Political Power-Play at its best — the case study of biomethane in Germany
Thomas Horschig* Daniela Thran *”
* Deutsches Biomasseforschungszentrum (DBFZ), Torgauer Strae 116, 04347 Leipzig, Germany
> UFZ Helmholtz Center for Environmental Research, Permoserstrake 15, 04318 Leipzig, Germany
thomas. horschig@ dbfz.de
Abstract
Biomethane is a gaseous and biogenic energy carrier, chemically identical to natural gas.
When used as natural gas substitute, it can contribute to significant GHG savings depending
on substrate utilization, cross compliance and system operation. Being an important part of
Germanys climate protection program the development of a national biomethane market was
pushed by several incentives via energy and climate policies and led to a rapid growth in
biomethane producing facilities. Recent adaptions of the installed support schemes no longer
guarantee sufficient compensation for the production of renewable energy. A system
dynamics model was developed to evaluate future policies on their environmental and
economic efficiency. Market participants want to shift from biomethane use in combined heat
and power plants to new markets. The model will be able to simulate future energy and
climate policies, estimate their success in terms of sustainable capacity development to
support policy makers for the development of efficient support schemes that incite a further
sustainable and economic efficient biomethane market development.
Keywords: biomethane, renewable energy, energy system, policy analysis
1. Introduction
Biomethane is biogenic methane chemically identical to natural gas. It is produced either via
an upgrading process of raw-biogas or upgrading of synthetic natural gas. Raw-Biogas is
produced through anaerobic digestion (AD) of several feedstocks like energy crops, manure,
sewage, plant residues or organic waste. Synthetic natural gas is produced via thermochemical
gasification of lignin-rich biomass like straw and wood. Whereas the production of raw-
biogas through AD and the following upgrading process to biomethane is widely applied
worldwide the thermochemical pathway to produce biomethane (often called bio-SNG) is not
yet market-implemented. Being chemical identical to natural gas biomethane as well as bio-
SNG can substitute this fossil energy carrier in each application (CHP plants, direct heat and
transport sector). When used as natural gas substitute, biomethane as well as bio-SNG can
contribute to significant GHG savings (Majer, 2011) ; (Rénsch, 2011). In order to achieve
progress on climate protection the German Government relies on a mix of policies and
instruments to decarbonize the overall energy system with the aim of greenhouse gas (GHG)
emission reduction of at least 40 % till 2020 and 80 % - 95 % till 2050 in comparison to the
level of 1990 (Horschig and Szarka, 2015). Biomethane as an opportunity for the
decarbonization of the power, heat and transport sector was part of widely applied support
schemes to incite R&D efforts and market development. The most important support
schemes, the way the government interfered as market creator and the role of market failures
as a barrier of the biomethane market development can be looked up in (Horschig and Szarka,
2015). In this paper the focus shall be laid on how system dynamics can help to predict
market developments during times of political power-play. Support schemes for biomethane
were first installed in 2004. Since then the level of support was changed each two to three
years. From 2014 on the support is no longer sufficient for a further capacity development of
biomethane. There is huge uncertainty in the market if new support schemes will be installed
in not yet widely decarbonized markets like the heat sector, seldom features like the high level
of flexible energy supply and storage possibilities will be priced or if a European-wide trade
will be established. But there is not only uncertainty among market participants. Possible
impacts of new support schemes in terms of capacity development, sustainability, new
technologies or to avoid over-compensation have to be considered and estimated. Both,
market participants and decision-makers can benefit from system dynamics. With a sound
system dynamics model it is possible to derive scenario-driven simulation results on the
impact of new governmental market-influences.
Besides the fact that biomethane increases energy security and diversity and represents an
option for energy storage the main justification for the support of a market development is the
fact that biomethane can contribute to significant greenhouse gas savings depending on the
scope of application (where natural gas is substituted) and the supply chain. A comprehensive
study of Majer et al. (2011) analyzed several different value chains and determined possible
greenhouse gas savings (figure 1). Of course, greenhouse gas emission calculations are
strongly dependent on assumptions. Figure 1 displays possible greenhouse gas emission
savings of biomethane used in different value chains. Internal energy usage means that
internal heat and power, that arises during the energy conversion process is used for the
energy provision whereas external heat and power usage means the external supply of energy.
Highest savings can be achieved in the combined heat and power supply chains with organic
waste as feedstock. Savings of more than 100% can be achieved when organic waste is used
for energy provision instead of landfilling it (avoided emissions).
OwnE/n
OW/ED
OWE
OW/EE/p
RR/MIIEh
RR/MIE)
RRMEEh
RRMEBp
2
&
z
a
3
2
=
8
60% 80% 100% 120% 140% 160%
RR renewable resources TE internal energy usage M_ manure
P power shareinCHP OW organic waste
h heat share in CHP EE_ external energy usage
Figure 1 Greenhouse gas emission savings of biomethane against fossil reference in %
(following (Majer, 2011))
1.1 Biomethane market
Since 2004 a variety of support schemes (main influence by Renewable-Energy-Source-A ct)
fostered a biomethane market development. The first biomethane plant went on grid in 2006.
In the past ten years more than 180 biomethane plants were constructed feeding-in more than
110,000 Nm?/h (~ 8 TWhpa). In this way the largest biomethane market worldwide raised.
With the argument of too high costs for the power production out of biomethane the main
support was decreased in 2014. Compensation which is necessary due to higher costs of
biomethane in comparison to natural gas is no longer sufficient. Nonetheless the advantages
and possibilities of biomethane for the task of decarbonizing the energy system make it worth
to analyze the historic and current market situation, implement upcoming technologies and
transfer this into a system dynamics simulation model. In this way it is possible to derive
insights into market behavior and scenario-driven simulation results of possible futures for the
biomethane market. Results of political power-play can then be simulated and estimated.
200 140.000
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2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
number of biomethane plants —curulative feed-in capacity
Figure 2 Development of the German biomethane market (“Biogasanlagen zur Biomethan-
Produktion FNR-Mediathek,” 2015)
1.2 Aims and objectives
It is the aim of this work to analyze the historic market development of biomethane in
Germany till the present moment. Results are transferred to a system dynamics simulation
model. A future technology, the thermochemical production of biogenic synthetic natural gas
(bio-SNG) is implemented via a learning-curve & market adoption sub-model. After
completing the model building process scenarios are implemented and simulated. The
scenarios are designed in a way to reflect market predictions and hopes of participants.
Market shares of biomethane in the heat and transport sector are simulated as well as future
shares of bio-SNG under varying boundary conditions. Finally greenhouse gas savings for
each scenario are calculated.
1.3 Recent work on the topic
System Dynamics suits well for the task of simulating policies and political power-play in
renewable energy markets. Coupling System Dynamics with evaluation tools and methods
enables the simulation of medium-term effects of potential futures and the analysis of
interactions of economic and environmental interactions and feedbacks (Spyridaki and
Flamos, 2014). However, Aslani et al. (2014) stated, only little research has been done using
the System Dynamics methodology for dynamic modelling of renewable energy policies
involving energy and climate policies (Aslani et al., 2014). Possible explanations are
complexities when it comes to combining energy and climate policies and modeling their
feedbacks and the limitations of bottom-up approaches conceming the implementation of
market failures. SD Activities concerning Renewable Energies were done by Movilla et al,
Hsu et al and Barisa et al. ((Barisa et al., 2015; Hsu, 2012; Movilla et al., 2013)). Hsu
developed a System Dynamics model to verify renewable energy promoting policies for
cost/benefit analysis, COz-reduction targets and budget limitations (Hsu, 2012). His
simulation results show how the Taiwanese CO. mitigation goal can be reached by a proper
policy combination. Barisa et al. (2015) developed a System Dynamics model for the dynamic
simulation of the Latvian biodiesel market that is facing a new vision of the Latvian
goverment with the plan to end subsidizing the market but increasing the consumption of
biofuels (Barisa et al., 2013). Sanches-Pereira & Gomez (2014) used the SD methodology in
an analytical framework modeling the Swedish biofuels system (Sanches-Pereira and Gomez,
2014). Their results indicate different pathways for decision makers to reach national
renewable fuels goals. Jeffers et al. (2013) developed a SD model to analyze the US
bioenergy feedstock market for the biofuels and biopower industries (Jeffers et al., 2013).
These are regulated by several policies. They simulated how the market for bioenergy
feedstock will react on policy changes. Concluding it can be said that there are several
different SD models that include a part of a renewable energy system. Some deal with
biofuels others with the generation of renewable power. Renewable heat is the topic of only
few research papers. Until now there is no research paper of a dynamic simulation model for
the whole renewable energy market. Within the presented approach we will show that it is
possible to develop a dynamic simulation model that is capable of simulating policy changes
with effects to the three energy sectors power, heat and transport. The German biomethane
market will serve as the case example.
1.4 Political Intervention — a double-edged situation
If there was the political will to decarbonize the energy system, including the power and heat
supply as well as the transport sector, there are two main options to do so. On the one hand
there is the possibility to increase the price for fossil fuels, and hence include the external
costs. On the other hand, new technologies can be subsidized by money for R&D or
compensation for the production of green energy. One reason for the price difference (in most
cases) between fossil fuels and green energy is market failures. More work on this topic has
been done by (Brown, 2001; Fisher and Rothkopf, 1991; Jaffe et al., 2005). The current
market situation is mainly determined by subsidies. Although there is a mechanism in force
that considers extemal costs for fossil fuels, its impact is comparably low (European Union
Emission Trading Scheme). This is because the price for polluting the environment is way too
low to foster investment in renewable energy. Therefore, the scenarios used to simulate future
developments of the German biomethane market consider both possibilities to intervene in
markets.
2. Methods
We developed a system dynamics model of the German biomethane market. The first step
was to analyze the existing market, its structure and barriers and drivers. This research was
concluded in the set-up of a conceptual model. A research paper including a detailed
description as well as a figure of the conceptual model has recently been submitted to a
scholarly journal. In the final version of this manuscript, subject to acceptance, we will refer
to it. The next step was to transfer the conceptual model to a causal loop diagram. Details of it
can be looked up in (Horschig and Szarka, 2015). In the following the causal-loop-diagram
was transferred to a stock-and-flow diagram. The model was calibrated with historical data.
During the development of the stock-and-flow model several experts in the field of
biomethane were consulted to ensure a sound model building process. The learning-curve and
market-adoption sub-model of bio-SNG was calibrated with data of an associated PhD
project. The system dynamics model will be able to calculate scenario-dependent overall
greenhouse gas emission savings. The included natural gas flow sub-model can determine in
which scope of application natural gas will be substituted. In this way, the environmental
most beneficial scenario can be determined. In addition, the most economic beneficial
scenario is measured by avoided greenhouse gas emissions per input of compensation.
2.1. Scenarios
Until now we defined three different scenarios. The first scenario is the base scenario, where
the current situation is implemented into the model. This scenario was used to calibrate the
model. The second scenario is the green heat scenario. In this scenario it is simulated which
effect a direct subsidy would have to the use of biomethane for heating purposes. The third
scenario is the green transport scenario. This scenario is defined by a direct subsidy for
biomethane in the transport sector. Boundary conditions and main assumptions are equal for
all scenarios. The modeled time horizon is 2000-2030.
3. Results
Until now only the base scenario is implemented into the system dynamics model. The
simulation results are displayed in figure 3. It has to be mentioned that the compensations for
the production of biomethane are guaranteed for 20 years. After expiration of these payments
a nearly complete re-investment in the infrastructure (biomethane plant,etc.) is necessary.
However, current compensation schemes are not sufficient to re-finance this re-investment.
An ongoing biomethane production cannot be realized.
biomethane capacity development in Germany [base scenario]
140000
e
120000 &
=
~ 100000 8
3 hi 80000 3
s eo000
al =
€ 40000 2
5 3
a + 20000 3
5
2006 2010 2014 2018 2022 2026 2030
number of biomethane plants Germany — feed-in capacity Germany
Figure 3 Simulation results of base scenario
The simulation results indicate that there will be a gradually decrease of biomethane
production plants and thus feed-in capacity. Of course, current greenhouse gas savings of
2,800kt CO2eq per year will decrease, too.
The simulation results of the green heat scenario and the green transport scenario will be
shown at the annual conference in Delft. Furthermore, the final version of this manuscript,
subject to acceptance, will include these results. In addition, the simulation results of an
adapted emission trading scheme (section 1.4) will be shown at the conference, too.
4. Discussion & Conclusion
The aim of the presented research was to develop a market simulation model for the German
biomethane market that is capable of simulating future policies and determine the most
environmental and economic beneficial one. This model shall be seen as a step to a renewable
energy system model (including the sectors power, heat and transport) that is able to simulate
economic and environmental effects of new policies. So far, the presented approach was
calibrated with historical data and reproduces them well. The base scenario was implemented
and results indicate not the best future for biomethane in Germany. The bio-SNG sub-model
works well, too. Simulation results for different future bio-SNG prices and how they affect
the biomethane market were summarized in a scholarly paper, recently submitted to a journal.
Subject to acceptance, the reference will be in the final version of this paper.
Summarizing it can be said that simulation results of the base scenario indicate that without
further support the biomethane market in Germany will disappear in a mid-term period. To be
realistic, a future support of biomethane for power production only won't happen. Bioenergy
is too valuable for this purpose and competing renewable power supplying technologies like
wind power or solar power are cheaper. Although a comparison of wind power and solar
power with bioenergy is not fair because bioenergy can provide energy independently on
short-term weather effects. Promising markets for biomethane are seen in the heat and
transport sector (Deutsche Energie-A gentur GmbH, 2014). The results of our simulations
(green heat scenario and green transport scenario) will show decision makers the amount of
support that is needed for a further capacity development and hence an ongoing
decarbonization of the heat and transport sector. Biomethane can be an option for the
decarbonization of the inner-city heavy duty traffic that cannot be replaced by electric
vehicles. Simulation results will be shown at the conference and presented in the final version
of this paper, subject to acceptance.
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