188
6 Experimental Validation of the Methodology
Table 6.9 Comparison of the parameter configurations in the mean value and the optimization scenario (1-min resolution)
Optimization Scenario
(1-minute resolution)
Mean Value Scenario
(1-minute resolution)
IdleOptimizer_Line1
44,8 min
45,9 min
IdleOptimizer_Line2
19 min
19 min
StandbyOptimizer_Line1
19 min
19 min
StandbyOptimizer_Line2
19 min
19,03 min
IdleOptimizer_L1wash
120 min
120 min
IdleOptimizer_L2wash
120 min
120 min
with mean values. It is also conceivable to integrate individual machines for which
there are no exact energy profiles but only mean values into an optimization
scenario with exact energy consumption profiles.
6.3.6 Financial Evaluation of the Increase in Energy Efficiency
This section assesses the financial evaluation of the energy savings that can be
achieved by the methodology. The data with a resolution of one data record per
minute is used as the basis for this. This is since the data produced results of
the same quality as the higher resolution data but was recorded by the machine
controller and thus no additional costs were incurred in this scenario for the
purchase and installation of external measuring equipment. As already described at the beginning of this chapter, Bosch purchases 95% of the total energy
needed externally. The details of the framework agreement for the electricity supply are available for the evaluation of the optimization results. Table 6.10 lists
all relevant consumption-dependent energy price components. The power price
(electricity costs power ), which is essential to financially evaluate the peak reduction, is known but only for the currently required grid capacity. Since there is no
information on price differentiation depending on the height of the grid capacity,
it is not possible to financially assess the savings achieved by the reducing the
energy consumption peaks. As the grid capacity costs account for about 12% of
the total energy costs per months, the missing option to financially evaluate this
data is quite disturbing but assumptions about possible price differentiations do
not contribute to a realistic assessment. For this reason, the financial evaluation
of the peak reduction is omitted.
6 Experimental Validation of the Methodology
Table 6.9 Comparison of the parameter configurations in the mean value and the optimization scenario (1-min resolution)
Optimization Scenario
(1-minute resolution)
Mean Value Scenario
(1-minute resolution)
IdleOptimizer_Line1
44,8 min
45,9 min
IdleOptimizer_Line2
19 min
19 min
StandbyOptimizer_Line1
19 min
19 min
StandbyOptimizer_Line2
19 min
19,03 min
IdleOptimizer_L1wash
120 min
120 min
IdleOptimizer_L2wash
120 min
120 min
with mean values. It is also conceivable to integrate individual machines for which
there are no exact energy profiles but only mean values into an optimization
scenario with exact energy consumption profiles.
6.3.6 Financial Evaluation of the Increase in Energy Efficiency
This section assesses the financial evaluation of the energy savings that can be
achieved by the methodology. The data with a resolution of one data record per
minute is used as the basis for this. This is since the data produced results of
the same quality as the higher resolution data but was recorded by the machine
controller and thus no additional costs were incurred in this scenario for the
purchase and installation of external measuring equipment. As already described at the beginning of this chapter, Bosch purchases 95% of the total energy
needed externally. The details of the framework agreement for the electricity supply are available for the evaluation of the optimization results. Table 6.10 lists
all relevant consumption-dependent energy price components. The power price
(electricity costs power ), which is essential to financially evaluate the peak reduction, is known but only for the currently required grid capacity. Since there is no
information on price differentiation depending on the height of the grid capacity,
it is not possible to financially assess the savings achieved by the reducing the
energy consumption peaks. As the grid capacity costs account for about 12% of
the total energy costs per months, the missing option to financially evaluate this
data is quite disturbing but assumptions about possible price differentiations do
not contribute to a realistic assessment. For this reason, the financial evaluation
of the peak reduction is omitted.