University College London Complex Built Environment Systems Bartlett School of Graduate Studies 1...

1

University College London

Complex Built Environment Systems Bartlett School of Graduate Studies

Using ESDS data for

Energy and Environment Modelling

ESDS International Conference 2006

27th November 2006

Mark [email protected]

Complex Built Environment Systems


2



Contents

• What are the energy models and databases for?

• Modelling: software environment and data access

• Sample models and projects

• Considerations for improvement to databases

3



Introduction

Energy and environment modelling and databases used to develop energy and environment policy.

• Objectives of modelling: develop economic strategies for– controlling environmental impacts– improving energy security

• Dimensions of modelling:– physical flows in time and space– Impacts; emissions, other– costs

• Models used: – National energy systems– Sectoral; electricity, buildings, transport, aviation– International; energy trading, aviation, Large Point Sources of emission

4



Some databases important to this work

Content Source GeogSocioeconomic Population, GDP IEA GlobalEnergy Energy flows IEA Global

Fuel reserves EIA GlobalFuel reserves BP GlobalRenewable energy potential WEC Global

Technologies Power stations Platts GlobalCoal power stations IEACR Global

Impacts Point source emisisons EU EU15Large point sources IEA GlobalCarbon dioxide emission CDIAC GlobalEmissions EMEP Europe

5



General software environment and data access methods

• General software environment chosen is Microsoft Office using Visual Basic for Applications (VBA) because

– it is integrated

– everyone has it, which makes it easier to exchange data and provide programmes/models for others to use and appraise

• General method

– Excel ‘front end’ as it provides graphical feedback for fast error correction using eyeballs

– VBA subroutines (in Excel) are used to extract and manipulate data using the Access database engine

6



PROJECT: Energy Scenarios for the EU25

For the Swedish Environmental Protection Agency

Investigate energy strategies for the EU25 that achieve multiple environmental and energy goals at low or minimum overall cost.

Goals:

• Environment

– global warming

• Kyoto and other targets for basket of 6 greenhouse gases including CO2

– EU atmospheric pollution legislation

• National Emission Ceilings (NECs) for SO2, NOx, etc

• Ozone

• Large Combustion Plant Directive

• Air quality standards

– other pollutants

• Energy security

– minimum imports of finite fuels

• Economic; least cost to meet objectives

7



Process

• Generate energy scenarios

– define policy objectives

– collect base data

– develop assumptions about policy options

– run scenarios

– output results data

• energy flows through different sectors and technologies

• costs and emissions

• Translate energy flow data into a format for the RAINS model (which calculates emission reduction costs).

• International Institute for Applied Systems Analysis (IIASA) run RAINS

8



Technical basis: SEEScen: Society, Energy, Environment Scenario model

SEEScen is applicable to any large country having IEA energy statistics

SEEScen calculates energy flows in the demand and supply sectors, and the microeconomic costs of demand management and energy conversion technologies and fuels

SEEScen is a national energy model that does not address detailed issues in any demand or supply sector.

Method• Simulates system over years, or

hours given assumptions about the four classes of policy option

• Optimisation under development

HISTORY

FUTURE

COSTS

INPUTS / ASSUMPTIONS

IMPACTSENERGY

IEA dataEnergyPopulation, GDP

Other dataClimate, insulation...

Delivered fuel

End use fuel mix

End use efficiency

Delivered fuel by end use

Useful energy

Socioeconomic

Useful energy

Delivered energy

Lifestyle change

Demand

End use fuel mix

End use efficiency

Conversion

Primary energy

Supply efficiency

Emissions

Capital

Running Distribution losses

Supply mix

Trade

Conversion

9



Demand drivers (PRIMES) - population

• The EU25 population is forecast to grow slowly to a peak in 2015, after which it gradually declines.

0

50

100

150

200

250

300

350

400

450

500

1995 2000 2005 2010 2015 2020 2025 2030

SWESLVSLKPRTPOLNLDMLTLVALUXLTUITAIREHUNGREGBRFRAFINESTESPDNKDEUCZRCYPBELAUT

M

10



Electricity: generationFinite fuelled electricity-only generation replaced by renewables and CHP. Proportion of fossil back-up

generation depends on complex of factors not analysed with SEEScen.

0

200

400

600

800

1000

1200

140019

90

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

PJe

S_Fos

L_FueOil

G_Fos

N_Nuc

S_Bio

L_Bio

G_Bio

S_MunRef

E_Hydro

H_Geothe

H_Solar

E_Wave

E_Tide

E_Wind

Pump_E

S_Fos

L_Fos

G_Fos

S_

L_

G_

GBR: TechBeh: Electricity : Output : PJe

11



Energy: primary supply: technical and behavioural measures

0

10000

20000

30000

40000

50000

60000

70000

8000019

90

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

PJ

CYPMLTGBRSWEESPSVNSVKPRTPOLNLDLUXLTULVAITAIRLHUNGRCDEUFRAFINESTDNKCZEBELAUT

ALL COUNTRIES: TechBeh : Primary

12



Environment: EU25 CO2 emission

Large reductions in CO2 feasible.

0

500

1000

1500

2000

2500

3000

3500

400019

90

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

0

CYPMLTGBRSWEESPSVNSVKPRTPOLNLDLUXLTULVAITAIRLHUNGRCDEUFRAFINESTDNKCZEBELAUT

ALL COUNTRIES: TechBeh : Environment: : National : CO2

13



UK Energy flow chart: 2050SENCO GBR : TechBeh : Y2050

Trade Extraction Fuel processing Electricity and heat Delivered Sectors Useful energyEnvironment

Waste energy

Trd_G

Trd_E

Trd_L

Ext_G

Ext_S

Biomass

Solid

Wind

TideWave

Solar

Biowaste

BiomassBiomass

proc

Refinery

S_BioL_Bio

Liq

Wind

TideWaveSolar

Waste

CHPDHFuI

ElOnly

Auto

CHPDH_H

Auto_H

Gas

G_CHP

H_Solar

Solid

Elec

Heat

L_CHP

Liq

Biomass Food

Res_G_CHPRes_H_Solar

Res_E_

Ser_G_CHPSer_H_SolarSer_E_

Ind_G_Ind_G_CHP

Ind_H_SolarInd_S_Ind_E_

Ind_L_Ind_L_CHP

Oth_G_

Tra(nat) ETra(nat) L

Tra(int) L

Mot W

El equip

Proc W

Light

H>120C

H<12-C

Cooking

Water H

Space H

Space AC

Cool

CO2

14



UK Energy flow chart: Animation 1990 to 2050

15



UK energy, space and time illustrated with EST

16



UK energy, space and time illustrated with EST : animated

17



Electricity trade

• An extensive continental grid already exists

• The diversity of demand and supply variations increases across geographical regions

• What is the best balance between local and remote supply?

InterEnergy model• Trade of energy over links

of finite capacity• Time varying demands and

supply• Minimise avoidable

marginal cost• Marginal cost curves for

supply generated by model such as EleServe

Electricity trade

18



InterEnergy – animated trade

Animation shows programme seeking minimum cost for one period (hour)

19



PROJECT: Large Point Sources of Emissions For the Swedish NGO on Acid Rain

• Assemble database of all large facilities; historical emissions, technical data, location

• Calculate costs of reducing emissions

• Estimate health costs of emissions

• Perform cost-benefit analysis

20



Considerations for improving databases

• Meta-database; a database of databases (ESDS and other) so we know what is out there

– Coding: standard codes, mapping of codes

– Definition mapping ; one-one, one-many (e.g. sectors)

• Addition/integration/linking of other databases to ESDS

• Issues of access: convenience, cost

• Software: extraction to most common programmes (Access, Excel, Visual Basic…)

• Data imaging: charts, maps

University College London Complex Built Environment Systems Bartlett School of Graduate Studies 1...

Documents

Transcript of University College London Complex Built Environment Systems Bartlett School of Graduate Studies 1...