Energy Markets and Quantitative Methods Padova, 17.Oct. 19 - - PowerPoint PPT Presentation

Energy Markets and Quantitative Methods Padova, 17.Oct. 19

hugo@energyquantified.com +47 9187 7970

Disclaimer

“The opinions expressed in this presentation and on the following slides are solely those of the presenter and not necessarily those of Energy Quantified (EQ). EQ does not guarantee the accuracy or reliability of the information provided herein.”

Content

• What to expect from the future RES data and

analysis vendors, and how it could provide an

• pening for closer integration of academia

into the business

• Some of the development
• What is it foreshadowing?
• Integration of academia into the business

• 1. Regimes: regulation vs Market based

distribution of production…

• 2. Capacity constraints: and Implicit,

Explicit, FB, Exogenous…

• 3. Market behavior: Strategic behavior /

gaming

• 1. Climate: Heating, Cooling, Windchill
• 2. Installed capacity: Wind SPV,
• 3. Historical prices / embedded behavior:

heating system industry structure

• 4. Social pattern: E.g. workhours and Holidays
• 5. Observed weather: (Kalman filtering)

What are we doing: Weather2Price

Systemic connecti

• n

Systemic connecti

• n

Data for the full picture

Everything you need to be in power

RES development

At the outset: “It can’t go on”….

• Wind power
• SPV
• Small Run-of-River
• CHP etc etc

Concerns, among many:

• Too expensive
• Hard to regulate / balance

My take: It will most probably continue to grow.

• Political support
• Commercially viable

RES development

“a landslide of changes”

• Trading moving from “Cal 2” to next minute
• PPA has taken the role trading previously had.
• Olga and Oleg is coming. (our analyst persona)
• Two PhDs, in math and programming,
• “Do not tell me the price, I will find the solution.”
• Give me input, the starting point.

How confident am I?

• Left a “secure, well payed job”. Started all over.
• We follow the plan, involving:

Provide the best possible high-resolution data.(and price) One year after launch, Sep. 2018, EQ provides hundreds of users their daily data requirement.

Integration to academia

“Making the full circle”

I stared career in Statistics Norway. Its purpose is to:

• Provide data and foundation for research
• Public and private companies (Commerce)

Then went on to the vendor industry as of 1999. (EQ’s siblings) Today the vendors (at least EQ)

• Commands a growing “surplus” of data
• We can never use our systems and data to it’s fullest potential

It is an opportunity to corporate. The stage is set

• Vendors have matured. Academia may be ready too.
• Start simple: Input for students thesis. (EQ does this)
• For larger projects: Find questions that could/should be answered,

and provide data and infrastructure that is otherwise behind commercial walls.

Some examples on what vendors can provide:

“Economics of scale makes the difference”

• Data, better than actuals.
• Enable you to study intra-hour effects.
• Infrastructure to manage large data

amounts from a laptop

Actual data is not good enough Why consider using synthetic 1/3

• “No data” is metered.

It is calculated by TSO or other bodies

• Some make a good job. Others make a poor job.
• E.g using identities/definitions:

Consumption = Production + Import – Export

Available only 72% – 98% Notoriously erroneous Consumption in SE = SE1 + SE2 + SE3 + SE4 Consumption, same date SE1

Wind and Solar may be embedded or just “off”

Solar embedded in NL consumption

Consider using synthetic 2/3

Wind power in NL is just way too low.

• Data from “all countries” MORE OR LESS show these and other

kinds of idiosyncrasies.

• Measurement error/calculation errors
• Definitions that varies
• Various time steps and step definition: e.g. measured at the start,

the end or as an average of the last hour?

• Geo-location/areas may be omitted or partly overlapping

As a vendor EQ removes these problems. Bringing order into chaos. One common definition for all areas, for each variable, One common time step (15 minutes).

Consider using synthetic 3/3

• Historical data prone to revisions
• Same for EQ’s Backcasts and Synthetic

Moving into the hour Alt: Why is EQ doing intra hour models

EQ claims: Knowing the intra-hour profile in detail is important.

• Better understanding of intra-hour markets.

Intraday on 30 or 15 min, German 15 min spot market.

• Improves the forecast on fundamentals on hourly

level, too.

• Spot price forecasts (on H level) will probably

improve too.

• Fundamentals are better described.
• But also the price formation, EQ believes.

Get ready for 15min. Not an EQ hype.

Hourly resolution, increasingly considered inadequate.

PPT, taken from ENERGINET DK

Taking a look at the intra-hour variation Germany

…Looking at the numbers they do look small… (Week 37_17 in Germany

Zoom in, and, Not so small at all. Zoom in on a day Zoom in on hour 4 - 12

Hourly avg. on increasing path:

• above 00 and 15
• undecided for 30
• below 45 and next 00

Dif H – 15min may be amplified or cancelled out by other variables

Residual load. Workdays. Jul Aug 17

Intra hour variation. DE. 1/2 Residual Load. ( Con – Wind - Solar)

Dif = Hourly – 15Min

Positive: The hourly average is above the “correct” number Negative : The hourly average is above the “correct” number

Residual Load Workdays. Jan Feb 18 This is for DE.

• High Solar High Wind
• 5-6 % may seem little.
• Within hour same hour,

from -5% to +5%, a spread of 10%.

• Context: Consumption models run
• n a 1-2% error

Dif Percent

15 30 45 00 Max 847 584 1539 3028 Min

• 545
• 1097
• 2779
• 1690

Max 2,1 % 1,6 % 4,3 % 5,5 % Min

• 1,5 %
• 2,1 %
• 5,2 %
• 4,3 %

MWh/h/ 15 min Percent 15 min step

Max and Min, (H - 15min). MWh/h and %. Weekdays

Intra hour variation. DE. 2/2 Residual Load. The True Spread

• The averages hide the variation.
• May even shift from positive to negative.
• Dif varies by context, from day to day, with

weather and time of the year.

• Even the mid. point, 30 min, exhibits large

variation

Dif 00 min Dif 30 min

Conclusion:

• Dif is substantial. Frequent lager than the modelling error on h level
• Dif varies from day to day, by context, weather and season
• Dif is not symmetrical. Linear interpolation on h-level is not advisable.
• Need to simulate Dif for all variables on a consistent Intra hour model

system in order spot the positive and negative correlation.

• Conclusion. ½

Why higher resolution matters

EQ observes that intra hour modeling:

• 1. Has a significant positive effect on fundamental

models on hourly level.

• 2. Positive impact from splining weather parameters

too.

• 3. EQ sees that intra-hour models improve the spot

models for hourly level too.

Theory, market intuition and experience suggest this.

The whole “value chain”: . Weather >> Fundamentals >> Price models

How to tap into large data sources

• No need to upload data
• Minimum time from idea to test/model

1.Implicit

Pan European studies made easy Geographical coverage

Frequency of update:

Continuously >> Always updated price expectations. EQ evaluate price movers outside of your area too.

• 2. Explicit
• 3. Combo, Impl. Expl
• 4. Flowbased
• 5. Quasi Exogenous

Five flow types

May study local influence too

You pick and chose:

• Your Stations, points (longitude-latitude)
• Your Areas (many stations weighted together)

EQ develops, operates and maintains :

• Web pages, consistent with what you see for

your country

• Data feed in the same API and Excel integrator.

Variables delivered:

• All weather driven variables.
• Seasonal Normals
• All EQs alternative forecasts
• Backcasted history
• In 15 minutes resolution

Coming: Climate info for your locations Lead time: Historical benchmarks Data search

Si Si Si Si

Integration to academia

“About time we get started”

Making the full circle. From University of Oslo via Statistics Norway to EQ, providing data to academia. There is an opportunity to corporate and harvest mutual benefits.

• Interested in elaborating on ideas?
• Visit monteleq.com or write to

hugo@energyquantified.com

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Mob: +47 91 87 79 70