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NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Gea-Bermúdez, Juan; Das, Kaushik; Pade, Lise-Lotte; Koivisto, Matti; Kanellas, Polyneikis
Publication date:2019
Document VersionPublisher's PDF, also known as Version of record
Link back to DTU Orbit
Citation (APA):Gea-Bermúdez, J., Das, K., Pade, L-L., Koivisto, M., & Kanellas, P. (2019). NSON-DK Day-Ahead marketoperation analysis in the North Sea region towards 2050. Technical University of Denmark.
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NSON-DK Day-Ahead market operation
analysis in the North Sea region towards 2050
NSON-DK
Deliverable 3.1
Juan Gea-Bermúdez, DTU Management
Kaushik Das, DTU Wind Energy
Lise-Lotte Pade, DTU Management
Matti Koivisto, DTU Wind Energy
Polyneikis Kanellas, DTU Wind Energy
DTU Wind Energy E-0193
Dec 2019
Authors: Juan Gea-Bermúdez1, Kaushik Das2, Lise-Lotte Pade1, Matti Koivisto, and
Polyneikis Kanellas2
Title: Day-Ahead power market operation analysis in the North Sea region towards 2050
Department: 1DTU Management. 2DTU Wind Energy
DTU Wind Energy E-0193
Dec 2019
Summary:
This report analyses the impact of: a) the penetration of VRE towards 2050, and b) the
offshore grid architecture, on the DA market operation in the North Sea region.
Additionally, the impact of the optimization method used is also studied.
The results from the DA simulations towards 2050 showed a considerable penetration of
VRE in the energy system, reducing drastically the use of fossil fuels. This penetration
reduces considerably the emissions of the studied energy system in the countries in focus
at the expense of increasing its costs due to especially the high increase in CO2 cost. The
penetration of VRE and its associated grid development lead to great change in the
operation of the system and electricity price distribution. More trade, and more efficient
hydro dispatch are some of the key features of the energy system in 2050. The results
show that the technology type that is likely to profit most from this VRE integration is
hydro reservoirs, whereas the one with more challenges is likely to be condensing power
plants. The latter will most likely not be able to be profitable participating only in the
energy markets towards 2050.
On the other hand, the impact of the offshore grid architecture in the DA market operation
is found rather limited. Energy-wise the offshore grid scenario results in slightly higher
penetration of VRE, higher reduction of emissions, more efficient energy trade, and less
operational costs towards 2050 to cover the same demand. Nevertheless, the offshore grid
architecture seems to be the most cost-efficient way to operate the future energy system
of the North Sea region, especially to integrate offshore wind and hence its development
should be encouraged. For the offshore grid scenario to become real, there is great need
for international cooperation though.
The optimization method can have a considerable influence on the curtailment of the
system. When LP method is used, less curtailment takes place. The use of the MIP
algorithm lead to the most realistic hourly operation of the power plants, at the expense
of increasing considerably the computational time. Introducing RMIP improved
considerably the results with respect to the LP approach, at the cost of increasing a bit the
computational time. Therefore, it seems like, unless the analysis of detailed hourly
operation of individual units is of great importance, the RMIP approach is the most
convenient.
Contract no.:
Grant no. 64018-0032 (EUDP,
Danish energy Agency)
Project no.:
43277
Sponsorship: EUDP (previously
ForskEL)
ISBN: 978-87-93549-61-6
Pages: 48
Tables: 10 (+ 10 in the appendices)
References: 12
Technical University of Denmark
Department of Management
Akademivej
Building 424
2800 Lyngby
Denmark
Telephone 45 25 48 00
www.sustainability.man.dtu.dk/english
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Preface The work presented in this report is deliverable D3.1 of the North Sea Offshore Network – Denmark
(NSON-DK) project. The report is prepared in collaboration between DTU Wind Energy and DTU
Management. The report includes as an appendix an update to the deliverable D2.1.
The NSON-DK project is funded by grant no. 64018-0032 under the EUDP program administrated by the
Danish energy Agency (previously under ForskEL). It is carried out as a collaboration between DTU Wind
Energy (lead), DTU Management and Ea Energy Analyses.
Lyngby and Risø, Denmark, 1st November 2019
Juan Gea Bermúdez, Kaushik Das, Lise-Lotte Pade, Matti Koivisto, and Polyneikis Kanellas
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Contents
1. INTRODUCTION 6
2. OVERVIEW OF THE NSON-DK SCENARIOS 7
2.1 Countries in focus 7
2.2 Project-based and offshore grid scenarios 7
3. DAY AHEAD MARKET OPERATION MODELLING 9
3.1 The Day Ahead market clearing problem 9
3.2 Balmorel 10
3.3 Market actors modelling 10 3.3.1 Demand 10 3.3.2 Supply 10 3.3.3 Storage 11 3.3.4 Electricity transmission 11
3.4 Unit Commitment modelling 11
3.5 Planned maintenance and storage content modelling in rolling seasonal horizon mode 12
3.6 VRE generation simulation 12
4. DAY-AHEAD MARKET OPERATION SIMULATIONS 13
4.1 Annual electricity balance 13
4.2 Electricity trading 15
4.3 Electricity curtailment 16
4.4 Emissions 18
4.5 Economic analysis 18 4.5.1 Electricity prices 18 4.5.2 Generator’s revenue in the electricity market 21 4.5.3 Operational costs 23 4.5.4 Congestion rent 24 4.5.5 Missing peak power 24
4.6 Hourly electricity balance 25
4.7 Influence of optimization method 28 4.7.1 Electricity prices 29 4.7.2 Generation 30 4.7.3 Computational time 31
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
5. CONCLUSION 32
REFERENCES 32
APPENDIX A: SUPPORTING TABLES 34
APPENDIX B: UNIT COMMITMENT ASSUMPTIONS 40
APPENDIX C: NSON-DK ENERGY SYSTEM SCENARIOS – FINAL VERSION
(UPDATE TO EDITION 2 SCENARIO REPORT) 42
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
1. Introduction Europe is pushing towards a CO2 free energy system. To achieve this, Variable Renewable Energy (VRE)
are key, especially solar and wind energy. Particularly, in the North Sea a large development of offshore
wind and its associated grid is expected. The expected massive VRE penetration is likely to challenge the
operation of the electricity markets.
The electricity system in Europe is composed of energy, and power markets, depending on the commodity
traded. This report focuses on the Day Ahead (DA) electricity market, which is the one where most of the
energy is traded. The DA market is run daily. In it, buyers estimate the amount of energy required for each hour of the following day and assess how much they are willing to pay for it. On the other hand, sellers
estimate the amount of energy that they will be able to offer for every hour of the next day. Buyers and
seller will then participate on the market by submitting their bids, and the hourly prices will be cleared.
Prices reflect the opportunity cost of the participants. If there is no transmission congestion, hourly prices
will be the same for all the regions; else, they will differ.
In many European markets, like Nord Pool [1], block bidding is allowed. There are several types of block
bidding strategies, but the idea behind them is that a market participant making a block bid offers a certain
volume and price on the condition that it will be accepted for a number of consecutive hours. This is
especially relevant for thermal power plants, which generally have significant start up and shut down costs.
Block bids allow them to make sure they will recover these fixed costs.
The penetration of VRE is challenging the operation of traditional thermal power plants due to several facts. On the one hand, thermal power plants are generally profitable only when running a number of consecutive
hours, as explained before. On the other hand, thermal power plants tend to face technical constraints such
as maximum ramping or minimum output requirements. Additionally, the opportunity cost of VRE is
normally lower than the thermal power plant’s one, which means that generally VRE will be dispatched
before most thermal power plants. The penetration of VRE is likely to lead to non-flexible thermal power
plants with high operational costs out of the DA market, and probably not being profitable and shutting
down, which might challenge system adequacy. Moreover, with the penetration of VRE in the DA market,
balancing needs close to real time are expected to increase due to the forecast errors. This combined with
presumably less available thermal power in the market will again challenge security of supply.
The objective of this report is to analyse the impact of offshore grid architecture on the Day Ahead market
operation in the North Sea region towards 2050, with a special focus on Denmark. The scenarios used for this analysis are based on the ones developed in WP2 of NSON-DK project [2], which have as starting
point [3]. An updated version of the results are used in this report and is shown in Appendix C. The NSON-
DK project studies the impact on the system of this development in the short-, medium-, and long-term,
focusing on the Danish power system. The results from the analysis on the DA electricity market operation
will be the starting point for the analysis of the balancing needs in close to real time markets towards 2050
which will be part of WP3 of the NSON-DK project.
Full year system cost minimizations are performed with the energy system Balmorel [4] to simulate the DA
market operation of the North Sea region for the years 2020, 2030 and 2050. The optimizations are carried
out with a rolling seasonal horizon approach of one day to try to replicate the daily operation of the DA
market. The technologies whose behaviour cannot be accurately replicated with this approach, like long-
term storage, are modelled with an especial method combining both short-term and long-term perspective.
The Unit Commitment (UC) methodology is included to obtain a realistic behaviour of thermal power
plants. The VRE generation modelling is carried out using the DTU Wind Energy’s CorRES tool [5].
The results focus on energy related output, such as energy balance, curtailment, etc. Key economic figures
are also shown. The influence of the optimization method on key results is also analysed.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
2. Overview of the NSON-DK scenarios This section provides a short overview of the NSON-DK scenarios used in this report to simulate the DA
market. A full description of the assumptions behind these scenarios can be found in [2]. Updates to the
original scenario report have been made and are fully described in Appendix C and [6]. Additionally, the
importance of the planning horizon and grid architecture has been studied in [7].
2.1 Countries in focus
The NSON-DK project focuses on the North Sea region. Countries analysed in detail are: Denmark (DK),
Norway (NO), United Kingdom (UK), Netherlands (NL), Belgium (BE) and Germany (DE). For UK, the
energy system of Great Britain (GB) is modelled, so the numbers refer to GB. The countries are shown on map in Figure 1. Even though Denmark is the main focus in the NSON-DK project, the other countries are
important when analysing the electricity market operation since Denmark is heavily interconnected.
The electricity generation and transmission development of the countries in focused are optimized in WP2
of the NSON-DK project, whereas the development of other surrounding, i.e. Sweden, Finland, Estonia,
Latvia and Lithuania, are not optimized but taken from the Nordic Energy Technology Perspective 2016
report Error! Reference source not found..
Figure 1: The countries analysed in detail in the NSON-DK scenarios are highlighted in the map.
2.2 Project-based and offshore grid scenarios
A project-based scenario and an offshore grid scenario are developed in the NSON-DK project. These
scenarios are differentiated by the allowed offshore grid structure, as shown in Figure 2. The offshore grid
scenario has more options in the investment optimization [2]. Including radial connections as possibilities
also in the offshore grid scenario creates a competition of radial connections and an integrated solution.
Figure 2: Schematic view of project-based (“radial”) and offshore grid (“meshed”) connection structures [8]. In the project based NSON-DK scenario, only the “radial” type of connections are allowed; in the offshore grid scenario, both “radial” and “meshed” connections are allowed.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
The aggregated electricity generation capacity development per fuel type towards 2050 in the countries in
focus for the project-based and offshore grid scenarios is depicted in Figure 3. The scenarios in 2020 are
identical, since only the development by 2030 and 2050 are optimized. The offshore grid shows by 2050
10.3 GW more of wind offshore capacity, 7.8 GW less of wind onshore, 6.5 GW less of solar PV, and 8.2 GW less of fossil thermal power, than the project-based scenario of 2050. The reduced need for fossil
thermal power in the offshore grid scenario suggests that this scenario is more efficient to provide flexibility
to the system than the project-based one. Both scenarios show a large penetration of VRE towards 2050.
Figure 3: Installed electricity capacity development per fuel and scenario in the countries in focus (GW).
Figure 4: Project-based scenario: transmission lines in 2030 and 2050 (GW) between regions visible in the map. On-land lines in green and C2C offshore lines in orange.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
The resulting transmission development in the North Sea region towards 2050 for the Project-based and
Offshore grid scenarios is depicted in Figure 4 and Figure 5, respectively. Overall, both scenarios show
high level of interconnection between the countries in focus, especially to Norway, whose hydro power is
used not only to cover its domestic demand, but to provide flexibility to the other countries. It is interesting to observe that in the offshore grid scenario, the transmission capacity is split into direct Country to Country
(C2C) and hub connected lines. Hub connected lines have the advantage that their transmission capacity
could be used for both wind offshore dispatch and C2C trade.
Figure 5: Offshore grid scenario: transmission lines and hubs in 2030 and 2050 (GW) between regions visible in the map. On-land lines in green, C2C offshore lines in orange, lines related to the meshed grid in light blue and hubs in dark blue.
3. Day Ahead market operation modelling
3.1 The Day Ahead market clearing problem
The DA market clearing is an equilibrium problem where every actor tries to maximize their own profit.
The actors of the problem (demand, supply and transmission) are linked through the electricity balance
constraint. However, under the assumption of perfect competition this problem is equivalent to solving a
social welfare maximization problem, since the Karush–Kuhn–Tucker (KKT) conditions are identical.
Electricity prices are obtained from the shadow price of the electricity balance equations.
In this report, for the sake of simplification we assume perfect competition, and hence, strategic behaviour
will not be considered.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
When modelling the DA market operation, it is important to try to capture as accurately as possible the
costs and constraints faced by all market participants, since they influence their opportunity costs used
when bidding on the market.
In this report, we use the energy system tool Balmorel [4]. to solve this problem. We do this from a private economic perspective, although for simplicity we remove all taxes, subsidies, and tariffs except the tax
corresponding to CO2 emission.
3.2 Balmorel
Balmorel [4] is an energy system tool, deterministic, open source, with a bottom approach. It has been
traditionally used to model the electricity and district heating sector, although it is currently being developed
to increase its capabilities and include more sectors. The geographical representation in Balmorel is
countries, which are composed of regions, which are composed of areas.
The latest version of the code, BB4, is used to model the DA market clearing of the full year. To do so, the
option seasonal rolling horizon optimization inside the model is applied. This approach optimizes the
energy dispatch season per season, linking the results from the previous season to the next one, so continuity
is assured. In this paper, a season represents a day. In order to capture the behaviour of agents that do not
simply have a short-term perspective when participating in the DA market, such as long-term energy
storage, specific conditions are applied for them. They are explained in section 3.5.
3.3 Market actors modelling
This subsection describes the modelling of the different actors that are part of the electricity market. Since district heating dispatch is also part of the optimization, as if there is a unique integrated market, the agents
involved in it will also be explained.
3.3.1 Demand
The traditional electricity demand is considered for simplicity inelastic, i.e. infinite price bid, whereas sector
coupling via electricity to district heat technologies, such as heat pumps or electric boilers, is optimized.
Assuming traditional electricity demand inelastic does not deviate much from today’s situation, although
this assumption would be less suitable with increasing penetration of Electric Vehicles (EV) and demand
side management. District heating demand is also considered inelastic.
3.3.2 Supply
The supply side consists of dispatchable and non-dispatchable technologies, depending on whether their
power generation can be easily planned and controlled or not, respectively. Dispatchable technologies
include power-only and cogeneration thermal power plants, boilers, heat pumps, geothermal, and
hydroelectric power with reservoirs, whereas non-dispatchable technologies are VRE, e.g. solar, wind, hydro run of river, tidal, and wave power. The generation of these technologies is limited by their power
capacity (MW) and their availability. The availability factor is modelled in an hourly bases with a factor
from 0 to 1. For thermal power plants, this factor is composed of two parts: planned maintenance and
stochastic outage. Planned maintenance is optimized (see section 3.5). The hourly stochastic outage are
based on the number of units and forced outage probability. For simplicity, outages are assumed to last for
1 hour. For non-thermal power plants, the availability factor is assumed to be 1. Wind and solar time series
include stochastic outage in them.
To obtain a realistic behaviour of thermal power plants the use of the Unit Commitment (UC) methodology
is necessary. A linear programming approach would not be able to fully capture start-up costs nor ramping
constraints, for instance. This methodology, implemented in Balmorel, is explained in detail in section 3.4.
Hydroelectric power with reservoirs is modelled as a generation technology linked to an interseasonal
storage with limited energy capacity, that receives seasonal energy inflows. The model optimizes the
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
production and level of storage along the year, with the condition that the level of the storage at the
beginning of the simulated year equals the level at the end. How the level of hydro interseasonal storage
are calculated when performing a rolling seasonal horizon mode can be read in section 3.5.
VRE is modelled with time series that provide the available energy during each time step. Curtailment is allowed. Capturing the correlation between the regions in in the model is very important. That is why the
solar PV and wind time series are calculated with the tool CorRES [5]. The time series used in this report
correspond to the DA forecast. Detailed explanation of the calculation of the methodology used in the
CorRES tool can be read in section 3.6.
3.3.3 Storage
Storage technologies offer flexibility to the system by arbitraging, i.e. moving energy from time steps with low prices to others with high prices, earning money with the difference. This process is assumed to have
losses, which depend on the specific technology.
In Balmorel, two type of electricity storages are modelled: intraseasonal and interseasonal. Interseasonal
storages are meant to move energy between seasons, whereas intraseasonal storage are meant to move
energy inside the same season. Hydro seasonal storage is a special type of interseasonal storage, as
explained in 3.3.2. Generally, interseasonal storage in Balmorel has the condition that the level of the
storage at the beginning of the year and at the end needs to be the same, whereas intraseasonal storage has
the condition that the level at the beginning of the season and at the end of it needs to be the same. However,
to try to obtain a realistic behaviour in the DA market of these two types of storages when applying a rolling
seasonal horizon approach, a more complex methodology has been applied, see section 3.5 for details.
3.3.4 Electricity transmission
Electricity trade is allowed in Balmorel and is limited to the available transmission capacity between the
regions. This available capacity in each time step is the product of the Net Transfer Capacity and the
availability of each connection in that time step.
3.4 Unit Commitment modelling
Introducing UC in the optimization allows for an improved representation of conventional generations, at
the cost of increasing considerably computational complexity due to the use of on/off integer variables. The
methodology applied for this paper includes minimum production, fixed hourly operation costs, minimum
on/off time, start up and shut down costs, and ramping constraints.
Figure 6: Electrical efficiency of several technology types as a function of their load [12].
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
The efficiency of the power plant is dependent on their load. Below a given loading, the efficiency of the
power plant is extremely low and therefore its operation is not feasible. Modelling the efficiency as a
function of the load increases the complexity of the problem dramatically, since the efficiency as a function
of the load is generally non-linear. As a proxy, a minimum fuel consumption and a fixed hourly cost are used in this paper to drive power plants to be in the feasible region that matches the corresponding efficiency
assumption.
Technologies have a limited rate of change of their production. Gas turbines and engines are quite fast,
whereas steam turbines are generally lower due to the steam generation process occurring in the boiler.
Boilers burning solid fuels are especially constraint, and generally require the use of liquid/gas fuel to start-
up operation.
Some power plants require a long time to be able to start/stop delivering energy for the system. For example,
the start-up time is generally influenced by the temperature state of the power plant (cold, warm, hot). If a
plant has been off for a long time, it will require a longer time and energy to bring the temperature of their
components to steady state, so it can operate normally. Stop times are generally less important and can be
linked for instance to ramping constraints. Minimum on/off constraints are introduced to model these limitations. Start-up and shut down costs are meant to represent the cost incurred by power plants when
they start or cease operation, respectively. There are many factors affecting these costs, being off time one
of them. For the sake of simplicity, the dependence of off time in minimum start-up time is not directly
modelled and the numbers corresponding to cold start-up are utilized. As a proxy, shut down costs are
assumed to be equal to start-up costs to try to reduce the impact of disregarding the influence of off time in
start-up costs and time.
To analyse the importance of introducing the UC methodology when modelling the DA market, three
different runs are performed: 1) Unit Commitment with MIP (UC-MIP), 2) Unit Commitment with RMIP
(UC-RMIP), and 3) LP without Unit Commitment (NO-UC). The importance of UC is shown in [12],
where it is found that the cycling costs could be reduced by 40% if they are fully considered in the
optimization. Appendix B shows the data assumptions on Unit Commitment.
3.5 Planned maintenance and storage content modelling in rolling
seasonal horizon mode
When simulating the DA market, it is important to try to capture the relative long-term perspective of
storage technologies, and maintenance of power plants. Optimal planned maintenance and storage content are dependent on opportunity costs, which are largely influenced by expectations of market prices along
the year. In order to maximize profits, short-term storage will tend to perform daily arbitrage, long-term
storage will tend to perform weekly arbitrage, and power plants will be inoperative due to maintenance
during seasons with low prices.
In this paper, these expectations are obtained by performing a full year dispatch optimization. From the full
year run, the storage level at the beginning of each day, and the units off due to maintenance are saved.
This optimization is performed with a RMIP method with UC, using the full year with 1 every 3 hours due
to computational complexity. The storage levels at the beginning of each day, and the units off due to
planned maintenance, are then loaded and forced when performing the rolling seasonal horizon
optimization.
3.6 VRE generation simulation
The CorRES tool [5] is used to simulate VRE generation time series. The tool is based on reanalysis data
obtained from the Weather Research and Forecasting (WRF) model [9], with meteorological downscaling,
stochastic fluctuation simulation and conversion from meteorological data to VRE generation time series
as described in [5].
For the modelling in CorRES, technical VRE generation parameters, such as wind turbine hub heights, are
required to estimate the capacity factors (CFs) of VRE generations in the different analysed areas.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
In addition to estimating Capacity Factors (CFs), CorRES can model the spatiotemporal dependencies in
VRE generation, as has been demonstrated, e.g. in [5][10][11]. The modelling provides to Balmorel VRE
generation profiles where the correlation structures of wind and solar PV generation, and the correlations
between wind and solar PV generation, are modelled.
The tool is used to generate the DA forecast timeseries used in this report. These timeseries are generated
together with the Hour Ahead (HA) and Available power in Real Time (RT), which are used in the future
NSON-DK report on balancing market analysis. An example of regional simulation of available wind,
forecast (DA and HA), is shown in Figure 7.
Figure 7: Example regional simulation of available wind generation and forecast (DA = day-ahead; HA = hour-ahead).
4. Day-Ahead market operation simulations This section shows the results from the Day-Ahead market operation applying the methodology described
in section 3 in terms of energy balance and economy for the countries in focus. The influence of the
optimization method is also analysed. Some results are only shown for a few regions for illustrative purposes and to limit the size of this report. All costs are expressed in €2012. Supporting tables for most of
the figures can be found in Appendix A.
4.1 Annual electricity balance
Figure 8: Annual electricity balance per scenario, country in focus, and energy scenario (TWh).
The annual electricity balance development towards 2050 in each of the countries in focus is shown in
Figure 8 for both scenarios. In both scenarios, the sum of the net exports of these countries is negative in
all the years, which means that they are net importers from the countries non in focus. However, these
positive net imports correspond to 2-6% of the total load, which is relatively small. These net imports in
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
the offshore grid scenario are a bit lower than in the project-based scenario, which makes them less
dependent on the countries non in focus. Among the countries in focus, DK and NO operate as net exporters
in most of the years, whereas the others tend to be net importers. The case of NO is especially relevant
since the ratio gross load and net export gets closer to 1 towards 2050. The influence of the scenarios in the total numbers is relatively small. However, country-wise there are a few differences, especially for NL and
DE. In the offshore grid scenario, DE builds more generation capacity than in the project-based one,
especially wind offshore, which leads to less need for imports. The case of NL is the opposite. For NL, its
net imports increase considerably whereas DE’s decrease.
On the other hand, the aggregated electricity generation per year, scenario, and fuel in the countries in focus
is depicted in Figure 9. The penetration of VRE towards 2050 is remarkable regardless of the scenario, at
the expense of decreasing the use of fossil fuels. Only natural gas is still used by 2050. The tiny oil usage
in 2030 and 2050 corresponds solely to the use of back-up power (more details about this back-up power
can be read in section 4.5.5). Linked to the capacity development, the main difference between the scenarios
is that in the offshore grid scenario, wind offshore is higher and wind onshore lower than in the project-
based scenario. One can see that although rather similar, the contribution of VRE in the offshore grid scenario is a bit higher. This penetration leads to a considerable increase of the share of VRE generation
towards 2050. The share of electricity generation for different aggregated types in the countries in focus
per year and scenario is depicted in Table 1. The results show that the share of CO2 free generation increases
from 64% in 2020 to 91% and 92% in 2050 in the project-based and offshore grid scenarios, respectively,
decreasing the share of fossil fuels (non-CO2 free), especially coal generation. This considerable increase
is largely influenced by the penetration of VRE, especially wind. Wind offshore is the technology with
largest increase towards 2050 in the scenarios.
Figure 9: Electricity generation per year, fuel, and scenario in the countries in focus (TWh).
Table 1: Share of electricity generation for different aggregated types in the countries in focus per year and scenario. The numbers might not sum 100% due to rounding.
Year Scenario
CO2 free Non-CO2 free
VRE HYDRO +NUCLEAR +BIOFUEL
SUM NATGAS COAL + OIL SUM SOLAR WIND-ONS WIND-OFF SUM
2020 Any 6% 17% 7% 30% 34% 64% 15% 21% 36%
2030 Project-based 10% 24% 23% 57% 27% 85% 15% 0% 15%
Offshore grid 10% 23% 25% 58% 27% 85% 14% 0% 15%
2050 Project-based 15% 26% 31% 71% 20% 91% 9% 0% 9%
Offshore grid 14% 24% 34% 73% 19% 92% 8% 0% 8%
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
4.2 Electricity trading
The comparison of the electricity trading between scenarios is not straight forward due to the different grid
configurations. In the project-based configuration, all the flows are Country to Country (C2C), whereas in
the offshore grid configuration, apart from direct C2C flows, there is also Hub to Country (H2C), Hub to
Hub (H2H), Country to Hub (C2H) flows, and offshore wind generation in the hubs. Additionally, the fact
that the hubs belong to different countries increases the complexity of the analysis. For the sake of performing a simple comparison of the C2C flows between the scenarios in the countries in focus all the
hubs are aggregated as if they are a unique country and the wind generation from offshore-hubs is removed.
The total C2C flow in the offshore grid scenario is calculated as the direct C2C flow and the indirect one
via hubs, which is equal to C2H.
The influence of the scenario and year in the total C2C electricity aggregated electricity trade across
countries in focus can be seen in Table 2. The results show a considerable increase of the total C2C flow
towards 2050. The increase from 2020 to 2030 in both scenarios is remarkable and is affected by the
penetration of VRE. Comparing the total C2C trade between the scenarios, one can see that the C2C trade
in the offshore grid scenario is lower than in the project-based. Even though the direct C2C trade is in
principle more efficient than the indirect C2C trade (shorter lines), the fact that the offshore grid is more
flexible in dispatching offshore wind connected to the hubs compensate for this drawback, leading to less need for direct C2C flows, and therefore, a more efficient dispatch. The results also show that the meshed
grid of hubs is mainly used to dispatch the offshore wind generated in them, especially towards 2050, since
the share of indirect C2C trade in total C2C trade is 5.8% and 5.3% in 2030 and 2050 respectively.
Nevertheless, the offshore grid scenario seems to require much higher international cooperation between
the countries for the correct operation of the flows in the offshore grid. For example, the Danish hub is
strongly used to move energy between NO and DE.
The utilization of the Viking Link, interconnection of 1.4 GW between DK and GB is shown in Figure 10
and Figure 11 for each year in the project-based and offshore grid scenarios, respectively. In both scenarios,
the line is mainly used to move energy from DK to GB, although in the offshore grid, the utilization of the
line to move energy from GB to DK is larger than in the project-based. Furthermore, the utilization of this
line seems to reduce towards 2050 in both scenarios, which questions the profitability of this line towards
2050.
Figure 10: Project-based scenario: Interconnection DK-GB utilization per year (Viking link).
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Figure 11: Offshore grid scenario: Interconnection DK-GB utilization per year (Viking link).
Table 2: Country to Country (C2C) electricity trade in the different scenarios and years among the countries in focus.
Year Scenario Direct C2C trade
(TWh) Indirect C2C trade
(TWh) Total C2C trade (TWh)
Share of indirect C2C trade in total
C2C trade (%)
2020 Any 302.1 - - -
2030 Project-based 542.3 - - -
2030 Offshore grid 477.5 29.5 507.0 5.8
2050 Project-based 554.3 - - -
2050 Offshore grid 493.3 27.4 520.7 5.3
4.3 Electricity curtailment
The aggregated electricity curtailment per year, source and scenario in the countries in focus is depicted in
Figure 12, whereas the disaggregation per months can be seen in Figure 13. The results show that, regardless
of the scenario, the total curtailment decreases slightly from 2020 to 2030, and increases considerably by
2050. This trend is most likely related to two factors: 1) penetration of VRE in the system and 2)
transmission level. By 2030, both the transmission and VRE development are quite high, however, from
2030 to 2050 the VRE penetration is much higher than the development of new transmission lines. The
source of curtailment is wind onshore and offshore. Hydro or solar PV curtailment are found negligible. Even though the level of generation of wind onshore and offshore are relatively similar, most of the
curtailment is coming from wind offshore. This is most likely because wind offshore is assumed to have
higher operational costs. In real life, most likely the share of wind offshore in total curtailment would be
lower.
The share of wind curtailment with respect to the total available wind production follows the same trend as
the curtailment, lower in 2030 compared to 2020 and higher in 2050 than in 2030 regardless of the scenario
(Table 3). These shares are relatively low and similar between the scenarios, with a maximum of 6.5% by
2050 in the offshore grid scenario. Even though the offshore grid scenario has more curtailment than the
project-based scenario, it is worth noticing the offshore grid scenario integrates more wind in the system.
In the offshore grid scenario, for instance by 2030 there are 16.5 TWh more of wind generation but only
1.1 TWh more of curtailment than in the project-based scenario (Table 4).
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Figure 12: Annual electricity curtailment per source, year, and scenario in the countries in focus (TWh).
On the other hand, from the monthly curtailment plot (Figure 13), it can be observed that by 2020, most of
the curtailment takes place in winter, reducing gradually towards the middle of the year, and increasing
again towards the end. There is however a small peak in June, probably linked to low demand situations.
Nevertheless, towards 2050 the curtailment increases but distributes along the year, making June the month
with highest curtailment in both scenarios. It seems that, by 2050, the penetration of VRE is so high, that low demand situations are quite likely to lead to curtailment. The pattern of curtailment is very similar
between the scenarios. This potential curtailment, together with its annual distribution, will most likely be
very relevant for sector coupling purposes towards 2050.
Figure 13: Monthly electricity curtailment, year, and scenario in the countries in focus (TWh).
Table 3: Curtailment analysis for each scenario and year.
Year Scenario Wind curtailment (TWh) Wind generation
(TWh) Share of wind curtailment in total
available wind production (%)
2020 Any 10.6 295.8 3.5%
2030 Project-based 6.8 568.5 1.2%
2030 Offshore grid 7.9 585.0 1.3%
2050 Project-based 43.1 679.5 6.0%
2050 Offshore grid 49.5 707.2 6.5%
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Table 4: Curtailment analysis. Difference with project-based scenario.
Scenario Year Wind curtailment difference (TWh) Wind generation difference (TWh)
Offshore grid 2030 1.1 16.5
2050 6.4 27.7
4.4 Emissions
The development of the aggregated CO2 emissions from the electricity and district heating market in the
countries in focus per year, scenario and technology type can be seen in Figure 14. In both scenarios, the
emissions are reduced around 72% in 2030 and 84% in 2050 with respect to 2020 levels. Most of the
reduction is coming from less emissions from condensing and CHP power plants, which are gradually
replaced with VRE in the scenarios. Since the development of the heating sector is not optimized, we can
see that the emissions from boilers are almost constant with time.
Figure 14: CO2 emissions in the countries in focus per year, scenario and technology type (Mton CO2).
4.5 Economic analysis
The economic results coming from the Day-Ahead market simulations are presented in this subsection. It
is worth mentioning that the investment costs are excluded from the numbers since they are not part of the
optimization. Additionally, the optimization is performed from a private economic point of view, although
for simplification, the only tax included is the CO2 tax. Subsidies and grid tariffs are also excluded.
4.5.1 Electricity prices
The cumulative probability curves for the electricity prices in each scenario and year, for the regions DK1,
NO2, and GB are shown in Figure 15, Figure 16, and Figure 17 respectively. There are three common trends
in all studied regions and scenarios: 1) average prices increase from 2020 to 2030 and decrease from 2030
to 2050, 2) the volatility of prices increases towards 2050, and 3) the prices in these regions are lower in
the offshore grid scenario than in the project-based. This development is linked to the CO2 price
assumptions and the penetration of VRE in the energy system. From 2020 to 2030, even though the penetration of VRE is large, the use of fossil fuels is still considerable and the CO2 price experiences a big
increase (from 6 €/t CO2 to 76.7 €/t CO2), leading to increase of prices. Due to transmission expansion, the
average prices of the regions get closer to each other. Although the price in the regions in the different
scenarios follow similar trends, their development is not identical. In the offshore grid scenario, the increase
in average price from 2020 to 2030 is much higher in NO2 (from 16.6 €/MWh to 54.7 €/MWh) and DK1
(from 17.7 €/MWh to 57.9 €/MWh) than in GB (from 35.6 €/MWh to 59.4 €/MWh). Analogously, the
decrease in average price from 2030 to 2050 follows the same pattern: in NO2 price decrease goes from
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
57.8 €/MWh to 29.9 €/MWh, in DK1 price decreases from 57.9 €/MWh to 37.9 €/MWh, and in GB price
decreases from 59.4 €/MWh to 42.7 €/MWh.
Figure 15: Probability distribution function of the hourly electricity price in DK1 for each year and scenario.
Figure 16: Probability distribution function of the hourly electricity price in NO2 for each year and scenario.
Figure 17: Probability distribution function of the hourly electricity price in GB for each year and scenario.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
On the other hand, the correlation between the share of wind and solar in the electricity generation and the
electricity prices in DK1 for the different years for the offshore grid scenario is shown in Figure 18, Figure
19, and Figure 20. The wind production of the DK hub is not included. From the graphs, two clear trends
can be observed. The first one is that the hours with high share of wind and solar increase towards 2050, which is due to the capacity development. The second one is that, overall, high hourly share of wind and
solar leads to hourly low electricity prices.
Figure 18: Offshore grid scenario: Correlation between hourly electricity price and share of wind+solar production in the hourly production in DK1 in 2020.
Figure 19: Offshore grid scenario: Correlation between hourly electricity price and share of wind+solar production in the hourly production in DK1 in 2030.
Figure 20: Offshore grid scenario: Correlation between hourly electricity price and share of wind+solar production in
the hourly production in DK1 in 2050.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
4.5.2 Generator’s revenue in the electricity market
The influence of the scenario and year on the specific revenue that each generator type obtains by selling
their electricity in the Day-Ahead market in the countries in focus is shown in Figure 21. Since CHP thermal
plants are part of both the power and district heat market, the income from selling heat is not included, and
the costs corresponding to heating production are also excluded. These heat costs have been calculated from
a simple energy approach, i.e. the share of heat production in total energy production (heat and electricity)
are excluded. Other electricity storage is pumped hydro.
Figure 21: Specific revenue in the Day-Ahead market for different technology types for each year and scenario for the aggregation of the countries in focus (€/MWh).
Overall, the results show that the difference between the scenarios is rather small for all the years. One can
observe an increase in specific energy revenue in 2030 with respect to 2020 and decrease in 2050 compared
to 2030. This trend is analogous to the electricity price trend. Moreover, hydro reservoirs seem to be the
technology benefiting most from the massive integration of VRE by 2030, whereas by 2050 pumped hydro
is the most profitable. This can be explained by the change in role of hydro storage towards 2050. In 2020,
it is mainly used for covering national demand, especially in NO, since international interconnections are
limited, but towards 2050, the use of hydro for balancing purposes increases considerably, which increases
its value. On the other hand, thermal condensing plants seem to be the technology most affected by the
penetration of VRE, since by 2050 their specific energy revenue gets negative, which means that they would not be able to recover their fixed costs by only participating in the Day-Ahead markets, and hence, would
be more likely to shut-down. The loss of market value of VRE with its penetration can be seen in the graph;
the specific energy revenue by 2050 is around 50% lower than in 2030 for VRE.
In Table 5, the specific revenue in the DA market for each technology type, year, and scenario are shown.
The table provides additional insight on the variability of the country-in-focus-wise specific revenue. The
results show that the technology types with highest specific revenue variability per country is hydro
reservoirs and thermal power plants. Furthermore, by 2020 wind offshore and solar PV are not capable of
recovering their fixed costs in DE, which suggests that most likely they benefit from subsidies that are not
included in the optimization. On the other hand, condensing thermal power plants become less and less
profitable towards 2050, as suggested before. The difference between the scenarios is rather small. It is
interesting to see that the variability of the specific revenue for VRE decreases towards 2050, also possibly
linked to the grid expansion.
On the other hand, the influence of the scenario and year on the average specific capacity revenue of
different electricity generator types of the countries in focus is shown in Figure 22. This numbers reflect the
revenue that the generators get per installed power capacity. For each technology type, these values are
calculated by summing the revenue of each technology type and dividing it by its corresponding aggregated
power capacity. The year development of these results is in line with specific revenue values of Figure 21,
although the relative magnitude of the values between technologies differs in the two figures. For example,
the specific capacity revenue of Solar PV is much lower than wind technologies, whereas their specific
revenue are very similar. These results are driven by the different capacity factors of the technologies, and
the economic value of their hourly production.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Table 5: Specific revenue in the DA market per technology type, year, scenario and country in focus (€/MWh)
Year Scenario Technology type BE DE DK GB NL NO
2020
Any
Hydro reservoir 22.8 37.2 13.4
Hydro run-of-river 20.9 17.2 21.2 7.1
Other elect. storage 13.9 27.0
Solar PV 7.5 -1.0 -5.1 16.8 4.3
Thermal CHP 7.2 4.3 9.4
Thermal condensing 14.4 -0.4 7.6 11.8
Wind offshore 12.9 -0.2 2.0 21.9 12.9 4.4
Wind onshore 13.1 3.2 3.8 24.4 14.5 7.6
2030
Project-based
Hydro reservoir 73.3 72.1 62.2
Hydro run-of-river 51.2 54.8 51.4 45.2
Other elect. storage 35.4 38.2
Solar PV 44.3 40.6 41.2 40.2 47.8
Thermal CHP 28.6 26.6 27.0
Thermal condensing -5.4 -10.1 29.4 13.2
Wind offshore 39.6 39.8 38.4 40.4 38.8 40.2
Wind onshore 37.6 37.6 36.6 40.5 38.6 43.8
Offshore grid
Hydro reservoir 74.1 72.0 62.7
Hydro run-of-river 50.8 54.1 50.8 44.6
Other elect. storage 36.8 38.6
Solar PV 44.3 40.0 37.6 39.8 47.9
Thermal CHP 28.4 27.1 26.9
Thermal condensing -8.5 -9.7 28.6 17.1
Wind offshore 39.7 37.3 37.9 39.1 37.9 38.9
Wind onshore 37.5 36.6 36.8 39.2 38.0 43.6
2050
Project-based
Hydro reservoir 84.0 78.7 42.7
Hydro run-of-river 34.2 38.5 34.0 15.7
Other elect. storage 48.6 47.2
Solar PV 22.9 16.6 18.8 21.3 22.2
Thermal CHP 24.9 7.3 14.3
Thermal condensing -14.3 -21.1 -0.9 -151.8
Wind offshore 21.4 17.2 18.4 24.2 20.1 15.4
Wind onshore 17.3 18.0 15.9 23.9 18.4 18.4
Offshore grid
Hydro reservoir 85.1 75.5 42.5
Hydro run-of-river 30.6 34.4 29.9 14.7
Other elect. storage 49.1 47.3
Solar PV 18.5 12.5 16.5 17.7 18.4
Thermal CHP 23.4 8.8 14.9
Thermal condensing -23.7 -19.8 -0.5 -362.9
Wind offshore 18.7 13.7 15.5 20.3 15.4 15.4
Wind onshore 15.6 15.9 14.3 20.7 15.8 18.2
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Figure 22: Specific power capacity revenue for different technology types for each year and scenario for the aggregation of the countries in focus (€/MWh).
4.5.3 Operational costs
The impact of the scenario and year in the operational costs of the studied energy system of the countries in focus is shown in Table 6. The energy specific operational costs are also shown to ease the comparison.
The disaggregation of the operational costs of the system are shown in Table 7. The results show that both
the absolute and energy specific operational costs, which include fixed O&M, variable O&M, fuel, and
CO2 emissions costs, increase in 2030 with respect to 2020, and decrease in 2050 compared to 2030. The
increase in 2030 can be explained with the assumption on the CO2 price, which assumes a high increase of
the emissions price towards 2050. The decrease in 2050 is explained with a decrease in fuel costs linked to
a higher penetration of VRE. The offshore grid scenario is cheaper in both 2030 and 2050 in terms of
operational costs, around 400 m€/year and 700 m€/year respectively.
Table 6. Operational costs analysis per year and scenario.
Year Scenario Operational
costs (b€) El. Demand
(TWh) District Heat demand
(TWh) Total energy
demand (TWh) Specific operational
costs (€/MWh)
2020 Any 24.2 1257.5 185.8 1443.3 16.8
2030 Project-based 31.5 1282.2 177.9 1460.1 21.6
2030 Offshore grid 31.1 1282.3 177.9 1460.2 21.3
2050 Project-based 26.7 1245.1 153.5 1398.6 19.1
2050 Offshore grid 26.0 1245.8 153.5 1399.3 18.6
Table 7. Disaggregated operational costs analysis per year and scenario (b€).
Year Scenario O&M FIXED O&M VARIABLE CO2 TAX Total
2020 Any 8.2 14.1 1.9 24.2
2030 Project-based 9.7 15.0 6.8 31.5
2030 Offshore grid 9.6 14.8 6.6 31.1
2050 Project-based 10.6 9.4 6.7 26.7
2050 Offshore grid 10.6 9.1 6.3 26.0
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
4.5.4 Congestion rent
The development of the congestion rent for selected interconnections of the North Sea region towards 2050
is shown in Table 8. The congestion rent in every hour is a function of the price different between two
market zones, the losses of the line and the energy flow. The specific congestion rent is the congestion rent
divided by the size capacity of the line. It is not straight forward to compare the scenarios, since in the
offshore grid case C2C flow can occur by utilizing the hubs.
The results show a similar trend in both scenarios, the congestion rent increases from 2020 to 2030, and
decreases from 2030 to 2050. This development is linked to the electricity prices and transmission grid
expansion. In all these lines there is a flow direction in which the congestion is predominant. The connection
from DK to other countries, and NO to other countries seem to be the most congested ones. In the case of
the connection between DK and NO, the congestion rent NO to DK is larger than the one from DK to NO.
The C2C connections generating highest revenues per GW installed are the lines connecting DK and NL,
GB and NO, and NO and DE, since they have the highest congestion rents in the studied years. On the other
hand, the C2C connections generating the lowest revenues per GW installed are the lines connecting DK
and NO, and DK and GB. The connection DK-GB decreases its value by 2050 in both scenarios. The
offshore grid seems to have some impact on the profitability of the lines connecting GB and DK, and DK
and NO.
Table 8. Congestion rent per year and scenario for selected interconnections in the North Sea region.
Interconnection
Size (GW) Congestion rent (million €) Specific congestion rent (million €/GW)
Any Project-based Offshore grid Any Project-based Offshore grid Any Project-based Offshore grid
2020 2030 2050 2030 2050 2020 2030 2050 2030 2050 2020 2030 2050 2030 2050
DK-GB - 1.4 3.4 1.4 1.4 - 365.8 488.3 351.6 190.0 - 261.3 143.6 251.1 135.7
GB-DK - 1.4 3.4 1.4 1.4 - 63.2 72.4 80.5 48.8 - 45.1 21.3 57.5 34.9
Sum - 1.4 3.4 1.4 1.4 - 429.0 284.5 305.6 83.3 - 306.4 83.7 218.3 59.5
DK-NL 0.7 0.7 0.7 0.7 0.7 98.0 231.1 124.8 241.9 139.6 140.0 330.1 178.3 345.6 199.4
NL-DK 0.7 0.7 0.7 0.7 0.7 0.9 10.8 6.7 6.7 3.6 1.3 15.4 9.6 9.6 5.1
Sum 0.7 0.7 0.7 0.7 0.7 98.9 241.9 131.5 248.6 143.2 141.3 345.6 187.9 355.1 204.6
DK-NO 1.7 1.7 1.7 1.7 1.7 49.1 140.6 27.6 116.7 24.3 28.9 82.7 16.2 68.6 14.3
NO-DK 1.7 1.7 1.7 1.7 1.7 102.1 223.0 204.0 343.9 280.2 60.1 131.2 120.0 202.3 164.8
Sum 1.7 1.7 1.7 1.7 1.7 151.2 363.6 231.6 460.6 304.5 88.9 213.9 136.2 270.9 179.1
GB-NO 1.4 5.8 5.8 3.5 4.5 10.2 160.9 46.8 100.4 49.9 7.3 27.7 8.1 28.7 11.1
NO-GB 1.4 5.8 5.8 3.5 4.5 168.6 1817.4 1164.0 1129.8 915.5 120.4 313.3 200.7 322.8 203.4
Sum 1.4 5.8 5.8 3.5 4.5 178.8 1978.3 1210.8 1230.2 965.4 127.7 341.1 208.8 351.5 214.5
DE-NO 1.4 11.2 11.2 5 5 45.4 256.8 82.1 131.6 29.2 32.4 22.9 7.3 26.3 5.8
NO-DE 1.4 11.2 11.2 5 5 83.0 3036.7 2027.7 1527.8 1060.5 59.3 271.1 181.0 305.6 212.1
Sum 1.4 11.2 11.2 5 5 128.4 3293.5 2109.8 1659.4 1089.7 91.7 294.1 188.4 331.9 217.9
4.5.5 Missing peak power
The capacity development scenarios used in this report come from the updated version of WP2 of the
NSON-DK project (Appendix C). Results reported in that report did not include Unit Commitment nor
security of supply constraints due to high computational complexity. Additionally, wind and solar
timeseries used in this report are different compared to the ones used in WP2. The ones in this report
correspond to the Day-ahead (DA) forecast, since they are linked to future power balancing analysis.
Because of these reasons, when running the full year missing back-up power is likely to be found.
Therefore, expensive back-up power is added to all the regions of the system to avoid infeasibilities. The
size and use of this back-up power are a measure of the adequacy of the approach used in the capacity
optimization.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
The results coming from the missing maximum back-up power and peak demand per scenario and year in
the relevant regions of the countries in focus can be seen in Table 9. The regions not shown has either zero
or negligible missing capacity. The missing capacity in the year 2020 is probably due to the change in
timeseries and MIP approach, since investments are not optimized in that year, but taken directly from the
Nordic Energy Technology Perspective 2016 report.
The results show that the missing back-up power increases towards 2050, coinciding with the decrease of
thermal power in the system. The missing capacity is relatively high in some regions, e.g. NL with 41% of
the peak demand in the offshore grid scenario, although as shown in Figure 9, the use of this peak-power
in annual energy terms is extremely small. The scenario with overall higher missing capacity is the offshore
grid. Due to the better integration of VRE with the meshed grid, this scenario lead to less need for thermal
capacity. However, it seems that this better integration underestimated the need for thermal power in the
system. After adding the missing capacity GWs to the scenarios, the amount of thermal power reach similar
levels in the scenarios.
These results suggest that energy wise, the capacity optimization is adequate, although capacity wise it
seems to be too optimistic. Sensitivity analysis are required to study further needs for back-up power, since VRE time series can have a large influence on these results. It should also be noted that flexibility from
demand and sector coupling is not modelled in this study.
Table 9: Maximum back-up power production and peak demand per scenario and year in the relevant regions of the
countries in focus (GWh/h).
Region
Maximum back-up power (GWh/h) Peak demand
(GWh/h)
2020 2030 2050 2020 2030 2050
Any Project-based Offshore grid Project-based Offshore grid
BE_R 0.0 0.0 3.4 0.6 14.1 14.4 13.9
DE_CS 0.0 2.2 68.1 69.1 66.9
GB_R 0.5 0.0 0.0 0.0 60.9 62.1 60.1
NL_R 0.1 0.0 5.2 5.3 18.6 18.6 18.0
4.6 Hourly electricity balance
The hourly electricity balance for four representative days for the years 2020, 2030, and 2050, for the
regions DK1, NO2, and GB, for different scenarios are depicted in Figure 23, Figure 24, Figure 25, Figure
26, and Figure 27. The graphs include disaggregated generation and demand, as well as electricity prices.
These graphs do not include the generation at the hubs, since these are assumed to be independent regions.
This is why in Figure 26 there is no wind offshore production in NO2.
In both scenarios the penetration of VRE (mostly wind) replaces thermal generation in the studied regions
towards 2050, especially in GB, whose share of condensing power by 2020 is considerably high. The hourly
exports in DK1 and NO2 increase in the project-based scenario towards 2050. This increase is linked to the
penetration of VRE. In DK1, this penetration takes place already by 2030, whereas in NO2 it gradually
increases towards 2050. The endogenous electricity demand, which includes Power-To-Heat and electricity
storage loading used for district heating purpose, grows towards 2050 in DK1, although in NO2 GB it is
very limited. This limited sector coupling is part of the assumptions behind the investment scenarios.
In the graphs it can be observed how situations with low residual load generally lead to low prices, whereas
high residual load situations are linked to high electricity prices. This relation is easier to observe in GB in
2020, since in that year the interconnection to other countries is quite limited. Towards 2050, the correlation
is harder to observe in specific regions since the countries are quite interconnected, and hence, requiring the analysis of broader areas to obtain coherent conclusion. The increased volatility in prices towards 2050
can also be observed in all the regions. By 2020, in the days depicted in the figures electricity prices tend
to be more constant, whereas in 2030 and 2050 the prices seem to change with higher frequency.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Figure 23: Project-based scenario: Hourly dispatch and electricity prices for 4 representative days of each year in DK1.
Figure 24: Offshore grid scenario: Hourly dispatch and electricity prices for 4 representative days of each year in DK1.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Figure 25: Project-based scenario: Hourly dispatch and electricity prices for 4 representative days of each year in NO2.
Figure 26: Offshore grid scenario: Hourly dispatch and electricity prices for 4 representative days of each year in NO2.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Figure 27: Project-based scenario: Hourly dispatch and electricity prices for 4 representative days of each year in GB.
Figure 28: Offshore grid scenario: Hourly dispatch and electricity prices for 4 representative days of each year in GB.
4.7 Influence of optimization method
This section shows the influence on key results of the method used in the optimization for the Offshore grid
scenario. The three optimization methods studied are Linear Programming (LP) without UC, Relax Mixed
Integer Programming (RMIP) with UC, and the one used in the previous results, i.e. Mixed Integer
Programming (MIP) with UC.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
4.7.1 Electricity prices
The influence of the optimization method in the cumulative probability curves for the electricity prices in
each scenario and year, for the regions DK1, NO2, and GB is shown in Figure 29, Figure 30, and Figure
31 respectively. The results show that the influence of the optimization method decreases towards 2050.
The reason for this is the decrease of thermal power plants capacity and utilization towards 2050, since they
are the ones affected by the UC constraints. Compared to the MIP approach used in this paper, using an LP
method without UC seems to underestimate the hours with low-mid prices, as well as the hours with peak
prices. By excluding minimum production, ramping limits, or minimum on off time constraints, we neglect
both the capability of thermal plants to react fast, and the value that it has for some power plants to stay
online, even if this means that they will be losing money for some hours. On the other hand, using an RMIP method leads to much closer results to the MIP approach regardless of the year and the region. When using
the RMIP approach, the full UC costs are considered, and hence the optimization leads to similar aggregated
results.
Figure 29: Offshore grid: Probability distribution function of the hourly electricity price in DK1 for each year and optimization method.
Figure 30: Offshore grid: Probability distribution function of the hourly electricity price in NO2 for each year and optimization method.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Figure 31: Offshore grid: Probability distribution function of the hourly electricity price in GB for each year and
optimization method.
4.7.2 Generation
The influence of the optimization method on the annual aggregated generation of the different technology
types for the offshore grid scenario in the countries in focus per year are shown in Table 10. First, it is worth
mentioning that in relative terms with respect to the MIP results, these annual variations are quite small.
The value of MIP is probably easier to identify when analysing the operation of individual units, instead of
aggregated ones.
Overall, the results show that when the UC constraints are neglected with the LP approach, the generation of VRE increases considerably (i.e. less curtailment). Also, thermal condensing and other electricity storage decrease their production, and CHP units sometimes produce more and others less. Since the LP without UC approach did not consider the limitations and costs of thermal power plants, it led to higher volatility of the thermal generation. On the other hand, the RMIP with UC approach leads to slightly lower wind offshore generation in all the years and thermal
producers varying their generation differently depending on the year. These results are highly linked to the main
characteristics of the energy system in the different years: e.g. share of VRE penetration, level of
transmission, UC constraints, and variable costs, among others. These characteristics change considerably
in the different years. For instance, 2020 is characterized by low share of VRE, low level of
interconnections, costs of coal cheaper than gas, and high nuclear capacity. Towards 2050, the share of
VRE increases, as well as the interconnections, gas costs become lower than coal, and nuclear capacity
decreases. Sensitivity analysis need to be performed to answer these issues, however these are out of the
scope for this report.
Figure 32: Offshore grid: Nuclear production in four consecutive days in GB in different years. Impact of optimization method.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
The hourly results show a different story. Even though in annual levels the difference is not very high, when
observing the hourly operation of the units, important differences are observed, especially when LP without
UC is introduced. As depicted in Figure 32Error! Reference source not found., where the nuclear production for four consecutive days in GB is shown for different years and optimization methods, when
not including UC costs, i.e. using LP without UC method, nuclear power tends to be more volatile, starting
up and shutting down with higher frequency. The difference between the optimization with MIP and RMIP
is not significant for aggregated nuclear power plants in GB. On the other hand, the volatility of the nuclear
production towards 2050, in the shown days is relatively high, especially in 2030. This fact suggests that
towards 2050, the traditional operation of nuclear plants is challenged with the penetration of VRE.
Table 10: Offshore grid scenario: influence of optimization method in aggregated electricity generation per technology type in the countries in focus. Difference between method and reference method (MIP) (TWh).
LP without UC RMIP with UC
Technology type 2020 2030 2050 2020 2030 2050
Hydro reservoir 0.0 0.0 0.0 0.0 0.0 0.0
Hydro run-of-river 0.0 0.0 0.0 0.0 0.0 0.0
Other elect. storage -0.4 -0.4 -0.2 0.0 0.1 0.0
Solar PV 0.0 0.0 0.0 0.0 0.0 0.0
Thermal CHP 1.7 -4.7 -2.2 -1.8 -2.9 -0.9
Thermal condensing -4.3 -4.9 -3.2 0.0 0.1 -0.9
Wind offshore 0.9 4.3 13.9 0.0 0.0 0.0
Wind onshore 0.3 0.0 0.1 0.0 0.0 0.0
Total -1.8 -5.6 8.5 -1.9 -2.7 -1.8
4.7.3 Computational time
The influence of the optimization method on the average computational time of the optimization for a
simulation of a day for the offshore grid is shown in Table 11. The results showed that including UC
constraints increases computational time, as expected. However, using MIP results in considerably larger
computational time than RMIP. The difference between RMIP and MIP approach decreases towards 2050,
which is linked to the reduction of number of thermal plants towards 2050 in this scenario.
Table 11. Average optimization time for a simulation of day in the offshore grid scenario per year and optimization method (seconds)1.
Optimization method 2020 2030 2050 Average
LP without UC 3.47 4.55 3.21 3.74
RMIP with UC 30.29 51.04 44.56 41.96
MIP with UC 293.89 264.93 152.51 237.11
1 The relative gap limit assumed for the MIP method is 0.00001. A time limit of 660 seconds is also
established in case the optimization got stuck. The solver used was CPLEX LP1. The optimizations are
performed in High Performance Computing clusters (10 nodes were used). More information on
https://www.hpc.dtu.dk/?page_id=2520
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
5. Conclusion This report analyses the impact of: a) the penetration of VRE towards 2050, and b) the offshore grid
architecture, on the DA market operation in the North Sea region. Additionally, the impact of the
optimization method used is also studied.
The results from the DA simulations towards 2050 shows a considerable penetration of VRE in the energy
system, reducing drastically the use of fossil fuels. This penetration reduces considerably the emissions of
the studied energy system in the countries in focus at the expense of increasing its costs due to especially
the high increase in CO2 cost. The penetration of VRE and its associated grid development lead to great change in the operation of the system and electricity price distribution. More trade, and more efficient hydro
dispatch are some of the key features of the energy system in 2050. The results show that the technology
type that is likely to profit most from this VRE integration is hydro reservoirs, whereas the one with more
challenges is likely to be condensing power plants. The latter will most likely not be able to be profitable
participating only in the energy markets towards 2050.
On the other hand, the impact of the offshore grid architecture in the DA market operation is found rather
limited. Energy-wise the offshore grid scenario results in slightly higher penetration of VRE, higher
reduction of emissions, more efficient energy trade, and less operational costs towards 2050 to cover the
same demand. Nevertheless, the offshore grid architecture seems to be the most cost-efficient way to
operate the future energy system of the North Sea region, especially to integrate offshore wind and hence
its development should be encouraged. For the offshore grid scenario to become real, there is great need
for international cooperation though.
The optimization method can have a considerable influence on the curtailment of the system. When LP
method without UC is used, less curtailment takes place. The use of the MIP algorithm lead to the most
realistic hourly operation of the power plants, at the expense of increasing considerably the computational
time. Introducing RMIP improved considerably the results with respect to the LP approach, at the cost of
increasing a bit the computational time. Therefore, it seems like, unless the analysis of detailed hourly
operation of individual units is of great importance, the RMIP approach is the most convenient.
References [1] Nordpool. Available at https://www.nordpoolgroup.com/
[2] M. Koivisto, J. Gea-Bermúdez, (2018), “NSON-DK energy system scenarios – Edition 2”, DTU Wind Energy, available at:
http://orbit.dtu.dk/files/160234729/NSON_DK_WP2_D2.1.Ed2_FINAL.pdf (accessed July 2019).
[3] Nordic Energy Technology Perspectives 2016 report: http://www.nordicenergy.org/project/nordic-energy-technology-
perspectives/ (accessed on 1 October 2019)
[4] F. Wiese, R. Bramstoft, H. Koduvere, A. Pizzaro Alonso, O. Balyk, J.G. Kirkerud, A. G. Tveten, T. F. Bolkesjö, M. Münster,
H. Ravn. (2018), Balmorel open source energy system model, Energy Strategy Reviews, vol. 20, pp. 26-34, April 2018.
Available at: https://doi.org/10.1016/j.esr.2018.01.003 (accessed 16 July 2019)
[5] M. Koivisto, K. Das, F. Guo, P. Sørensen, E. Nuño, N. Cutululis and P. Maule, (2018). “Using time series simulation tool for
assessing the effects of variable renewable energy generation on power and energy systems”, WIREs Energy and Environment.
https://doi.org/10.1002/wene.329
[6] M. Koivisto et al. (2019), North Sea offshore grid development: Combined optimization of grid and generation investments
towards 2050”, IET Renewable Power Generation, accepted for publication, 2019.
[7] Juan Gea-Bermúdez et al. (2019)., “Optimal generation and transmission development of the North Sea region: impact of grid
architecture and planning horizon”, Energy, early access, 2019.
[8] European Commission, Study of the benefits of a meshed offshore grid in Northern Seas region. Available at:
http://ec.europa.eu/energy/en/content/benefits-meshed-offshore-grid-northern-seas-region (accessed on July 2019)
[9] W. Skamarock, J. Klemp, J. Dudhia, D. Gill, D. Barker, M. Duda, X. Huang, W. Wang and J. Powers, “Description of the
advanced research WRF version 3,” Boulder, Colorado, USA, 2008.
[10] E. Nuño, P. Maule, A. Hahmann, N. Cutululis, P. Sørensen and I. Karagali, “Simulation of transcontinental wind and solar PV
generation time series”, Renewable Energy, vol. 118, pp. 425-436, April 2018.
[11] M. Koivisto, P. Maule, E. Nuño, P. Sørensen, N. Cutululis, “Statistical Analysis of Offshore Wind and other VRE Generation
to Estimate the Variability in Future Residual Load”, Journal of Physics: Conference Series, vol. 1104, no. 1, 012011, 2018
[12] K. Van den Bergh, E. Delarue, (2015). “Cycling of conventional power plants: Technical limits and actual costs”, Energy
Conversion and Management, vol. 97, pp. 70-77, June 2015. https://doi.org/10.1016/j.enconman.2015.03.026
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Appendix A: Supporting tables Table 12: Installed power capacity per year, fuel and scenario in the countries in focus (GW).
Year Scenario Country WATER SOLAR WIND-
ONS WIND-
OFF BIOFUEL NUCLEAR NATGAS COAL OIL TOTAL
2020 Any
BE 0.6 3.4 2.3 1.6 2.5 5.9 1.3 17.6
DE 5.8 52.0 49.5 7.4 8.5 25.2 17.7 0.5 166.6
DK 0.9 4.1 1.7 1.6 1.1 0.8 0.1 10.2
GB 1.8 11.5 11.9 10.5 4.9 39.1 0.2 79.9
NL 0.0 1.9 4.9 1.1 5.2 0.5 17.8 4.9 36.4
NO 33.2 3.5 0.0 0.3 0.7 37.7
Total 41.4 69.8 76.2 22.2 22.8 6.4 84.0 24.7 0.7 348.3
2030
Project-based
BE 0.6 8.6 4.4 6.0 1.2 6.1 0.7 27.6
DE 5.8 84.3 59.0 7.8 8.6 57.9 8.8 0.2 232.6
DK 2.2 6.5 6.2 1.5 4.8 0.6 0.0 21.8
GB 1.8 17.9 20.0 30.2 2.4 10.2 29.5 0.1 112.1
NL 0.0 12.7 4.9 9.2 2.6 0.5 14.5 2.4 46.9
NO 33.2 11.4 4.0 0.3 0.7 49.5
Total 41.4 125.7 106.2 63.5 16.7 10.7 113.4 12.5 0.4 490.4
Offshore grid
BE 0.6 8.6 4.4 6.0 1.2 7.2 0.7 28.7
DE 5.8 79.6 57.3 21.3 8.6 56.2 8.8 0.2 237.9
DK 1.3 6.5 7.0 1.5 4.8 0.6 0.0 21.7
GB 1.8 17.9 20.0 27.7 2.4 10.2 28.1 0.1 108.2
NL 0.0 12.7 4.9 1.1 2.6 0.5 11.0 2.4 35.3
NO 33.2 8.0 6.2 0.3 0.7 48.4
Total 41.4 120.1 101.1 69.2 16.7 10.7 108.0 12.5 0.4 480.1
2050
Project-based
BE 0.6 19.8 4.4 6.0 7.6 38.4
DE 6.3 103.7 64.0 16.2 8.3 54.5 0.1 253.1
DK 9.0 6.5 8.1 0.1 7.8 31.5
GB 1.8 37.2 20.0 33.8 3.2 30.5 126.5
NL 0.0 12.7 7.5 16.3 5.5 42.1
NO 35.1 11.4 11.5 0.3 0.7 59.0
Total 43.8 182.4 113.9 91.9 8.7 3.2 106.7 0.1 550.6
Offshore grid
BE 0.6 15.1 4.4 6.0 10.0 36.2
DE 6.3 97.0 62.3 41.0 8.3 52.8 0.1 267.8
DK 9.0 6.5 8.9 0.1 7.8 32.3
GB 1.8 42.0 20.0 33.2 3.2 25.1 125.3
NL 0.0 12.7 4.9 1.7 2.1 21.4
NO 35.1 8.0 11.4 0.3 0.7 55.6
Total 43.8 175.9 106.1 102.2 8.7 3.2 98.5 0.1 538.5
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Table 13: Annual electricity balance per scenario, country in focus, and energy scenario (TWh).
GENERATION GROSS LOAD NET EXPORT
Scenario Country 2020 2030 2050 2020 2030 2050 2020 2030 2050
Project-based
BE 72 64 65 84 85 83 -12 -22 -17
DE 608 487 466 562 572 561 46 -85 -96
DK 33 63 69 37 42 44 -5 21 25
GB 287 313 275 338 345 334 -51 -32 -58
NL 59 86 98 110 113 109 -51 -27 -11
NO 152 196 232 126 124 114 26 71 118
TOTAL 1211 1209 1205 1257 1282 1245 -46 -73 -40
Offshore grid
BE 72 60 59 84 85 83 -12 -25 -23
DE 608 536 551 562 573 562 46 -36 -11
DK 33 67 73 37 42 44 -5 24 29
GB 287 303 276 338 345 334 -51 -42 -57
NL 59 50 34 110 113 109 -51 -63 -75
NO 152 196 221 126 124 114 26 72 107
TOTAL 1211 1212 1215 1257 1282 1246 -46 -70 -31
Table 14: Electricity generation per year, fuel and scenario in the countries in focus (TWh).
Year Scenario SOLAR WIND-ONS WIND-OFF WATER BIOFUEL NUCLEAR COAL NATGAS OIL Total
2020 Any 68.3 206.0 89.8 169.1 64.2 179.4 257.9 176.8 0.0 1211.5
2030
Project-based 124.6 290.6 277.9 168.9 80.4 80.4 4.6 181.5 0.0 1208.9
Offshore grid 119.2 276.3 308.7 168.9 79.9 79.6 5.0 174.6 0.0 1212.3
2050 Project-based
182.1 310.8 368.7 178.8 37.7 18.9 0.1 108.2 0.0 1205.4
Offshore grid 175.7 288.7 418.5 178.8 36.5 17.5 0.2 99.4 0.0 1215.2
Table 15: Average electricity price per year and scenario in the regions of the countries in focus (€/MWh).
2020 2030 2030 2050 2050
Region Any Project-based Offshore grid Project-based Offshore grid
BE 28.1 58.5 58.1 42.3 38.7
DE_CS 22.1 59.8 59.0 44.3 40.3
DE_ME 18.7 59.7 59.0 43.7 40.2
DE_NE 16.6 54.6 54.4 36.5 35.1
DE_NW 17.2 57.6 56.8 37.9 36.2
DK1 17.7 55.7 55.6 36.2 35.0
DK2 17.3 55.7 55.6 37.3 36.2
GB 36.3 57.7 56.6 41.5 37.7
NL 28.4 58.7 58.0 42.0 38.0
NO1 16.6 55.8 55.1 30.9 30.0
NO2 16.6 55.6 54.7 32.7 31.7
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
NO3 16.4 54.8 55.4 29.0 28.4
NO4 15.8 54.4 55.5 29.0 28.4
NO5 16.4 55.1 54.2 29.0 28.5
Table 16: Annual generation per source, year, country in focus and scenario (TWh).
Year Scenario Country SOLAR WIND-ONS WIND-OFF WATER BIOFUEL NUCLEAR COAL NATGAS OIL Total
2020 Any
BE 3.2 5.8 7.0 1.7 47.5 6.8 0.0 72.1
DE 52.2 129.3 25.4 22.1 36.3 60.0 218.7 64.4 0.0 608.5
DK 0.8 12.7 7.5 5.7 4.2 1.7 32.6
GB 10.5 33.4 45.1 5.3 16.1 68.3 108.3 0.0 286.9
NL 1.5 13.8 4.8 0.1 5.8 3.5 28.2 1.7 0.0 59.4
NO 11.0 0.0 140.0 0.3 0.8 152.1
2030
Project-based
BE 8.5 11.1 26.7 1.7 1.6 0.3 14.0 63.8
DE 84.2 155.5 33.1 21.8 44.8 2.3 145.3 487.1
DK 2.1 20.2 27.2 7.5 1.1 5.2 63.3
GB 17.1 55.9 133.2 5.3 10.9 77.0 14.0 0.0 313.3
NL 12.7 13.8 39.3 0.1 14.4 3.4 0.9 1.3 0.0 85.8
NO 34.1 18.4 140.0 1.2 1.8 195.5
Offshore grid
BE 8.5 11.1 26.8 1.7 1.6 0.3 10.5 0.0 60.5
DE 79.6 150.5 94.9 21.8 44.8 2.8 141.7 536.2
DK 1.3 20.2 31.2 7.5 1.1 5.3 66.7
GB 17.1 55.9 122.8 5.3 10.8 76.3 15.1 0.0 303.2
NL 12.7 13.8 4.8 0.1 14.0 3.3 0.9 0.1 49.6
NO 24.8 28.3 140.0 1.2 1.8 196.1
2050
Project-based
BE 19.7 11.1 24.4 1.7 8.5 0.0 65.3
DE 104.1 168.2 60.3 23.5 36.6 0.1 73.0 0.0 466.0
DK 9.3 20.2 32.5 0.7 6.0 68.7
GB 36.3 55.9 139.4 5.3 18.9 19.4 0.0 275.2
NL 12.7 21.3 63.0 0.1 0.6 0.0 97.7
NO 34.1 49.1 148.2 0.4 0.6 232.5
Offshore grid
BE 15.1 11.1 25.2 1.7 6.3 0.0 59.4
DE 97.5 163.0 163.0 23.5 35.3 0.2 69.0 0.0 551.5
DK 9.3 20.2 36.9 0.7 6.2 73.4
GB 41.1 55.9 139.3 5.3 17.5 17.0 0.0 276.2
NL 12.7 13.8 7.1 0.1 0.1 0.0 33.8
NO 24.8 46.9 148.2 0.4 0.7 221.1
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Table 17: Annual electricity curtailment per source, year, country and scenario (TWh).
Scenario Country
WIND-ONS (TWh) WIND-OFF (TWh) WIND TOTAL (TWh) Share in total available wind
production (%)
2020 2030 2050 2020 2030 2050 2020 2030 2050 2020 2030 2050
Project-based
BE 0.0 0.0 0.4 2.7 0.0 0.4 2.7 0% 1% 8%
DE 2.5 0.5 0.6 7.7 2.0 13.1 10.3 2.4 13.6 7% 1% 6%
DK 0.0 0.3 1.2 4.9 0.3 1.2 4.9 1% 3% 9%
GB 0.0 1.9 11.8 0.0 1.9 11.8 0% 1% 6%
NL 0.0 0.0 0.6 7.5 0.0 0.6 7.5 0% 1% 9%
NO 0.0 0.0 0.0 0.0 0.2 2.6 0.0 0.2 2.6 0% 0% 3%
Total 2.6 0.5 0.6 8.0 6.3 42.5 10.6 6.8 43.1 4% 1% 6%
Offshore grid
BE 0.0 0.0 0.3 1.8 0.0 0.3 1.8 0% 1% 5%
DE 2.5 0.4 0.8 7.7 3.7 28.0 10.3 4.1 28.8 7% 2% 9%
DK 0.3 1.0 4.3 0.3 1.0 4.3 1% 2% 8%
GB 0.0 1.2 9.2 0.0 1.2 9.2 0% 1% 5%
NL 0.0 0.0 0.2 0.0 0.0 0.2 0% 0% 1%
NO 0.0 0.0 1.2 5.2 0.0 1.2 5.2 0% 2% 7%
Total 2.6 0.4 0.8 8.0 7.5 48.7 10.6 7.9 49.5 4% 1% 7%
Table 18: Annual transmission flow between countries per scenario and year (TWh). Country OTHER represents the aggregation of the countries not in focus.
Year Scenario Country from BE DE DK GB NL NO OTHER
2020 Any
BE
0.3
5.9 11.3
0.3
DE 5.5 129.9 8.1
35.0 4.9 21.3
DK
5.5 3.6
5.5 4.3 3.1
GB 0.8
1.4 0.6 0.1
NL 1.3 0.1 0.1 5.2
0.5
NO
5.4 6.4 10.6 5.0 44.8 9.3
OTHER 21.8 19.8 8.5 32.1
0.5 126.0
2030
Project-based
BE
13.3
4.3 3.4
1.0
DE 5.0 177.2 2.8
8.1 9.2 23.3
DK
18.2 4.8 6.4 4.0 3.9 7.0
GB 10.2
1.4
17.3 6.0 4.9
NL 1.9 14.2 0.3 4.1
1.1
NO
47.3 3.5 28.0 15.7 130.6 8.0
OTHER 26.5 43.0 10.5 28.5
11.3 127.2
Offshore grid
BE
14.2
2.7 2.5
1.1
DE 4.6 199.0 3.4 9.7 63.8 12.2 23.6
DK
34.1 7.9 6.1 4.3 6.3 7.0
GB 10.8 1.7 1.8
12.5 4.1 6.1
NL 3.7 16.5 0.1 3.5
0.1
NO
46.5 17.5 29.5 4.1 145.2 8.0
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
OTHER 26.4 42.6 10.6 27.2
11.4 126.0
2050
Project-based
BE
15.9
2.7 1.4
1.1
DE 3.6 190.1 6.0
6.3 8.8 29.1
DK
10.3 6.6 12.8 2.8 2.2 14.6
GB 9.4
2.6
9.2 4.1 9.5
NL 4.7 17.9 0.3 5.6
2.6
NO
43.4 4.2 26.4 22.9 131.6 41.2
OTHER 20.6 65.8 5.1 45.7
2.0 190.9
Offshore grid
BE
14.4
0.9 1.5
1.0
DE 2.6 239.5 6.3 21.9 85.7 13.3 29.7
DK
30.9 9.1 4.8 3.3 5.8 14.8
GB 10.6 3.0 1.7
8.6 5.1 13.8
NL 6.4 18.0 0.2 2.4
0.3
NO
40.4 17.0 30.8 3.6 151.9 41.9
OTHER 21.5 67.3 5.3 39.4
2.2 191.0
Table 19: CO2 emissions per country in focus, year, scenario and technology type (Mton).
Year Scenario Country Thermal CHP Thermal condensing Heat boilers Total
2020 Any
BE
5.8
5.8
DE 141.7 74.8 6.2 222.7
DK 4.8
2.3 7.1
GB
52.2
52.2
NL
26.9
26.9
NO 0.5
0.2 0.7
2030
Project-based
BE
4.9
4.9
DE 54.6 10.9 2.7 68.2
DK 3.7
1.7 5.4
GB
6.2
6.2
NL
2.2
2.2
NO 0.9
0.3 1.2
Offshore grid
BE
3.8
3.8
DE 54.5 10.2 2.7 67.4
DK 3.8
1.7 5.5
GB
6.5
6.5
NL
1.7
1.7
NO 0.9
0.3 1.3
2050 Project-based
BE
2.8
2.8
DE 29.6 4.4 3.1 37.1
DK 2.8
1.1 3.9
GB
6.6
6.6
NL
0.2
0.2
NO 0.4
0.1 0.4
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Offshore grid
BE
2.2
2.2
DE 28.8 3.8 3.1 35.7
DK 2.9
1.1 4.0
GB
5.8
5.8
NL
0.1
0.1
NO 0.4
0.1 0.4
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Appendix B: Unit Commitment assumptions
Table 20: Unit Commitment assumptions per technology group used in the optimizations.
Tech. group Fuel Year
invested Ramp_Up ((MWe
ramped/hour)/MWe) Ramp_down ((MWe
ramped/hour)/MWe) Min. Output (% capacity)
Min. on
time (h)
Min. off
time (h)
Start up cost (€2012/MW)
Shut down cost
(€2012/MW)
Fixed hourly cost
(€2012/MW)
Planned maint. (hours)
Forced outage
(-)
BOILER BIOGAS 2020 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER BIOGAS 2030 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER BIOGAS 2040 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER BIOGAS 2050 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER BIOGAS EXIST. 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER BIOOIL 2020 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER BIOOIL 2030 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER BIOOIL 2040 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER BIOOIL 2050 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER BIOOIL EXIST. 180% 180% 15% 0 0.1 11.68 11.68 1 504 3%
BOILER COAL 2020 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER COAL 2030 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER COAL 2040 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER COAL 2050 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER COAL EXIST. 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER FUELOIL 2020 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER FUELOIL 2030 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER FUELOIL 2040 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER FUELOIL 2050 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER FUELOIL EXIST. 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER LIGHTOIL 2020 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER LIGHTOIL 2030 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER LIGHTOIL 2040 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER LIGHTOIL 2050 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER LIGHTOIL EXIST. 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER LIGN 2020 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER LIGN 2030 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER LIGN 2040 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER LIGN 2050 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER LIGN EXIST. 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER LNG 2020 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER LNG 2030 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER LNG 2040 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER LNG 2050 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER LNG EXIST. 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER MW 2020 180% 180% 70% 1 8 14.60 14.60 1 487 1%
BOILER MW 2030 180% 180% 70% 1 8 14.60 14.60 1 437 1%
BOILER MW 2040 180% 180% 70% 1 8 14.60 14.60 1 437 1%
BOILER MW 2050 180% 180% 70% 1 8 14.60 14.60 1 353 1%
BOILER MW EXIST. 180% 180% 70% 1 8 14.60 14.60 1 504 1%
BOILER NG 2020 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER NG 2030 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER NG 2040 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER NG 2050 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER NG EXIST. 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER PEAT 2020 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER PEAT 2030 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER PEAT 2040 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER PEAT 2050 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER PEAT EXIST. 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER STRAW 2020 180% 180% 50% 0 0.3 14.60 14.60 1 672 4%
BOILER STRAW 2030 180% 180% 50% 0 0.3 14.60 14.60 1 672 4%
BOILER STRAW 2040 180% 180% 50% 0 0.3 14.60 14.60 1 672 4%
BOILER STRAW 2050 180% 180% 50% 0 0.3 14.60 14.60 1 672 4%
BOILER STRAW EXIST. 180% 180% 50% 0 0.3 14.60 14.60 1 672 4%
BOILER WOOD 2020 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER WOOD 2030 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER WOOD 2040 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER WOOD 2050 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER WOOD EXIST. 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER WOODCHIPS 2020 180% 180% 20% 0 0.3 14.60 14.60 1 336 3%
BOILER WOODCHIPS 2030 180% 180% 20% 0 0.3 14.60 14.60 1 336 3%
BOILER WOODCHIPS 2040 180% 180% 20% 0 0.3 14.60 14.60 1 336 3%
BOILER WOODCHIPS 2050 180% 180% 20% 0 0.3 14.60 14.60 1 336 3%
BOILER WOODCHIPS EXIST. 180% 180% 20% 0 0.3 14.60 14.60 1 336 3%
BOILER WOOD_PELLETS 2020 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER WOOD_PELLETS 2030 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER WOOD_PELLETS 2040 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER WOOD_PELLETS 2050 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER WOOD_PELLETS EXIST. 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER WOOD_WASTE 2020 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER WOOD_WASTE 2030 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER WOOD_WASTE 2040 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER WOOD_WASTE 2050 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER WOOD_WASTE EXIST. 180% 180% 40% 0 0.3 14.60 14.60 1 504 3%
BOILER BIOOIL 2020 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER BIOOIL 2030 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER BIOOIL 2040 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER BIOOIL 2050 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
BOILER BIOOIL EXIST. 180% 180% 15% 0 0.1 11.68 11.68 1 67 1%
COMBINEDCYCLE COAL 2020 300% 300% 30% 1 1 29.20 29.20 1 386 3%
COMBINEDCYCLE COAL 2030 300% 300% 30% 1 1 29.20 29.20 1 336 3%
COMBINEDCYCLE COAL 2040 300% 300% 30% 1 1 29.20 29.20 1 336 3%
COMBINEDCYCLE COAL 2050 300% 300% 30% 1 1 29.20 29.20 1 336 3%
COMBINEDCYCLE COAL EXIST. 300% 300% 30% 1 1 29.20 29.20 1 420 3%
COMBINEDCYCLE FUELOIL 2020 300% 300% 30% 1 1 29.20 29.20 1 386 3%
COMBINEDCYCLE FUELOIL 2030 300% 300% 30% 1 1 29.20 29.20 1 336 3%
COMBINEDCYCLE FUELOIL 2040 300% 300% 30% 1 1 29.20 29.20 1 336 3%
COMBINEDCYCLE FUELOIL 2050 300% 300% 30% 1 1 29.20 29.20 1 336 3%
COMBINEDCYCLE FUELOIL EXIST. 300% 300% 30% 1 1 29.20 29.20 1 420 3%
COMBINEDCYCLE NG 2020 300% 300% 30% 1 1 29.20 29.20 1 386 3%
COMBINEDCYCLE NG 2030 300% 300% 30% 1 1 29.20 29.20 1 336 3%
COMBINEDCYCLE NG 2040 300% 300% 30% 1 1 29.20 29.20 1 336 3%
COMBINEDCYCLE NG 2050 300% 300% 30% 1 1 29.20 29.20 1 336 3%
COMBINEDCYCLE NG EXIST. 300% 300% 30% 1 1 29.20 29.20 1 420 3%
GASTURBINE BIOGAS 2020 600% 600% 23% 0.23 0.23 21.90 21.90 1 504 2%
GASTURBINE BIOGAS 2030 600% 600% 20% 0.2 0.2 21.90 21.90 1 420 2%
GASTURBINE BIOGAS 2040 600% 600% 20% 0.2 0.2 21.90 21.90 1 420 2%
GASTURBINE BIOGAS 2050 600% 600% 20% 0.2 0.2 21.90 21.90 1 420 2%
GASTURBINE BIOGAS EXIST. 600% 600% 25% 0.25 0.25 21.90 21.90 1 504 2%
GASTURBINE FUELOIL 2020 600% 600% 23% 0.23 0.23 21.90 21.90 1 504 2%
GASTURBINE FUELOIL 2030 600% 600% 20% 0.2 0.2 21.90 21.90 1 420 2%
GASTURBINE FUELOIL 2040 600% 600% 20% 0.2 0.2 21.90 21.90 1 420 2%
GASTURBINE FUELOIL 2050 600% 600% 20% 0.2 0.2 21.90 21.90 1 420 2%
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
GASTURBINE FUELOIL EXIST. 600% 600% 25% 0.25 0.25 21.90 21.90 1 504 2%
GASTURBINE LIGHTOIL 2020 600% 600% 23% 0.23 0.23 21.90 21.90 1 504 2%
GASTURBINE LIGHTOIL 2030 600% 600% 20% 0.2 0.2 21.90 21.90 1 420 2%
GASTURBINE LIGHTOIL 2040 600% 600% 20% 0.2 0.2 21.90 21.90 1 420 2%
GASTURBINE LIGHTOIL 2050 600% 600% 20% 0.2 0.2 21.90 21.90 1 420 2%
GASTURBINE LIGHTOIL EXIST. 600% 600% 25% 0.25 0.25 21.90 21.90 1 504 2%
GASTURBINE NG 2020 600% 600% 23% 0.23 0.23 21.90 21.90 1 504 2%
GASTURBINE NG 2030 600% 600% 20% 0.2 0.2 21.90 21.90 1 420 2%
GASTURBINE NG 2040 600% 600% 20% 0.2 0.2 21.90 21.90 1 420 2%
GASTURBINE NG 2050 600% 600% 20% 0.2 0.2 21.90 21.90 1 420 2%
GASTURBINE NG EXIST. 600% 600% 25% 0.25 0.25 21.90 21.90 1 504 2%
GASTURBINE BIOOIL 2020 600% 600% 23% 0.23 0.23 21.90 21.90 1 504 2%
GASTURBINE BIOOIL 2030 600% 600% 20% 0.2 0.2 21.90 21.90 1 420 2%
GASTURBINE BIOOIL 2040 600% 600% 20% 0.2 0.2 21.90 21.90 1 420 2%
GASTURBINE BIOOIL 2050 600% 600% 20% 0.2 0.2 21.90 21.90 1 420 2%
GASTURBINE BIOOIL EXIST. 600% 600% 25% 0.25 0.25 21.90 21.90 1 504 2%
STEAMTURBINE_SUBCRITICAL BIOGAS 2020 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL BIOGAS 2030 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL BIOGAS 2040 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL BIOGAS 2050 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL BIOGAS EXIST. 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL BIOOIL 2020 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL BIOOIL 2030 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL BIOOIL 2040 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL BIOOIL 2050 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL BIOOIL EXIST. 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL COAL 2020 90% 90% 25% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL COAL 2030 90% 90% 25% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL COAL 2040 90% 90% 25% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL COAL 2050 90% 90% 25% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL COAL EXIST. 90% 90% 25% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL FUELOIL 2020 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL FUELOIL 2030 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL FUELOIL 2040 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL FUELOIL 2050 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL FUELOIL EXIST. 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL LIGHTOIL 2020 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL LIGHTOIL 2030 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL LIGHTOIL 2040 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL LIGHTOIL 2050 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL LIGHTOIL EXIST. 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL LIGN 2020 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL LIGN 2030 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL LIGN 2040 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL LIGN 2050 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL LIGN EXIST. 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL MW 2020 90% 90% 20% 0.5 0.5 36.50 36.50 1 403 1%
STEAMTURBINE_SUBCRITICAL MW 2030 90% 90% 20% 0.5 0.5 36.50 36.50 1 370 1%
STEAMTURBINE_SUBCRITICAL MW 2040 90% 90% 20% 0.5 0.5 36.50 36.50 1 370 1%
STEAMTURBINE_SUBCRITICAL MW 2050 90% 90% 20% 0.5 0.5 36.50 36.50 1 302 1%
STEAMTURBINE_SUBCRITICAL MW EXIST. 90% 90% 20% 0.5 0.5 36.50 36.50 1 420 1%
STEAMTURBINE_SUBCRITICAL NG 2020 180% 180% 40% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL NG 2030 180% 180% 40% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL NG 2040 180% 180% 40% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL NG 2050 180% 180% 40% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL NG EXIST. 180% 180% 40% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL NUCLEAR 2020 60% 60% 40% 3 24 58.40 58.40 1 504 3%
STEAMTURBINE_SUBCRITICAL NUCLEAR 2030 60% 60% 40% 3 24 58.40 58.40 1 504 3%
STEAMTURBINE_SUBCRITICAL NUCLEAR 2040 60% 60% 40% 3 24 58.40 58.40 1 504 3%
STEAMTURBINE_SUBCRITICAL NUCLEAR 2050 60% 60% 40% 3 24 58.40 58.40 1 504 3%
STEAMTURBINE_SUBCRITICAL NUCLEAR EXIST. 60% 60% 40% 3 24 58.40 58.40 1 504 3%
STEAMTURBINE_SUBCRITICAL PEAT 2020 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL PEAT 2030 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL PEAT 2040 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL PEAT 2050 90% 90% 40% 1 3 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL PEAT EXIST. 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL STRAW 2020 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL STRAW 2030 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL STRAW 2040 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL STRAW 2050 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL STRAW EXIST. 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOOD 2020 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOOD 2030 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOOD 2040 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOOD 2050 90% 90% 40% 1 3 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOOD EXIST. 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOODCHIPS 2020 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOODCHIPS 2030 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOODCHIPS 2040 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOODCHIPS 2050 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOODCHIPS EXIST. 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOOD_PELLETS 2020 90% 90% 15% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOOD_PELLETS 2030 90% 90% 15% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOOD_PELLETS 2040 90% 90% 15% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOOD_PELLETS 2050 90% 90% 15% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOOD_PELLETS EXIST. 90% 90% 15% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOOD_WASTE 2020 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOOD_WASTE 2030 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOOD_WASTE 2040 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOOD_WASTE 2050 90% 90% 40% 1 3 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL WOOD_WASTE EXIST. 90% 90% 40% 1 2 36.50 36.50 1 504 3%
STEAMTURBINE_SUBCRITICAL BIOOIL 2020 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL BIOOIL 2030 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL BIOOIL 2040 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL BIOOIL 2050 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUBCRITICAL BIOOIL EXIST. 180% 180% 20% 1 2 29.20 29.20 1 504 3%
STEAMTURBINE_SUPERCRITICAL COAL 2020 180% 180% 15% 1 3 36.50 36.50 1 437 2%
STEAMTURBINE_SUPERCRITICAL COAL 2030 180% 180% 15% 1 3 36.50 36.50 1 437 2%
STEAMTURBINE_SUPERCRITICAL COAL 2040 180% 180% 15% 1 3 36.50 36.50 1 437 2%
STEAMTURBINE_SUPERCRITICAL COAL 2050 180% 180% 10% 1 3 36.50 36.50 1 437 2%
STEAMTURBINE_SUPERCRITICAL COAL EXIST. 180% 180% 18% 1 2 36.50 36.50 1 437 2%
42 NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
Appendix C: NSON-DK energy system scenarios –
Final version (update to Edition 2 scenario report)
C.1 Introduction
The NSON-DK project studies how the future massive offshore wind power and the associated offshore
grid development will affect the Danish power system in the short, medium and long term. This report
describes the energy system scenarios developed in NSON-DK WP2, with focus on the North Sea region
and especially on Denmark. The scenarios specify developments towards 2050, with 2030 providing a
medium-term view of the future; assumed development by 2020 is taken from external sources.
This report is an update to the NSON-DK Edition 2 scenario report 0. It shows the final updates to the
scenario modelling in the NSON-DK project, and presents the final scenarios to be analysed in the other
work packages. Majority of the scenario modelling remains the same as in the Edition 2 report; however,
updates are made on 1) selection of hours for system optimisation; 2) scaling of wind and solar generation
time series and load time series in the optimisation; and 3) Danish hub is introduced in the integrated
offshore grid scenario.
The developed scenarios provide the basis for the other WPs of the NSON-DK project. When studying, for
example, balancing and need for reserves (WP3) or system adequacy (WP4) in the future, information about
the expected generation capacities and transmission development are needed. In addition to focusing on
Denmark, information about the expected developments in the surrounding countries is needed as modern
power systems are highly interconnected; this is especially important in the Danish case with strong
connections to other countries.
C.2 Updates to the scenario modelling
This chapter presents the updates to the scenario modelling. Most of the modelling is the same as shown in
0. However, three aspects of the modelling are updated, as shown in the following three sections.
C.2.1 Selection of hours for investment optimisation
In this updated version of the investment scenario modelling, eight weeks spread-over-the-year, taking one
every two hours, are found as the convenient amount of time steps to perform the investment optimisation.
The approach utilised to select the time steps is to run different sets of eight weeks spread-over-the-year
with one every two hours for the project-based scenario (i.e., the scenario with the least complexity). The
average investment results of these sets are defined as the reference, and the set with results closest to the
reference is chosen for the optimisation. The selected hours are used in the modelling of all the scenarios
to make the results comparable.
In the previous version of this report, four full weeks spread-over-the-year were used 0. By doubling the
number of weeks, but reducing by half the amount of hours, the complexity of the problem in terms of
number of variables remains the same. However, with this change the representation of high/low VRE
generation hours with high/low load over large geographical areas is improved, reducing considerably the
variability of the results with respect to the weeks used.
C.2.2 Scaling of wind and solar generation and load time series
This subsection shows the updated scaling of wind and solar generation and load time series compared to
the simple linear scaling presented in 0. The scaling is applied in Balmorel investment optimisation when
full year of data cannot be used due to calculation time limitations.
The hourly electricity load and VRE generation time series are scaled using probability integral
transformations to match the probability distributions of the full year of data. The transformations are
applied as follows for a time series yt,fullYear, which denotes full year of data for a single VRE source or load,
e.g., offshore wind generation of western Denmark. First, the cumulative distribution function (CDF) of yt,fullYear, i.e., FfullYear, is estimated (Matlab function paretotails [2] was used with an empirical distribution
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050 43
fit covering all the observations). Then, the reduced time step selection is applied and yt,reduced is obtained;
yt,reduced represents the same VRE generation source or load as yt,fullYear, but with less time steps. The CDF
of yt,reduced, i.e., Freduced, is estimated using the same method as explained above. Following the approach
used, e.g. in [3], yt,reduced is transformed to 𝑦𝑡,reduced∗ as
𝑦𝑡,reduced∗ = 𝐹fullYear
−1 (𝐹reduced(𝑦𝑡,reduced)). (1)
𝑦𝑡,reduced∗ are the data used in the Balmorel investment optimization. When applied for all VRE sources and
loads, (1) makes the marginal distributions of the reduced time step data follow the same marginal
distributions observed in the full year data set. An additional step is taken after applying (1); namely, the
maximum value of 𝑦𝑡,reduced∗ is replaced by the maximum value of yt,fullYear. This makes sure that the reduced
time step data has exactly the same maximum value as observed in the full year data (for each VRE source
and load).
Figure 33: Investable hub-connected offshore wind capacities in the integrated offshore grid scenario. The offshore wind power plants (OWPPs) connected to UK and NLw are British, and the ones connected to UK and NLe are Dutch.
Dogger Bank and Horn Sea consist of British OWPPs, and all hubs ending in “Win” consist of German OWPPs.
C.2.3 Introduction of Danish offshore hub to the modelling
Compared to 0, a Danish hub is introduced to the integrated offshore grid scenario. Even though Danish
offshore wind power plans are close to shore and can be connected with standard HVAC, the Danish hub is considered in the optimisation as it is situated in the middle of the very important Norway-Germany
transmission corridor (see Figure 34); it has thus potential to integrate offshore wind power and additional
transmission capacity. Also, the Norwegian hub investment limits have been increased to 2 GW. 2 GW is
the limit up to which the economies of scale affect hub investments costs 0; with these limits, offshore wind
can be integrated to the Norwegian hubs with the same M€/MW cost as in the other hubs. The final
investable offshore hub GW are shown in Figure 33.
44 NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
C.3 Resulting final scenarios
This chapter describes the final NSON-DK scenarios. Transmission expansion towards 2050 and installed
generation GW are presented for the different generation sources in the project based and the integrated
offshore grid scenario.
C.3.1 Project-based scenario
This section describes the resulting final project-based scenario for the NSON-DK project.
Figure 34. Project-based scenario: transmission lines in 2030 and 2050 [GW]. On-land lines in green and country-to-country offshore lines in orange.
The transmission line development towards 2050 in the project-based scenario is shown in Figure 34. It can
be seen that the connections from Norway (NO) to the other countries are strengthened significantly, mostly
to combine the flexible Norwegian hydropower to the increasing VRE generation shares in all countries. Significant transmission capacity development is also seen between Germany (DE) and UK via Belgium
(BE) and Netherlands (NL).
The generation development towards 2050 in the project-based scenario is shown in Table 21. It can be
observed that the development of VRE towards 2050 is remarkable, leading to a decrease of around 50%
of thermal condensing power in the countries analysed with respect to 2020 levels. Wind onshore capacity
reaches its full potential in most of the countries by 2030, which suggests that it is the most attractive source
of electricity in these countries.
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050 45
Table 21: Project-based scenario: installed VRE and fossil condensing capacities [GW].
Country
Solar PV Offshore wind Onshore wind Thermal Condensing
Starting point
2030 2050 Starting
point 2030 2050
Starting point
2030 2050 Starting
point 2030 2050
BE 3.4 8.6 19.8 1.6 6.0 6.0 2.3 4.4 4.4 9.7 8.0 7.6
DE 52.0 84.3 103.7 7.4 7.8 16.2 49.5 59.0 64.0 35.2 28.8 15.2
DK 0.9 2.2 9.0 1.7 6.2 8.1 4.1 6.5 6.5 0.1 0.0 0.0
GB 11.5 17.9 37.2 10.5 30.2 33.8 11.9 20.0 20.0 53.1 42.2 33.7
NL 1.9 12.7 12.7 1.1 9.2 16.3 4.9 4.9 7.5 28.4 20.0 5.5
NO 0.0 0.0 0.0 0.0 4.0 11.5 3.5 11.4 11.4 0.0 0.0 0.0
SUM 69.70 125.7 182.4 22.3 63.4 91.9 76.2 106.2 113.8 126.5 99 62
C.3.2 Integrated offshore grid scenario
This section describes the resulting final integrates offshore grid scenario for the NSON-DK project.
Figure 35. Integrated offshore grid scenario: transmission lines and hubs in 2030 and 2050 (GW). On-land lines in green, country-to-country offshore lines in orange, and lines related to the meshed grid in blue (hub size in dark blue).
The transmission expansion in the integrated offshore grid scenario towards 2050 is shown in Figure 35. It
can be seen that Norwegian, German and Danish hubs are built. It needs to be noted that in the optimised
system the DK hub was not connected to DK (only to NO and DE). However, it was considered that it
46 NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050
would not be feasible to build offshore wind generation in the Danish waters without a connection to DK;
for this reason, a 1 GW connection to DK was assumed to occur by 2030 in the model; however, otherwise
the investments were optimised.
The generation development towards 2050 in the offshore grid scenario is shown in Table 22. Comparing the results with the project-based scenario, it can be seen that the offshore grid topology ends up with more
wind offshore and less wind onshore and solar PV than the project-based solution, and that the offshore
grid requires less thermal condensing power (5.4 GW and 8.1 GW less in 2030 and 2050, respectively).
These results suggest that it is more cost-effective to provide part of the required flexibility to integrate
large shares of renewable energies with an offshore grid transmission development than to build condensing
power. It is interesting to see that the wind offshore capacity installed is split into hub-connected and
radially connected. This suggests that there are several offshore locations where wind offshore does not
require hubs to be profitable. The country with the highest amount of hub-connected wind offshore by 2050
is DE.
Figure 35 shows that the increased connections from NO and GB to continental Europe are provided by
both radial lines and by utilising transmission via the hubs in the integrated offshore grid scenario. The NO and DK hubs are situated so that they can be integrated to important transmission corridors between the
different countries, and this possibility is utilised in the Balmorel optimisation. The DE hubs are also used
as part of the transmission infrastructure in the North Sea region. Especially by 2050, the DE hubs see very
large installations of wind generation, in large part to feed both DE and NL which both lack generation due
to assumed decommissioning of thermal units.
Table 22: Integrated offshore grid: installed VRE and fossil condensing capacities [GW].
Country
Solar PV
Offshore wind
Onshore wind Thermal Condensing (in brackets: share of hub-connected)
Starting point
2030 2050 Starting
point 2030 2050
Starting point
2030 2050 Starting
point 2030 2050
BE 3.4 8.6 15.1 1.6
(0%) 6.0
(0%) 6.0
(0%) 2.3 4.4 4.4 9.7 9.1 10.0
DE 52.0 79.6 97.0 7.4
(0%) 21.3
(64%) 41.0
(77%) 49.5 57.3 62.3 35.2 27.1 13.5
DK 0.9 1.3 9.0 1.7
(0%) 7.0
(14%) 8.9
(23%) 4.1 6.5 6.5 0.1 0.0 0.0
GB 11.5 17.9 42.0 10.5 (0%)
27.7 (0%)
33.2 (0%)
11.9 20.0 20.0 53.1 40.9 28.3
NL 1.9 12.7 12.7 1.1
(0%) 1.1
(0%) 1.7
(0%) 4.9 4.9 4.9 28.4 16.5 2.1
NO 0.0 0.0 0.0 0.0
(0%) 6.2
(97%) 11.4
(52%) 3.5 8.0 8.0 0.0 0.0 0.0
SUM 69.70 120.1 175.8 22.3 (0%)
69.3 (30%)
102.2 (39%)
76.2 101.1 106.1 126.5 93.6 53.9
C.3.3 System cost differences between the scenarios
The total nominal annual system cost difference of the integrated offshore grid scenario with respect to the
project-based scenario is presented in Table 23. The total cost differences of the scenarios are disaggregated
into transmission investments costs, generation investments costs, Operation and Maintenance (O&M)
variable costs, and O&M fixed costs. Transmission investments include those lines that can be used as
interconnectors between regions, therefore radially connected offshore wind power is not part of this group,
but it’s included in the generation investment group. These cost differences are shown for each of the
optimisation years, i.e., 2030 and 2050, and for the average of these two years.
The results show that the offshore grid case seems more cost effective than the project-based case, leading
to average savings of 139 m€2012/year. The integrated offshore grid scenario requires higher investments
in interconnections compared to the project-based one. However, it shows savings in O&M costs, which
compensate the increase in investment costs. Nevertheless, compared to O&M costs of the system, the
savings of an offshore grid configuration compared to a project-based configuration are relatively small
when considering purely the DA market. The additional value of an offshore grid configuration in for
NSON-DK Day-Ahead market operation analysis in the North Sea region towards 2050 47
example, for ancillary services needs, as well as the impact of different weather years should be further
studied. The cost of the CAPEX of the missing back-up power capacity in each of the scenarios is not
included.
Table 23: Cost difference in the nominal annual system costs with respect to reference (Project-based).
Cost difference with respect to project-based (m€2012/year)
Scenario Year Trans. Invest. Gen. Invest. O&M Var. O&M Fixed TOTAL
Integrated offshore grid
2030 251 -43 -366 -42 -200
2050 478 105 -660 0 -77
Avg. 365 31 -513 -21 -139
C.4 Conclusion
This report has described the energy system scenarios developed in the NSON-DK project. Majority of the
scenario modelling remains the same as in the Edition 2 report 0. However, updates were made on selection
of hours for investment optimisation and scaling of wind and solar generation time series and load time
series. In addition, a Danish hub was introduced to the integrated offshore grid scenario.
The final NSON-DK scenarios were presented. It was shown the integrated offshore grid scenarios shows
somewhat lower overall system costs compared to the project-based scenario. In the integrated scenario,
both radial lines and utilisation of transmission via the hubs provide interconnections between the countries. Germany sees the biggest difference between the integrated offshore grid and project-based scenarios, with
installed offshore wind increasing to 41 GW by 2050 in the integrated scenario.
C.5 References
[1] M. Koivisto, J. Gea-Bermúdez (2018), “NSON-DK energy system scenarios – Edition 2”, DTU Wind Energy. Available at:
http://orbit.dtu.dk/files/160234729/NSON_DK_WP2_D2.1.Ed2_FINAL.pdf
[2] MathWorks, “paretotails”, description available online at: https://se.mathworks.com/help/stats/paretotails.html (accessed on 14
March 2019).
[3] M. Koivisto, J. Seppänen, I. Mellin, J. Ekström, R. Millar, I. Mammarella, M. Komppula, M. Lehtonen, “Wind Speed Modeling
Using a Vector Autoregressive Process with a Time-dependent Intercept Term”, International Journal of Electrical Power and
Energy Systems, vol. 77, pp. 91-99, May 2016.
Acknowledgements
Poul Sørensen, Professor, DTU Wind Energy, Project Leader of NSON-DK
The authors would like to thank Poul Sørensen for helping with the report.
Lars Møllenbach Bregnbæk, Partner, Ea Energy Analyses a/s; Chief Modelling Expert, China National
Renewable Energy Centre (CNREC)
The authors would like to thank Lars Møllenbach Bregnbæk for giving important and helpful comments on
the NSON-DK scenario modelling.
Nina Dupont, Consultant, Ea Energy Analyses a/s;
The authors would like to thank Nina Dupont for giving important and helpful comments on the NSON-
DK scenario modelling.
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