NSON-DK Day-Ahead market operation analysis in the North ... · Authors: Juan 1Gea-Bermúdez , Kaushik Das2, Lise-Lotte Pade1, Matti Koivisto, and Polyneikis Kanellas2 Title: Day-Ahead

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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.

https://orbit.dtu.dk/en/publications/d8c3addb-57e5-4f8e-b698-6111b1e223c8

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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

[email protected]

www.sustainability.man.dtu.dk/english

http://www.sustainability.man.dtu.dk/english


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


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


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


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.


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.


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.


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.


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


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].


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.


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


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%


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).


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).


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%


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


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.


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.


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.


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




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




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




Solar PV 22.9 16.6 18.8 21.3 22.2


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




Solar PV 18.5 12.5 16.5 17.7 18.4


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


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


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.


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.


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.


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.


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.


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.


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.


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

https://www.hpc.dtu.dk/?page_id=2520


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

https://www.nordpoolgroup.com/

http://orbit.dtu.dk/files/160234729/NSON_DK_WP2_D2.1.Ed2_FINAL.pdf

http://www.nordicenergy.org/project/nordic-energy-technology-perspectives/

http://www.nordicenergy.org/project/nordic-energy-technology-perspectives/

https://doi.org/10.1002/wene.329



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


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


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


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


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


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


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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 EXIST. 90% 90% 40% 1 2 36.50 36.50 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 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.


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.


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


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


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:


[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.


https://se.mathworks.com/help/stats/paretotails.html

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consulting in the field of wind energy. Our activities develop new opportunities and technology for the global and Danish exploitation of wind energy. Research

focuses on key technical-scientific fields, which are central for the development, innovation and use of wind energy and provides the basis for advanced education

at the education.

We have more than 240 staff members of which approximately 60 are PhD students. Research is conducted within nine research programmes organized into three

main topics: Wind energy systems, Wind turbine technology and Basics for wind energy.

Technical University of Denmark

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4000 Roskilde

Denmark

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[email protected]

www.vindenergi.dtu.dk

Documents

NSON-DK Day-Ahead market operation analysis in the North ... · Authors: Juan 1Gea-Bermúdez , Kaushik Das2, Lise-Lotte Pade1, Matti Koivisto, and Polyneikis Kanellas2 Title: Day-Ahead