Maximum uptime constraint #859
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| @@ -373,6 +373,13 @@ def _create(self, group=None): | |||
| if g[2].nonconvex.minimum_uptime is not None | |||
| ] | |||
| ) | |||
| self.MAXUPTIMEFLOWS = Set( | |||
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Must be placed close to
as the _non_convex_flow.py was refactored into _non_convex_flow_block.py| @@ -545,6 +552,34 @@ def _min_uptime_rule(block, i, o, t): | |||
| self.MINUPTIMEFLOWS, m.TIMESTEPS, rule=_min_uptime_rule | |||
| ) | |||
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| def _max_uptime_rule(block, i, o, t): | |||
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Here it seems it should be set similar to
It seems that here we would also need a def _max_downtime_constraint(self) to wrap around max_uptime_rule
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This PR adds a maximum uptime constraint for NonConvex flows.
The motivation is a scheduling problem for industrial processes, if you have e.g. a cyclic industrial process (block profile) with a certain time between each process and a fixed length of the process. With a
maximum_uptimeattribute and in combination with the existingminimum_downtimeand other attributes, it is possible to model this problem within oemof.solph. See the example.The API is an additional attribute at the NonConvex class:
The constraint itself is based on the rule that the sum of
maximum_uptime+1 consecutive binary decision variables of the flow need to be smaller or equal the maximum operation timemaximum_uptime.If there is nothing against the implementation of this constraint, I would continue to implement docstrings, tests, etc. I would a appreciate a short feedback on that.
p.s.: This constraint would allow the scheduling of block profiles. We would be also interested to schedule fixed but fluctuating profiles. Any ideas?