icis webinar - price sensitivity analysis with neural networks
DESCRIPTION
The power markets are full of what if’s. The impact of renewable generation on spot power prices has naturally generated a great deal of volatility in the markets. Inputs and assumptions such as power demand, changing weather forecasts, and available capacities are just some of the key drivers that help predict the price of power. But what if there is more wind generation than expected? What happens if demand for power turns out to be stronger than anticipated? While uncertainties in the market cannot be eliminated, they can be identified, quantified and their impact assessed on positions and portfolios. The goal of this webinar is to explain how Neural Networks power price models can help to assess the sensitivities that can impact spot prices in the German day ahead market and how you can use this to your advantage.TRANSCRIPT
Power price sensitivity analysis
with Neural Networks for German
day ahead spot trading Free webinar 10.07.2014
Jonathan Scelle
Senior Analyst EU Power Markets
Sebastian Stütz
Lead Analyst Power
www.icis.com
Content
PART I – Concept of modeling spot prices with Neural Networks
PART II – Sensitivity: Price expectations under varying conditions?
Your Questions
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Spot price forecast model – June, 14th & 15th 2014
June, 14th 2014 – 1-day-ahead:
June, 15th 2014 – 2-day-ahead:
Ø MAE hourly: Ø MAEbase: 1.51 € 0.77 €
Ø MAE hourly: Ø MAEbase: 3.44€ 2.95 €
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How to model power prices?
Market Challenges
Uncertainty in
renewable gener-
ation and power
demand
High day to day
price volatility
Negative prices
Source: ICIS Power Portal
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How to model power prices?
Which prices to forecast?
Spot vs. forward market (spot market highly volatile with renewable challenges)
OTC, daily auction
Specific power market problems to address
State of information before auction gate closure
How to model hours? Separate prediction / 24h prediction?
(number of inputs in model / complexity / overfitting?)
Multiple day ahead => feed same model / new model?
Negative prices
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Why use Neural Networks?
NNs can learn from sample data
NNs are data driven self-adaptive models which determine their function based on sample data
No a-priori assumptions are needed
NNs can generalize
NNs can produce reasonable outputs for previously unseen data
NNs are universal function approximators
NNs can deal with non-linear relationships
NNs are successfully used for a wide variety of tasks
Facial Recognition
Text analysis
Technical process control
Medical diagnosis
Stock market forecasts
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Price model key summary
Data prepared with power market insight
We aggregate raw input series to prepared series like residual demand
We apply averages and self-developed indicators for key power factors, e.g.
indicators for degree of utilization of merit order
Considered inputs
available capacities (EEX after scope correction with BNetzA figures)
power demand forecast (own Neural Network based model for DE/AT)
wind and solar production forecasts (own model)
fuel and carbon price levels
Import/export flows*
multiple weather variables (based on high resolution GFS-WRF)
efficiency factors of power plants
* Import / Export explicit modeling is running project.
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Implicit stack / merit order approximation
Our model is not trained to forecast absolute prices but to learn price
gradients in the merit order, visible through auction results (extension to
bidding curves in plan)
The trained “model” can be described as an experienced view on price
gradients at the price setting parts of the merit order
Hence, the model is capable of predicting price changes from
drops/increases in e.g. residual load or available capacities
In order to distil changes in historic data we normalize always based on
each latest week. Our running forecasts take into account latest days and
weeks and long-term trends.
Source: Risø DTU
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Advantages / disadvantages of Neural Networks as
power price models
Pros
Decreases need for explicit
assumptions
How do you model in your
stack…
Actual efficiencies and
capacities of each plant?
Inland transportation costs
Topping turbines
Combined heat and process
steam generation
Must run conditions
Transferable to other markets
Constantly learning
Cons
Require long series, structural
change of market mechanisms
(e.g. capacity market) would
be a problem
Computationally expensive
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Models in practise: ICIS Power Portal
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Neural Network based price forecast model –
backtesting
1-day-ahead:
2-day-ahead:
Ø MAE hourly: Ø MAEbase: 3.72 € 2.23 €
Ø MAE hourly: Ø MAEbase: 4.01€ 2.91 €
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In a perfect world, inputs would be always right
In Power markets: Most key inputs have to be estimated, too
Estimations change over time and with more insight
Sometimes, inputs are not even clear ex-post –
What’s the German power demand?
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Example for input forecast:
ICIS Power Demand Forecast (DE/AT)
Based on Neural Networks, trained to match the demand data supplied by
entsoe.eu and apg.at
Utilises a high resolution weather forecast data derived from the world-wide
operational GFS (Global Forecasting System) model
Considers the time of the year as well as a variety of date-depending factors
Effects that directly affect the population – like weather and holidays – are weighted
accordingly for each minor region within the countries
Updated 4 times a day starting 3:04 [GMT]
Period MAE
Jan 2014 1741 MW
Feb 2014 1914 MW
Mar 2014 1501 MW
Apr 2014 1846 MW
Jan-Apr 2014 1750 MW 30,000
40,000
50,000
60,000
70,000
80,000
90,000
actual_demand DE+AT (entsoe.eu, APG) forecasted_demand DE+AT
Last week of April 2014 (latest actual publication by ENTSOE.EU)
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Example I (28-06-2014)
0
1000
2000
3000
4000
5000
6000
1 3 5 7 9 11 13 15 17 19 21 23
MW
Hour
actual wind predicted wind
0
5000
10000
15000
20000
25000
1 3 5 7 9 11 13 15 17 19 21 23
MW
Hour
actual solar predicted solar
Wind
Solar
0
10000
20000
30000
40000
50000
60000
70000
1 2 3 4 5 6 7 8 9 101112131415161718192021222324
MW
Hour
actual demand predicted demand
0
10000
20000
30000
40000
50000
60000
1 2 3 4 5 6 7 8 9 101112131415161718192021222324
MW
Hour
actual residual demand predicted residual demand
Demand
Residual Demand
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Content
PART I – Concept of modeling spot prices with Neural Networks
PART II – Sensitivity: Price expectations under varying conditions?
Your Questions
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Sensitivity: What would be the price under other conditions?
“What you feed into is what you get”
Single forecasts are tools, not final market views
For a specific input, expectations vary (multiple models/state of information)
Our aim as a data service provider
Enable to widen methodology scope
Enable to improve internal market views
=> Give the ability to adjust and see the changes in the price
Enable to trade
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Key power market drivers and what factors to adjust?
Residual Load
= Demand to be
covered by
conventional
power plants
Wind feed-in
Solar feed-in
Power demand
Public behavior,
holidays
Outages
Efficiencies
Fuel prices
Net interconnection flows
Weather
Marginal costs and
supply bidding
behaviour Ramping costs
Supply structure
costs at volume
Manual short-term inputs
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The concept of residual load
Power demand
€/MWh
Wind, Solar, Net import flow
Residual load
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Which input changes can be explained by shifting
residual load against the stack?
Price insight is generated by shifting the remaining
consumption against the conventional (ANN: implicit) stack
Change in power demand?
Change in renewables?
Change in net interconnection flow?
Change in available capacity of baseload power plants (new builds)?
Changes in the plant efficiencies and fuel prices?
Source: Risø DTU
Adjustable with
residual load
shift concept?
No
Approximated by
ANN learning
process
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Residual load vs EPEX price
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Example I (28-06-2014)
0
1000
2000
3000
4000
5000
6000
1 3 5 7 9 11 13 15 17 19 21 23
MW
Hour
actual wind predicted wind
0
5000
10000
15000
20000
25000
1 3 5 7 9 11 13 15 17 19 21 23
MW
Hour
actual solar predicted solar
Wind
Solar
0
10000
20000
30000
40000
50000
60000
70000
1 2 3 4 5 6 7 8 9 101112131415161718192021222324
MW
Hour
actual demand predicted demand
0
10000
20000
30000
40000
50000
60000
1 2 3 4 5 6 7 8 9 101112131415161718192021222324
MW
Hour
actual residual demand predicted residual demand
Demand
Residual Demand
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0
10000
20000
30000
40000
50000
60000
70000
1 2 3 4 5 6 7 8 9 101112131415161718192021222324
MW
Hour
actual residual demand
predicted residual demand
predicted residual demand:scenario RD +5GW
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 101112131415161718192021222324
Eur/
MW
h
Hour
actual auction price
predicted auction price
auction price: scenario RD +5GW
Example I (28-06-2014) – Usage of input adjustments
Residual Demand
Auction Price
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Summary
Varying estimations require options to test changes in price models when
inputs change
Many key fundamental drivers changes can be modelled by left-right
shifting of residual load against the stack
Opportunity to gain confidence on expected price changes/risks for trading
on changing fundamental expectations
Source: Risø DTU
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New application by ICIS / Tschach Solutions
Input scenarios
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Underlying inputs
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Result of full day +5GW shift in demand
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Content
PART I – Concept of modeling spot prices with Neural Networks
PART II – Sensitivity: Price expectations under varying conditions?
Your Questions
www.icis.com
Your Questions