University College London Complex Built Environment Systems Bartlett School of Graduate Studies 1 Using ESDS data for Energy and Environment Modelling ESDS International Conference th November 2006 Mark Barrett Complex Built Environment Systems University College London

Complex Built Environment Systems Bartlett School of Graduate Studies 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

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

University College London Complex Built Environment Systems Bartlett School of Graduate Studies 4 Some databases important to this work

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

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

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

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

University College London Complex Built Environment Systems Bartlett School of Graduate Studies 9 Demand drivers (PRIMES) - population The EU25 population is forecast to grow slowly to a peak in 2015, after which it gradually declines.

University College London Complex Built Environment Systems Bartlett School of Graduate Studies 10 Electricity: generation Finite fuelled electricity-only generation replaced by renewables and CHP. Proportion of fossil back-up generation depends on complex of factors not analysed with SEEScen.

University College London Complex Built Environment Systems Bartlett School of Graduate Studies 11 Energy: primary supply: technical and behavioural measures

University College London Complex Built Environment Systems Bartlett School of Graduate Studies 12 Environment: EU25 CO2 emission Large reductions in CO2 feasible.

University College London Complex Built Environment Systems Bartlett School of Graduate Studies 13 UK Energy flow chart: 2050

University College London Complex Built Environment Systems Bartlett School of Graduate Studies 14 UK Energy flow chart: Animation 1990 to 2050

University College London Complex Built Environment Systems Bartlett School of Graduate Studies 15 UK energy, space and time illustrated with EST

University College London Complex Built Environment Systems Bartlett School of Graduate Studies 16 UK energy, space and time illustrated with EST : animated

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

University College London Complex Built Environment Systems Bartlett School of Graduate Studies 18 InterEnergy – animated trade Animation shows programme seeking minimum cost for one period (hour)

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

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