A central basis for the simulation is a detailed database on cost potential curves of renewable electricity generation technologies in Germany. An overview of the potential curve is given in Figure 7-5.
In the given simulation platform a major player is an investment planner that determines the expected income of renewable investment options based on the available potential, the re-quired interest rate and the available support. Based on this information, a production request is generated which contains all investment options with a positive annuity. An important pa-rameter for the investment planner is the applied interest rate for the calculation. Based on a recent study presented by Held (Held et al., 2006), an interest rate of 6.6 % is assumed for the German feed-in system. Since the German feed-in system is based on nominal support values, the impact of inflation has to be taken into account. Based on an analysis of the development of inflation over the past 50 years, an inflation rate of 2 % is assumed (Sensfuß, 2004).
The "Renewable Energy Source-Plant Producer" builds new renewable power plants accord-ing to the available production capacity. Requests exceedaccord-ing the production capacity are not fulfilled. If the requests for new plants exceed the production capacity, the plant producer considers building new production facilities based on the remaining potential for the renew-able technology and its own requirements concerning the expected utilization of new
produc-tion facilities. The expansion of producproduc-tion facilities is also limited by a maximum value rep-resenting the real world restrictions to the expansion of production facilities. In case of wind energy the maximum annual extension of production facilities is limited. Thereby the "RES-Plant producer" of the simulation is used as an aggregate for all the planning and construction capability of a country needed to carry out projects for the construction and operation of re-newable power plants. Interaction with construction and planning capabilities of neighbouring countries are not taken into account in the current version of the model.
Two smaller modules seek to integrate the aspect of technology learning and the dampening effect of planning and authorization procedures. The module for technology-learning simu-lates the impact by adjusting the cost of renewable power plants by an annual reduction factor of 1.5 % reflecting the average annual cost reduction presumed in the Renewable Energy Source Act. In principle a technology learning based on learning curves could also be inte-grated, but the additional benefit is questionable as the model only simulates the German de-velopment. An adequate picture of the learning effects would require a lot of external learning which can only be integrated as external input parameter. The authorization module reflects the dampening effect if the installed capacity gets close to the limitations of the available technological potential. In reality it gets increasingly difficult to utilize the remaining poten-tial if a large part of the generation potenpoten-tial is already utilized. Authorization procedures last longer and it is more difficult to discover places which are e. g. suitable for wind power plants. In the given version this aspect is integrated by two factors. The first factor determines the possible utilization of the generation potential which can take place without the dampen-ing effect. The second parameter determines the actual dampendampen-ing effect in terms of the pro-duction request sent by the investment planner. Both parameters are determined in a calibra-tion procedure. An overview of the model structure and the applied formulas is given in Figure 7-5 and Formula 7-3.
0 5,000 10,000 15,000 20,000 25,000 30,000
2,600 2,500
2,400 2,300
2,200 2,100
2,000 1,900
1,800 1,600
1,400 1,200 Utilization in full load hours
Cumulated potential in MW
Figure 7-5: Potential curves of wind energy in Germany
Source: own illustration, data: renewable potential database of the Fraunhofer Institute for Systems and Inno-vation Research
Cost 3. Send production request 7. Determine investment ()
RES-Plant Producer m Production capacity v Utilization factor
µ Max. annual capacity growth 5. Produce RES power plants () 6. Deliver plants ()
8. Evaluate pr. capacity () 9. Extend production facilities ()
Technology Learning - q Annual learning rate 10. Update Cost data ( ) 3. Send production request 7. Determine investment ()
RES-Plant Producer m Production capacity v Utilization factor
µ Max. annual capacity growth 5. Produce RES power plants () 6. Deliver plants ()
8. Evaluate pr. capacity () 9. Extend production facilities ()
RES-Plant Producer m Production capacity v Utilization factor
µ Max. annual capacity growth 5. Produce RES power plants () 6. Deliver plants ()
8. Evaluate pr. capacity () 9. Extend production facilities ()
Technology Learning - q Annual learning rate 10. Update Cost data ( )
Technology Learning - q Annual learning rate 10. Update Cost data ( )
Technology Learning - q Annual learning rate 10. Update Cost data ( )
Figure 7-6: Structure of the developed simulation platform
Source: own illustration
Formula 7-3: Mathematical representation of simulation investment processes
1. Predict the annuity of the potential income of an investment option within the cost potential curve
2. Calculate annuity of the cost
d
3. Determine production request )
5. - 7. Produce renewable power plants
⎩
( )
8. - 9. Extend production facility
⎪⎪
10. Update cost data q
Variables Unit Indices
a = Annuity of the expected income [Euro] t = Time step in years b = Annuity of cost [Euro] d = Index of potential step c = Variable cost of the technology [Euro/MWh]
e = Feasible potential or renewable gen.
capacity [MW]
f = Feed-in tariff [Euro/MWh]
g = Installed capacity [MW]
h = Available capacity in potential step [MW]
k = Investment [Euro/MW]
l = Interest rate of soft loans [%]
m = Maximum output of production facility [MW]
p = Potential limit [MW]
q = Learning factor [None]
r Production request [MW]
s = Authorized production request [MW]
u = Utilization [Hours]
x = Annual income [Euro]
γ = Growth damper [None]
δ = Interest rate [%]
µ = Maximum annual growth of the pro-duction facilities
[MW]
ν = Required utilization of new production facilities
[Years]
σ = Share of soft loans [%]
φ = Projected market price for electricity [Euro/MWh]
ψ = Lifetime of the technology [Years]