![](bg6.png)
Although the above results are for a simple
simulation model, it was shown that the backward
simulator can catch the unfavorable conditions and
can narrow down the area of such conditions.
To make Figs 8 and 9, we need to judge the
simulation trace displayed in Fig.7 whether it
continues forever or not for the feasible cases.
Probably, we have to set some time limit for each
case’s calculation, beyond which the simulator
automatically judges the case feasible.
7 CONCLUSION AND FUTURE
WORK
We designed the forward and backward simulator
with capability of using numerical simulation
models, backward range processing and case
branching. The implemented simulator shows
validity of the backward simulation for a simple case
of dynamic pricing control model planned for smart
grid.
We need to have many experiences by applying
the simulator to practical applications. Multiple user
simulation (Fig.10) is the top on our list, which
requires multiple branching components and a
backward sum component. We have to estimate
processing time of our backward simulator in the
case of large number of branches. Also, we plan to
design automatic judging algorithm for cases of
continuing or vibrating simulation. And also,
externalization of internal state variables will be
demanded in a complex system simulation.
i
i
o
o
1
i
o
2
i
i
i
temp
user1
heater1
price
user2
heater2
o
o
o
1
o
+
o
con-p
con-t
2
o
r
r
i
1
o
2
o
Figure 10: A backward simulation structure for two users.
ACKNOWLEDGEMENTS
This work was partly supported by JSPS KAKENHI
Grant Number 25540006. The authors wish to thank
the reviewers for their valuable comments.
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