144
5 Development of a Simulation-based Methodology …
Now the peak optimization experiment can be setup as follows: The objective
function is the minimization of the maximum peak found in the energy data flow
set, the permanent documentation of the current energy flow of the production
line. The number of iterations is set to 60, as this covers all iterations possible
when varying the offset parameter between zero and five minutes in three-secondsteps (Figure 5.40). In order to illustrate the effect of the offset parameter, it is
initially only applied to one machine. In principle, it is possible to determine the
offset in parallel for all machines in relation to each other. If, however, the machines are to be switched on manually by the calculated offset, it will be difficult to
implement this in practice. The consideration of several offset parameters in practice therefore requires the automated transfer of the offset values to the machine
control system.
The machine that is looked at, can be selected in the main table by removing
the ignore-flag in the parameter settings. The idle and standby optimizer parameters are set to the best feasible solution of the total consumption optimizer
experiment, which is run first. The parameters are set to the type fixed will not
be varied during the peak optimization experiment. The model time is set to a
four-week period.
The order of the execution of the defined optimization experiments is dependent on the priorities of the optimization objectives. The objective with the highest
priority is run first, as the optimization parameters are then fixed in the following
experiment executions. For the case study, the highest priority gets the optimization of the overall energy consumption. The peak reduction is then performed
for the optimized total energy consumption scenario without affecting the total
consumption value.
The methodology can be summarized using the following steps:
Step 1: Modeling of different aspects of the production simulation.
Step 2: Implementation of production and energy data in the model.
Step 3: Definition of required optimization parameters for the simulation.
Step 4: Definition of objective functions and setup of optimization
experiments.
Step 5: Execution of the optimization experiment according to the priority of
the objective functions. The optimum parameters are copied into the
simulation model and the following optimization experiments (as fixed
parameters).
5 Development of a Simulation-based Methodology …
Now the peak optimization experiment can be setup as follows: The objective
function is the minimization of the maximum peak found in the energy data flow
set, the permanent documentation of the current energy flow of the production
line. The number of iterations is set to 60, as this covers all iterations possible
when varying the offset parameter between zero and five minutes in three-secondsteps (Figure 5.40). In order to illustrate the effect of the offset parameter, it is
initially only applied to one machine. In principle, it is possible to determine the
offset in parallel for all machines in relation to each other. If, however, the machines are to be switched on manually by the calculated offset, it will be difficult to
implement this in practice. The consideration of several offset parameters in practice therefore requires the automated transfer of the offset values to the machine
control system.
The machine that is looked at, can be selected in the main table by removing
the ignore-flag in the parameter settings. The idle and standby optimizer parameters are set to the best feasible solution of the total consumption optimizer
experiment, which is run first. The parameters are set to the type fixed will not
be varied during the peak optimization experiment. The model time is set to a
four-week period.
The order of the execution of the defined optimization experiments is dependent on the priorities of the optimization objectives. The objective with the highest
priority is run first, as the optimization parameters are then fixed in the following
experiment executions. For the case study, the highest priority gets the optimization of the overall energy consumption. The peak reduction is then performed
for the optimized total energy consumption scenario without affecting the total
consumption value.
The methodology can be summarized using the following steps:
Step 1: Modeling of different aspects of the production simulation.
Step 2: Implementation of production and energy data in the model.
Step 3: Definition of required optimization parameters for the simulation.
Step 4: Definition of objective functions and setup of optimization
experiments.
Step 5: Execution of the optimization experiment according to the priority of
the objective functions. The optimum parameters are copied into the
simulation model and the following optimization experiments (as fixed
parameters).