
techniques:
o Short –term and cross-commodity portfolio
optimization
o Unbalances bidding optimization
o Prompt prices forecasting and modeling
Techniques:
o Hybrid modelization
(statistics+fundamental)
o Thick quantitative (Neural Networks,
Fuzzy Logic),
o Operation research
o Game theory
ETRM company structure: activities

10. Model a risk hedging approach to offset the volume and price risk for the producer
Model a risk transfer mechanism to get the producer rid of the price and volume risks
Estimation of Risk Management fee
MWh
(S-Fee)*Q*t
MWh
Pz*Vol
S*Q*t PUN*Q*t
Producer
OTC
PUN = IPEX electricity hourly price (€/MWh)
Pz = IPEX zonal electricity hourly price (€/MWh)
Q = Hourly hedging level (MW);
Vol = Wind farm production (MWh)
S = Strike (€/MWh);
t = time-frame (h)
PUN*(Vol-Q*t) Fee*Q*t
(Fixed Income)
Price risk Volume risk
MtM
Risk Management application: Wind risk management

11. Margin vs TOP real testcase (Y-)
Wind velocity and power timeseries of a farm in a given
time-window and market level
Wind velocity and Power forecast:
- Modelling a synthetic wind-velocity process
- Modelling a synthetic hourly power production process
1
Risk Analysis:
- Montecarlo simulation assess optimal hedging level (Qopt)
- Montecarlo simulation, with hedging (Qopt) to assess producer Fee e VaR
3
IPEX PUN price forecast:
- Set-up of a monthly parametric mean-reverting PUN process (Peak- Off Peak –
Holydays switch) with Jumps
2
Quantitative algorithm

12. Portfolio Management application:
Hedging VPP by a multi-strike call option
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Max capacity: 1100MW
Min capacity: 462MW
Must run constraints
Option on CSS = PW-IT – 2*PSV - CO2
Ramp-up from Min to Max: 2 hours
Real asset flexibility: Strip of daily call options Peak/Off-peak
CSS (€/MWh)
Volume in the money
(TWh)
VPP EXERCISE
40
45
50
55
60
65
70
75
80
85
90
1 3 5 7 9 11 13 15 17 19 21 23
PUN 2013
Media PUN orario 2013
Media OFF PEAK orario 2013
Media PEAK orario 2013

13. Dynamic – Programming priciple
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The best combination of contingent claims, aims to optimize the value function:
• Vt(st) = value function (i.e. cash flow, S-P)
• = admissible state variables in the system
• = control variables (electricity price, gas price, CO2, CV)
• = transition function
• = cash flow in the state upon taking decision
The stochastic-dynamic programming algorithm solves the Hamilton – Jacobi-
Bellman (HJB) equation, by iteratively optimizing the expectation of the
continuation value:

14. Dynamic – Programming priciple
9/12/2013
14
Presentation Title
The best combination of contingent claims, aims to optimize the value function:
• Vt(st) = value function (i.e. cash flow, S-P)
• = variabili di stato ammissibili
• = variabili di controllo (prezzo elettricità, gas, CO2, CV)
• = funzione di transizione
• = cash flow generato nello stato prendendo la decisione

15. Hedging VPP by a multi-strike call option
Strike calibration
E@RA: (Average – P05) earning of the naked asset
E@RP: (Average – P05) earning of option + asset
0%
2%
4%
6%
8%
10%
12%
14%
16%
-60 -40 -20 0 20 40 60 80 100 120 140 160
Average Cashflowdistribution
ASSET AS+ OP Multi Strike AS+ OP 1 Strike M€
15
HCR* = 1- (E@RP/E@RA)
0.00
1.00
2.00
3.00
4.00
5.00
6.00
-30.00 -20.00 -10.00 - 10.00 20.00 30.00 40.00
Asset based approach
Asset
Option 1 Strike
0.00
1.00
2.00
3.00
4.00
5.00
6.00
-30.00 -20.00 -10.00 - 10.00 20.00 30.00 40.00
Multi Strike approach
Asset
Option Multi Strike
TWh
CSS IT
€/MWh
CSS IT
€/MWh
HCR* = 60% HCR* = 93%
The optimal strike combination is the one
that maximizes the objective function:
Hedge cover ratio
TWh

16. Strategy for QA & Analytics desk development
Capital Intensive Knowledge Intensive
Outsourcing
and consultancy
investments
Human and
intellectual capital
investment
- Dependency on external entities
- Technology change risk
- More expensive
Direct KPI monitoring
Reduced responsibility
Full delegation
Flexibility
Less expensive
Long term investments
Adoption of knowledge and
technology brokers
- Larger development time (?)
- Need for know-how retention

17. 17
“Enhance the knowledge and technology transfer
between research, industry and innovation”

18. Manuele.monti@gdfsuez.it
@ Manuele Monti
# EquanT Bootcamp
www.equantbootcamp.com