The document models and analyzes Germany's electricity generation system up to 2040 under different scenarios using the EnergyPLAN macro-modeling tool. The model was validated by comparing historical data from 2003-2012 to model results, which showed minimal differences. Three scenarios were simulated: Business as Usual, 50% renewable electricity share by 2040, and 60% CO2 reduction by 2040. The 60% CO2 reduction scenario was selected as best using multi-criteria analysis. Optimization of installed power capacity for 2020 and 2040 under the best scenario found renewable sources would account for over 50% of capacity by 2020 with 46% cost savings, and over 75% of capacity by 2040 with 71% cost savings.

1. Energy Models and Scenarios: predicting Germany’s
electricity production system in 2040
Okoroma Justice (MSc.), and Bello Taofeeq (MSc.)
Petroleum Engineering- Politecnico di Torino, Italy
1. ABSTRACT
The modeling, simulation and optimization of Germany’s electricity production system
was done using the EnergyPLAN macro-modelling tool. Firstly, statistical data were
collected for a proper description and modelling of Germany’s macroeconomic and
energetic system, and a suitable driver was identified for further analysis. The model of
the electricity generating system (Reference Energy System, energy balance) of Germany
for ten years (2003 to 2012) was built in the EnergyPLAN environment, with the help of a
few assumptions and approximations. Analysis of the historical statistical data of
Germany was then performed, and the results computed with EnergyPLAN was
compared to the historical trends, as a necessary check for correct model match and
validation. Definition, simulation and comparative discussion of three different scenarios
(including Business as Usual) were done, considering different targets (CO2 Emissions
and reduction of fossil fuel consumption, in line with Germany’s policy) and suitable set
of commodities to be exploited in Germany. The best scenario was selected by applying
the Multi-Criteria Method, and finally, optimization of the composition of the installed
power capacity in 2020 and 2040 according to the target of the selected best scenario, to
obtain the cheapest configuration of the whole electricity production system (coal, gas,
nuclear, renewables).
Upon building the model, it was observed that the differences between the historical
data and model results were minimal, implying that the model is correct and could be
used to forecast the trends of same parameters (RES of Electricity Production, Share of
Renewables, and CO2 Emissions) for Germany’s electricity generating system, beyond
2012. Projected simulations showed increase in electricity production from RES, increase
in share of renewables, and decrease in CO2 emissions. Among the three scenarios
(Business as usual, 50% RES share in electricity production, and 60% reduction in CO2
from electricity production), the best scenario was the 3rd. Finally, optimization yielded
a reduced total cost for the optimized case as compared to the actual scenario, total cost
savings were 46% and 71% for 2020 and 2040 respectively; the renewable share also
increased. These findings proved that EnergyPLAN tool is useful for macroscale
modelling.
2. BACKGROUND
The German power system is relatively large in Europe. According to the “Renewable:
2014 Global Status Report[1], Germany also had the highest share of renewable power
in Europe in terms of installed capacity, and in fact, is the country with the third largest
amount of installed renewables capacity (excluding hydro) in the world. The European
Union (EU) has set the goal of reducing GHG emissions 20% below 1990 levels by 2020.
In view of this, Germany set a target of reducing GHG emissions by at least 40%
compared to 1990 levels by 2020. Also, the Energiewende aims to reduce greenhouse
gas emissions economy-wide by 80–95% by 2050 [2].
Under the European Renewable Energy Directive, Germany has been allocated an 18%
target for the share of gross final energy consumption to be met by renewable energy by
2020. In 2014, the Renewable Energy Act was amended, with the goal of continuing
progress towards Germany’s renewable energy targets, while controlling cost.
3. OBJECTIVES
The aim of this work is to model and analyze Germany’s electricity generation system,
simulate its evolution based on different scenarios up to 2040; and as such, investigate
whether the goals set by the Energiewende are feasible . However, this work is not
exhaustive since it focuses on the electricity generation only (no heating, no transport),
and as such, is limited in content and applications.
4. METHODS
In order to achieve the above aim, the following eight (7) steps were taken:
1. Firstly, collection of data, and description of Germany’s macroeconomic and
energetic system on the basis of available statistical data, this is relevant for
identifying suitable driver for future projections and further analysis.
2. Build in the EnergyPLAN environment the model of the electricity generating system
(Reference Energy System, energy balance) of the Germany for the past 10 years.
3. Analysis of the historical/statistical data of Germany, and comparing the historical
trends with the results computed with EnergyPLAN; important for model validation.
4. Definition, simulation and comparative discussion of three different scenarios
(including Business As Usual), considering key targets (reduce fossil fuel consumption,
in this case) and suitable set of commodities to be exploited in Germany.
5. Simulation of the evolution of energy demand and supply panorama of Germany,
according to the different scenarios.
6. Selection of the best scenario by applying the Multi-Criteria Method.
7. Optimization of the composition of installed power capacity in 2020 and 2040 based
to the target of the selected best scenario.
5. RESULTS
5.1 MODEL VALIDATION
6. CONCLUSIONS
In conclusion, we successfully modeled the Germany electricity production system and
also projected the trends for the future as regards the best and optimal scenario for the
country, this was done with a good touch of reality and reasonable sense of assumptions
and approximations.
References: [1] Ren21 (2014). Renewables 2014 Global Status Report. Available At http//www.ren21.Net/therenewables-2014-global-status-report-is-now-available/ [Last Accessed: May 30 2016]
[2] Agora Energiewende, Report On The German Power System, Version 1.01, Country Profile. Agora Energiewende, Berlin, Germany. February, 2015
[3] Savoldi L. et al, Macroscale modelling with EnergyPLAN, Models and Scenarios for Energy Planning (AA.2015-2016). Dipartimento Energia, Politecnico di Torino.
INPUT DATA REQUIRED FOR ENERGYPLAN:
A lot of efforts were made to obtain the required input data for EnergyPLAN tool; along
the line, some reasonable assumptions and approximations were made. Also, a few
scaling factors and calibration factors were defined. This section highlights the
assumptions and justify the approximations.
Nonetheless, a huge amount of input data was obtained from the following sources: the
International Energy Agency (energy and cost data), Eurostat (national accounts and
energy data), BP Statistical Review of World Energy (energy data), the World Bank
(national accounts and energy data), European Network of Transmission System
Operators for Electricity (energy data), and International Renewable Energy Agency
(energy data)
5.2 SIMULATION AND ANALYSIS OF SCENARIOS
• Scenario 1: Business as Usual (BAU) up to 2040
• Scenario 2: 50% RES Share in electricity production by 2040
• Scenario 3: 60% reduction in CO2 from electricity production by 2040
5.3 BEST SCENARIO, USING
MULTI-CRITERIA METHOD:
5.4 OPTIMIZATION USING LINEAR PROGRAMMING
The differences between the historical
data and model results are not much. This
implies that the model is correct and can
be used to forecast the trends of same
parameters (Renewable share of
electricity, share of renewables and CO2
emissions) for Germany’s energy systems,
beyond 2012.
For 2020, The renewable sources
accounted for more than 50% of the
installed power capacity. The cost savings
was 46%.
For 2040, the renewable sources
accounted for more than 75% of the
installed power capacity. The cost savings
was 71%.