Scenario modelling as a tool for planning sustainable urban energy systems

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Scenario Modelling as a Tool for Planning Sustainable Urban Energy

Systems

BEN AMER, Sara1

1 Department of Management Engineering, Technical University of Denmark, Denmark Abstract: Past decades have seen a continued increase in the urban population, coupled with growing CO2 emissions from cities. Meanwhile, efficient solutions for renewable energy supply and demand management in urban areas are being developed and implemented. The cities showing a will to carry out energy plans and achieve ambitious climate goals require tools such as energy system models to support decision-making in sustainable urban energy planning. The aim of this paper is to examine urban energy scenario modelling with the use of Balmorel, a least-cost optimization model for electricity and district heat sectors analysis. Computer tools for energy systems scenario modelling on a world-, national and regional scale have been used in quantitative analysis for a few decades already. However, recent emergence of a notion of sustainable and smart cities has revealed the need for city-scale energy modelling frameworks, allowing the assessment of e.g. the role of decentralized generation. Besides, in order to analyse cities, discussing open questions such as system boundary is vital. This study attempts to use a redesigned national-scale energy modelling tool to handle urban energy systems. The focus is on Greater Copenhagen area, Denmark, which has a number of renewable energy-related goals. The aim of this examination is to determine, compare and contrast alternative energy scenarios for the focus area, concentrating on reducing CO2 emissions and decreasing the dependence on fossil fuels. This paper argues that energy scenario modelling is a suitable method to quantitatively assess current and future urban electricity- and heat systems from the point of view of increased deployment of renewable energy sources. The outcomes of the techno-economic scenario analysis can be further applied in analyses on selected socio-economic impacts (e.g. employment effects, fuel poverty etc.) of urban energy transitions. Keywords: Urban energy systems, Sustainable energy planning, Scenario modelling, Quantitative models

1 Introduction

While the global population is expected to exceed 9 billion by 2050, cities and towns are estimated to account

for almost 70% of the world population in 2050, compared to 50% now (UN, 2012). Urban areas emit up to

70% of worldwide greenhouse gas emissions (UN, 2011), with their energy systems among the main

contributors. Nonetheless, cities are also places of exchanging flows of information, people, and resources that

can facilitate innovation and transition to sustainable solutions (GEA, 2012), such as energy efficiency and

renewable energy.

Although sustainable energy has been a policy target on the EU and national scale for last decades, it is only

recently that the urban energy policy development has started to gain momentum in Europe (Keirstead & Shah,

2013). A stable political situation of local governments, as well as high citizen involvement, encourages cities to

implement ambitious energy plans, often facilitated by participation in various organisations, such as ICLEI,

Eurocities, Covenant of Mayors and other that have climate- or clean energy- focused programs. Scandinavia is

a leader in this field, with Sweden implementing first legislation on non-obligatory energy plans in towns in

1970s, as a way of coping with the oil crisis (Nilsson & Mårtensson, 2003). In Denmark, energy planning

initially focused on heat supply planning, and in 1979 legislation in this area was implemented (DEAb, n.d.). A

growing interest in strategic municipal energy planning contributes to the importance of city-scale energy plans

in Denmark. Nonetheless, other than non-academic reports, not many energy scenario-related inquiries

concerning Greater Copenhagen have been published.

Modern planning research needs to play a role in supporting cooperation on a local and community level,

developing plans and policies and engaging various stakeholders. The local level is important, because this is

where both the problem and the possible solutions arrive from (Blanco, et al., 2009). If urban structure is

developed together with energy infrastructure, local planning can be more successful in realising CO2 reduction

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goals (Rydin & Hamilton, 2011). Moreover, while creating a positive environment for creation of "green jobs",

local authorities are ideally positioned to assure quality and back finances required for such projects (Calderon

& Keirstead, 2012).

Cities showing a will to carry out energy plans and achieve ambitious climate goals require methods and tools

such as scenario planning and energy system modelling to support sustainable urban energy planning. To date,

however, the number of sufficient tools to fully analyse the complexities of urban energy systems and to

facilitate cross-optimisation of various components in a strategic planning perspective is limited (Blanco, et al.,

2009) (Mirakyan & De Guio, 2013).

The aim of this paper is to examine urban energy scenario modelling with the use of Balmorel, a linear

optimization model for electricity and heat sector analysis. This study seeks to determine and evaluate

alternative energy scenarios, concentrating on reducing CO2 emissions and decreasing the dependence on fossil

fuels in the Greater Copenhagen area, Denmark. The results of this examination provide insights into

Copenhagen's energy future and highlight possible steps towards a low-carbon city that may be also relevant to

other cities in the world.

The present work describes the current energy situation (as of year 2013) and a reference and low-carbon

scenario for medium-term (year 2025) perspective developed for the city. The analysed pathways will result in

different energy mixes, investment choices and system costs, depending whether a business-as-usual or low-

carbon scenario is chosen.

This paper has been divided into five parts:

Section 1 introduces the topic of this article.

Section 2 begins by laying out the background and then looks at policy frameworks relevant to the focus areas.

Section 3 describes methodology used.

Section 4 reports results.

Section 5 forms a discussion of the results and conclusions, including limitations of the study and further work.

2 Background

2.1 Greater Copenhagen

The Greater Copenhagen area is located on the island of Zealand, Denmark. For modelling purposes in this

study it encompasses the Copenhagen Municipality and 16 other nearby towns (Frederiksberg, Gentofte,

Gladsaxe, Ballerup, Rødovre, Glostrup, Albertslund, Høje Taastrup, Roskilde, Solrød, Greve, Ishøj,

Vallensbæk, Brøndby, Hvidovre and Tårnby) that are connected to the same district heating net. The total

number of inhabitants in the modelled area is 1.3 million.

Although divided into three sales companies, the heat transmission network forms one single coherent system,

supplying distribution networks in the Greater Copenhagen area, which then serve district heating customers in

17 municipalities.

Regarding the type of technology used, district heat is produced either in combined-heat-and-power plants

(CHP), also called cogeneration plants, or in heat-only boilers. It can also be stored in heat storage tanks.

District heating production units in the metropolitan area can be divided into:

• (politically) prioritized plants : three combined-heat-and-power (CHP) waste-fired incineration plants, one

incineration cogeneration plant using dewatered sludge from waste treatment, and a geothermal

demonstration plant;

• base load – cogeneration plants meeting most of the continuous energy demand: one coal/wood pellets-

fuelled, one multi-fuel and one natural gas-fired, and two heat storage tanks ;

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• peak and reserve load (only run if demand is very high): four major peak and reserve load boilers at the

cogeneration plants that due to their low operation cost are used often in case of high demand; 30 boilers

acting as a reserve for base load - typically in case of winter power plant outages (Varmelast.dk, n.d.)

The figure 2.1 below depicts the fuel share in district heating in Greater Copenhagen in 2013.

Figure 2-1 Fuel mix in district heating production in Greater Copenhagen. Data from (Varmelast.dk, n.d.)

Solid biomass (wood waste, wood pellets and straw) dominated the mix, followed by coal, natural gas and

municipal waste for incineration. Oil and geothermal heat played only a minor role. The total energy

consumption for district heating in the Greater Copenhagen area was 34.5 PJ, corresponding to 20 % of the total

heat consumption in Denmark (Varmelast.dk, n.d.).

Electricity generation located in the Copenhagen area comes mainly from cogeneration plants and wind power

farms: Middelgrunden in the Øresund strait (the Sound). The city is well-connected with rest of the island and

the country with a number of electricity distribution and transmission cables.

2.2 Policy frameworks

2.2.1 EU policy framework

In 2008, the EU adopted The Climate and Energy Package that set the following targets for the European Union

by 2020: a 20% reduction in greenhouse gas (GHG) emissions from 1990 levels; increasing the share of energy

consumption from renewables to 20%; and improving the energy efficiency by 20% (EC, 2014c). A number of

directives have been implemented (concerning energy efficiency, renewables etc.) to ensure that the countries

are on the right track to comply with the goals. In 2014 a new target of 40% reduction for domestic GHG

emissions for 2030 was proposed together with a renewable energy target at European level of at least 27% (EC,

2014b). Besides, the EU has plans for 80% - 90% reduction in GHG emissions and a near-zero carbon energy

system in 2050 (EC, 2014d).

The EU recognizes that participation of cities is desirable for implementing the climate and energy goals. Thus

it has supported many projects and initiatives concerning sustainability in urban areas, but there is still

consistency lacking in the European urban policy agenda and subsidy programmes, which currently can be

summed up under subjects such as: sustainable mobility, environmental protection and CO2 reduction (EC,

2014a). Ongoing efforts, as for instance document entitled "Towards an Integrated Urban Agenda for the EU",

are aiming to fill the gap (EU, 2014).

2.2.2 Danish policy framework

Energy has been an important direction of policy in Denmark throughout the last and current century, first due

to the lack of domestic fossil fuel resources , then due to the shortage of imported fuels, e.g. during World Wars

and in the 1970s'. In 1976 the first national energy plan was ratified. (ESRU, 2002) Throughout the years, the

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structure of Danish energy infrastructure has also undergone transition from centralised power plants to more

localised CHP plants and increased wind power deployment. (Lund, 2010)

Danish energy policy is founded on consensus, expressed in a series of governmental Energy Agreements

(OECD/IEA, 2011). The "Energy Strategy 2050" from 2011 establishes a vision of Denmark 100% independent

of fossil fuels in 2050, based upon scenarios analyses conducted for the Danish Commission on Climate Policy

(DEAa, n.d.). The "Energy Agreement" from 2012 details the efforts towards fossil fuels independency by

agreeing on 50% of electricity from wind power and decreasing the energy consumption by more than 12 % in

2020 compared to 2006 (KEB, 2012).

2.2.3 Strategic energy planning

In Denmark in the last few years a growing interest in energy planning on a municipal scale and its relation to

the central planning schemes has occurred, under a notion of "strategic energy planning". An example of a

process of creating a strategic municipal energy plan is shown in the following figure 2.2.

Figure 2-2 Strategic municipal energy planning process. Modified from (DEA, 2013)

Scenarios appear both in the mapping and analysis part of the process of developing the strategic energy plans.

Besides, modelling is a helpful tool to quantify these scenarios and thus use this knowledge in further steps.

In the strategic planning, coordination between central and local activities is crucial for ensuring an overall

energy system transition and avoiding sub-optimisation. Changes on the central level (e.g. subsidies) may make

some local level actions more or less feasible financially, so support from central level and better coordination

between various municipalities are needed. Moreover, municipalities should take an active role in policies

creating and get flexibility in distributing new, local incentives (Sperling, et al., 2011).

2.2.4 Policy framework of Copenhagen

In its "CPH 2025 Climate Plan", developed in collaboration with business, citizens, NGOs and universities,

Copenhagen set a strategy to become the world's first carbon neutral capital in 2025. The Plan was adopted in

2012. "Carbon neutrality" is defined as follows: net carbon emissions have to equal zero, achieved by both

reduced emissions and compensation in periods when production e.g. from windmills is greater than

consumption. Moreover, the city is planning to purchase some wind turbines that are not geographically located

in the municipality (City of Copenhagen, 2012) (City of Copenhagen, 2014).

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The main focus areas of the plan are: energy consumption, energy production, mobility, and city administration

initiatives. Among the projected outcomes there are (City of Copenhagen, 2012):

• Carbon neutral districting heating and cooling by 2025

• Decreasing energy consumption of commercial buildings by 20 %, households by 10 %, and public

buildings by 40 %

• Halving of street-lighting energy consumption

• 100% of the city’s electricity consumption from renewables

Moreover, Copenhagen is among the six so-called Danish "Environmental Municipalities" that have made an

agreement on achieving completely CO2-neutral electricity and heat supply by 2025, with an interim goal of

25% reduction in 2015, in relation to 2006. (Miljoekommunerne, n.d.)

3 Methodology

3.1 Scenario analysis

Herman Kahn is widely recognised to have started using the notion of "scenario" in the 1950's and a decade later

him, together with Anthony Wiener, and, independently, Gaston Berger, were among the first ones to publish on

scenario planning (Chermack, et al., 2001). As a result of strategic planning's inability to foresee various events

of that time, scenario planning became a popular method in academia and business (Chermack, et al., 2001)

(Nielsen & Karlsson, 2007) (Schoemaker, 1995). For instance, scenario analysis in Shell has helped in

identifying possible energy and political fluctuations and has been used at the same level as market analyses to

influence the corporation's strategy, affecting especially the examination of risk (Cornelius, et al., 2005).

(Ramírez & Selin, 2014) use the statistics on rising amount of articles in the scenario area as evidence of the

renewed interest of research community in this approach. According to (Wright, et al., 2013), if more attention

is paid to evidence-based assessment, scenario planning has a chance to become a more valuable decision

support method.

To date researchers have introduced a myriad of explanations of a notion of a scenario, but in this paper a

definition adopted from (Nielsen & Karlsson, 2007) and (Ramírez & Selin, 2014) is used: a purposeful depiction

of possible future energy systems and pathways to achieve them.

(Börjeson, et al., 2006) and (Nielsen & Karlsson, 2007) classify the types of scenario studies into three main

groups. These are:

• Predictive: result in forecasts and business-as-usual scenarios; the analyst may ask: "What will happen?"

• Explorative: result in a number of parallel futures; the analyst may ask: "What can happen?"

• Anticipative/normative: development of desirable or feared visions and pathways of reaching them; the

analyst may ask: '"How can a specific target be reached?"

Relevance, coherence, likelihood and transparency of scenarios are important factors (Godet & Roubelat, 1996).

The main actors dealing with scenarios in the energy field are: academia, think-tanks, planners, as well as

policymakers, organisations and private firms. The application of scenarios in the decision-making process in

the energy sector is crucial. Energy investments are usually planned well in advance, due to their path

dependency (linked to technological and monetary reasons). Moreover, accurate prediction of the future

technology prices and policy is impossible and the views and requirements of energy systems actors evolve

constantly. Thus scenario analysis, with its aim to look at consequences of long-term decisions is a suitable tool

for energy field in transition (Nielsen & Karlsson, 2007).

The table 3.1 below shows main features of the two types of scenarios conducted in the energy field.

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Table 3-1 Quantitative and qualitative scenarios. Based on (Nielsen & Karlsson, 2007)

Quantitative Qualitative

Computer model-based Descriptive

Technical, economic aspects Social, political, cultural aspects

Calculating economic, environmental and technical impacts

Discussing radical innovation and its background

While quantitative scenarios usually focus on techno-economic analysis conducted using computers, qualitative

scenarios are more descriptive and concentrate on "softer" aspects, such as societal, political and cultural

transitions. Certainly, a mixture of the two approaches can also be the case.

Scenario development is largely influenced by three aspects: views of the analysts (e.g. regarding what is a

sustainable technology), uncertainties, and resulting assumptions. These can be for instance: economic and

social changes affecting energy demand (e.g. will a dramatic increase in home-working affect the energy

demand in houses and workplaces?); fuel resources potential and prices, technological progress in energy

recovery, storage, transport; environmental impacts and risks of energy supply and production; security of

supply and political turmoil in fossil fuel-supplying regions (e.g. in Russia or Middle East and North Africa);

population and economic growth in developing countries and its influence on energy. Nonetheless, the

discussion on the social characteristics (e.g. influence of lifestyle changes) is frequently lacking in the energy

scenarios, which may be regarded as a weakness in comparison to the full potential of the scenario method

(Nielsen & Karlsson, 2007).

The table 3.2 exemplifies how scenarios in the energy domain can be applied. Undoubtedly, the division lines

are fluid and many scenarios belong simultaneously to two or more categories.

Table 3-2 Use of energy scenarios. Based on (Nielsen & Karlsson, 2007)

Main purpose How used Examples

Strategic planning Improve preparedness for future events

Strategic planning e.g. in Shell, including the newest ones developed in 2013 (Shell, 2013) and city-focused (Shell & Centre for Liveable Cities, 2014)

Long-term energy forecasts Forecasting fuel prices, future technologies etc.

IEA's World Energy Outlook (IEA, 2013) Technology Data for Energy Plants (DEAc, n.d.)

Energy planning activities Analysing different scenarios and roadmaps for advocacy and goal-setting

Normative scenario planning, backcasting), e.g. IPCC near-term and long-term climate projections contained in (IPCC, 2013) used for displaying climate change risks and consequences; Scenarios of the Danish Commission on Climate Change Policy (Klimakomissionen, 2010)

Energy systems transition is a public policy matter that in many cases requires public support for high

investments. One of the main weaknesses of the use of scenario planning is considered to be lack of real policy

significance mainly because of the difference between long-term scenarios and shorter-term policy run

perspective (Hughes, 2013). This however is not observed in Denmark, where scenarios not only influence the

debate among companies, the energy agency, municipalities and central government, but also have an impact on

legislation (see (DEAa, n.d.)). This is primarily due to an open political process and a willingness of Danish

policy-makers of most political parties to engage in the energy system transition. Moreover, this approach shows

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that the way that scenarios are conducted in Denmark (i.e. quantitative computer-tool-based analysis) make it a

suitable tool for planning feasible energy transitions.

Shifting focus to the city-scale energy scenarios, the literature contains several examples of using optimisation

tools other than the one applied in this study. These are: using the Long-Range Energy Alternatives Planning

tool (LEAP) in Cape Town (Winkler, et al., 2006), in Beijing (Feng & Zhang, 2012), and in Bangkok

(Phdungsilp, 2010), EnergyPLAN in Aalborg (Østergaard, et al., 2010) and Frederikshavn (Østergaard & Lund,

2011) and MarkAL-TIMES in Pesaro (Italy) (Comodi, et al., 2012).

Generally, the scenario analysis process can be divided into the following iterative phases: background analysis

(e.g. literature review, interviews), scenario development (using an unrestrained method or a more prescribed

one, e.g. SWOT, Delphi etc.), conducting a scenario (using qualitative or quantitative methods) and results

(demonstration and discussion of scenario outcomes).

The figure 3.1 depicts the methodology of scenario analysis used in this study, divided into background work,

scenario development, conducting the scenario and scenario outcomes.

Figure 3-1 Phases of scenario analysis. Inspired by (Fontela, 2000) and (Karlsson & Meibom, 2008)

The background analysis relied on a literature review of energy scenarios, and previously conducted informal

talks and semi-structured interviews with the utilities and municipalities. This phase formed a backbone for

scenario development, where knowledge on scenarios and input data and theory (e.g. economic aspects)

contained in the model was used. Scenarios were conducted in an iterative process of modelling and adjustment.

Finally, the outcomes were techno-economic results of modelling of the power and district heating system that

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can further facilitate the decision-making process. In this way, the aforementioned definition of a scenario as

purposeful depiction of possible future energy systems and pathways to achieve them is applied.

In order to see what kind of consequences are connected with the whole Greater Copenhagen area becoming

CO2 neutral or not, two types of scenarios were modelled for year 2025. First scenario is a reference one, where

all the fossil fuel investments are allowed and no specific goal for CO2 emission is set (other than carbon tax).

The alternative scenario is a low-carbon one, where no fossil fuels can be used in electricity and district heat

generation not only in the city of Copenhagen, but also in remaining municipalities of the area. Besides, a

current scenario for 2013 was also modelled to check consistency of the model with statistical data (calibration)

and to be able to claim that the results generated by the model are valid (validation).

3.2 Energy systems modelling

The applicability of energy systems modelling was investigated, using Balmorel tool. The modelling tool was

chosen in order to observe the techno-economic influence of changing the fuel mix in the Greater Copenhagen

area as part of the Danish and Nordic energy system.

Computer tools for energy systems scenario modelling on a world-, national and regional scale have been used

in quantitative analysis for a few decades already. The approach of scenario modelling of energy systems is

used, e.g. by the International Energy Agency (IEA, 2014).

As stated by (Hiremath, et al., 2007), among the benefits of computer models are: transparent assumptions, logic

in computing and possibility to account for many aspects in the same time. The exploratory approach and ability

to calculate and display possible consequences (Herbst, et al., 2012), such as e.g. required investments, make

energy models well-suited for serving the quantitative scenario analysis. In this sense, scenario modelling allows

to see a comprehensive quantitative picture of selected impacts connected with a specific policy. However, to

represent an energy system, many assumptions are made, meaning that while developing complexity, the model

may not become more accurate or its results more correct than those derived from a simpler model, as

exemplified by (Craig, et al., 2002).

The computer tool used in this study, Balmorel, has originally aimed at modelling heat and electricity systems in

the Baltic Sea region. It is an open-source linear programming optimisation modelling tool. Optimisation is a

versatile approach, used in planning, engineering design, as well as allocation of resources. (Bazmi & Zahedi,

2011) Linear programming is relevant to model aspects such as e.g. technology choice in view of cost

minimisation and reliability maximisation (Baños, et al., 2011).

Balmorel simulates generation, transportation and consumption of electricity and heat according to the scenarios

chosen. The model is written in GAMS (General Algebraic Modelling System) language; see (GAMS, 2014). It

has been applied in the Northern and Central Europe, for subjects such as: expansion of district heating, heat

pumps investments, electricity transmission, heat savings etc., for more details see (Balmorel, n.d.).

The modelling tool simulates the energy system's supply and demand on market basis and optimizes the

operation of generation units, aiming to achieve a state of balance between the surplus of consumers and

producers in the energy sector rather than in the wider economy (partial equilibrium). It is a bottom-up model,

because it focuses on comprehensive technology depictions. The focal point of optimisation is to achieve the

most cost-effective mix of technologies bounded by constraints such as e.g. technical limitations for CHP (e.g.,

relation between heat and power production), emission limits, and required fuel type use. (Ravn, 2001)

(Connolly, et al., 2010) (Herbst, et al., 2012).

The essential input data for Balmorel is: type and performance of generation units, fuel types and costs,

investment and operation and maintenance and emission costs, losses and limits of distribution and

transmission, taxes on fuels etc. The energy infrastructure is geographically divided into countries, regions and

areas. This is done to show costs and/or technical bottlenecks related to the transport of electricity and heat in

Denmark, Sweden, Finland and Norway, which are connected to the same electricity market (Nord Pool). In the

redesigned model version, Eastern Denmark (Zealand) is divided into two main regions: Greater Copenhagen

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and rest of Eastern Denmark. A temporal distinction allows representing intermittency of technologies such as

wind power, variation in demands and storages. Balmorel's flexibility is expressed by a possibility to alter input

parameters (Karlsson & Meibom, 2008) or constraints.

Energy systems modelling in this paper is done by modelling a number of energy scenarios concentrating on

urban area of Greater Copenhagen, connected with the rest of the Nordic electricity market.

3.3 Main input data and assumptions

Since a model is just a representation of reality, many assumptions and simplifications must be made to reduce

the computational time, while depicting the complexity of the real world at least to a certain degree.

Most of the data in the model and underlying data assumptions were updated and collected for a project called

"The role of district heating in the future Danish energy system" (EA Energy Analyses, 2013). The simulation

encompasses not only The Greater Copenhagen area, but also the rest of Denmark, as well as countries linked

with it via substantial transmission lines and the common Nordic electricity market: Sweden, Norway and

Finland. This is done to avoid sub-optimisation, and to produce more reliable results.

Supply

The data on existing technologies is based on statistics from the Danish Energy Agency and Danish TSO

Energinet.dk. The data on future technologies (typology, efficiencies, costs etc.) is from the Danish Energy

Agency's Technology Catalogue. The fossil fuel prices come from International Energy Agency's World Energy

Outlook 2012, New Policies Scenario. The source for biomass prices is a report by the Danish Energy Agency.

(DEA, 2011) Although the fuel prices in the longer term follow market projections, in some cases there are local

adjustments to represent short-term contracts signed between plants and fuel providers.

The data on biomass resources available is based on reports of the European Environmental Agency and model

developers' assumptions on the share available to the power and district heating sector.

Other input data, such as the yearly number of full load hours for renewables is based on reports by the Danish

Energy Agency.

Demand

The input data for electricity and district heat demand (98% of overall demand in the city of Copenhagen) is

based on previous analyses, among other the work of the Danish Commission on Climate Change Policy. The

only exception is Greater Copenhagen area, where demand is calculated as multiplication of the average

electricity consumption per capita and the number of people in the municipalities covered by the Greater

Copenhagen district heating scheme. While the electricity demand in the model is assumed to steadily increase

(about 1% yearly), the heat demand slowly increases until 2035, then decreases to portray the expected building

energy savings. Losses in electricity distribution are 6%, while in heat distribution: 20%.

Other

The model allows investments in plant retrofitting to use another fuel, e.g. from coal to biomass. A unit can be

decommissioned if not profitable anymore - when revenues can no longer cover both variable and fixed

operating costs.

The currency used for the outputs is Euro 2013.

4 Results

4.1 Calibration – year 2013

The model calibration is understood as the adjustment of input data and optimisation so that the simulated

reference year 2013 is validated by being consistent with available statistics. Calibration is usually a time-

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consuming iterative process, but also a useful one – it shows the discrepancies between the model and reality.

Nonetheless, model calibration is rarely discussed in publications, possibly due to the time/space constraints. A

complete replication of reality is impossible, thus some tolerance is acceptable.

To check the model, two aspects were chosen: fuel mix in the district heat generation and electricity price, due

to data statistics available. The following figure 4.1 depicts results of the model calibration.

Figure 4-1 Fuel mix in district heating

A comparison of the figure above with the figure 2.1 reveals that there is only 2-4 per cent discrepancy between

the model and reality, in case of biomass, municipal waste, natural gas and coal share. The overall fuel use is

almost the same in the model as in reality, thus 36 PJ. The lack of fuel oil can be explained by its very high cost

in the model, almost twice the price of wood waste. One has to remember that the fuel mixes used change from

year to year – and even more so, as multiple fuel plants use various fuels in various configurations.

The model calculated the average marginal system operation cost for 2013 for Greater Copenhagen

(DK_CA_KBH-reg) to be €45.6/MWh (see figure 4.2). The average electricity spot price on the Nordic

electricity market for Denmark was around €40/MWh (Nord Pool Spot, 2013), therefore the model calculation

does not deviate too significantly from real costs.

Figure 4-2 Electricity price throughout the weeks of the year 2013

The conclusion from the aforementioned results is that the model calibration is successful. Therefore, one may

expect that the results of future scenarios will also be bearing less uncertainty than they would if using a model

without calibration.

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4.2 Preliminary scenarios for 2025: Reference and low-carbon scenario

The figure 4.3 below depicts electricity generation in the reference scenario. Biomass dominates with almost

55% share, but there is still a significant share of coal and natural gas (21.5%) in the electricity generation fuel

mix.

Figure 4-3 Electricity generation in the reference scenario, divided by fuels [TWh]

Figure 4.4 below presents electricity generation in low carbon scenario. The total generation is much lower here

(the electricity demand in the modelled area is the same in both scenarios) and this may be because there is few

times lower net electricity export to the neighbouring region on the Zealand island. Wind contributes with

almost 50% of electricity generation, followed by municipal waste, biomass and solar cells.

Figure 4-4 Electricity generation in the low-carbon scenario, divided by fuels [TWh]

Heat generation in reference scenario is mainly based on biomass, especially wood pellets and wood waste, and

municipal waste in CHP plants (see figure 4.5).

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Figure 4-5 Heat generation in the reference scenario, divided by fuels [PJ] Heat = heat storage, Electric=heat pumps

Figure 4.6 below shows heat generation in the low-carbon scenario.

Figure 4-6 Heat generation in the low-carbon scenario, divided by fuels [PJ] Heat=heat storage, Electric=heat pumps

In this scenario, heat pumps are most significant contributors (a little above 40%), followed by municipal waste

and then the remaining energy sources.

New electricity and heat investments in the reference scenario are shown in the table 4.1 below. The size of

these investments is comparable to the amount of fuels used for electricity generation, as is in the figure 4.3.

Heat storage tanks are the only chosen district heating investment in this scenario.

Table 4-1 Electricity and heat investments in the reference scenario

Electricity

Wood CHP Wood pellet CHP

[MW] 444 927

Heat

Heat storage

[MWh] 42 115

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Electricity and heat investments in the low-carbon scenario are presented in the table 4.2 below. Here, the

investments are more varied than in the alternative scenario. While the biomass investments are much more

modest here, the heat investments include not only heat storage, but also heat pumps and solar heating.

Table 4-2 Electricity and heat investments in the low-carbon scenario

Electricity

Straw CHP Wood CHP

[MW] 19 211

Heat

[m2] Solar heating

2 214

[MW] Heat pumps

1 165

[MWh] Heat storage

31 433

For system costs and CO2 emissions in the two alternative scenarios, see table 4.3 below.

Table 4-3 Selected system costs and CO2 emissions

Reference Low-carbon

Variable O&M (m€) 46 33

Fixed O&M (m€) 174 84

Capital Cost (m€) 312 322

CO2 Emissions

(kilotons)

2 126 407

Both the variable and the fixed operation and maintenance costs are lower in case of the low-carbon scenario,

but the capital costs are lower in the reference scenario. It seems that the main reason for lower O&M cost in the

low-carbon scenario is simply less electricity generation, while higher capital cost may be due to the more

investments in renewable generation, such as heat pumps and solar heating. One has to be aware of the fact that

system costs can be described by also other costs, such as fuel costs, thus more analysis need to be made to

choose a better alternative from an economic point of view. The CO2 emission are more than 5 times lower in

the low-carbon scenario, since the only source of them is municipal waste incineration (which is assumed 59%

non-fossil).

4.3 Conclusion of the preliminary scenarios

What matters in scenario comparison are the dissimilarities between scenarios that enable evaluation of various

policy measures. The preliminary scenarios analysed differ in many aspects, concerning fuel mix and system

costs, as well as CO2 emissions. It seems that from the point of view of biomass availability and reducing CO2

emissions, the low-carbon scenario is a better choice than the reference.

The presented results are only a small part of the number of results that Balmorel calculates. Still, this modelling

tool proves to be suitable to characterise and model municipal-scale energy scenarios. However, modelling of

14

urban energy systems is a challenging task, if one wants to achieve a most optimal solution both on a local and

national level, since the electricity flows in the international electricity market make it difficult to see the origin

of each electron in the system. All in all, this analysis should not be treated as a final projection of what

happens if the Greater Copenhagen area becomes CO2 neutral or not, but rather as an indication of how carbon

emission reductions and various renewables can be implemented. Besides, the high investments required can be

avoided if more attention is given to energy savings, be it insulation, demand side management, efficient

appliances or efficient energy plants.

5 Conclusions

The aim of this paper was to determine and evaluate alternative energy scenarios, concentrating on reducing

CO2 emissions and decreasing the dependence on fossil fuels in the Greater Copenhagen area, Denmark. Energy

scenarios were modelled with use of Balmorel, a linear optimization model for electricity and heat sector

analysis. This work described the current energy situation (as of year 2013) and a reference and low-carbon

scenario for medium-term (year 2025) perspective developed for the city.

The model calibration conducted shows some discrepancies with the statistics, but within an acceptable range of

2-4%. One has to remember that a model is always a simplification of a system and too complicated models

usually require time and/or significant computational resources. Detailed statistics, e.g. regarding total fuel use

in the simulation area are also lacking, which in turn may be due to the fact that plants operate on a market basis

and are not interested in sharing detailed information on their performance.

The analysed scenarios resulted in different energy mixes, investment choices and system costs, depending on

whether a reference or low carbon scenario was selected. Taking into consideration biomass availability and

CO2 emission reductions, it seems that the low carbon scenario is a more optimal choice than the reference. The

results of this examination provide insights into Copenhagen's energy future and highlight possible steps

towards a sustainable city that may be also relevant to other cities in the world.

Overall, optimisation techniques are relevant for facilitating solutions for energy policy challenges, and this

complies with conclusion of the review authored by (Bazmi & Zahedi, 2011). Nonetheless, quantitative energy

scenario modelling has a number of limitations. The simulations rely on statistical data and assumptions, thus

uncertainties are involved in the process. Therefore, this method should not be the only one used to make

decisions, but should be supplied by other analyses. Moreover, scenarios could be developed in consultation

with stakeholders, to see what aspects are most relevant to be simulated. It would be beneficial to conduct the

study with municipalities to obtain more detailed data, e.g. for household-level energy usage.

While in the energy field it is widely practised to use techno-economic modelling tools and assumptions, the

discussion on the social characteristics (e.g. influence of lifestyle changes) is usually lacking, which may be

regarded as weakness in comparison to the full potential of the scenario method. Least-cost optimisation does

not show how stakeholders interact and influence each other, because there relations are complex and non-

linear. A relatively new modelling approach, called agent-based modelling, can be used to represent these

complicated activities.

There are a number of aspects that this paper does not touch upon, but that are worthwhile to consider in future

work. The outcomes of the techno-economic scenario analysis can be further applied in analyses on selected

socio-economic impacts (e.g. employment effects, fuel poverty etc.) of urban energy transitions, either by

macro-economic models or spreadsheet calculations. A stakeholder analysis may be useful for identifying

implementation enablers and barriers of analysed scenarios (see for example (Ben Amer, 2011).

Acknowledgements

I would like to thank Per Sieverts Nielsen for valuable feedback, Jesper Werling and Karsten Hedegaard for

making it possible to use the model and Hans Ravn and Olexandr Balyk for advice.

15

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………………………………………………………………………………………………………………...

Sara Ben Amer holds a Master's degree in Energy Planning and Management from Aalborg University and is

currently employed as a PhD candidate in the Climate Change and Sustainable Development Group, Systems

Analysis Division, Department of Management Engineering at the Technical University of Denmark. Her PhD

thesis focuses on energy scenarios modelling and sustainable urban energy systems transitions.

Documents

Scenario modelling as a tool for planning sustainable urban energy systems