# The effect of the Austrian-German bidding zone split on unplanned cross-
border flows

Theresa Graefe Ulm University, Institute of Sustainable Management, 89081 Ulm,
Germany<EMAIL_ADDRESS>
###### Abstract

In 2013, TSOs from the Central European Region complained to the Agency for
the Cooperation of Energy Regulators because of increasing unplanned flows
that were presumed to be caused by a joint German-Austrian bidding zone in the
European electricity market. This paper empirically analyses the effects of
the split of this bidding zone in 2018 on planned and unplanned cross-border
flows between Germany, Austria, Poland, the Czech Republic, Slovakia, and
Hungary. For all bidding zones, apart from the German-Austrian one, planned
flows increased. Further, I find that around the policy intervention between
2017 and 2019, unplanned flows between Germany and Austria as well as for the
Czech Republic and Slovakia decreased. However, for Poland increasing
unplanned flows are found.

Keywords: European electricity market; Bidding zone configuration

##  Introduction

Until September 2018, Germany, Austria, and Luxembourg formed a joint bidding
zone in the European power market. The countries thus traded electricity at a
uniform price. Since much wind and solar power was generated at quasi-zero
marginal costs in northern Germany, and traders were able to sell and buy as
much electricity as they wanted, a huge amount of flows was scheduled from
Germany to Austria without the need to allocate transmission capacities at
this border (ACER, 2015). Already in 2013, a joint study of Transmission
Systems Operators (TSOs) from the Central East European (CEE) region came to
the conclusion that the electricity in many hours did not flow as it was
traded.111The study was conducted by TSOs from Hungary (MAVIR), Poland (PSE),
the Czech Republic (ČEPS), and Slovakia (SEPS). Instead, Poland, the Czech
Republic, Slovakia, and Hungary had to host physical electricity flows that
were traded between Germany and Austria (ČEPS et al., 2013). Thus, in 2013,
those TSOs complained to the Agency for the Cooperation of Energy Regulators
(ACER) because of increasing unplanned flows that threatened grid stability in
the host countries.222The Agency for the Cooperation of Energy Regulators
(ACER) is a decentralized EU agency that ensures the integration of national
energy markets and the implementation of legislation in the member states
(ACER, 2021). It is located in Ljubljana, Slovenia.

In September 2018, Germany and Austria were eventually forced to split their
joint bidding zone by the agency. Hence, two separate prize zones resulted:
Austria and a German / Luxembourgian zone, further referred to as ’Germany’,
since Luxembourgian demand and trading activities can be seen as negligible.
Through the split, the electricity trading process between Germany and Austria
was changed. Along with the scheduling of flows, in separate auctions,
transmission capacities now had to be bought. That means a restriction of
trading opportunities for electricity traders. Politically, one goal of the
split of the bidding zone was to reduce unplanned flows through the host
countries (ACER, 2016). Thus, the primary objective of this paper is to
analyze the impact of the split of the German-Austrian bidding zone on planned
and absolute unplanned flows between Germany, Austria, Poland, the Czech
Republic, Slovakia, and Hungary based on hourly data for two years around the
splitting event. Apart from the unique event, the analysis of the joint
bidding zone is interesting as the area was by far the largest consumer and
trader in the European electricity market. In 2013, the bidding zone alone
caused $65.5\%$ of the total yearly consumption in the CEE region (ACER,
2015).

I find increasing planned flows for the complaining countries using a baseline
OLS regression model. Between Germany and Austria, as expected, planned flows
declined substantially. Regarding absolute unplanned flows, I find reductions
for the German-Austrian border, as well as for the Czech Republic and
Slovakia. For Hungary, absolute unplanned flows are not altered through the
policy intervention. For Poland, absolute unplanned flows after the bidding
zone split increase. The results are confirmed by the usage of a larger set of
control variables and Post Lasso Double Selection and Partialling-out
approaches, except for Poland, where the positive effect diminishes.333I use
Post-Lasso Double Selection and Partiallin-out as implemented by Chernozhukov,
Hansen, and Spindler (2019). They therefore provide the hdm package on R which
can be found under the following link:
https://cran.r-project.org/web/packages/hdm/index.html. Also, further
separating unplanned flows per border indicates that the situation improved
for the Czech Republic concerning Germany and Austria, whereas unplanned flows
between Poland and Germany and the Czech Republic rather increased. Therefore,
I conclude that the policy intervention did indeed reach the intended goal of
reducing unplanned flows for Slovakia and most of the Czech Republic. For
Hungary, no effects are found and for Poland, it seems like the policy
intervention was leading to increasing absolute unplanned flows. Still, for
Poland, the situation remains the least clear.

The remainder of this paper is structured as follows: In section 2, a summary
of the related literature is provided. Section 3 describes the institutional
setup of European cross-border electricity trading. Section 4 demonstrates the
hypothesis and the estimation procedure, before in section 5 the used data and
some descriptive statistics are presented. In section 6, the main empirical
results are shown and discussed. In section 7, Post Lasso Double Selection and
Partialling-out are performed as robustness checks, which confirm the main
results. The paper ends with some concluding remarks.

##  Related Literature

So far, the literature covering cross-border electricity flows is rather
sparse. ČEPS et al. (2013) descriptively quantify unplanned flows based on
historical values for the CEE region. Taniguchi (2020) compares the role of
renewable electricity generation systems in Denmark and Japan on commercially
scheduled interconnector flows with a multiple regression model. Further,
Skånlund et al. (2013) use a correlation analysis of the central European
power system to assess unplanned flows connected to the German-Austrian
bidding zone.

Moreover, several drivers of electricity flows related to the German-Austrian
bidding zone are identified in the literature: By using principal component
analysis and local polynomial regression, Zugno, Pinson, and Madsen (2013)
find that wind power generation especially from Germany has a substantial
effect on physical cross-border flows in the European market. In line with
that, Jónsson, Pinson, and Madsen (2010) describe that the increase in
renewable electricity production in northern Germany pushes through the grids
of neighboring countries. Skånlund et al. (2013) find correlations between
wind feed-in in northern Germany as well as scheduled flows between Austria
and Germany with unscheduled flows between Germany and Poland. Signh et al.
(2016) investigate unplanned flows between Germany, Austria, Poland, and the
Czech Republic with an optimal power flow simulation based on data from the
year 2013. They conclude that unplanned flows through Poland and the Czech
Republic are mainly caused by northern German wind generation in combination
with a poor transmission infrastructure at the German-Austrian interconnector.
ACER (2019) adds the absence of sufficient coordination of the capacities, and
APG (2021) argues that lagging transfer grid development consequently
reinforced unplanned electricity flows. In 2017, after the decision of the
ACER to split the joint bidding zone, the negatively affected TSOs made a
press release public. In this, they describe that instead of coordinated
capacity allocation, as demanded by the ACER, a bilateral agreement between
Germany and Austria about the congestion management at their border was taken.
The TSOs thus raise the concern that the split of the bidding zone alone may
not lead to an improved situation as without coordinated capacity allocation
there is still a preference for energy traded between Germany and Austria
(ČEPS et al., 2017).

Some other papers cope with the topic of bidding zone reconfiguration in a
more general way. Bemš, Králík, and Knápek (2016) qualitatively describe,
which criteria should be applied in examining the effectiveness of bidding
zones. The authors mention congestion rents, remedial actions, price
differentials and volatility and transition costs, as well as social welfare.
Borowski (2020) compares the traditional electricity system that is based on
bidding zones with a nodal model with local electricity prices at each node.
He argues that nodal pricing sets better price signals and fosters investment
in the electricity market. Another strand of literature covers the integration
of markets in the European Union: Antweiler (2016) shows that scheduled
electricity exports and imports are mainly driven by price differences between
two bidding areas. Gugler, Haxhimusa, and Liebensteiner (2018) and Keppler,
Phan, and Le Pen (2017) studied the integration of European electricity prices
and found that increasing variable renewable electricity production for
example in northern Germany disturbs market integration, leading to a higher
frequency of unplanned flows, congestion occurrences, and higher price
differentials. Keppler, Phan, and Le Pen (2017) thereby show that market
coupling causes converging prices between France and Germany but that this
effect is counteracted by a high amount of renewable intermittent electricity
production in Germany. To the best of my knowledge, so far, there is no
analysis of the effects of bidding zone splits and no approach that uses
unplanned flows as a dependent variable in an empirical analysis.

##  Cross-border trading in the European electricity market

When power is traded at the electricity exchange, the outcome is referred to
as commercially traded or planned flows. Planned flows are day-ahead
commercial exchanges based on matched nominations from previous time horizons
(ENTSO-E Transparency Platform, 2021). The real movement through the grid is
represented by physical flows. Planned and physical flows can deviate, as
electricity does not necessarily flow as it is traded. Instead, electricity
follows the path of least resistance, which depends on the available transfer
capacities in the grid. Until September 2018, the joint Austrian-German
bidding zone existed. Figure 1 shows pre-treatment averages of hourly planned
and physical flows in the observed region, measured in GWh.444The pre-
treatment sample reaches from September 2017 until the policy intervention in
September 2018. The averages are calculated as the sum of physical and planned
flow in both directions, e. g. from Germany to Austria and from Austria to
Germany. The observed sample in this study covers cross-border flows between
Germany, Austria, Poland, the Czech Republic, Slovakia, and Hungary.

Figure 1 shows that the deviation in averages of pre-treatment physical and
planned flows is small between Hungary and Austria and between Slovakia and
Hungary. ČEPS et al. (2013) already discussed that unplanned flows at the
Hungarian and Slovakian borders are within an acceptable range. Instead, they
identify Poland and the Czech Republic as the two countries that are mainly
affected by unplanned transactions related to the joint bidding zone.
Descriptively, the pre-treatment averages seem to confirm this. The largest
deviations can be found at the German-Austrian border. Here, average planned
flows ($6.16$ GWh) largely exceed average physical flows ($1.52$ GWh). The
same, but to a lesser extent, is true for the connections Germany-Czech
Republic and Slovakia-Czech Republic. For the rest, physical flows are on
average larger than planned flows (DE-PL, AT-CZ, PL-CZ, PL-SK). Hence, before
the bidding zone split, in many hours power was scheduled from lower-priced
northern Germany to demand centers that were located in the South of Germany
and Austria.

Figure 1: Average pre-treatment planned and physical cross-border flows

As intra-zonal available transfer capacities often were not sufficient,
physical flows are presumed to follow a path through eastern Europe,
especially through Poland and the Czech Republic.555By intra-zonal capacities,
transmission capacities between Germany and Austria are meant as well as
transmission capacities within Germany. Since those flows were not planned by
the host countries, they are called unscheduled or unplanned flows (ACER,
2018). Those unplanned flows are calculated as the difference between physical
flows and commercially traded flows in hour t (ENTSO-E, 2014). The index i
here either stands for the border (Germany-Austria) or the country (the Czech
Republic, Poland, Slovakia, or Hungary):

$\begin{array}[]{ll}unplanned_{i,t}=physical_{i,t}-planned_{i,t}\end{array}$
(1)

Unplanned flows are problematic if the source and the sink of those
transactions are in the same bidding zone.666ACER (2018) call those kind of
flows loop flows, as they stem from bidding zone internal exchanges but travel
through neighboring bidding zones. In that case, unplanned flows do not impact
the net exchange position of neighboring countries which means, that the hosts
do not get compensated for accommodating those unplanned physical movements
through their grid (Bemš, Králík, and Knápek, 2016). ACER (2015) states that
between 2011 and 2014, around $48\%$ of the flows that were scheduled between
Germany and Austria were flowing through neighboring countries. For the years
2011 and 2012, ČEPS et al. (2013) find that about $50\%$ of the scheduled
flows within the German-Austrian bidding zone were adversely affecting Poland
and the Czech Republic. To reduce structural congestion in the host countries,
ACER (2015) suggested the split-up of the German-Austrian bidding zone and the
introduction of coordinated capacity allocation at the German-Austrian border
which was eventually conducted in September 2018. With the separate bidding
areas, traders are only able to buy and sell as much electricity between
Germany and Austria, as transmission capacities are available. The capacities
at this border are limited to $4,900$ MWh (Bundesnetzagentur, 2021).

There are several reasons for the decision: Unplanned flows afflict external
costs to the host countries, as they have to be incorporated into the grid
(ACER, 2015). The capacities that would be available for commercial cross-
border trading may have to be reduced by the affected TSOs. So, the host
countries cannot use their transmission capacities in other, more lucrative
transactions (Bundesnetzagentur, 2021). This means that the TSOs lose benefits
compared to a situation with sufficient transfer capacities. According to ACER
(2016), this is reinforced due to unequal treatment of electricity exchanges
of flows between and within bidding areas: The handling of internal exchanges
within a bidding zone is discriminatory concerning cross-zonal exchanges
between two different bidding zones. While exchanges between different bidding
areas are constrained by the TSO through the allocation of transfer
capacities, within a bidding zone, consumers and generators contract
electricity in an unconstrained manner. Also, the network is forced to
accommodate all physical flows that stem from intra-zonal exchanges whereas
cross-zonal exchanges have disadvantages in network access (ACER, 2016). This
induces costs of reduced capacities for trading in the host countries and a
loss of congestion revenues for the TSOs. As well, the affected countries face
costs related to the security of supply through expensive remedial actions.
For example, the host countries have to reduce their production to ensure grid
stability. This inflicts costs for re-dispatching, counter trading, and
curtailment (ACER, 2015). According to Skånlund et al. (2013), those re-
dispatch actions are expensive and negatively affect network security. ACER
(2018) further states that the force to offer less cross-zonal capacity for
trade due to unequal treatment of electricity exchanges reduces the efficiency
of the market. Moreover, wrong signals are created that do not foster
investment in the development of the transmission system (ACER, 2015).

##  Hypothesis and Empirical Strategy

In this section, I describe the econometric estimation procedure to
investigate, how the bidding zone split affected planned and unplanned cross-
border flows in the observed region. I here follow the argumentation of the
complaining TSOs from the CEE region and the ACER, that unplanned flows before
the split are related to the joint German-Austrian bidding zone. Therefore,
several hypotheses are to be tested. First of all, I use a baseline
specification that is estimated by Ordinary Least Squares estimation. The
dependent variable $planned_{i,t}$ is planned flows for the border, which is
the case for flows between Germany and Austria or a country $i=1,2,...n$ in
hour $t=1,2,...,T$:

$\displaystyle
planned_{i,t}=\alpha_{0}+\alpha_{1}\delta_{t}+\alpha_{2}{X}_{i,t}+\tau_{t}+\eta_{i,t}\qquad\qquad(2)$

Further, $\delta_{t}$ is a dummy variable that indicates whether an
observation is pre- (0) or post-treatment (1). Thus, $\hat{\alpha}_{1}$ is the
estimate for the parameter of interest. Moreover, $X_{i,t}$ is a vector of 107
control variables. For the four German TSOs 50Hertz, Tennet, TransnetBW, and
Amprion and the Austrian APG, forecasted onshore and offshore wind generation
data is collected. As well, on that spatial level, forecasted solar PV
generation and residual load values are part of $X_{i,t}$. Aggregated at the
country level, electricity generation from other sources (e. g. lignite,
biomass, …) for Germany is included. Renewable and fossil electricity
generation for Austria, the Czech Republic, Poland, Slovakia, and Hungary is
added along with loads and residual load differences between the countries.
Solar PV and wind generation are further allowed to take a quadratic form. As
the chosen variables might be correlated over time, affecting the dependent
variable, they have to be included. If they vary over time, they otherwise
would bias the estimate of interest if not controlled for. Further, I assume
all control variables to be exogenous.777Electricity generation can be seen as
exogenous source of variation of (un-)planned cross-border flows. According to
Cló, Cataldi, and Zoppoli (2015), short-term electricity demand can be seen as
exogenous, too. With $\tau_{t}$, fixed effects for the hour of the day, the
month of the year, and the weekday are added. $\eta_{i,t}$ is an error term. I
form the following hypotheses:

Hypothesis 1: After the policy intervention, planned flows at the German-
Austrian border will decrease.

Hypothesis 2: After the policy intervention, planned flows for Poland and the
Czech Republic will increase.

Hypotheses 1 and 2 reflect the expectations of the behavior of commercially
traded (or planned) flows following the policy intervention. According to
Bundesnetzagentur (2021), with the split of the joint bidding zone, the
transmission capacities (and thus commercially scheduled flows) between
Austria and Germany are limited to $4,900$ MW. As there has been no limitation
of commercially traded flows before the split, those planned flows are
expected to go down. For the estimation, planned flows from Germany to Austria
and in the opposite direction are aggregated. As cheap electricity is still
produced in Northern Germany after the policy intervention and traders will
still be interested in trading the electricity at a low price, I expect, that
trading activities will remain at a high level. Since the policy intervention
forces traders to reduce the scheduled flows between Germany and Austria, I
expect to find increasing planned flows for the complaining countries after
the policy intervention. To observe the effect of the bidding zone split,
cross-border trading activities are aggregated at the country level.888For the
Czech bidding zone (CZ), I sum up the following planned flows: CZ-DE, DE-CZ,
PL-CZ, CZ-PL, CZ-AT, AT-CZ, CZ-SK, SK-CZ. For the Polish bidding zone (PL)
flows from PL-DE, DE-PL, CZ-PL, PL-CZ, SK-PL, and PL-SK are aggregated. For
Slovakia (SK) flows from SK-PL, PL-SK, CZ-SK, SK-CZ, HU-SK, and SK-HU, and for
Hungary (HU) AT-HU, HU-AT, SK-HU, HU-SK are summed up. Although Austria and
Slovakia share a border, no electricity trading data is found for the
interconnection between the two countries.

Secondly, I define absolute unplanned flows $|unplanned_{i,t}|$ as the
dependent variable:

$\displaystyle|unplanned_{i,t}|=\beta_{0}+\beta_{1}\delta_{t}+\beta_{2}{Z}_{i,t}+\tau_{t}+\varepsilon_{i,t}\qquad\qquad(3)$

As described, unplanned flows are calculated as the difference between
physical and planned flows. Every deviance of physical from planned flows is
probable to cause difficulties for the countries that have to cope with them.
As I am mainly interested in problems for the providers and all unplanned
flows are problematic, I take absolute values for the analysis. $Z_{i,t}$
again is a vector of 107 control variables. Instead of forecasted renewable
generation values from the German and Austrian TSOs, here the difference
between forecasted and actual generation values are taken as controls.
Further, if absolute unplanned flows for Poland, the Czech Republic, Slovakia,
or Hungary are the dependent variable, scheduled flows from Germany to Austria
and in the opposite direction are added as additional control variables, as in
the literature they are identified as a driver of unplanned flows in the
region. I form two more hypotheses:

Hypothesis 3: After the policy intervention, absolute unplanned flows at the
German-Austrian border will go down.

As ČEPS et al. (2013) already noticed for 2011 and 2012, and Figure 1 shows
for the pre-treatment period, planned flows between Germany and Austria
massively exceed physical flows at the German-Austrian. If planned flows at
the border indeed decreased and physical flows stayed the same after the
policy intervention, I expect the negative unplanned flows to become less
negative. In absolute terms, this means decreasing unplanned flows.

Hypothesis 4: After the policy intervention, absolute unplanned flows for
Poland and the Czech Republic will go down.

The split of the joint bidding zone was targeted at unplanned flows in the
region. If the policy intervention was successful, absolute unplanned cross-
border flows for the negatively affected countries are expected to decrease.
This is the main hypothesis to test the success of the policy intervention.

##  Data and Descriptive Statistics

All data used in this study are publicly available and joined at hourly
resolution for Germany, Austria, Poland, the Czech Republic, Hungary, and
Slovakia. The sample reaches from 01st October 2017 until 30th September 2019,
covering a balanced two-year period around the policy intervention. The data
frame thus comprises 17,518 observations.999As there are only a few missing
values and the sample is large, missing values are replaced by column means.
The policy intervention took place at midnight between 30th September and 1st
October 2018. That results in 8,759 data points before the split of the
German-Austrian bidding zone and 8,759 afterwards. Physical and commercial
trade data are taken from ENTSO-E Transparency Platform, as well as
electricity generation data for all countries. Additionally, more detailed
actual and forecasted renewable generation values for the four German TSOs and
APG are taken from the Bundesnetzagentur. Also, residual load values for the
TSOs stem from this source. Unplanned flows and residual load differences are
calculated manually. Unplanned flows are computed as described in equation
(1).101010For instance, absolute unplanned flows between Germany and Austria
are calculated as
$|physicalflows_{DE\\_AT}-plannedflows_{DE\\_AT}|+|physicalflows_{AT\\_DE}-plannedflows_{AT\\_DE}|$.
For the residual load differences, residual loads first have to be obtained.
For Austria, values are taken from Bundesnetzagentur. For Germany, residual
load values of the four German TSOs are aggregated. For Poland, the Czech
Republic, Slovakia, and Hungary forecasted load values are taken from ENTSO-E
Transparency Platform and wind and solar PV generation is subtracted. For the
six countries, then residual load differences are calculated. All data are
given in Gigawatt-hours (GWh).111111Data from ENTSO-E Transparency Platform
are accessed in 12/2020 (https://transparency.entsoe.eu/); data from
Bundesnetzagentur are accessed in 01/2021
(https://www.smard.de/home/downloadcenter/download-marktdaten).

Table 1 depicts the averages and standard deviations of planned cross-border
flows, measured in GWh, before and after the policy intervention.

| Before split | After split |  Difference
---|---|---|---
|  Mean |  S. D. |  Mean |  S. D. |
Planned flows (GWh): |  |  |
DE-AT |  6.16 |  1.05 |  2.81 |  1.53 |  -3.35
CZ |  3.71 |  0.77 |  4.08 |  0.85 |  0.37
PL |  0.46 |  0.33 |  0.56 |  0.37 |  0.1
SK |  2.68 |  0.62 |  2.74 |  0.7 |  0.06
HU |  1.44 |  0.37 |  1.69 |  0.36 |  0.25
Table 1: Descriptive statistics of planned cross-border flows

The data are aggregated for the German-Austrian border, Poland, the Czech
Republic, Slovakia, and Hungary as described in section 4. The table shows
that descriptively planned flows massively decreased for the German-Austrian
interconnection after the split of the bidding zone. The difference in the
averages amounts to $-3.35$ GWh. For the four other bidding zones, average
planned cross-border flows increased. Descriptively, the largest increase is
shown for the Czech Republic with $0.37$ GWh, followed by Hungary with $0.25$
GWh. For Poland and Slovakia, the increases are quite small with $100$ and
$60$ MWh. Apart from that, for all units, except for Hungary, the standard
deviation after the policy intervention goes up compared to before.

Table 2 descriptively compares pre- and post-treatment averages of absolute
unplanned flows. One can see, that descriptively absolute unplanned flows
decreased for the German-Austrian border, in line with hypothesis 3. The same
applies to Slovakia. Regarding the Czech Republic, and Hungary the differences
are close to zero. For Poland, the comparison shows a slight
increase.121212Figure 3 in Appendix A plots unplanned cross-border flows (not
absolute values). It shows that indeed unplanned flows between Germany and
Austria are utterly negative before the split of the bidding zone. After the
policy intervention, values are closer to zero and often within a positive
range. Plotting unplanned flow for the Czech Republic instead of absolute
values shows that also here, values are often negative but rather distributed
around zero. It looks like unplanned flows after the split vary more,
especially in the negative direction. For Poland, the development looks quite
similar but after the intervention unplanned flows seem to be a little more
positive. For Slovakia and Hungary, unplanned flows seem to vary less and
negative values appear to become less frequent after the bidding zone split.

| Before split | After split |  Difference
---|---|---|---
|  Mean |  S. D. |  Mean |  S. D. |
Absolute unplanned flows (GWh): |  |  |
DE-AT |  4.64 |  1.4 |  1.45 |  0.93 |  -3.19
CZ |  1.03 |  0.79 |  1.09 |  0.8 |  0.06
PL |  1.15 |  0.6 |  1.29 |  0.67 |  0.14
SK |  0.7 |  0.65 |  0.49 |  0.44 |  -0.21
HU |  0.34 |  0.31 |  0.3 |  0.24 |  -0.04
Table 2: Descriptive statistics of absolute unplanned flows

##  Results

I estimate equation (2) by Ordinary Least Squares estimation to test
hypotheses 1 and 2 for the data set. Table 3 presents the results for the
estimator of the parameter of interest $\hat{\alpha}_{1}$ with planned flows
between Germany and Austria (DE-AT), for the Czech Republic (CZ) and Poland
(PL) (and additionally for Slovakia (SK), and Hungary (HU)) as the dependent
variables. I find estimators that are significant at the $1\%$ level for the
joint German-Austrian bidding zone, Poland, and
Hungary.131313Heteroskedasticity and autocorrelation robust standard errors
after the Newey-West (1994) method in parentheses. Further, all coefficients
of the zone splitting dummy $\delta$ receive estimates in the expected
directions. As formulated in hypothesis 1, the effect of the bidding zone
split on planned flows between Germany and Austria is strongly and
significantly negative. The reduction amounts to $2.914$ GWh.

Dependent variable: Planned flows
---
|  DE-AT |  CZ |  PL |  SK |  HU
|  (1) |  (2) |  (3) |  (4) |  (5)
delta (zone splitting dummy) |  $-$2.914∗∗∗ |  0.139 |  0.350∗∗∗ |  0.073 |  0.281∗∗∗
|  (0.230) |  (0.173) |  (0.056) |  (0.119) |  (0.060)
Controls |  Yes |  Yes |  Yes |  Yes |  Yes
Fixed Effects |  Yes |  Yes |  Yes |  Yes |  Yes
Observations |  17,518 |  17,518 |  17,518 |  17,518 |  17,518
Adjusted R2 |  0.811 |  0.305 |  0.353 |  0.403 |  0.452
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Standard errors are HAC robust. Lags are chosen with the Newey-West (1994)
method.

Table 3: Regression results planned flows

On average planned flows between Austria and Germany are $2.914$ GWh lower in
post-treatment hours compared to pre-treatment hours. With respect to pre-
treatment average, this is equal to a reduction of $47\%$. As well, for the
four observed countries, planned flows increased after the policy
intervention. The largest and significant effect arises for Poland, where
post-treatment planned flows on average increased by $0.35$ GWh (or by
$+76\%$) compared to pre-treatment hours. This effect is much larger than the
sole comparison of pre- and post-treatment averages as is done in Table 1.
Also, for Hungary, the effect is strong with $0.281$ GWh (respectively
$+11\%$) and nearly equals the simple time differences. For the Czech Republic
and Slovakia the estimated treatment effects are as well positive but not
significant. The Czech increase in relative terms is quite small with only
$3\%$. But here, already before the treatment, planned flows were around eight
times as high as in Poland, so the starting point for that country is already
at a high level. Still, the estimates for the split coefficients are well away
from zero. Accordingly, hypotheses 1 and 2 cannot be rejected. Table 7 in
Appendix B further shows the impact of the bidding zone split on physical
flows.

To answer the question of how the bidding zone split influenced absolute
unplanned flows, moreover equation (3) is estimated by OLS. Table 4 presents
the regression results. Hypothesis 3 reflects the expectation that absolute
unplanned flows between Germany and Austria declined through the policy
intervention. Indeed, I find a strong negative and significant effect of
$-2.696$ GWh for that border, so hypothesis 3 cannot be rejected.

Dependent variable: Absolute unplanned flows
---
|  DE-AT |  CZ |  PL |  SK |  HU
|  (1) |  (2) |  (3) |  (4) |  (5)
delta (zone splitting dummy) |  $-$2.969∗∗∗ |  $-$0.408∗∗∗ |  0.312∗∗ |  $-$0.314∗∗∗ |  0.025
|  (0.245) |  (0.147) |  (0.132) |  (0.092) |  (0.052)
Controls |  Yes |  Yes |  Yes |  Yes |  Yes
Fixed Effects |  Yes |  Yes |  Yes |  Yes |  Yes
Observations |  17,518 |  17,518 |  17,518 |  17,518 |  17,518
Adjusted R2 |  0.790 |  0.330 |  0.517 |  0.388 |  0.306
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Standard errors are HAC robust. Lags are chosen with the Newey-West (1994)
method.

Table 4: Regression results absolute unplanned flows

Compared to the pre-treatment average, the reduction is equal to $64\%$. The
complaining countries that claimed to suffer the most from unplanned flows in
the CEE region were Poland and the Czech Republic. If the policy intervention
had the intended effect, I expect absolute unplanned flows to decrease for the
two countries, as stated in hypothesis 4. According to the regression results,
hypothesis 4 can be significantly confirmed at the $1\%$ level for the Czech
Republic. On average, absolute unplanned flows are $408$ MWh ($-39.6\%$) lower
in post-treatment hours compared to pre-treatment hours. The results are also
in line with those from the planned and physical flows regressions. Before the
split, on average one observed negative unplanned flows. As shown, planned
flows increased slightly more than physical flows which should result in an
overall decrease in (absolute) unplanned flows.141414Also, for Slovakia, I
find a significant reduction of $314$ MWh, which means a decline of $44.9\%$
compared to pre-treatment averages.For Hungary, the effect is only small and
insignificant ($25$ MWh or $7.4\%$). What is not as expected, is that for
Poland, the effect is strong and significantly positive (significant at the
$5\%$ level). Here, on average absolute unplanned flows increased by $312$ MWh
after the policy intervention. Compared to the pre-treatment average of
$1.150$ MWh this is equal to an increase of $27.1\%$. Hence, the results
suggest, that hypothesis 4 is rejected for Poland. 151515Interestingly, this
is not in line with the former regressions. In Poland, unplanned flows before
the policy intervention were positive ($1.6$ GWh), meaning that physical flows
were higher than planned flows. Through the policy intervention, planned flows
increased more than physical flows ($0.35$ GWh compared to $0.28$ GWh). Thus,
the difference between physical and planned flows should become smaller, which
should lead to decreasing (absolute)unplanned flows.Still, Table 4 does not
indicate that a relief through the bidding zone split is apparent.

To better understand where the unplanned flows exactly occur, I re-estimate
equation (3) but with separated values per border. The results are shown in
Table 5.

Dependent variable: Absolute unplanned flows separated per border
---
|  CZ-DE |  CZ-PL |  CZ-AT |  CZ-SK
delta |  $-$0.078 |  0.201∗∗∗ |  $-$0.234∗∗∗ |  $-$0.297∗∗∗
|  (0.092) |  (0.054) |  (0.069) |  (0.080)
Adjusted R2 |  0.225 |  0.419 |  0.741 |  0.424
|  PL-DE |  PL-SK |  SK-HU |  HU-AT
delta |  0.156∗∗ |  $-$0.045 |  0.028 |  0.040
|  (0.071) |  (0.036) |  (0.032) |  (0.030)
Adjusted R2 |  0.502 |  0.498 |  0.147 |  0.225
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Standard errors are HAC robust. Lags are chosen with the Newey-West (1994)
method.

Table 5: Regression results absolute unplanned flows per border

CZ-DE stands for the sum of absolute unplanned flows from the Czech Republic
to Germany and in the opposite direction, from Germany to the Czech Republic.
Observing the borders in the region separately, yields a more diverse picture.
For the Czech Republic, reductions in unplanned flows stem from
interconnections with Germany ($-78$ MWh), and mainly from the
interconnections with Austria ($-234$ MWh) and Slovakia ($-297$ MWh). Still,
absolute unplanned flows increased significantly between the Czech Republic
and Poland by $201$ MWh. As well, unplanned flows between Poland and Germany
grew by $156$ MWh. Results for PL-SK, SK-HU, and HU-AT are small and
insignificant. Hence, also the analysis of the directed flows does indicate
that the situation improved for Slovakia and at large also for the Czech
Republic. Only concerning Poland, the situation of absolute unplanned flows
worsened, as the country has to incorporate more unplanned flows into its grid
compared to before the policy intervention.

Figure 2 further decompose the effect of the policy intervention on absolute
unplanned flows. Here, unplanned flows at the specific borders are shows.
Details about the OLS regressions can be found in Table 8 in the Appendix. One
can see, that very small changes occurred in connection with Hungary and
mainly with Slovakia. In the lower part of the picture, more decreasing
absolute unplanned flows are found: at the Austrian-German and the Czech-
Slovakian interconnection. Also flows from the Czech Republic to Germany and
Austria show significant reductions. Nevertheless, the OLS results suggest
that there are also positive developments regarding absolute unplanned flows.

Figure 2: OLS results of directed absolute unplanned flows

Those mainly seem to be connected to Poland. Interestingly, the increase seems
to take place in more than one direction: from Poland to the Czech Republic
($114$ MWh), and from the Czech Republic to Poland ($92$ MWh); from Poland to
Germany ($102$ MWh) and also the estimated coefficient for flows from Germany
to Poland is positive with $78$ MWh but not significant. Hence, the regression
analysis with OLS suggests that for Poland there is still a deviation between
planned flows and physical movement through the grid. Moreover, the increase
in the deviation seems to be connected to Germany and the Czech Republic.

##  Robustness Checks

In the baseline OLS regression, I use 107 control variables, like residual
load, conventional electricity generation or the difference between forecasted
and actual renewable generation values. It is well conceivable that for
example wind or solar PV feed-in changes over the seasons. For example solar
PV feed-in might have varying effects on the outcome variable in summer and in
winter months as well as between day and night hours. Normally, one could
think of much more flexible specifications of control variables by adding for
example interaction effects between solar PV fluctuation and the hour of the
day or the month. However, if one would use all the possible interaction
effects, this would lead to a large set of over 4,000 control variables. They
namely are electricity generation values for Germany, Austria, Poland, the
Czech Republic, Hungary, and Slovakia (60 variables), load (6 variables) and
residual load differences (8 variables). At the TSO level, the data set
comprises actual and forecasted solar PV, offshore and onshore feed-in as well
as the difference between forecasted and actual feed-in (36 variables).
Further, residual load data for the TSOs were collected (5 variables).
Residual load, residual load differences, and renewable generation are allowed
to take a quadratic form as well (52 variables). Moreover, I allow
interactions of all features (apart from the quadratic terms) with the month
of the year (1,380 variables) and the hour of the day (2,760 variables).
161616I re-estimate the equations (2) and (3) with a larger number of possible
regressors $p$ stored in the vectors $\dot{X}$ and $\dot{Z}$:
$\displaystyle\dot{planned}_{i,t}=\alpha_{0}+\alpha_{1}\delta_{t}+\alpha_{2}\dot{X}_{i,t}+\zeta_{i,t}\qquad\qquad(4)$
$\displaystyle\dot{|unplanned|}_{i,t}=\beta_{0}+\beta_{1}\delta_{t}+\beta_{2}\dot{Z}_{i,t}+\xi_{i,t}\qquad\qquad(5)$
So as a robustness check I use Post-Lasso Double Selection and Partialling-out
approaches by Chernozhukov, Hansen, and Spindler (2019) to select from the
large set of potential features. A good introduction to the modern machine
learning methods can also be found in Ahrens, Hansen, and Schaffer (2020). For
the usage of the package ‘hdm‘ in R, I refer to Spindler, Chernozhukov, and
Hansen (2016).

If the variable selection is applied to the data set with planned flows
between Germany and Austria as the outcome, 95 out of 4,200 possible features
are selected with an adjusted $R^{2}$ of 0.80.171717Here $y_{i}$ is regressed
on the vector of all possible controls $\dot{X}$ by Post-Lasso estimation in
order to select the most predictive features $s$. To control for time trends,
hours, weekdays, and months are added as dummy variables. The split
coefficient delta is not included into the set of potential controls. 7 of the
selected features are conventional production values from Germany and Austria,
The largest fraction of the selected features is made up by interactions of
solar PV and wind generation from Germany and conventional generation values
from CZ/PL/SK/HU with months. Also, some German and Austrian conventional
generation variables and their interactions with hours are chosen. Further,
onshore and offshore wind feed-in for the 50Hertz and Tennet regions are
selected. Renewable generation from the other countries plays a minor role as
well as residual load and load differences. Further, 5 non-interacted
renewable generation variables in linear and 7 in quadratic form are chosen by
the approach. One can also find 19 interactions with hours of the day . The
biggest part of them is made up by interactions of hours with the difference
between Austrian forecasted and actual PV generation (11 interactions). That
is not what one would expect, as planned flows should be rather driven by
forecasted generation only. 35 selected features are moreover interactions of
various variables with months. If instead Polish planned flows are used as the
dependent variable, 124 features are selected. The adjusted $R^{2}$ is 0.421,
which is higher than the one found for the OLS regression in Table 3. Again,
the largest part is made up by interactions with hours (57) and months (46).
Especially, coal generation from Germany and actual and forecasted offshore
and onshore wind production from Tennet and 50Hertz are taken into account.
Once more, it seems not so plausible, that here actual instead of forecasted
generation is selected.

In Table 6, results from the baseline OLS regression are shown together with
results from the Double Selection and Partialling-out approaches are presented
with planned flows between Germany and Austria, for the Czech Republic,
Poland, Slovakia, and Hungary as the dependent variables.

Dependent variable: Planned flows
---
|  DE-AT |  CZ |  PL |  SK |  HU
|  (1) |  (2) |  (3) |  (4) |  (5)
OLS |  $-$2.914∗∗∗ |  0.139 |  0.350∗∗∗ |  0.073 |  0.281∗∗∗
|  (0.230) |  (0.173) |  (0.056) |  (0.119) |  (0.060)
Adjusted $R^{2}$ |  0.811 |  0.305 |  0.353 |  0.403 |  0.452
Double |  $-$3.865∗∗∗ |  0.031 |  0.073∗∗∗ |  $-$0.085. |  0.171∗∗∗
Selection |  (0.108) |  (0.062) |  (0.019) |  (0.046) |  (0.023)
Partialling |  $-$1.447∗∗∗ |  $-$0.012 |  0.116∗∗∗ |  $-$0.138∗∗∗ |  0.097∗∗∗
out |  (0.067) |  (0.049) |  (0.022) |  (0.035) |  (0.020)
Adjusted $R^{2}$ |  0.804 |  0.331 |  0.421 |  0.458 |  0.495
Note: | Signif. codes for OLS: ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
| Signif. codes for Double Selection and Partialling out: ∗∗∗ 0.001 ∗∗ 0.01 ∗
0.05

Table 6: Regression results OLS, Double Selection, Partialling out - planned
flows

For planned flows between Germany and Austria, the direction and significance
level of the treatment effect found by OLS are confirmed. Post Lasso Double
Selection yields a more negative and Partialling-out a less negative
coefficient. As well, for Poland and Hungary, the significance and direction
of the OLS treatment effect are approved. Still, for both bidding zones, the
modern methods find smaller effect sizes. The baseline OLS estimation results
in insignificant estimates for the Czech Republic and Slovakia. For the
former, this finding is confirmed. Here, the sign of the coefficient flips for
Partialling out but stays relatively close to zero and insignificant.
Concerning Slovakia, Double Selection yields a negative but insignificant
effect. Partialling out as well finds a negative effect, but it becomes
significant. Hence, for Slovakia, the size and sign of the effect of the
bidding zone split remain rather unclear.

Regarding unplanned flows, table 7 shows, which variables are chosen by the
approach, when I use absolute unplanned flows between Germany and Austria
(column 2) and Poland (column 3) as the dependent variable. In column 2,
conventional electricity production seems to play a larger role. Renewable
electricity generation instead is found when having a closer look at the
interaction effects. For instance, the approach selects 15 interactions
effects of German wind and solar PV generation with the month of the year.
Overall, 77 variables are chosen. If Polish absolute unplanned flows is the
dependent variable, 94 features are selected. Again, the main part is made up
by interactions with the month of the year. 18 of them are made up by German
and Austrian wind and solar PV interactions.

Category |  Number of |  DE-AT |  PL
---|---|---|---
|  potential features |  |
|  (1) |  (2) |  (3)
Wind/PV German TSOs/APG |  36 |  1 |  3
Other gen. DE/AT |  22 |  9 |  6
Wind/PV PL/CZ/SK/HU |  4 |  0 |  1
Other gen. PL/CZ/SK/HU |  34 |  8 |  10
(Residual) load (differences) |  19 |  2 |  1
Quadratic terms |  52 |  2 |  3
planned flows DE-AT |  2 |  - |  2
Interactions with hour |  1,380 |  13 |  21
Interactions with month |  2,760 |  42 |  47
sum |  4,200 |  77 |  94
Note: The table shows the number of potential control variables which are
chosen by the
Double selection approach when I use absolute unplanned flows between Germany
and Austria
(column 2) or Poland (column 3) as the outcome. (The 28 quadratic terms are
not interacted
with hour or month.)

Table 7: Examples of variable selection Double Selection Approach

Further, table 8 shows regression results for the basline OLS, Double
Selection and Partialling-out with absolute unplanned flows as the outcomes.

Dependent variable: Absolute unplanned flows
---
|  DE-AT |  CZ |  PL |  SK |  HU
|  (1) |  (2) |  (3) |  (4) |  (5)
OLS |  $-$2.696∗∗∗ |  $-$0.408∗∗∗ |  0.312∗∗ |  $-$0.314∗∗∗ |  0.025
|  (0.254) |  (0.147) |  (0.132) |  (0.092) |  (0.052)
Double |  $-$3.459∗∗∗ |  $-$0.579∗∗∗ |  -0.072 |  $-$0.437∗∗∗ |  $-$0.038
Selection |  (0.071) |  (0.063) |  (0.045) |  (0.04) |  (0.025)
Partialling |  $-$2.748∗∗∗ |  $-$0.495∗∗∗ |  0.039 |  $-$0.255∗∗∗ |  $-$0.008
out |  (0.065) |  (0.054) |  (0.037) |  (0.033) |  (0.021)
Note: | Signif. codes for OLS: ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
| Signif. codes for Double Selection and Partialling out: ∗∗∗ 0.001 ∗∗ 0.01 ∗
0.05

Table 8: Regression results OLS, Double Selection, Partialling out - absolute
unplanned flows

Both machine learning methods confirm the directions and significance levels
of the results found by OLS for Germany-Austria, the Czech Republic, and
Slovakia. For the German-Austrian border, the Double Selection method finds a
more negative estimate ($-3.459$ MWh). Applying Post-Lasso Partialling out
does not alter the OLS result a lot. For the Czech Republic, the estimated
treatment effects becomes more negative for Partialling out ($-0.495$ MWh) and
even more for the Double Selection approach ($-0.597$ MWh). For Slovakia,
Partialling out estimates a smaller ($0.255$ MWh) and Double Selection a
larger ($0.437$ MWh) negative treatment effect. For Hungary, the estimates
become slightly negative, but are still close to zero. As well, the estimated
coefficients stay insignificant. Concerning Poland, the found effect is still
positive but much smaller and insignificant when Partialling out is applied.
Using the Double Selection approach, the estimated coefficient becomes even
negative but also insignificant. Thus, except for Poland, the results are
confirmed by the machine learning approach. For Poland, thus the effect of the
bidding zone split on absolute unplanned flows remains the least clear.

##  Conclusion

The split of the German-Austrian bidding zone gives a unique opportunity to
observe the effect of a trading regime change on unplanned cross-border flows.
The joint Austrian-German bidding zone was split in October 2018. In this
analysis, I exploit the effect of this policy intervention on cross-border
flows between Germany, Austria, the Czech Republic, Poland, Slovakia, and
Hungary. I show that planned (or scheduled) cross-border flows decreased by
nearly $3$ GWh at the German-Austrian border. For the other countries, the
policy intervention was leading to increasing planned flows, where the
strongest effect can be found for Poland, followed by Hungary. For absolute
unplanned flows, the treatment effects are more diverse. Indeed, absolute
unplanned flows between Germany and Austria substantially declined by $2.7$
GWh. Moreover, the situation for the Czech Republic and Slovakia improved.
Here, a reduction in absolute unplanned flows of $40\%$ respectively $45\%$
compared to the pre-treatment mean is found after the policy intervention. The
results are significant for all observed units but for Hungary, where the
effect size is close to zero.

Nevertheless, it seems like Poland is still suffering from a high amount of
absolute unplanned flows. Further, it is unclear, whether the situation for
this country even worsened after the split of the joint bidding zone. I find a
large and significant increase in Polish unplanned flows after the policy
intervention by applying OLS with a large set of control variables ($+312$
MWh). Using an even larger set of potential controls and applying machine
learning techniques, yields smaller and in one case even insignificant
effects. Further decomposing flows that are related to Poland does also not
show a clear cause of increasing absolute unplanned flows. Thus, with regard
to Poland, further analyzes are needed. It would also be interesting to study,
whether a further split up of Germany into two price zones would resolve the
situation to understand the causes of the lacking decrease (or probably even
increase) in absolute unplanned flows for Poland which should be part of the
political concern and future research.

This analysis gives insights into the effect of the split of a large bidding
zone on planned and absolute unplanned cross-border flows. In future research,
it would be interesting to also look at the financial implications of the
split of the German Austrian bidding zone. It may be that through the policy
intervention, the need for re-dispatch actions declined with the reduction of
absolute unplanned flows. This decline can be analyzed and expressed
monetarily.

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##  Appendix A Appendix A: Additional figures

Figure 3: Unplanned cross-border flows

The plots in Figure 3 display unplanned cross-border flows between Germany and
Austria and for Poland, the Czech Republic, Slovakia, and Hungary between
October 2017 and October 2019. The black vertical line marks the policy
intervention. The horizontal curved line again is a smoother function.
Unplanned flows are calculated as the difference of the sums of physical minus
the sums of planned flows for the respective unit.

During peak hours, the electricity demand generally is higher than during off-
peak hours.181818On-peak hours are often defined between 8 A. M. and 8 P. M.
on weekdays, whereas off-peak hours are the night hours between 8 P. M. and 8
A. M. on weekdays and all hours on weekends (Moran et al. 2011). Figure 4
gives an overview of how absolute unplanned flows vary over the day and on
different weekdays. For every hour of the day, therefore one average value
over the whole sample (pre- and post-treatment) is calculated and plotted for
each day of the week. One can see, that average absolute unplanned flows are
on the highest levels in the Czech Republic and on the German-Austrian border.
The development is quite stable in the Czech Republic throughout the day as
well as between weekdays. One can not observe great outliers. Overall it still
seems like unplanned flows are more stable in peak hours and slightly increase
and vary more in off-peak hours. At the German-Austrian border, unplanned
flows are on a much lower level on the weekend. Moreover, absolute unplanned
flows reach a plunge in the early morning hours. They then increase on
weekdays, stay on a quite stable level for the peak hours and realize the
highest levels between 4 and 8 P. M. In Hungary, absolute unplanned flows are
low and quite evenly distributed throughout the day. Also, the weekday does
not seem to have a big influence of the amount of unplanned flows. A small
peak can be observed in the early morning hours. The same holds for Slovakia,
nevertheless here flows are on a higher level and the peak in the morning
hours is more pronounced than in Hungary. For Poland again unplanned flows are
lowest on the weekend. Also one can see that again the development throughout
the day is quite stable and on a lower level within peak hours. Overall, apart
from the German-Austrian interconnection, absolute unplanned flows
descriptively seem to be somewhat higher and less stable in off-peak hours,
while they are quite steady and on a lower level in peak hours.

Figure 4: Absolute unplanned hourly cross-border flows per weekday

##  Appendix B Appendix B: Additional tables

Table 9 gives an overview of the regression of physical flows as dependent
variable. The rest of the regression specification is as described in equation
(3?).

Dependent variable: Physical flows
---
|  DE-AT |  CZ |  PL |  SK |  HU
|  (1) |  (2) |  (3) |  (4) |  (5)
delta |  $-$0.126 |  0.088 |  0.280∗∗ |  0.284∗∗ |  0.218∗∗∗
|  (0.081) |  (0.151) |  (0.137) |  (0.138) |  (0.074)
Observations |  17,518 |  17,518 |  17,518 |  17,518 |  17,518
Adjusted R2 |  0.829 |  0.693 |  0.482 |  0.727 |  0.712
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01 |
Standard errors are HAC robust. Lags are chosen with the Newey-West (1994)
method.

Table 9: Regression results physical flows

For the estimation, physical flows that represent the movement of electricity
flows through the grid, are the dependent variables. The rest of the
regression specification stays as in equation (2). Only instead of forecasted
renewable generation for the German and Austrian TSOs, actual production
values are taken into account as control variables. One can observe that
physical flows between Germany and Austria decreased but to a much lesser
extent than planned flows. The estimated coefficient is insignificant and
$-126$ MWh for that border. Here, the sign is contrary to the one presented in
Table 1, where sole averages are computed. For the Czech Republic, physical
flows slightly increase after the policy intervention ($88MWh$). Still, also
here the estimated coefficient is insignificant. Larger and significant
increases in physical flows are estimated for Slovakia ($284$ MWh), Poland
($280$ MWh), and Hungary ($218$ MWh).

In Table 10, absolute unplanned flows are further separated into directed
flows per interconnection.

Dependent variable: Directed absolute unplanned flows per border
---
|  CZ-DE |  DE-CZ |  CZ-AT |  AT-CZ
delta |  -0.113 |  0.035 |  $-$0.191∗∗∗ |  $-$0.043
|  (0.074) |  (0.068) |  (0.060) |  (0.028)
Adjusted R2 |  0.411 |  0.192 |  0.761 |  0.636
|  PL-CZ |  CZ-PL |  PL-DE |  DE-PL
delta |  0.138∗∗∗ |  0.063∗∗ |  0.073∗∗∗ |  0.083
|  (0.049) |  (0.027) |  (0.027) |  (0.068)
Adjusted R2 |  0.490 |  0.565 |  0.519 |  0.521
|  PL-SK |  SK-PL |  CZ-SK |  SK-CZ
delta |  $-$0.044 |  $-$0.0001 |  $-$0.099∗∗ |  $-$0.198∗∗∗
|  (0.037) |  (0.005) |  (0.047) |  (0.048)
Adjusted R2 |  0.517 |  0.193 |  0.391 |  0.423
|  SK-HU |  HU-SK |  AT-HU |  HU-AT
delta |  0.028 |  $-$0.042 |  0.002 |  0.039∗∗
|  (0.032) |  (0.027) |  (0.028) |  (0.019)
Adjusted R2 |  0.147 |  0.343 |  0.234 |  0.372
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Standard errors are HAC robust. Lags are chosen with the Newey-West (1994)
method.

Table 10: Regression results absolute unplanned flows per border

To analyze the effect of peak hours, I re-estimate equation (3) but add a
dummy for peak hours $peak_{t}$ that is equal to 1 for on-peak and equal to 0
for off-peak hours. Also, I interact the split dummy $\delta_{t}$ with
$peak_{t}$:

$\displaystyle
unplanned_{i,t}=\theta_{0}+\theta_{1}\delta_{t}+\theta_{2}peak_{t}+\theta_{3}\delta_{t}\times
peak_{t}+\theta_{4}X_{i,t}+\pi_{t}+\nu_{i,t}\qquad\qquad(5)$

With $\pi_{t}$, only fixed effects for the month of the year and the weekday
are added. With that specification it is possible to test, whether absolute
unplanned flows depend on peak hours ($\theta_{2}\neq 0$) and whether the
effect of the split coefficient depends on peak hours ($\theta_{3}\neq 0$).
The results are presented by table 11.

| Dependent variable: Absolute unplanned flows per border
---|---
|  DE-AT |  CZ |  PL |  SK |  HU
|  (1) |  (2) |  (3) |  (4) |  (5)
peak |  0.121∗ |  $-$0.009 |  $-$0.268∗∗∗ |  0.010 |  0.042∗∗
|  (0.070) |  (0.051) |  (0.035) |  (0.029) |  (0.020)
delta |  $-$2.656∗∗∗ |  $-$0.301∗∗ |  0.248∗∗ |  $-$0.281∗∗∗ |  0.044
|  (0.221) |  (0.144) |  (0.122) |  (0.092) |  (0.052)
peak:delta |  $-$0.169∗∗ |  0.068 |  0.231∗∗∗ |  0.094∗∗∗ |  0.040∗
|  (0.082) |  (0.061) |  (0.044) |  (0.036) |  (0.022)
Observations |  17,518 |  17,518 |  17,518 |  17,518 |  17,518
Adjusted R2 |  0.786 |  0.329 |  0.515 |  0.383 |  0.306
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Standard errors are HAC robust. Lags are chosen with the Newey-West (1994)
method.

Table 11: Regression results peak hours

Peak shows the estimated effects of on-peak hours before the treatment. For
the Czech Republic and Slovakia, the found coefficients are close to zero. The
estimated coefficient for Poland is strongly negative and significant. Here,
the largest effect is found which means that absolute unplanned flows are
lower by $268$ MWh in peak hours compared to off-peak hours before the
treatment. For flows between Germany and Austria, the effect size is positive
and significant at the $10\%$ level. Here, absolute unplanned flows are $121$
MWh higher in pre-treatment peak hours compared to pre-treatment off-peak
hours. For Hungary, the effect is also positive but much smaller. delta gives
the estimated effect for post-treatment off-peak hours. The largest
coefficient is found for DE-AT ($-2.66$ GWh). I further find negative effects
for the Czech Republic and Slovakia. For Poland and Hungary, it is positive.
The interaction term delta:peak allows the population effect of post-treatment
values on absolute unplanned flows to depend on on-peak hours. For all columns
I find a value that is not $0$. The differential between on-peak post-
treatment hours relative to off-peak pre-treatment hours is positive for
Poland ($211$ MWh), and Hungary ($126$ MWh). For flows between Germany and
Austria and for the Czech Republic and Slovakia, it is negative ($-2.704$ GWh;
$-242$ MWh; $-177$ MWh).191919The differential between on-peak post-treatment
hours and off-peak pre-treatment hours is calculated as the sum of
$\hat{\theta}_{1}$, $\hat{\theta}_{2}$ and $\hat{\theta}_{3}$.

ČEPS et al. (2013) and ACER (2015) argue, that unplanned flows in the region
mainly occur when scheduled flows from Germany to Austria exceed $3,000$ MWh.
Thus, the data is filtered for hours where commercially traded flows on the
respective border exceed this threshold. This is the case for 11,898 of the
17,518 observed hours, of which 8,045 are pre-treatment and 3,853 are post-
treatment hours. Thus, in around $92\%$ of pre-treatment hours scheduled flows
between DE-AT exceeded the threshold whereas this is the case for only $44\%$
of post-treatment hours. So, the occurrence of those high planned flows
between Austria and Germany substantially declined. The regression results are
shown in Table 12.

Dependent variable: Absolute unplanned flows per border
---
|  DE-AT |  CZ |  PL |  SK |  HU
|  (1) |  (2) |  (3) |  (4) |  (5)
All |  $-$2.733∗∗∗ |  $-$0.301∗∗ |  0.349∗∗∗ |  $-$0.277∗∗∗ |  0.038
|  (0.232) |  (0.144) |  (0.132) |  (0.093) |  (0.051)
Observations |  17,518 |  17,518 |  17,518 |  17,518 |  17,518
Adjusted R2 |  0.788 |  0.335 |  0.519 |  0.385 |  0.310
Filtered |  $-$1.820∗∗∗ |  $-$0.365∗∗∗ |  0.354∗∗∗ |  $-$0.280∗∗∗ |  0.046
|  (0.194) |  (0.148) |  (0.129) |  (0.096) |  (0.056)
Observations |  11,898 |  11,898 |  11,898 |  11,898 |  11,898
Adjusted R2 |  0.800 |  0.426 |  0.564 |  0.419 |  0.316
Note: |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Standard errors are HAC robust. Lags are chosen with the Newey-West (1994)
method.

Table 12: Regression results all and filtered observations

If the data is filtered as described, the largest deviation in the treatment
effect in comparison with the regression with all data points is found for DE-
AT. Here, the estimated treatment effect is $-1.820$ or $39.2\%$ instead of
$-2.733$ GWh ($-58.9\%$). For the Czech Republic, the estimator becomes more
negative. For the other countries, filtering the data does not alter the
results. Still, the adjusted $R^{2}$ goes up for all regressions with the
filtered data. It seems like, more variation in absolute unplanned flows can
be explained through the explanatory variables in hours with a high amount of
scheduled flows between Germany and Austria.