RELATIVE BALANCING CONTRIBUTION
OF HYDROPOWER PLANTS AND RIVERS
REVISION 2
Report Number VRD-R19:2015-Rev2
Joakim Lönnberg and Johan Bladh
2016-01-20
VATTENFALL R&D
Power Technology
Relative balancing contribution of hydropower plants and
rivers - Revision 2
From
Date
Serial No.
Vattenfall R&D
Power Technology
2016.01.20
VRD-R19:2015Rev2
Author/s
Security class
Project No.
Joakim Lönnberg and Johan Bladh
None [C1]
RD.17.04.09
Customer
Reviewed by
Vattenfall AB
Public and regulatory affairs
Claes Hedenström
Issuing authorized by
Key Word
No. of pages
balance power, dispatch optimization, flexible 19
generation, hydropower production planning, net
load, nuclear decommissioning, relative balancing
contribution, regulated rivers, renewable energy,
residual load, security of supply, wind power
integration
Appending pages
1
Distributionlist
Company
Department
Name
Number of
Vattenfall AB
PRA
Claes Hedenström
PDF
It is recommended to read this report in colour
Revision history
Serial No.
Author(s)
Description
VRD-R19:2015-
J.Lönnberg,
Original report
Rev1
J.Bladh
VRD-R19:2015-
J.Lönnberg,
New treatment of data described in Sec. 2.2.2.
Rev2
J.Bladh
Separate
horizons
for
week
and
month
replaced by one multiday horizon. New results
based on the new calculation scheme. New
sections 3.5.5 and 3.5.6.
Abstract
Load and varying renewable energy sources are balanced by dispatchable energy
sources such as thermal and hydro, where the latter is known for its superior
flexibility. There must be enough dispatchable energy to balance the residual load in
all time frames from seconds to years. In this report, a method is suggested to quantify
the relative balancing contribution from dispatchable energy sources in multiple time
frames. Focus here is the contribution from Swedish hydropower plants and rivers to
balance the Swedish residual load in time frames ranging from one day to one year.
Here, the method is used to visualise the balancing contribution from the entire
Swedish hydropower fleet as well as from some individual plants; to categorise
hydropower plants with respect to their balancing characteristics; and to rate and rank
Swedish hydropower plants and rivers based on their balancing contribution. Specific
examples are discussed and a few important remarks are made to facilitate correct
interpretation of the numerical results.
It is shown that hydropower is the major contributor to balancing the Swedish residual
load in all the studied time frames. It is likely that the value of these capabilities will
increase even more as the share of wind and solar power production continues to
grow. The method presented in this report is a useful tool to visualise and compare the
balancing contribution from dispatchable power plants, which should have many
applications in the ongoing transformation of the energy system.
Table of Contents
Page
1
INTRODUCTION
1
2
POWER SYSTEM BALANCING
2
2.1
The residual load and its variations
2
2.2
The relative balancing contribution
4
2.2.1
Linearity
4
2.2.2
Multiple time frames
4
3
APPLICATIONS
3.1
Visualisation of the balancing contribution from the aggregate
Swedish hydropower fleet during the period 2008-2014
3.2
6
Categorisation of hydropower plants with respect to balancing
contribution
3.3
6
7
Visualisation of the balancing contribution from selected hydropower
plants during the period 2001-2014
8
3.3.1
Diurnal regulators / High-capacity plants
8
3.3.2
Multiday regulators / Buffer plants
9
3.3.3
Seasonal regulators / Tap plants
11
3.3.4
Energy plants
11
3.4
Numerical valuation and ranking of plants and rivers
3.5
Remarks
15
3.5.1
Historical data yields past-time performance
15
3.5.2
Individual plant valuation presumes a system perspective
15
3.5.3
Changing operating conditions must be evaluated per river
15
3.5.4
Correct comparison presumes identical residual load
15
3.5.5
Regulated river systems compensates for their own natural
negative balancing contribution
3.5.6
13
16
Balancing contributions to and from neighboring countries
are partly hidden
16
4
SUMMARY AND RECOMMENDATIONS
17
5
CONCLUDING REMARKS
18
Appendices
APPENDIX A
Number of Pages
Schematic view of Lule river
1
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1
Introduction
Balance power needs to be provided at all times to maintain equilibrium between the
production and consumption. The growing share of varying renewable energy sources
is expected to increases the need of balance power, i.e., power from dispatchable
production sources. In Sweden, these dispatchable sources are primarily thermal
power and hydropower where the latter is known for its superior balancing properties.
When balance power resources exist abundantly in relation to the demand, it can be
discussed and evaluated in rather general terms. However, as the need increases and
the margins gradually disappears, it becomes essential to be more specific – to develop
a common understanding of what balance power is and what properties that affects it.
This short report suggests a method to rate the balancing contribution from a
dispatchable power source based on time-series production data. The work presented
here is an excerpt from an ongoing and much more comprehensive effort at Vattenfall
R&D aiming to increase the knowledge about the flexibility and regulation
characteristics of Vattenfall’s hydropower assets.
A recent publication from Vattenfall R&D and Uppsala University [1] deals with the
short-term (1-14 days) energy storage need induced by residual load variations in a
system with large wind and solar power penetration. Other efforts have been devoted
to develop a model based tool for simulation and analysis of operation patterns in the
Lule River [2], and a numerical framework to quantify flexibility and regulation
capability [3]. These works are based on simulations and are focused on how the
hydropower system is affected by increasing amounts of wind power on a short-term
horizon (21 days).
This report was first issued in 2015 [4], taking the numerical framework from [3] one
step further by introducing multiple time frames. In this second version, a simple data
filtering method is introduced to separate the different horizons from each other.
Furthermore, in Revision 2, the relative regulation contribution is calculated on three
characteristic horizons instead of four; the separate week and month time frames in [4]
have been replaced by one multiday time frame. The new data treatment scheme is
described in Section 2.2.2.
Although, this work is devoted to the balancing contribution from Swedish
hydropower plants and rivers, the method as such is not limited to hydropower
systems. It can be used to quantify the balancing contribution (positive or negative)
from any load or power source. Nor is it limited to any particular time frames
considered here. Given sufficient length and resolution of the input data, it can be
applied to any time frame, both shorter and longer.
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2
Power system balancing
This section presents the numerical analysis framework.
2.1
The residual load and its variations
The need for balance power is quantified by the residual load (also called net load)
defined as
𝑃𝑅𝑅𝑅 = 𝑃𝐿𝐿𝐿𝐿 − 𝑃𝑊𝑊𝑊𝑊 − 𝑃𝑆𝑆𝑆𝑆𝑆 , (1)
where PLoad is the consumption, PWind is the wind power production and PSolar is the
solar power production (not considered in this study). Consequently, it is also the need
for power from dispatchable sources such as hydro and thermal, where the latter is less
suitable for variable production. In the Nordic countries, the residual load variations
are mainly expected to be balanced by the power production from regulated river
systems. In a balanced power system
𝑁𝑃
�
𝑃𝑘 = 𝑃𝑅𝑅𝑅 ,
𝑘=1
(2)
where Pk is the production in dispatchable plant k and NP is the total number of
dispatchable plants.
The residual load is a combination of variations and contains a wide range of
frequencies. It can easily be realised that the length and resolution of the time-series
data determines what frequencies that are dominating. For example, seasonal
variations are always present but they are not observable in the data for a single week.
To be able to see them, data from at least one year is needed.
Figure 1 shows the load, the wind power production and the resulting residual load
during a single week. The consumption generally follows the same trend with similar
variations from day to night and from weekday to weekend, whereas the wind power
production is stochastic. The residual load is a combination of the two according to
Equation (1). We know that a seasonal variation is there, but we do not see it.
Conversely, diurnal variations is present in yearly data series if the resolution is high
enough, but the amplitude of the seasonal variation might be much larger and will thus
get a much larger impact, visually and numerically. Figure 2 shows a two-year
window of the residual load. The seasonal variation, largely governed by temperature
variations, seems to be dominating. The average wind power production is statistically
higher during the winter which reduces the need of seasonal balance power. Wind
power has a small, but positive balancing contribution on a yearly basis.
It can be realised that balancing of the residual load must be evaluated on multiple
time scales to include all types of variations and balancing power.
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Figure 1.
Consumption, wind power production and resulting residual load between
29/09/2014 and 5/10/2014. The predictable pattern of the consumption dominates
the residual load as the penetration of wind power is low. Note the different
vertical scales.
Figure 2.
Consumption, wind power production and resulting residual load for 2013 and
2014. The residual load peaks during the winter months as the cold temperature
increases the consumption. Note the different vertical scales.
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2.2
The relative balancing contribution
The relative balancing contribution from a power source (one plant or a group of
plants) k was introduced in [3] as the covariance between its power production Pk and
the residual load as time series of n elements as follows
𝐶𝑅,𝑘 {n} =
𝑐𝑐𝑐[𝑃𝑅𝑅𝑅 {𝑛}, 𝑃𝑘 {𝑛}]
.
𝑣𝑣𝑣[𝑃𝑅𝑅𝑅 {𝑛}]
(3)
The maximum value for the covariance, corresponding to a complete balancing of the
residual load, is by definition the variance of the residual load. Hence, normalising by
the residual load variance yields a number between minus one and one, where one
corresponds to a balanced system. A plant with a constant production or a production
which is uncorrelated with the residual load would get a value equal to zero. This
should not be misinterpreted as if it is useless from a balancing perspective; its energy
contribution is required to maintain balance. If some of that energy disappears, it must
be supplied by another controllable source. This is commented further later on.
2.2.1
Linearity
The covariance is linear by definition, such that
𝑐𝑐𝑐(𝑃𝑅𝑅𝑅 , 𝑃1 + 𝑃2 ) = 𝑐𝑐𝑐(𝑃𝑅𝑅𝑅 , 𝑃1 ) + 𝑐𝑐𝑐(𝑃𝑅𝑅𝑅 , 𝑃2 ) ,
(4)
where P1 and P2 is the production from plant 1 and 2 respectively. Consequently, the
relative balancing contribution of two separate plants is the sum of their individual
contributions.
2.2.2
Multiple time frames
It can be realised from the discussion in Section 2.1 that the length of the time series
data gives different weights to different time frames. Moreover, as a consequence of
the hourly resolution of the data, the diurnal pattern is always present regardless of
horizon, thus favouring plants with a high balancing contribution on the diurnal time
scale. Filtering the time series data using averages reduces the impact of the diurnal
pattern yielding a more accurate representation of the relative balancing contribution
in the multiday and seasonal time frames. Table 2-1 shows how the relative balancing
contribution can be obtained on each characteristic horizon by averages on different
time scales.
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Table 2-1.
Treatment of data for calculation of the relative balancing contribution in three
characteristic time frames.
Horizon
Diurnal
Resolution
1 hour
Size of
Step size; Number of
window
values per year
24 hours
1 day / 365
Comment
Measures
the
relative
to
diurnal
contribution
balancing,
e.g.,
variations
between day and night. The
values vary over time and
depend
on
for
instance
average flow and reservoir
levels.
Multiday
1 day
28
days.
1 day / 365 (if data
Measures
(24 hours)
(The value
exist from the last 13
is
days of the previous
associated
with
the
th
14 day.
the
relative
contribution
to
multiday
balancing,
e.g.,
load
year and the first 14
variations
between
work
days of the following
week
year)
variations caused by weather
and
weekend,
cycles etc. Intraday variations
are effectively removed.
Seasonal
1 week
52 weeks
1 day / 2
Measures
contribution
(168 hours)
balancing,
the
relative
to
seasonal
e.g.,
variations
between summer and winter.
Intraday
variations
and
intraweek
are
effectively
removed.
The results in Section 3 are derived as the average of the relative balancing
contribution calculated for each day during the studied period.
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3
Applications
In this section, a few applications of the proposed method are presented. In the last
subsection, a few important remarks are made that need to be considered for correct
interpretation of the numerical results.
3.1
Visualisation of the balancing contribution from the aggregate
Swedish hydropower fleet during the period 2008-2014
Figure 3 shows that the short-term balancing of the residual load (days and weeks) is
almost exclusively supplied by hydropower. On a very short horizon (diurnal) Sweden
exports balancing power (𝐶𝑅,𝑘 [1] > 1). Seasonal variations are balanced to 35 percent
by hydropower and the rest is mainly balanced by nuclear power, which is taken out of
operation in the summer period for maintenance, and Combined Heat and Power
(CHP), which follows the temperature variations. The solid line represents the average
contribution and the shaded area represents the interquartile range, i.e., the range
within which 50 percent (+/- 25) of the data points can be found.
Figure 3.
Balance curve for the aggregate Swedish hydropower fleet based on historical
production data between 2008 and 2014 [5]. The relative balancing contribution
is larger than one on short horizons (diurnal) as Sweden exports balancing
power on a daily basis.
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3.2
Categorisation of hydropower plants with respect to balancing
contribution
The suggested method can be used to categorise power plants with respect to their
balancing contribution. Figure 4 shows how the balance curves distinguish four
archetypes of hydropower plants; diurnal regulators, multiday regulators, seasonal
regulators and energy plants. A diurnal regulator (for instance Kilforsen) has the
largest balancing contribution on very short horizons while a multiday regulator
(Messaure) has the largest contribution on series of a couple of days. Seasonal
regulators (Vietas) contribute the most on a seasonal basis and energy plants (Boden)
contribute with energy rather than balance power.
0.03
Kilforsen
Messaure
Vietas
Boden
Relative balancing contribution
on given horizon
0.025
0.02
0.015
0.01
0.005
0
Diurnal balance
Figure 4.
Multiday balance
Seasonal balance
Balance curves of Kilforsen – a typical diurnal regulator, Messaure – a typical
multiday regulator, Vietas – a typical seasonal regulator and Boden – a typical
energy plant. The curves represent the average contribution from the period
2001-2014.
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3.3
Visualisation of the balancing contribution
hydropower plants during the period 2001-2014
from
selected
This section shows the contribution from a few example plants within each category
during the period 2001-2014 and describes briefly a few plant properties that explain
the observations. Appendix A shows a schematic image of Lule river and Table 3-1
presents technical data for some of the plants discussed below.
3.3.1
Diurnal regulators / High-capacity plants
Figure 5 shows three typical diurnal regulator plants, which exhibit their highest
balancing contribution on diurnal horizon. Diurnal regulators are characterised by a
large discharge factor 1 (low utilisation rate), which allows for a flexible dispatch
suitable for balancing the diurnal consumption variations and fast intermittent
production variations, e.g., unforeseen variations caused by wind power forecast
uncertainty.
Figure 5.
Balance curves for three typical diurnal regulators. Stornorrfors has a negative
balancing contribution on an annual horizon due to the local inflow from the
unregulated Vindel River.
The relative balancing contribution from Stornorrfors can be seen to be negative on a
yearly basis, which is caused by the inflow from the unregulated Vindel river – a
result of the fact that the natural river flow does not follow the consumption pattern.
Hence, if the river systems are redesigned to become more natural (less regulated),
balance power will be lost and must be obtained from other sources.
1
Discharge factor (or relative discharge capacity) is the relation between the installed
discharge capacity and the annual average discharge [3].
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3.3.2
Multiday regulators / Buffer plants
Figure 6 shows three plants that exhibit their highest balancing contribution to the
multiday horizon. In addition to a substantial discharge factor, such plants typically
have a larger reservoir than diurnal regulators, which gives them the ability to buffer
water for some time. This reduces the hydrological coupling in the river reach, thus
increasing the flexibility of the system. The ability to buffer water can be utilised in
several ways, e.g., to dampen, or even counter regulate, the upstream flow variations
in favour of the reach downstream. It can also be used to correct for planning errors.
Figure 6.
Balance curves for three multiday regulators, which typically have larger
reservoirs than diurnal plants. This comparatively large energy storage enables
them to balance residual load variations over longer periods of time. The large
reservoir also weakens the hydrological coupling between upstream and
downstream plants, thus enhancing the dispatch flexibility of the whole river
system.
A good example of a multiday regulator is Messaure, with a discharge factor 𝐹𝐷 =
2.24 and a comparatively large reservoir (see Table 3-1). Although the discharge
capacity of Messaure is large, it is still about 40% lower than the upstream plants
Porjus, Harsprånget and Ligga. Thus, Messaure buffers the inflow peaks and redispatch these volumes over a longer period of time (Messaure has a higher utilisation
rate). The capacity of Messaure and the size of its reservoir make it suitable for
balancing both diurnal load variations and weekly variations, gradually emptying its
reservoir during the work week (Monday to Friday) and recovering the reservoir
during the weekends.
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Plants with the ability to balance residual load variations stretching across several days
are particularly valuable to balance wind power production considering that weather
systems typically last a few (3-5) days. Replacing such ability would be expensive
considering the comparatively large energy volumes involved 2. The ability to correct
for planning errors should also be increasingly valuable to deal with wind power
forecast uncertainty.
Comparing Laxede and Porsi gives valuable insights about the impact of water rights.
As can be seen in Table 3-1, the two stations have roughly the same discharge
capacity, but the head is 1.3 times higher in Porsi, which makes a difference in power
and annual production. The relative balancing contribution should differ by about the
same factor, but as can be seen in Figure 6, the balancing contribution from Porsi is
twice that of Laxede on a short-term basis.
One explanation for this can be found in the water rights of Laxede and the
downstream plants Vittjärv and Boden. Laxede is by law required to counter regulate
the upstream flow variations so that the flow downstream follows the seasonal
variation as much as possible. In addition Laxede must be operated with respect to the
required minimum flow in Vittjärv and Boden.
Table 3-1.
Technical data for a few plants in Lule river taken from Appendix A.
Plant
Installed
Annual
power production
Head
Discharge
capacity
Average
discharge
Discharge
factor
Reservoir
size
[MW]
[GWh]
[m]
[m /s]
3
[m /s]
3
[]
[Mm ]
Vietas
325
1155
79(83)
540
215
2.51
<7000
Seitevare
225
785
180
135
58
2.33
1675
Porjus
440
1180
59
940
259
3.63
632
Harsprånget
940
2160
107
1040
259
4.02
6
Messaure
442
1820
87
615
274
2.24
53
Porsi
276
1145
33
975
458
2.13
27 (17)
Laxede
200
865
25
990
466
2.12
15
Vittjärv
30
185
6
680
495
1.37
13
Boden
80
455
13
680
495
1.37
0.8
2
3
Demand response in combination with thermal energy storage or batteries typically covers a
few hours.
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3.3.3
Seasonal regulators / Tap plants
Figure 7 shows the balance curves for three typical tap plants, which generally are
higher on annual time scales. Taps are positioned at the top of the reach and
characterised by very large reservoirs, collecting and redistributing the spring flood to
match the seasonal variation of the residual load. Taps control the long-term
production in the whole reach. Vietas and Seitevare are the taps of Lule river and
Gardikfors is one of the taps in Ume river.
Figure 7.
Balance curves for three tap-type plants. Taps control the long-term production
in the whole river by regulating the flow from the large season reservoirs at the
top. Therefore, they typically have a larger relative balancing contribution on the
seasonal horizon.
3.3.4
Energy plants
Figure 8 shows balance curves for three typical energy plants in Lule river; Vittjärv,
Boden and Parki. The magnitude of the balancing contribution is small (close to zero).
As pointed out previously, this should not be misinterpreted as if energy plants do not
provide balance power – all controllable power input balances the residual load. A
value close to zero simply means that the plant does not follow the residual load
variations. Energy plants are built and operated to supply energy rather than balance
power.
Energy plants are typically characterised by comparatively low heads, small reservoirs
and/or legal operation restrictions, which inherently makes them unattractive for
balancing purposes. Energy plants with some degree of operation freedom can be used
to counter regulate upstream flow variations at a small cost compared to the value of
the upstream regulation.
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In addition to the energy contribution itself, Energy plants are valuable providers of
ancillary services such as automatic frequency control, voltage support and inertia;
particularly at times when there are few other synchronous machines in operation (for
instance at night). Such capabilities are likely to become increasingly valuable as wind
and solar power enter the market on a large scale.
Figure 8.
Balance curves for three typical Energy plants. The balancing contribution is
somewhat higher for longer horizons due to the seasonal balancing of the
-3
whole reach. Note the 10 exponential along the ordinate.
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3.4
Numerical valuation and ranking of plants and rivers
Balance curve plots provide a good tool to visualise the balancing contribution from
various power sources. However, numerical values are better for valuation and
ranking of power plants and reaches. For numerical comparison, it is suggested that
the balance curve is approximated by three points corresponding to datasets of 24
hourly, 28 daily averages and 52 weekly averages values. Together, these three
numbers capture how the power source is operated and how it contributes to the
balancing of diurnal, multiday and seasonal residual load variations.
Table 3-2 shows a numerical comparison between the Vattenfall owned power plants
aggregated per river. Lule river can be seen to have the highest relative balancing
contribution on all characteristic horizons. Comparing the Ume river and the Indals
river, it can be seen that Ume contributes more to the short-term balancing, but less to
the seasonal. The reason for this is the relatively large inflow from the unregulated
Vindel river that empties into the Ume river upstream Stornorrfors, thereby forcing
Stornorrfors to operate on the inflow of water in addition to the spot price.
Table 3-2.
The average relative balancing contribution in the three characteristic time
frames: day, multiday and year. Column two shows the share of [installed
power;average production] from Vattenfall plants.
Vattenfall
Diurnal
Multiday
Seasonal
share
contribution
contribution
contribution
[%;%]
[%]
[%]
[%]
100;100
27.4
17.7
8.9
Ångerman
56;56
8.5
5.0
2.0
Ume
72;68
6.1
3.6
0.6
Skellefte
35;42
5.1
1.4
1.6
Indals
42;46
2.9
1.8
1.5
Gimån
100;100
1.0
0.7
0.4
Göta
39;49
0.5
0.6
0.8
Dal
14;15
0.3
0.1
0.2
River
Lule
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Table 3-3 shows a numerical comparison between some selected Vattenfall owned
plants. Harsprånget is the most potent source of balance power among Vattenfall’s
plants on all time scales. It has a large relative power capacity but a small reservoir
which pegs it to the operation of Porjus on mid-term time scales. Porjus has a large
reservoir on the shorter time scales, but relies on Vietas to store and dispatch the large
inflow from the spring flood. The tap plant Vietas and the energy plants Vittjärv and
Boden can be seen to increase their balancing contribution as the time frame is
expanded.
Table 3-3.
Relative balancing contribution of selected plants on the three characteristic
time frames. The numbers should be viewed in light of the discussion in Section
3.5.2.
Diurnal
Multiday
Seasonal
contribution
contribution
contribution
[%]
[%]
[%]
Harsprånget
7.9
4.1
2.2
Porjus
4.3
2.2
1.3
Letsi
3.7
1.1
-0.9
Kilforsen
2.9
1.4
0.6
Gallejaur
2.8
0.6
0.6
Messaure
2.5
2.5
1.5
Vietas
1.1
1.3
1.9
Forsmo
1.0
0.9
0.2
Akkats
1.0
0.2
-0.1
Ajaure
0.5
0.2
-0.3
Grundfors
0.5
0.4
0.5
Stadsforsen
0.4
0.3
0.3
Rusfors
0.3
0.2
0.1
Vittjärv
0.0
0.1
0.0
Boden
0.0
0.2
0.0
Power plant
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3.5
Remarks
In this section, a few important remarks are made, necessary for correct interpretation
of the results obtained.
3.5.1
Historical data yields past-time performance
As mentioned in the introduction, the use of historical input data yield results that
reflect use rather than potential. This is the reason for calling the quantity balancing
contribution rather than balancing capability or balancing capacity. The balancing
contribution could however be used to assess the balancing capability indirectly using
a simulation approach such as in [2] [3].
3.5.2
Individual plant valuation presumes a system perspective
The appropriateness of individual plant valuation is open for discussion. It can be
argued that the contribution from one plant is made possible by the regulation of
another; that some plants are deliberately operated to regulate the flow for the benefit
of others, thus obtaining low values for themselves. However, it can also be argued
that the placement along the river constitutes an operation limitation just as the
discharge capacity or prevailing water rights; that the plant must be operated a certain
way to obtain a greater value somewhere else. After all, it is the total contribution
from the river that matters from a system balancing point of view. Hence, numerical
valuation of whole rivers is safe as long as there are no hydrological connections
between them, e.g. common reservoirs. Numerical valuation of individual plants
presumes a system perspective where it is recognised that each plant is operated to
maximise the total contribution from the river.
3.5.3
Changing operating conditions must be evaluated per river
In light of the discussion in Section 3.5.2, the proposed method can be used to value
and rank the balancing contribution from individual hydropower plants. However, for
the same reason – hydrological coupling – it cannot be used to value and rank the
impact of changing operating conditions for the hydropower plants individually.
Changing the operating conditions in one plant may affect the whole river system.
Hence, the impact of changing the operating conditions or the legal constraints in one
power plant must be measured and evaluated per river.
3.5.4
Correct comparison presumes identical residual load
Relative balancing contribution means contribution in relation to the residual load
variations. Hence, fair comparison between different plants presumes that the residual
load is identical. Alternatively, the measure could be used to study for example how
the relative balancing contribution from one plant changes over time; however, the
differences in the residual load itself would be included in the result. If, for example,
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the amount of varying production increases over time, the residual load variance is
likely to increase. Then, the relative balancing contribution of one plant will decrease
even though the plant provides more balancing power in absolute terms. Then perhaps
it would be better not to normalize by the residual load variance, or to normalize by
the variance during some given period.
3.5.5
Regulated river systems compensates for their own natural negative
balancing contribution
The natural river flows are not in phase with the seasonal power demand variations.
Thus, the unregulated hydropower balancing contribution would be negative and of
non-negligible magnitude. This effect can be seen in Figure 5 as a negative seasonal
contribution from Stornorrfors, which partly has an unregulated inflow from the
Vindel river. Hence, on the seasonal scale, hydropower both covers its own natural
negative contribution and makes a significant contribution to balancing the national
residual load.
3.5.6
Balancing contributions to and from neighboring countries are partly
hidden
A substantial part (around 25 percent) of the multiday balancing contribution can be
attributed to power transmission to and from other countries. A positive balancing
contribution from international transmission links can be obtained either by importing
power when the Swedish residual load is high or by exporting power when the
Swedish residual load is low. The outcome is a result of price differences. A rough
analysis (left out here) shows that Sweden contributes to balancing diurnal and
multiday residual load variations in Denmark and Finland and that Norway contributes
to balancing the Swedish. Alternatively, Norwegian hydropower to some extent
balances the Finish and Danish residual load variations through the Swedish grid. It
should at least be noticed that treating Sweden as an electrical island does not give the
full picture.
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4
Summary and recommendations
The relative balancing contribution measures to what extent the power production
from a plant or a group of plants is correlated to the residual load, i.e., to what extent it
follows the residual load variations. Thus, the energy contribution is excluded and
must be valued separately. The relative balancing contribution is a linear function, i.e.,
the contribution from a group of plants is the sum of the contribution from the
individual plants.
The method presented can be used for numerical valuation and ranking of individual
plants’ contribution to balance the whole system if it is recognized that the operation
of a river is optimised as a whole. Each plant’s location in the river, favourable or
unfavourable, then becomes an operational constraint. The total contribution from
separate rivers can be safely compared and ranked as long as there are no hydrological
connections between them. For the same reason – hydrological coupling between the
plants – the marginal impact of operation condition changes, e.g., changed water
rights, must be evaluated per river and not per plant. The example plants discussed in
this report may give some ideas about how the hydrological conditions and water
rights affect the balancing contribution.
A plant or a group of plants provides balance power within multiple time frames
simultaneously. The balance curves suggested in this report is a good way to visualise
the relative balancing contribution on all time frames. For numerical valuation and
ranking of plants, it is suggested that the average relative balancing contribution is
calculated on three characteristic horizons – diurnal, multiday and seasonal.
If historical data is used, the relative balancing contribution measures use rather than
capability. In this work, a national perspective is taken by using the Swedish residual
load; however, the data set could be expanded to include other regions or subdivided
to get a finer spatial resolution.
As the amount of weather dependent power sources continues to grow, the short-term
residual load variations will increase [1] [2] [3], making the hydropower balancing
capabilities even more valuable. If the amount of nuclear power is reduced, the need
for seasonal balance power will increase3, i.e., some other controllable source with the
size of a few nuclear units, must run in the winter and stand in the summer to balance
the seasonal variation. If hydropower is to take on some of that job, the average flow
will be comparably larger in the winter, thus reducing the short-term balancing
capability. Hence, in the years to come, balancing capabilities are likely to become
increasingly valuable in all time frames.
3
The relative regulation contribution on 52 weekly averages, calculated as an average over the
years 2012-2014, is distributed as follows: Hydro 46 %, Nuclear 35 %, CHP 20 %, and
Transmission to other countries -1 %.
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5
Concluding remarks
The method presented in this report is easy to use if only the data is accessible.
Furthermore, it yields clear and transparent results that are easy to visualise, interpret
and compare. It has been well received by Swedish authorities and hydropower
operators 4.
The authors hope that the suggested method to calculate and visualise the balancing
contribution from dispatchable energy sources will become useful in the ongoing
discussions about for instance large-scale integration of renewables, nuclear
decommissioning, security of supply and the review of water rights.
4
The method was presented and discussed April 13, 2015, on a seminar arranged by the
Swedish Energy Agency (Energimyndigheten) and attended by representatives from the
Swedish Energy Agency, the Swedish Transmission System Operator (Svenska kraftnät),
Statkraft, Fortum, E-on, Skelleftekraft, Vattenregleringsföretagen, and Svensk
vattenkraftförening. It has also been presented for the Swedish Agency for Marine and Water
Management (Havs- och vattenmyndigheten) on Feb 18, 2015.
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References
[1] L. Saarinen, N. Dahlbäck och U. Lundin, ”Power system flexibility need induced
by wind and solar power intermittency on time scales of 1-14 days,” Renewable
energy, vol. 83, pp. 339-344, 2015.
[2] J. Lönnberg, “Short term regulating capacity and operational patterns of The Lule
River due to increasing wind power penetraion,” Uppsala University, Uppsala,
2014.
[3] J. Lönnberg and J. Bladh, “Flexibility and Regulation Capability of Hydropower
Systems to Balance Large Amounts of Wind Power,” in Wind integration
Workshop 14, Berlin, Germany, 2014.
[4] J. Lönnberg och J. Bladh, ”Relative balancing contribution of hydropower plants
and rivers. VRD-19:2015-Rev1,” Vattenfall R&D, Solna, 2015.
[5] SvK, “Elmarknad - statistik,” SvK, 2015.
http://www.svk.se/aktorsportalen/elmarknad/statistik/.
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[Online].
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Appendix A
Figure 9
Schematic view of Lule river.
Appendix A