dealing with the uncertainties in modelling the …...producing maps of uncertainty and map...

Dr. Michael Förster

Master Project Environmental Planning | 17.10.2013

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Dealing with the uncertainties in modelling the spatial competition of renewable energies

Master Project Environmental Planning 2013/14



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Background

The aims set for the European development of renewable energies are ambitious (20% until 2020 see e.g. Scarlat et al. 2013)

The different types of renewable eneries require potentially large areas. Especially in densly populated coutries there will be a competition in between the energy types as well as with already existing land-uses

There are policy-based aims in using renewable energies, but little thought is spend on how the areas could be supplied in the most efficient way.

All decisions of producing RE (from policy, commercial, environmental perspective) include a high degree of uncertainty



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Basis

With a knowledge-based approach it is possible to evaluate spatially explict the conflict and synergy areas of renewable energies within a region…

…. but there is no information about uncertainties and fuzzyness within the basic-data and the rule-base!

This is our starting point



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Goal of the project Explore the uncertainties of a given knowledge-based

spatial model for finding suitable areas of different renewable energies with Bayesian Networks (cooperation with partner project) of

GeNIe (http://genie.sis.pitt.edu/)

with Fuzzy Logic Tools (e.g. WinFact http://www.kahlert.com/web/wf8.php)

Vary the influence of policy, economic and environmental

influences on the spatial scale (e.g. scenario without any subsidies what is the spatial consequence?)

Producing Maps of uncertainty and map including these uncertainty (MacEachren et al. 2005)

Writing a scientific article (e.g. „Exploring uncertainty when spatial modelling the distribution of renewable energies“)

http://genie.sis.pitt.edu/

http://www.kahlert.com/web/wf8.php



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Goal of the project



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Project Area District Oberlausitz-Niederschlesien

5,000 km²

600,000 inhabitants

Rather rural district

Agriculture is dominating

Mining of brown coal was/ partly is common



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Data and Maps (Protected Areas)



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Data and Maps (Scenic beauty)



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Knowledge-base

suited

partly suited

Environm. restriction

Legal restriction

Suitability class Combination of Maps

Parameters: B = Soil information R = planning information BT = Biotope type and conserv. area L = scenic beauty

A minimum of a single Parameter has a legal restirction for an area

B OR R BT + + B R BT + + B R BT + + B R BT + +

B R BT + + B R BT + + B R BT + +

B OR R BT + + OR

B R BT + + B R BT + + B R BT + +

B OR R BT + + B R BT + + B R BT + + B R BT + +

OR

B R BT + + B R BT + + B R BT + +

OR

B R BT + + B R BT + + B R BT + +

OR

B R BT + + B R BT + + B R BT + +

L

L



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Evaluation of suitability(Scenic beauty)



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Aggregation of spatial competition



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Example Fuzzy Logic (suitability of soil for potatoe cultivation)

Input Membership Function Output Membership Function



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Example Fuzzy Logic (suitability of soil)

0 0,2 0,4 0,6 0,8 1,0

1,0 0,8 0,6 0,4 0,2

yres= y1H1+y2H2

H1+H2

H1 H2

y1 y2

yres

Pos

sibi

lity

Weighted average:



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1 2 3 4 5 6 7 8 9

1,0 0,8 0,6 0,4 0,2

Soil type

poss

ibilit

y po

tato

e

Example Fuzzy Logic (suitability of soil)



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First Working Steps

Evaluating the spatial implications of renewable energies for a county from different perspectives Planning perspecive Economic perspective Policy perspective Local stakeholder perspective Environmental perspective

Exploring the alreading available knowledge-base for spatial modelling conflicts and synergies

Exploring Tools for including uncertainty into the existing knowledge-base (possible extension)



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Further Working steps

Production of a method/map of optimizing spatial synergies of renewable energy production Including uncertain information Including scenarios (perspectives) Including different types of information

Production of a map of uncertainty (e.g. for different

methods like Bayesian Networks and Fuzzy Logic)



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Timetable and Plenary sessions (until excursion)

Date MF Topics Thursday, 17.10 X 1. Plenary session, administrative issues, presentation

of geo-data and existing knowledge-based approach

Friday, 18.10 Getting acquainted with the existing geo-data (1pm to 5 pm – small teaching pool)

Thursday, 24.10 x 2. Plenary Session, • topics for short presentations (different perspectives of

renewable energies production, different data-types), • presentation of GENIE/SMILE and WinFact as tools for

including uncertainty in the modelling process • Preparation of the excursion

Friday, 25.10 Getting acquainted with BN and Fuzzy tools Individual Working Week Thursday, 7.11 X 3. Plenary Session:

• Short presentations • First exchange on data and tools • Discussion about further methodological approach • Preparation of excursion

11.11.-15.11. X Excursion Week (Workshop within the study area?)



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Timetable and plenary sessions (until Christmas break) Date MF Topics Thursday, 21.11.

X 4. Plenary Session • Implementation of the workshop results (How to do…) • Feedback from Practisioners • First Plans of project reports and/or publication • Handing in essays

Friday, 22.11. 28./29.11. (X) Intensive GIS-work / Scientific writing (how to write a

manuscript)

5./6.12 X 5. Plenary Session • Feedback on essay texts • First results from GIS work

12./13.12. (X) Intensive GIS-work

19./20.12. X 6. Plenary Session • Project midterm evaluation

Christmas Break



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Excursion

Date: 11th to 15th of November (four weeks!!!!)

Suggestion: workshop to evaluate the different methods of measuring uncertainty in the Berlin area

Together with the pair project!

Two voluneteers?



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• Förster, M., Helms, Y., Herberg, A., Itzerott, S., Köppen, A., Kunzmann, K., Radtke, D., & Ross, L. (2008). A Site-related Analysis for the Production of Biomass as a Contribution to Sustainable Regional Land-use. Environmental Management, 41, 584-598

• Jongsawat, N., Poompuang, P., & Premchaiswadi, W. (2008). Dynamic Data Feed to Bayesian Network Model and SMILE Web Application. In, Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008. SNPD '08. Ninth ACIS International Conference on (pp. 931-936)

• MacEachren, A.M., Robinson, A., Hopper, S., Gardner, S., Murray, R., Gahegan, M., & Hetzler, E. (2005). Visualizing Geospatial Information Uncertainty: What We Know and What We Need to Know. Cartography and Geographic Information Science, 32, 139-160

• Scarlat, N., Dallemand, J.-F.o., & Banja, M. (2013). Possible impact of 2020 bioenergy targets on European Union land use. A scenario-based assessment from national renewable energy action plans proposals. Renewable and Sustainable Energy Reviews, 18, 595-606

• Uusitalo, L. (2007). Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling, 203, 312-318

References

dealing with the uncertainties in modelling the …...producing maps of uncertainty and map...

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