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The Use of Bottom-up Optimisation Models for Analysing the Transition to Low-Carbon Cities CREE - 6th Research Workshop Arne Lind, IFE v Motivation Urban development will play a dominant role in the mitigation of GHG in the Nordic region

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The Use of Bottom-up Optimisation Models for Analysing the Transition to Low-Carbon Cities

CREE - 6th Research Workshop

Arne Lind, IFE

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  • Urban development will play a dominant role in the mitigation of GHG

in the Nordic region

  • About 85% of the Nordic population live in urban areas
  • Buildings and infrastructure from the 1950s are mostly still in use
  • Massive effort is needed in retrofitting of existing buildings and the

decarbonisation of transport

  • Larger Nordic cities have a wider range of technology options

available to mitigate climate change

  • Can take leadership in the drive to achieve carbon neutrality across the

Nordic region

  • The Nordic region has considerable wind power potential close to

urban areas

  • Largely coastal population

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Motivation

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  • Introduction
  • Methodology
  • Detailed case studies
  • Results
  • Concluding remarks

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Content

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Part 1: Introduction

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Visualisation of Nordic CO2 emissions, 2013

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Oslo

  • Oslo: Capital of Norway
  • Climate targets in Oslo (more ambitious

than national targets):

  • 50 % emission reduction before 2030
  • No use of fossil fuels by 2050
  • No use of fossils in public transport

after 2020

  • Oslo is world leading in EV roll-out
  • 2000 public charging points
  • ¼ of the total national
  • One per 330 inhabitants

Oslo

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Energy consumption in 2009

0.0033 0.0013

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Households Services Primary industries Holiday cottages Industry Transport [TWh] Electricity Petroleum products Waste Biofuels Gas

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CO2 emissions: 1991 - 2013

0.2 0.4 0.6 0.8 1 1.2 1.4 1991 1995 2000 2005 2008 2009 2011 2012 2013 M t Total emissions Transport sector Stationary sector

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Focus areas

  • Urban development
  • E.g. planning of urban areas and public transport junctions
  • Infrastructure
  • Including energy stations for renewable fuels in transport (e.g. battery

charging, hydrogen and biofuels)

  • Transport
  • Focus on green transport fuels and reduced use of private cars
  • Buildings
  • Special focus on prohibiting the use of fuel oil and implementation of

energy efficiency measures

  • Energy production and distribution
  • E.g. new infrastructure for central heating and optimal utilisation of local

energy resources

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Part 2: Methodology

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Modelling approach

  • An energy system model (TIMES-Oslo) was developed in order to

analyse how Oslo can transform into a low-carbon city

  • TIMES-Oslo is a bottom-up, techno-economic model describing the

energy system in Oslo

  • Comprises a technology-rich basis for estimating energy dynamics over a

long-term, multi-period time horizon

  • Assumes perfect competition and perfect foresight and is demand driven
  • Aims to supply energy services at minimum global cost by making

equipment investments, as well as operating, primary energy supply and energy trade decisions

  • Base year: 2010
  • Model horizon: 2010 – 2050
  • The city of Oslo is represented as a single model region
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Methodology overview

Projections of drivers/ activity (A) Statistics:

* Energy * End-use * Drivers

Indicator (I)

* Base year * Development

Energy service demand E = A × I

TIM ES-Oslo

Conversion / Processes Transmission / Distribution Demand technologies

Scenarios

Predictive, exploratory and normative

M odel results:

* Energy production * Energy use * End-use technologies * Other

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Scenario assumptions

  • The analyses include all active national measures of today
  • Green certificate market
  • Enova policy measures
  • Energy taxes are kept constant at the 2014 level
  • Energy prices
  • The development in energy prices for imported energy carriers are

corresponding to the Current Policy Scenario (WEO2013)

  • The prices of electricity import/export, to and from Norway, are given

exogenous to the model

  • Kept constant at the 2014 level throughout the analysis
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Main scenarios

  • Reference scenario (REF)
  • Includes all current national policies
  • Used to illustrate the effects of the policies analysed in the other scenarios
  • 2 degree scenario (2DS)
  • Corresponds closely to the 2DS presented in NETP 2016 where a 85%

reduction trajectory is presented

  • Overall CO2 emission constraint is included for 2030 (50% red.) and 2050

(87% red.)

  • Oslo targets (CLI)
  • The targets include to halve the emissions of greenhouse gases before

2030, and to use no fossil fuels by 2050

  • The targets are added to the model as restrictions
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Climate and energy measures

Focus area\Sector -> Transport Building Energy sector Urban development T2: Improved infrastructure for public transport Infrastructure T3: Infrastructure for renewable transport fuels T6: Transferring freight from road to rail and ship Transport T1: No increase in vehicle-km T4: Support scheme for renewable fuels T5: Procurement of renewable transport services Buildings B1: Prohibition of fossil fuels for heating B3: Support scheme for passive houses B4: Support for energy efficiency measures E2: Energy storage in buildings Energy production and distribution B2: Providing areas for new energy solutions E1: Renewable energy production from local resources

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Part 3: Results

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CO2 emissions in Oslo

Reference scenario

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

[M t]

Total emissions Transport sector Stationary sector

Statistics Model results

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Energy consumption per sector

Reference scenario (2050)

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2DS: Emissions in transport decrease

  • but continues to be the main contributor to CO2

emissions in the future

Note: Emission from district heating is from waste incineration

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Household energy consumption for heating

1 2 3 4 5 6 REF REF 2DS CLI REF 2DS CLI 2010 2030 2050 [TWh]

Solar heat Oil boiler LPG boiler District heat Elc resistance HP a/ a HP w/ w Wood stove Pellets boiler

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0.5 1 1.5 2 2.5 3 3.5 4 4.5 REF REF 2DS CLI REF 2DS CLI 2010 2030 2050 [TWh] Sea transport (hydrogen) Sea transport (fossil fuels) Public transport (biodiesel) Public transport (diesel) Public transport (elc) Other transport (diesel) Other transport (biodiesel) HD trucks (biodiesel) LD trucks (electric) LD + HD trucks (diesel) LD + HD trucks (biodiesel blend) Gasoline (car + hybrid) Diesel car Electricity (car + hybrid) Biodiesel (car + blend)

Transport sector: Technology choices

= Fossil

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CO₂ emissions and energy consumption

  • compared to the reference scenario in 2050

50 55 60 65 70 75 80 85 90 95 100 Ref E2 B3 T1 T5 T6 T2 E1 B2 B4 T3 T4 B1 2DS CLI Relative CO2 emissions and energy consumption [%] Energy consumption CO2 emissions

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Focus areas

  • B1: Prohibition of use of fossil fuels for heating
  • Lowest emissions with a reduction of 0.49 Mt in 2050 (66% of REF)
  • More use of wood pellet boilers, district heating, woodstoves and electric

radiators

  • Only 7% of the emissions in 2050 come from the stationary sector
  • T4: Support schemes for implementation of renewable transport fuels
  • A reduction of 0.38 Mt CO2 in 2050 (74% of REF)
  • CO2 contribution is slightly lower for transport than for the stationary sector
  • B4: Financial support for energy efficiency measures
  • A reduction in energy use of 1.4 TWh (17.6%) in households
  • A reduction in energy use of 0.9 TWh (12.4%) for services
  • Also considerable CO2 emission reductions (79% of REF)
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Combination of independent measures

  • the goal in 2050 is not reached

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 2010 2020 2030 2050 [M t]

Ref scenario B1 + T4 B1 + T4 + T6 2030 target 2DS CLI

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Abatement costs

  • 2DS: Average abatement cost of 2300 NOK per ton CO2 removed
  • Consists of:
  • Energy system costs (e.g. investment and operational costs)
  • Costs related to disposal of oil boilers and tanks
  • Costs for retrofitting exiting buildings for obtaining better energy performance

standards

  • CLI: Average abatement cost of 2370 NOK per ton CO2 removed
  • Increased costs due to more use of hydrogen technologies
  • B4: Prohibition of use of fossil fuels for heating
  • Average abetment cost: 922 NOK/ton CO2
  • High feasibility
  • T4: Support schemes for implementation of renewable transport fuels
  • Average abetment cost: 448 NOK/ton CO2
  • High to medium feasibility (related to level of ambition)
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Limitations

  • The model results are based on a linear least cost model assuming

perfect competition and perfect foresight

  • Future technologies not invented are not included in the model
  • Human behaviour aspects are hard to incorporate properly
  • Particularly relevant when considering implementation of energy efficiency

measures

  • Infrastructure costs for non-energy items (e.g. tunnels or roads) are

not included in the model

  • Must be added after the analysis
  • Lack of other emissions beside CO2
  • In an urban area, the local air quality is clearly of high importance
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Part 4: Concluding remarks

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  • The city of Oslo has ambitious climate targets for the next 35 years
  • To meet these targets requires new infrastructure, transport solutions and

more energy efficient buildings that do not depend on fossil fuels

  • Key finding: The majority of the emissions from the stationary sector can

be removed at a low abatement cost (below 500 NOK/ton CO2)

  • Medium to high feasibility
  • Transport in the Oslo region must undergo huge changes if the target for

2050 shall be met

  • Increase transport demand must therefore be covered by either public

transport, bikes or by walking

  • Public transport must be based on renewable fuels
  • Requires implementation of a bicycle strategy by 2025
  • Renewable fuels must be used for all transport modes by 2050

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Concluding remarks

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  • Bottom-up optimisation models are well suited for analysing how urban

areas can develop sustainable energy systems for the future

  • Mode 1: Using the full technological richness provided by TIMES-Oslo
  • Two climate scenarios analysed (2DS and CLI)
  • The results provided the most cost-efficient way of reaching the climate

targets, where the dynamics between the sectors are taken into account

  • Disadvantage: Can be hard to identify both the underlying measures and the

relevant policy instruments required to obtain the solution

  • Mode 2: Several individual measures
  • Advantage 1: Full transparency of the model results for each individual case
  • Advantage 2: Easier for the decision makers to come up with relevant policy

instruments to implement different measures

  • Disadvantage: Sub-optimal systems can arise because interactions between

different systems are ignored

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The use of TIMES-Oslo in two different “modes”

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The two approaches were compared to study the effectiveness of the various measures

  • Effective measures: T3, T4, T5, B1, B3 and B4
  • These were found in the cost optimal solution of both the climate scenarios
  • Ineffective measures: E1, E2 and B2
  • Not found in any of the optimal solutions
  • Not effective for reaching the CO2 targets
  • Could provide other positive aspects, like e.g. reduced demand for grid

investments and increased security of supply

  • Not included in the overall optimisation: T1, T2 and T6
  • Contributes to reduced emissions
  • Not included since e.g. modal shifts must be handles exogenously

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Effectiveness of individual measures

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Thank you

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More information: arne.lind@ife.no

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