1. No category

Renewable Energy 80 (2015) 604e609
Contents lists available at ScienceDirect
Renewable Energy
journal homepage: www.elsevier.com/locate/renene
Quantifying the impacts of national renewable electricity ambitions
using a NortheWest European electricity market model
Driscoll b, B.P.O.
Gallacho
ir a
J.P. Deane a, *, A.
a
b
Energy Policy and Modelling Group, Environmental Research Institute, University College Cork, Ireland
Economic and Social Research Institute, Dublin, Ireland
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 15 November 2013
Accepted 22 February 2015
Available online
This work builds a comprehensive NortheWest European Electricity Market model for the year 2020 and
uses it to quantify the impacts of ambitious national renewable electricity targets. The geographical
coverage of the model comprises Germany, France, Belgium, Netherlands, Luxemburg, Great Britain and
Ireland. The model simulates the electricity market operation for the entire region at half hourly resolution and produces results in terms of electricity prices, cross border flows, emissions and associated
total system costs. The impact of two carbon prices is examined within the model. Results highlight the
policy challenges that arise when individual Member States formulate renewable energy plans in
isolation in the absence of integrated modelling of interconnected regions as cross border power flows
play a more significant role in market dynamics especially in the presence of geographically dispersed
variable renewable generation sources such as wind and solar. From a policy perspective results suggest
that based on these national plans, congestion will be present on a number of key lines at long periods
during the year.
© 2015 Elsevier Ltd. All rights reserved.
Keywords:
Electricity
European market integration
Market modelling
1. Introduction
The creation of a European internal electricity market is a priority of the European Union (EU). Since 1996, with the introduction
of the “First Legislative Package” in the Internal Energy Market
(Directive 96/92), there has been a move towards electricity market
integration between national markets with a focus on common
rules for generation, transmission and distribution of electricity.
Despite two EU Directives1 and the creation of the Council of European Energy Regulators (CEER) the DG Competition report on
energy sector inquiry found inefficiencies impeding a European
internal electricity market. Issues highlighted include a high degree
of market concentration, vertical integration, insufficient interconnecting infrastructure between nations and insufficient incentives to improve this as well as incompatible market design.
Furthermore a lack of simplified and standardised regulations has
been identified as a barrier to a European internal market [1,2].
* Corresponding author.
E-mail address: jp.deane@ucc.ie (J.P. Deane).
1
The First Legislative Package was adopted in 1996 and was replaced by the
Second Legislative Package in 2003. There has since been a Third Legislative
Package which was adopted in 2009.
http://dx.doi.org/10.1016/j.renene.2015.02.048
0960-1481/© 2015 Elsevier Ltd. All rights reserved.
Electricity market integration has been more successful in some
regions over others. The Nord Pool, which includes Norway,
Denmark, Sweden and Finland, has a tradition of cooperation and
as such enjoys a high capacity of interconnection. Most European
countries have significant institutional differences that mean power exchanges are inefficient, notably France with its near monopoly structure. In Ref. [3] the European integration process is
examined using the pricing-to-market (PTM) phenomenon to test
cross-border integration by comparing Norway and Switzerland's
respective external electricity trades, demonstrating Norway has
greater electricity market integration than Switzerland. Lise et al.
[4] investigate the liberalisation of the NortheWestern European
electricity market using the EMELIE model and conclude electricity
is not necessarily produced where cheapest to do so, as firms with
market power distort the market. Other research illustrating the
disparity between regions includes [5e7]. The previous research
indicates there is not uniform integration, and a single electricity
market as envisaged by the EU has not been reached.
Interconnection is a prerequisite for market integration across
Europe. Mitigating market power through interconnection could
reduce generation costs by reducing dependence on peaking plants
as well as giving greater security of supply. Much of the literature
considers specific case studies of a particular interconnector
J.P. Deane et al. / Renewable Energy 80 (2015) 604e609
utilising a variety of models. A static optimal dispatch model is
employed in Ref. [8] to study the effects of additional interconnection between Great Britain and Ireland and finds that private
investment in interconnection would be less than the socially
optimal level as the interconnector receives less revenue as interconnection increases. The META-Net modelling approach is used in
Ref. [9] to find interconnection between Korea and Japan results in
reduction of total system cost of electricity sectors but CO2 emissions are largely determined by generation plant mix and nuclear
energy policy. Ref. [10] modelled the optimal amount of investment
in new generation capacity, using a linear program, and optimal
investment in interconnection, using a mixed integer program, in a
case study for eight Northern European regions. It was found that
interconnection investment reduces total costs only when there is a
target for renewable generation. Further papers are available on
electricity market modelling trends in general; see Refs. [11,12].
While previous research has created market and operational
models of electricity markets such as those mentioned above
[8e10] this paper contributes to existing knowledge through
focussing on the NortheWestern European region with capacities
calibrated to the year 2020 with a particular focus on renewable
energy. Novel contributions of this work are the development and
dissemination of a freely available NortheWest European power
system database and model and the use of this model to analyse
National Renewable Energy Actions Plans (NREAP) from an integrated regional perspective.
This paper describes the development of (to the authors'
knowledge) the first detailed electricity market model of the year
2020 for the NortheWest European region including the countries
of Belgium, France, Germany, Ireland, Netherlands, Luxemburg and
Great Britain. Renewable Energy capacitates are aligned in the
model to National Renewable Energy Actions Plans (NREAP) submitted by each country to the European Union. The goal of the work
is to develop a comprehensive database of electricity power plant
in NortheWest Europe to improve the understanding of the
development of regional markets within the EU. The dataset is
freely available.2 Secondly the database and model are used to
determine the impacts of renewable electricity targets on electricity prices and regional flows within the study area.
Section 2 describes the software model used in this work. Section 3 describes the data sources and model configuration. The
results are presented in Section 4 and initial conclusions drawn in
Section 5.
2. Model
The software used throughout this work to solve the unit
commitment and dispatch problem is PLEXOS [13]. PLEXOS is a
power systems modelling tool used for electricity market modelling and planning [14e16]. PLEXOS, normally a commercial
modelling tool, is free to academic institutions for non-commercial
research. This software was chosen because it is a flexible platform
allowing user defined constraints. It is not a ‘black box’ and the
users can browse and verify the equations of the problem via a
diagnostic tool. The PLEXOS modelling tool is used by the Commission for Energy Regulation (CER) in Ireland to validate Ireland's
Single Electricity market and has a history of use in Ireland [17]. A
description of the model equations can be found in Ref. [16].
PLEXOS co-optimises hydro, thermal, renewable, and reserve classes. Modelling is carried out using mixed integer linear programming that aims to minimize an objective function subject to the
expected cost of electricity dispatch and a number of constraints.
2
Contact Corresponding author. for dataset.
605
The objective function of the model includes operational costs,
consisting of fuel costs and carbon costs; start-up costs, consisting
of a fuel offtake and a start cost; penalty costs for unserved energy
and for failing to meet reserve requirements. System level constraints consist of an energy balance equation ensuring supply (net
pumping demand) meets regional demand at each period. Water
balance equations ensure water flow within the pumped storage
units is conserved and tracked. Constraints on unit operation
include minimum and maximum generation, maximum and minimum up and down time and ramp up and down rates. Three Startup/shutdown profiles and times are enforced (hot, warm and cold).
In this specific analysis generating units are assumed to run up
instantaneously (ie no run up rates). We have tested the inclusion
of run up rates on model runs and found the impact on results to be
insignificant; however simulation times are much longer. They are
therefore excluded in these market simulations.
In chronological mode, PLEXOS solves for each period and
maintains consistency across the full problem horizon. Temporal
resolution settings in relation to solving are user defined and
flexible. Users can choose interval lengths of one minute to one
hour in hourly, daily or weekly periods over the full problem horizon (typically one year or more). For example, a model run with
an optimisation length of one hour and period of one day with a
horizon of one year will run 365 individual daily optimisations at a
resolution of one hour each. To avoid issues with intertemporal
constraints (i.e. unit commitment of large units and storage end
levels) at the simulation step boundaries, a ‘look ahead’ period is
used. ‘Look ahead’ means that the optimiser is given information
about what happens ahead of the period of optimisation, and then
solves for this full period (i.e. simulation period þ look ahead
period). However, only results for the simulation period are kept. In
this work the ‘Look ahead’ is set to 6 h. Pumped Storage units are
also optimized in the model. Within the model, maintenance
schedules for generation units can be fixed exogenously if a known
maintenance schedule is available, otherwise the model can
determine an optimal maintenance schedule based on the annual
maintenance rate and mean time to repair for each unit. The
objective function of the maintenance scheduling formulation is to
equalize the capacity reserves across all peak periods. Random
outages for units are calculated based on Monte Carlo simulations.
Outages occur at random times throughout the year with frequency
and severity defined by forced outage rate, mean time to repair and
repair time distribution. At simulation run time, PLEXOS dynamically constructs the linear equations for the problem using AMMO3
software and a solver to solve the equations. In this work, Xpress
MP [18] with a duality gap set to 0.1% is used. Within the PLEXOS
modelling tool, wind and other renewables are essentially treated
as ‘free’ generation (i.e. the marginal cost is zero), although this can
be changed by the user.
3. Data sources
A number of publicly available sources were drawn upon to
gather the large amount of information required for to develop the
2020 Northwest Europe Electricity model [19e25]. These sources
can primarily be divided into power plant technical data, renewable
energy installed capacities, projected interconnection capacity between each country and load profiles. These are discussed below.
In all, the current model and database contains over 900 individual power plants. Information on power plant capacity and type
were taken from Transmission Systems Operators (TSO's),
3
AMMO performs a similar role in PLEXOS as other mathematical languages such
as AIMMS, AMPL, or GAMS but is written exclusively for PLEXOS.
606
J.P. Deane et al. / Renewable Energy 80 (2015) 604e609
Table 1
Installed thermal capacity (MW) and number of thermal plant in each country for
2020.
MW capacity
AI
UK
Coal
855
21,384
Gas
4320
49,622
Nuclear
0
6078
Number of thermal power plant
Coal
3
13
Gas
24
76
Nucleara
0
5
FR
DE
BE
NL
2935
10,606
64,670
49,610
27,955
8052
470
11,000
5060
6652
22,919
504
5
26
58
141
249
6
2
69
6
10
66
1
a
Information on german nuclear 2020 capacity and phase out is taken from the
federal ministry for the environment, nature conservation and nuclear safety. For
the UK information is taken from the department of energy and climate Change.
Regulators, generation adequacy report and individual company
web sites. In cases where a Generation Adequacy Report provided
information only as far as 2018 it is assumed that this thermal capacity is unchanged for 2020. A breakdown by primary fuel type for
each country is provided in Table 1 for installed MW capacities and
number of thermal power plant.
Some jurisdictions such as the All Island4 (AI) system provide
very detailed information of power plant technical characteristics
[17], where possible direct information on thermal generation type
is used however in most countries detailed information is not
available and has to be inferred from best available sources. Each
power plant in the model is described by a maximum capacity, a
minimum stable level, start costs, minimum up and minimum
down time and where applicable ramp rates. Average heat rates are
used to describe the efficiency of each power plant. The efficiency of
each plant is inferred from the fuel type, size and where possible
the age of the plant. Information from Ref. [26] and the IEA-ETSAP
technology database [27] is used to approximate the average efficiency of each plant type by country. Generation plant that are
predicted to come online between 2012 and 2020 are generally
given the maximum efficiency for that plant type. Natural gas plant
that are less than 100 MW capacity are assumed to be OCGT,
whereas plant greater than this are assumed to be CCGT unless
specific information can show otherwise. Table 2 shows the range
of values that are used to determine actual plant technical characteristics. Intermediate values for start costs and efficiency between these ranges are linearly interpolated. Coal plant in the UK
are also assumed to have limitations on the annual number of
running hours and is reflected in a estimation of maximum annual
capacity factors of 38%. This is to try and capture the impact of the
Large Combustion Plant Directive and the Industrial Emissions
Directive.
Pumped storage plants are modelled as individual units (38
plants) with a pumping efficiency of 75% in all cases. All units are
also assumed to operate on a daily cycle where the storage reservoir
is forced to return to its initial level at the end of each day. This is
potentially a restrictive operational rule and further research will
look at the impact of relaxing this constraint.
Transmission within each country is ignored and a ‘copper plate’
assumption is made. Interconnection between each country is
included with values for 2020 sourced from ENTSO-E's Ten Year
Network Development Plan [22] and from regional TSO. Fig. 1
shows the MW interconnection capacities assumed between each
country for the target year 2020.
Within the model only net exports from France are considered
as boundary conditions for the study (i.e. imports/exports from all
4
The All Island System refers to the power system of both Northern and Republic
of Ireland. The system is currently operated as the Single Electricity Market.
Table 2
Technical characteristic for thermal power plant.
Fuel type
MW capacity
(MW) range
MSL
(MW)
Efficiency
Min Up/Down
time (hrs)
Start
cost (V000)
Coal
Coal
Coal
Coal
Gas
Gas
Gas
Gas
Gas
Gas
Gas
Gas
New Gas
Nuclear
50
100
300
600
25
50
100
150
200
400
600
800
800
e
30
40
120
240
10
20
40
60
80
160
240
320
320
e
36.20%
37.10%
38.00%
39.20%
32.00%
33.00%
35.00%
35.00%
55.00%
55.80%
56.60%
57.00%
58.00%
40.0%
6
6
6
6
6
4
4
4
4
4
4
4
4
24
V10
V20
V80
V150
V2
V5
V10
V15
V40
V120
V170
V200
V200
V250
other countries are ignored). This is done by imposing monthly
export targets on France which are based on historic net exports to
Spain, Switzerland and Italy from 2011. This is a major caveat on this
work as it assumes any changes in carbon tax will have no impact
on these historic flows. Within the model intertemporal constraints
such as monthly or annual capacity factors are decomposed to daily
constraints by firstly undertaking lower resolution simulations over
the course of the year and passing these target values to the daily
simulations.
Hourly load profiles for each region were obtained from ENTSOE [28] for the year 2011. These profiles were linearly scaled to match
the estimated gross final consumption of electricity as submitted by
each country as part of it NREAP submission [29]. Values for the
“additional energy efficiency” scenario are assumed in all cases.
These values are shown in Table 3.
Renewable energy capacities are also taken from the specific
country NREAP. These are shown in Table 4. Annual capacity factors
for each type of renewable generation derived from the report are
imposed within the model as shown in Table 5. Also monthly
Fig. 1. Projected interconnection between each country.
J.P. Deane et al. / Renewable Energy 80 (2015) 604e609
Table 3
Gross final consumption and peak demand for each country.
Table 6
Correlation between daily average predicted wind speed in each country.
Country
Gross final consumption (GWh)
Peak (MW)
BE
DE
FR
IE
LU
NL
UK
110,787
561,927
545,598
32,715
6617
135,850
376,812
18,056
88,586
105,035
5290
1248
22,122
67,393
Hydropower
Pumped storage
Geothermal
Solar PV
Concentrated solar
Marine
Onshore wind
Offshore wind
Solid biomass
Biogas
Bioliquids
Total CHP
UK
FR
IE
GB
NL
BE
FR
DE
AI
GB
NL
BE
FR
DE
100%
65%
33%
33%
33%
43%
e
100%
43%
43%
35%
48%
e
e
100%
88%
58%
65%
e
e
e
100%
56%
63%
e
e
e
e
100%
82%
e
e
e
e
e
100%
Table 7
Annual average shadow price (V/MWh) and for each country assuming a carbon
price of V20/tonne and V45/tonne.
Table 4
Installed renewable capacity (MW) in each country.
Installed capacity (MW) IE
607
DE
BE
NL
LX
234
4920 28,300
4309
140
68
44
292
2800
6800
7900
0
0 1300
0
0
80
298
4
0
0
0
2680
4860 51,753 1340
722
113
0
0
540
0
0
0
0
75
1300
380
0
0
135
0
4094 14,890 19,000 35,750 2320 6000
131
555 12,990
6000 10,000 2000 5178
0
91
3140
2382
4792 2007 2253
30
62
1100
625
3796
427
639
29
0
0
0
237
18
0
0
80
270
3007
3765
662
0
56
historic capacity factors for French hydro also from the year 2011
are imposed on this resource in France.
Hourly wind profiles for the study region were developed by the
wind forecasting company [30]. A number of historic ‘hindcasts’
were simulated for the year 2011 to derive hourly wind speeds at a
number of regions within each country. These wind speeds were
aggregated and combined with a standard power curve to produce
normalised hourly capacity factors for each country which were
then scaled to the annual reported capacity factors for each country
in each respective NREAP. The daily correlations for each region are
shown in Table 6 for onshore wind speeds.
Hourly solar profiles were obtained from the JRC [31] and again
scaled to match reported NREAP values. Fuel price are taken from
IEA World Energy outlook 2011 [32]. No transportation or seasonality of price is assumed. A range of pan European carbon prices
from V20\tonne to V45\tonne are simulated.
4. Results
The PLEXOS model was populated with individual unit characteristics and technical details. A number of simulations were undertaken to determine the resultant flows of electricity and market
Table 5
Estimated annual capacity factors (%) for renewable generation for each country.
Annual CF (%)
IE
UK
FR
DE
BE
NL
LX
Hydropower
Pumped storage
Geothermal
Solar PV
Concentrated solar
Marine
Onshore wind
Offshore wind
Solid biomass
Biogas
Bioliquids
Total CHP
34%
0%
0%
0%
0%
35%
29%
36%
86%
59%
0%
80%
15%
0%
0%
10%
0%
35%
26%
39%
75%
58%
0%
80%
29%
12%
68%
14%
21%
35%
24%
34%
65%
68%
0%
65%
53%
12%
63%
9%
0%
0%
23%
36%
59%
70%
70%
63%
36%
0%
95%
10%
0%
0%
21%
35%
54%
38%
16%
51%
31%
0%
0%
9%
0%
43%
25%
42%
61%
83%
0%
0%
32%
8%
0%
8%
0%
0%
21%
0%
72%
57%
0%
65%
SP (V20)
SP (V45)
AI
BE
DE
FR
GB
LU
NL
60.75
70.94
57.20
70.56
52.14
71.32
31.06
38.92
61.80
71.00
56.91
73.17
55.26
69.65
prices (as represented by shadow prices) of electricity under a
number of carbon price assumptions namely V20 and V45 per
tonne. In PLEXOS, shadow prices are automatically determined as
part of the solution to the optimisation problem. The price reported
represents the shadow price of the constraint that matches supply
and demand. This can be considered as the change in the objective
function for an incremental change in demand.
Table 7 shows the model derived annual system marginal prices
as approximated by the Shadow Price (SP) of electricity for each
region for a carbon tax scenario of V20 and V45 per tonne. Lowest
prices are observed in France with its large capacity of both nuclear
and renewable capacity. Higher prices are seen in both the AI and
GB system where gas fired generation are setting the price more
frequently. The addition a carbon tax of V45/tonne adds on average
an extra V13/MWh to shadow prices across the region.
Tables 8 and 9 present the resultant cross border interconnector
flows for both carbon price scenarios. France is a significant net
exporter of low cost electricity and its high interconnection to other
regions makes it attractive as an export market. However as shown
in Fig. 2 there are long periods of congestion (i.e. number of hours
the line flow is at the max flow) predicted on the Interconnector
Table 8
Annual imports and exports for each country for carbon price scenarion of V20/
tonne.
AI
BE
DE
FR
GB
LU
NL
Imports (GWh)
Exports (GWh)
Net export (GWh)
1541
39,284
26,821
2311
34,450
6647
16,873
2483
5474
28,004
98,086
4907
1238
14,255
942
33,810
1183
95,776
29,543
5409
2618
Table 9
Annual imports and exports for each country for carbon price scenarion of V45/
tonne.
AI
BE
DE
FR
GB
LU
NL
Imports (GWh)
Exports (GWh)
Net export (GWh)
1967
30,640
34,099
1984
28,480
5645
5127
2386
7163
7611
94,831
7204
320
14,946
419
23,477
26,488
92,847
21,276
5326
9819
608
J.P. Deane et al. / Renewable Energy 80 (2015) 604e609
Fig. 2. Number of hours that selected lines experienced congestion.
Table 10
Tonnes of CO2 emissions for each country.
AI
BE
DE
FR
GB
LU
NL
Total
production. Germany and the UK with a high portion of coal fired
generation are the largest absolute emitters in the region. Interestingly the CO2 emissions in Belgium increase significantly at the
higher carbon tax level, linked to the greater amount of exports.
The inclusion of an extra V25 of carbon tax is seen to reduce
emissions by approximately 50 MT.
Total generation cost is the cost including fuel, variable operations and maintenance costs, start and shutdown costs and emissions costs. Generation cost is the total variable cost of generation.
Total generation costs are primarily a function of fuel type and
system size and this is reflected in high total generation cost in
Germany. Total system costs rise by approximately 10 billion euros
when moving from a carbon tax of V20/tonne to V45/tonne across
the region. Approximately 7.8 billion euros of this increase in cost is
directly attributable to the change in carbon tax (Table 11).
5. Discussion and conclusion
CO2 emissions (V20/tonne)
CO2 emissions (V45/tonne)
13,292,776
3,879,131
236,575,446
5,313,165
97,804,477
220,357
52,659,860
409,745,212
8,409,913
7,736,829
197,255,119
1,882,753
96,148,749
217,730
45,214,760
356,865,853
lines particularly from France as power is wheeled through Belgium
into the UK or into The Netherlands and on to Germany. The level of
congestion places strong limitations on the ability of the system to
move electricity around efficiently. The impact of a higher carbon
price tends to reduce imports and exports across most countries;
this is primarily because of its impact on the price of baseload
generation such as coal and the reduced price difference between
coal and gas fired generation. Germany is an exception to this trend
as a higher carbon price drives an increase in imports particularly
from France this is accompanied by a reduction in exports from
Germany, particularly to Netherlands which reduces imports and
generates more from local gas fired generation. The UK also reduces
imports from France and Belgium in favour of CCGT's fired generation which is brought into merit by a higher carbon price.
Annual CO2 emissions for each region are presented in Table 10
while Fig. 3 displays the carbon intensity of each power system
measured as the total electricity produced in that region (including
for export) divided by the total emissions associated with that
The above analysis describes and details the development of a
2020 electricity market model calibrated to each country's NREAP
projections for the Northwest region of Europe. The focus of individual NREAPs is generally limited to impacts at a Member State
level. The value of this work is assessing the impacts at a regional
and inter-Member State level. The results highlight the importance
of integrated modelling of interconnected regions, as cross border
power flows play a more significant role in market dynamics
especially in the presence of geographically dispersed variable
renewable generation sources such as wind and solar. The results
show that the formulation of individual Member State National
Renewable Energy Plans can lead to issues on interconnectors at a
regional level. Flows on the interconnectors are an important
aspect of any future EU market and the results here shows that
congestion will be present on a number of key lines at long periods
during the year. This is especially true for France and neighbouring
regions as low cost nuclear and renewable electricity will flow to
higher priced regions. The wheeling of power through Belgium and
Netherland into either the UK or Germany is also seen at times.
The work also highlights the contribution that integrated
modelling can make to policy decisions by providing insight into
the impact of varying levels of carbon pricing and in particular how
this level of pricing impacts on total generation costs and emissions
reductions in each specific country.
Regional market prices, as inferred from the shadow price of
electricity, are naturally seen to be lowest in regions with strong
nuclear or renewable capacity. The impact of a higher carbon price
has a lower impact on these regions. The carbon intensity of the full
power system as presented here is expected to be approximately
236 gCO2/kWh for a carbon price of V20/tonne and reduces to 206
gCO2/kWh for an increased carbon price of V45/tonne. Germany
with its legacy of coal fired generation and the UK are the largest
emitters in absolute terms.
Table 11
Total generation costs (V000) for each country for both carbon tax scenarios.
Fig. 3. Carbon Intensity (g\kWh) for both carbon price scenarios.
AI
BE
DE
FR
GB
LU
NL
Total
Total generation costs V20/tonne
Total generation costs V45/tonne
V1,151,278
V1,095,898
V12,942,379
V3,357,793
V9,265,986
V42,378
V3,645,669
V31,501,381
V1,371,389
V1,868,779
V16,849,366
V3,247,612
V12,048,453
V47,358
V5,935,828
V41,368,784
J.P. Deane et al. / Renewable Energy 80 (2015) 604e609
This work presents the first important step in the analysis of
regional market integration in the EU. Further work will focus on
the where increased interconnection would have the most beneficial impacts under a range of carbon scenarios and market
structures within the study region.
Acknowledgement
The authors acknowledge the useful and constructive comments and ideas that were provided by Prof John FitzGerald and
Prof. Sean Lyons from the Economic and Social Research Institute
during the preparation of this paper.
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