A spatial and temporal correlation analysis of aggregate wind power in
an ideally interconnected Europe
Malvaldi, A., Weiss, S., Infield, D., Browell, J., Leahy, P., & Foley, A. M. (2017). A spatial and temporal
correlation analysis of aggregate wind power in an ideally interconnected Europe.
Wind Energy
,
192
(15), 315-
328. https://doi.org/10.1002/we.2095
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WIND ENERGY
Wind Energ.
(2017)
Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/we.2095
RESEARCH ARTICLE
A spatial and temporal correlation analysis of
aggregate wind power in an ideally interconnected
Europe
A. Malvaldi
1
, S. Weiss
1
, D. Infield
1
,J.Browell
1
, P. Leahy
2
and A. M. Foley
3
1
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK
2
School of Engineering, University College Cork, Cork, Ireland
3
School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast, UK
ABSTRACT
Studies have shown that a large geographic spread of installed capacity can reduce wind power variability and smooth pro-
duction. This could be achieved by using electricity interconnections and storage systems. However, interconnections and
storage are not totally flexible, so it is essential to understand the wind power correlation in order to address power system
constraints in systems with large and growing wind power penetrations. In this study, the spatial and temporal correlation
of wind power generation across several European Union countries was examined to understand how wind ‘travels’ across
Europe. Three years of historical hourly wind power generation data from 10 countries were analysed. The results of the
analysis were then compared with two other studies focused on the Nordic region and the USA. The findings show that
similar general correlation characteristics do exist between European country pairs. This is of particular importance when
planning and operating interconnector flows, storage optimization and cross-border power trading. Copyright © 2017 The
Authors Wind Energy Published by John Wiley & Sons Ltd
KEYWORDS
wind power generation; wind energy; interconnection; geographic diversity; correlation
Correspondence
A. Malvaldi, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK.
E-mail: alice.malvaldi@strath.ac.uk
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and
reproduction in any medium, provided the original work is properly cited.
Received 1 June 2016; Revised 8 December 2016; Accepted 30 January 2017
1. INTRODUCTION
Installed wind power capacity in Europe grew strongly in the decade to 2015, from 48 GW, at the end of 2005, to almost
142 GW by the end of 2015.
1
As the penetration of wind generation in power systems continues to increase, the uncertainty
and variability associated with wind power bring challenges for power system operators. Many previous studies have
addressed the problem of integrating variable wind power in existing power systems.
2–5
These works emphasized the
impact of geographic diversity of wind farms on power system operations. A large geographic spread of wind capacity
greatly reduces the aggregate wind power variability, as wind speeds experienced in different areas are not 100% correlated
over time. As the geographic spread increases, the distribution of aggregate wind power approaches a normal distribution
according to the central limit theorem.
6
As a result, the number of high-power or low-power output events in the aggregate
wind power time series is decreased, and this reduction in variability is referred as the ‘smoothing effect’.
The smoothing effect has been the focus of numerous studies.
6–15
For example, large geographical dispersal of installed
wind power capacity has been shown to be beneficial to power system operators in the study of Giebel.
7
Several studies
have also observed that linear cross-correlation coefficients of wind power output between pairs of wind farms or at system
level decrease exponentially with increased separation between the wind farms or systems.
6, 8–12, 14, 15
Hasche
12
shows
that the smoothing effect depends only on the region size and not on the number of wind farms. The correlation of wind
Copyright © 2017 The Authors Wind Energy Published by John Wiley & Sons Ltd
A spatial and temporal correlation analysis of aggregate wind power A. Malvaldi
et al.
power output between wind farms is influenced not only by separation distance but also by time scales. High-frequency
variations of wind power from different locations are random and uncorrelated, whereas correlation is high for longer
term fluctuations (several hours or more).
6,8,10,16
Moreover, the degree of correlation between wind farm outputs is also
influenced by diurnal weather variations and the movement of synoptic weather systems. Therefore, the correlation of wind
power output between different locations can exhibit a wide range of values from 1 day to another. Two locations can have
a very high correlation 1 day and little or negative correlation the next.
6,10,17
Many other factors may impact on the level of correlation between the power outputs of different wind farms. One of
these is the direction of separation between wind farms. There may be a greater similarity between outputs of wind farms
separated in one direction than in others, as shown in Tastu et al.
18
and Osborn et al.
19
In general, increased geographic diversity of wind power capacity and increased numbers of turbines may deliver several
benefits to power system operators. For example, the number of ramp events tends to decrease as the geographic diversity
of installed wind power capacity increases.
6,11,16,20,21
The relative requirement for ancillary services may also be reduced
because of the smoothing effect on aggregate wind power outputs, as shown in the analysis carried out by Ernst et al.
using data from the German ‘250-MW measurement program’.
8
Furthermore, geographic diversity and larger numbers of
turbines both lead to a lower temporal variability of aggregate power output, with decreased durations of high-power and
low-power events, and smaller wind forecast errors. However, high penetration of wind power also brings some issues
and challenges for transmission system operators (TSOs). Frequency control and system security are highly influenced by
wind power fluctuations in systems with very weak interconnection.
22–24
For example, on the island of Ireland, there is
10.26 GW of dispatchable generation capacity, including 3.02 GW
23
dispatchable wind plant and 950 MW of intercon-
nection capacity to Great Britain (GB). The TSOs in Ireland EirGrid and System Operator Northern Ireland (SONI) have
increased interconnection to the UK and between Northern Ireland and the Republic of Ireland with further plans to extend
to France to ensure security of supply and grid stability.
25
EirGrid and SONI
25
identified and examined the technical
operational issues with increasing wind penetration.
It is therefore desirable to encourage greater geographic diversity of wind power capacity. Several studies have examined
the opportunity to ‘smooth’ wind generation over time and to integrate it more efficiently and have suggested a common
balancing area for adjacent systems.
4–6,26
As a result, interconnection capacity between different countries and market
regions within the European Union (EU) and the USA has been increasing over the last number of years. For example,
within the EU, more than 20 GW of electricity interconnections have been built during the last decades and more than
50 GW are under construction or planned to be built, according to the projects of common interests (PCI).
27
A few studies have analysed the correlation between wind farms using actual wind power production data. Among
them, Holttinen
11
used real power production data from the Nordic countries of Denmark, Finland, Sweden and Norway to
perform a statistical analysis. However, in this study, the time series from Finland, Sweden and Norway had to be upscaled
more than 10-fold to create large-scale wind power production time series. This study found that increased geographical
spread of wind power capacity reduces the overall variability, and in particular, the number of periods of very high and
very low outputs decreases. A more recent study conducted by Louie
6
carried out extensive correlation analysis using
historical data from four North American system operators (i.e. BPA, ERCOT, MISO and PJM). In the study, a hypothetical
interconnection of the normalized wind power values for four North American systems is modelled. Louie
6
highlighted
three main conclusions. First, all correlations between systems or wind farms present similar characteristics such that
correlation increases with the averaging period and decreases exponentially with separation distance. Second, despite the
smoothing of wind generation over time by spatial aggregation, clear diurnal and seasonal patterns were still evident in the
aggregated wind generation time series. Third, the increase in installed wind power capacity did not affect the correlation
and statistical results of the study.
Foley et al.
26
analysed meteorological wind data from a number of Met Éireann and UK Meteorological Office recording
stations in the UK and the Republic of Ireland in order to examine wind variability across the British Isles. The situation
in the British Isles regarding interconnected power systems is somewhat similar, albeit at a smaller scale, to the North
American case.
In this study, the analysis by Foley et al.
26
is extended to wind power generation data from different countries in the
EU. Historical wind power data from 10 different EU Member States are used to carry out an extensive statistical analysis
across yearly, daily and hourly time-scales. The results are then compared with two previous studies by Holttinen
11
on
several Nordic countries and Louie
6
on four North American system operators. The study aims to investigate the impact
of an ‘ideally interconnected Europe’ modelled as the average wind power production of 10 selected EU countries, similar
to the hypothetical interconnection of the normalized wind power values modelled by Louie
6
for four North American
systems. The paper is organized into five sections. The background is overviewed in Section 1. The datasets used for the
study are described in Section 2. The statistical analysis methods are presented in Section 3. The results are discussed in
Section 4, and the conclusions drawn are provided in Section 5.
Wind Energ.
(2017) © 2017 The Authors Wind Energy Published by John Wiley & Sons Ltd
DOI: 10.1002/we
A. Malvaldi
et al.
A spatial and temporal correlation analysis of aggregate wind power
2. DATASET
This study used historical data of wind power generation from 10 European countries (i.e. Austria, Belgium, Denmark,
Germany, Great Britain, Ireland, Italy, Romania, Spain and Sweden). The analysis considered a 3 year period starting on 1
January 2012 and ending on 31 December 2014. Table I lists the time resolution and data source for each country selected.
The datasets collected have different time resolutions, ranging from 5 min up to 1 h. Thus, the original time series were
aggregated hourly, temporally synchronized and referenced to Greenwich Mean Time. Figure 1 presents a map of Europe
with the 10 countries, which are the focus of the analysis, highlighted in grey. The installed wind power capacity (MW) for
each of the 10 countries and the position of weighted centroid (i.e. blue circle) together with operational, under construction
and planned PCI high-voltage direct current interconnectors
27
are also shown.
The 10 countries were selected not only because of the availability of datasets but also because of the following: (i)
geographic diversity (i.e. terrain, size and location) on a north–south axis; (ii) the different installed wind power capacity
values; and (iii) the existence of inter-country PCI high-voltage direct current interconnections.
27
In addition, the geo-
Ta b l e I . Time resolution and source of wind power data for the 10 EU countries selected for this study.
Country Time resolution Source
Austria (AT) 15 min Austrian Power Grid (APG)
33
Belgium (BE) 15 min Elia
34
Denmark (DK) 1h Energinet.dk
35
Germany (DE) 15 min TransnetBW GmbH,
36
TenneT,
37
50 Hertz Transmission GmbH
38
and Amprion GmbH
39*
Great Britain (GB) 5 min Gridwatch
40
Ireland (IE) 15 min System Operator Northern Ireland (SONI)
41
and EirGrid
42
Italy (IT) 1h Terna S.p.A. - Rete Elettrica Nazionale
43
Romania (RO) 10 min Transelectrica
44
Spain (ES) 10 min Red Eléctrica de España (REE)
45
Sweden (SE) 1h Svenska Kraftnät
46
Figure 1. Map of Europe showing the 10 selected countries for this study, their installed wind power capacity (MW) and position of
weighted centroid (blue circle), together with the main electricity interconnectors from the PCI.
27
[Colour figure can be viewed at
wileyonlinelibrary.com]
*
Data from all the TSOs are based on a projection of the feed-in values of representative wind farms that are measured online.
Wind Energ.
(2017) © 2017 The Authors Wind Energy Published by John Wiley & Sons Ltd
DOI: 10.1002/we
A spatial and temporal correlation analysis of aggregate wind power A. Malvaldi
et al.
Figure 2. Map showing the main climates of Europe
29
; the 10 countries considered in this study are labelled by their names. [Colour
figure can be viewed at wileyonlinelibrary.com]
graphic diversity allows for very different weather systems and climates among the selected EU countries. The climatic
conditions range from subtropical climates, temperate climates and cold climates
28,29
as shown in Figure 2.
Table II shows the installed wind power capacity for each EU Member State at the end of 2012, 2013 and 2014, according
to the European Wind Energy Agency statistics annual reports.
1,30,31
The 10 countries selected for this study are high-
lighted in Table II. The total installed wind power capacity of these 10 countries was 103,019 MW at the end of 2014, which
was 79.82% of the total installed wind power (i.e. 129,060 MW) in the EU in 2014.
1,32
A common characteristic is that the
installed wind power capacity increased each year in each country. Thus, the impact of increasing wind power capacity on
correlation can also be investigated. The total installed wind power capacity in the 10 selected countries increased by 22%
over the 3 year period. It is important to note that, even though Table II reports the installed wind power capacity of the UK
and the Republic of Ireland, for this study, datasets were available for GB, Northern Ireland and the Republic of Ireland
separately. For the purpose of this analysis, GB comprises England, Wales and Scotland, and Ireland comprises of North-
ern Ireland and the Republic of Ireland reflecting the jurisdictions of the British Electricity Trading and Tariff Arrangement
and the Single Electricity Market on the island of Ireland. Moreover, the datasets for GB are relative to a selection of mon-
itored wind farms but not all wind farms installed in the country. Hence, the installed wind power capacity for GB used in
the analysis is lower than the one reported in Table II, namely, 6953 MW by the end of 2012, 8179 MW by the end of 2013
and 8618 MW by the end of 2014.
47
In the case of Germany, all four TSOs declared to have projected the wind generation
using reference sites in the control area and a calculation algorithm; therefore, the total installed capacity in the country is
used for the normalization. For the other countries, datasets used are relative to the total installed wind power capacity.
Wind Energ.
(2017) © 2017 The Authors Wind Energy Published by John Wiley & Sons Ltd
DOI: 10.1002/we