Incorporating Behavioural Responses within a Technology Optimization Energy Model

Transcription

1 Incorporating Behavioural Responses within a Technology Optimization Energy Model Ramachandran Kannan & Dr Neil Strachan Policy Studies Institute BIEE Conference, Oxford September 2006

2 Outline Introduction to UK MARKAL Modelling energy efficiency and conservation Incorporation of barriers Incorporation of behaviour Indicative modelling results Uncertainties and comments

3 Introduction: MARKAL MARKet ALlocation dynamic optimization model A least cost optimization model based on life-cycle costs of competing technologies (to meet energy demand services) Technology rich bottom-up model (e.g. end-use technologies, energy conversion technologies, refineries, resource supplies, infrastructure, etc) An integrated energy systems model Energy carriers, resources, processes, electricity/chp, industry, services, residential, transport, agriculture Range of physical, economic and policy constraints to represent UK energy system

4 UK MARKAL model ENERGY SOURCES TECHNOLOGY CHARACTERISTICS ENVIRONMENTAL CONSTRAINTS & POLICIES MARKAL Open and transparent Fully documented data sources Explicit sensitivity and uncertainty analysis TECHNOLOGY MIX FUEL MIX EMISSIONS SOURCES & LEVELS FUEL & EMISSION MARGINAL COSTS RANKING OF MITIGATION OPTIONS A key analytical input for the 2003 White Paper Model has been substantially updated for the 2006 Energy Review

5 Simplified & Partial MARKAL Reference Energy System (RES) Resource supply Fuel processing and supply Conversion Technology T&D networks End energy use Electricity Import Electricity Export Uranium Import Nuclear fuel processing Nuclear plants Coal mining Coal Remote power gen. (e.g. offshore wind) Hydro (large) Electricity transmission grid Micro generation Oil imports Domestic oil exploration Natural resources (e.g. wind, solar) Gas imports LNG terminal Wastes (e.g. MSW) Energy crops, Agro residue Refinery Landfill gas Sewage gas Diesel Fuel oil Natural gas Biogas Biomass Coal-fired plants Gas GTCC Diesel engine Duel fuel plants Biogas CCGT Small/micro hydro Wind Solar PV Biomass co-fired Distributed CHP CCS Electricity distribution grid Heat distribution pipelines Micro CHP Residential Auto power generation Industry Transport Service Residential Industry

6 Modelling energy efficiency and conservation Energy efficiency options are represented through efficient/advanced technologies Efficiency improvement Cost reduction (vintages vs. learning curves) Constraints on uptake Conservation measures Mimic reduction of energy service demand from a given conservation measure using dummy technology that deliver desired energy service without using any input energy Capital and O&M cost are represented Incentives, subsidies, opportunity costs can be incorporated

7 Energy efficiency vs. conservation Gas Electricity Boiler Existing stock Condensing Boiler 2005 Heat pumps - Existing Heat pumps 2010 Residential Heat Demand Heat Conservation measure District heating Loft insulation Cavity wall insulation

8 Representation of barriers Cost optimization models tend to choose all costeffective technologies and measures But many barriers prevent the penetration of energy efficiency and conservation Lack of information Uncertainty in performance Principal agent issues Sensitivity to upfront costs.. How is this incorporated in MARKAL?

9 Modelling of behavioural change Energy efficiency Constraining technology mix historical take-up of technologies (e.g. bell shape distribution of labeled appliances) Upper and lower limits (or diffusion curve) on technology penetration (to prevent overnight booming) Introducing high hurdle rate (25%) to delay investment decision Inclusion of subsidies or hidden costs Partial access to end-use sector Conservation measures Upper bounds for take-up of conservation Calibrated to near-term policy effectiveness

10 Behavioural response via UK MARKAL MACRO model ENERGY SOURCES TECHNOLOGY CHARACTERISTICS ENVIRONMENTAL CONSTRAINTS & POLICIES MARKAL USEFUL ENERGY SERVICES ENERGY PAYMENTS TECHNOLOGY MIX FUEL MIX EMISSIONS SOURCES & LEVELS FUEL & EMISSION MARGINAL COSTS RANKING OF MITIGATION OPTIONS LABOUR MACRO CAPITAL GDP CONSUMPTION INVESTMENT

11 UK MARKAL: Conservation options Selected residential sector conservation measures Loft insulation of various thickness Cavity insulation (pre & post 76) Solid wall insulation Double glazing windows Hot water cylinder insulation of various thickness Floor insulation.. Selected service sector conservation measures Double glazing Cavity wall insulation External wall cladding/insulation pitched roof insulation Programmable thermostats optimising Heating start times.. Industrial sector aggregated conservation Chemical, iron & steel, paper & pulp, ferrous metals, other industry By fuel: coal, oils, natural gas, derived fuels)

12 UK MARKAL: End-use data sources Key data sources for residential and service sectors conservation measures BRE DTI/DEFRA Environmental Change Institute, University of Oxford (e.g. 40% house, Carbon futures for European households) Market Transformation Programme Energy Saving Trust Policies incorporated EEC (Energy efficiency commitments), building regulations EU-ETS, RTO, REO, Climate levy etc

13 Residential sector conservation uptake 250 Use of Conservation in residential sector Note 1: Combined residential fuel consumption in 2010 is 1873PJ (or MTOE) i.e., 5.6% demand reduction in 2010 rising to 10.7% in PJ Equivalent CO2-60 Base Note 2: DEFRA projections for base-case uptake through 2020

14 Service sector conservation uptake Use of Conservation in service sector Note 1: Combined services fuel consumption in 2010 is 805PJ (or 19.3 MTOE) i.e., rising to 15.2% in 2050 PJ Equivalent Base CO2-60 Note 2: DEFRA projections for base-case uptake through 2020

15 Overall conservation measures CO2-60 constraint case: BAU, barrier removal and price induced conservation Services carbon price induced PJ Equivalent Residential carbon price induced Industry barriers relaxed Services barriers relaxed Residential barriers relaxed Services BAU Residential BAU Note: DEFRA projection for base-case uptake through 2020

16 Uncertainties and closing comments Models are not truth machines. They are intended to stimulate thought and deliver insights into complex realities Uncertainty/sensitivity analysis Ranges for input data (rather than point estimates) Tipping points and trade-offs via a set of runs rather than rely on one model output Optimisation issues Cost optimization is NOT price optimization Short term trends are not necessarily comparable with other model outputs Simplified consideration of non-market barriers and behavioural responses Efficiency/conservation cost considerations are essential Energy system interactions are essential

17 Thank you Ramachandran Kannan: Neil Strachan: