| Conditions | 11 |
| Total Lines | 316 |
| Code Lines | 196 |
| Lines | 143 |
| Ratio | 45.25 % |
| Changes | 0 | ||
Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
Complex classes like solph.constraints.storage_level.storage_level_constraint() often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
| 1 | """A constraint to allow flows from to a storage based on the storage level. |
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| 13 | def storage_level_constraint( |
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| 14 | model, |
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| 15 | name, |
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| 16 | storage_component, |
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| 17 | multiplexer_bus, |
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| 18 | input_levels, |
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| 19 | output_levels, |
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| 20 | ): |
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| 21 | r""" |
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| 22 | Add constraints to implement storage content based access. |
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| 23 | |||
| 24 | As a GenericStorage just allows exactly one input and one output, |
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| 25 | an additional bus, the multiplexer_bus, is used for the connections. |
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| 26 | Note that all Flow objects connected to the multiplexer_bus have to have |
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| 27 | a nominal_capacity. |
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| 28 | |||
| 29 | Parameters |
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| 30 | ---------- |
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| 31 | model : oemof.solph.Model |
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| 32 | Model to which the constraint is added. |
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| 33 | name : string |
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| 34 | Name of the multiplexer. |
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| 35 | storage_component : oemof.solph.components.GenericStorage |
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| 36 | Storage component whose content should mandate |
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| 37 | the possible inputs and outputs. |
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| 38 | multiplexer_bus : oemof.solph.Bus |
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| 39 | Bus which connects the input and output levels to the storage. |
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| 40 | input_levels : dictionary with oemof.solph.Bus as keys and float as values |
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| 41 | Dictionary of buses which act as inputs and corresponding levels |
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| 42 | output_levels : dictionary with oemof.solph.Bus as keys and float as values |
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| 43 | Dictionary of buses which act as outputs and corresponding level |
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| 44 | |||
| 45 | Verbose description can be found in https://arxiv.org/abs/2211.14080 |
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| 46 | """ |
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| 47 | |||
| 48 | def _outputs(): |
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| 49 | OUTPUTS = po.Set(initialize=output_levels.keys()) |
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| 50 | setattr(model, f"{name}_OUTPUTS", OUTPUTS) |
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| 51 | |||
| 52 | active_output = po.Var( |
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| 53 | OUTPUTS, model.TIMESTEPS, domain=po.Binary, bounds=(0, 1) |
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| 54 | ) |
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| 55 | setattr(model, f"{name}_active_output", active_output) |
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| 56 | |||
| 57 | constraint_name = f"{name}_output_active_constraint" |
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| 58 | |||
| 59 | def _output_active_rule(m): |
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| 60 | r""" |
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| 61 | .. math:: |
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| 62 | y_n \le E(t) / E_n |
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| 63 | """ |
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| 64 | for t in m.TIMESTEPS: |
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| 65 | for o in output_levels: |
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| 66 | getattr(m, constraint_name).add( |
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| 67 | (o, t), |
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| 68 | m.GenericStorageBlock.storage_content[ |
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| 69 | storage_component, t + 1 |
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| 70 | ] |
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| 71 | / storage_component.nominal_storage_capacity |
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| 72 | >= active_output[o, t] * output_levels[o], |
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| 73 | ) |
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| 74 | |||
| 75 | setattr( |
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| 76 | model, |
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| 77 | constraint_name, |
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| 78 | po.Constraint( |
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| 79 | OUTPUTS, |
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| 80 | model.TIMESTEPS, |
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| 81 | noruleinit=True, |
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| 82 | ), |
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| 83 | ) |
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| 84 | setattr( |
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| 85 | model, |
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| 86 | constraint_name + "build", |
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| 87 | po.BuildAction(rule=_output_active_rule), |
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| 88 | ) |
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| 89 | |||
| 90 | # Define constraints on the output flows |
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| 91 | def _constraint_output_rule(m, o, t): |
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| 92 | return ( |
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| 93 | m.flow[multiplexer_bus, o, t] |
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| 94 | / m.flows[multiplexer_bus, o].nominal_capacity |
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| 95 | <= active_output[o, t] |
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| 96 | ) |
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| 97 | |||
| 98 | setattr( |
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| 99 | model, |
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| 100 | f"{name}_output_constraint", |
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| 101 | po.Constraint( |
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| 102 | OUTPUTS, |
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| 103 | model.TIMESTEPS, |
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| 104 | rule=_constraint_output_rule, |
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| 105 | ), |
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| 106 | ) |
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| 107 | |||
| 108 | View Code Duplication | def _outputs_tsam(): |
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|
|
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| 109 | OUTPUTS = po.Set(initialize=output_levels.keys()) |
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| 110 | setattr(model, f"{name}_OUTPUTS", OUTPUTS) |
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| 111 | |||
| 112 | active_output = po.Var( |
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| 113 | OUTPUTS, |
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| 114 | model.TIMEINDEX_TYPICAL_CLUSTER_OFFSET, |
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| 115 | domain=po.Binary, |
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| 116 | bounds=(0, 1), |
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| 117 | ) |
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| 118 | setattr(model, f"{name}_active_output", active_output) |
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| 119 | |||
| 120 | constraint_name = f"{name}_output_active_constraint" |
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| 121 | |||
| 122 | def _output_active_rule(m): |
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| 123 | r""" |
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| 124 | .. math:: |
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| 125 | y_n \le E(t) / E_n |
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| 126 | """ |
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| 127 | for p, i, g in m.TIMEINDEX_CLUSTER: |
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| 128 | k = m.es.tsa_parameters[p]["order"][i] |
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| 129 | t = m.get_timestep_from_tsam_timestep(p, k, g) |
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| 130 | tk = m.get_timestep_from_tsam_timestep(p, k, g) |
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| 131 | for o in output_levels: |
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| 132 | getattr(m, constraint_name).add( |
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| 133 | (o, p, i, g), |
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| 134 | ( |
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| 135 | m.GenericStorageBlock.intra_storage_delta[ |
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| 136 | storage_component, p, k, g + 1 |
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| 137 | ] |
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| 138 | + m.GenericStorageBlock.inter_storage_content[ |
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| 139 | storage_component, i |
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| 140 | ] |
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| 141 | * (1 - storage_component.loss_rate[t]) |
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| 142 | ** (g * m.timeincrement[tk]) |
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| 143 | ) |
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| 144 | / storage_component.nominal_storage_capacity |
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| 145 | >= active_output[o, p, k, g] * output_levels[o], |
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| 146 | ) |
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| 147 | |||
| 148 | setattr( |
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| 149 | model, |
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| 150 | constraint_name, |
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| 151 | po.Constraint( |
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| 152 | OUTPUTS, |
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| 153 | model.TIMEINDEX_CLUSTER, |
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| 154 | noruleinit=True, |
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| 155 | ), |
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| 156 | ) |
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| 157 | setattr( |
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| 158 | model, |
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| 159 | constraint_name + "build", |
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| 160 | po.BuildAction(rule=_output_active_rule), |
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| 161 | ) |
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| 162 | |||
| 163 | # Define constraints on the output flows |
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| 164 | def _constraint_output_rule(m, o, p, k, g): |
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| 165 | t = m.get_timestep_from_tsam_timestep(p, k, g) |
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| 166 | return ( |
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| 167 | m.flow[multiplexer_bus, o, p, t] |
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| 168 | / m.flows[multiplexer_bus, o].nominal_capacity |
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| 169 | <= active_output[o, p, k, g] |
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| 170 | ) |
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| 171 | |||
| 172 | setattr( |
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| 173 | model, |
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| 174 | f"{name}_output_constraint", |
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| 175 | po.Constraint( |
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| 176 | OUTPUTS, |
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| 177 | model.TIMEINDEX_TYPICAL_CLUSTER, |
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| 178 | rule=_constraint_output_rule, |
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| 179 | ), |
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| 180 | ) |
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| 181 | |||
| 182 | if not model.TSAM_MODE: |
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| 183 | _outputs() |
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| 184 | else: |
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| 185 | _outputs_tsam() |
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| 186 | |||
| 187 | def _inputs(): |
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| 188 | INPUTS = po.Set(initialize=input_levels.keys()) |
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| 189 | setattr(model, f"{name}_INPUTS", INPUTS) |
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| 190 | |||
| 191 | inactive_input = po.Var( |
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| 192 | INPUTS, model.TIMESTEPS, domain=po.Binary, bounds=(0, 1) |
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| 193 | ) |
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| 194 | setattr(model, f"{name}_active_input", inactive_input) |
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| 195 | |||
| 196 | constraint_name = f"{name}_input_active_constraint" |
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| 197 | |||
| 198 | def _input_active_rule(m): |
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| 199 | r""" |
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| 200 | .. math:: |
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| 201 | \hat{y}_n \ge (E(t) - E_n) / E_{max} |
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| 202 | """ |
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| 203 | for t in m.TIMESTEPS: |
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| 204 | for i in input_levels: |
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| 205 | getattr(m, constraint_name).add( |
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| 206 | (i, t), |
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| 207 | ( |
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| 208 | m.GenericStorageBlock.storage_content[ |
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| 209 | storage_component, t |
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| 210 | ] |
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| 211 | / storage_component.nominal_storage_capacity |
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| 212 | - input_levels[i] |
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| 213 | ) |
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| 214 | <= inactive_input[i, t], |
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| 215 | ) |
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| 216 | |||
| 217 | setattr( |
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| 218 | model, |
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| 219 | constraint_name, |
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| 220 | po.Constraint( |
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| 221 | INPUTS, |
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| 222 | model.TIMESTEPS, |
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| 223 | noruleinit=True, |
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| 224 | ), |
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| 225 | ) |
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| 226 | setattr( |
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| 227 | model, |
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| 228 | constraint_name + "build", |
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| 229 | po.BuildAction(rule=_input_active_rule), |
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| 230 | ) |
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| 231 | |||
| 232 | # Define constraints on the input flows |
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| 233 | def _constraint_input_rule(m, i, t): |
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| 234 | return ( |
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| 235 | m.flow[i, multiplexer_bus, t] |
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| 236 | / m.flows[i, multiplexer_bus].nominal_capacity |
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| 237 | <= 1 - inactive_input[i, t] |
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| 238 | ) |
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| 239 | |||
| 240 | setattr( |
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| 241 | model, |
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| 242 | f"{name}_input_constraint", |
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| 243 | po.Constraint( |
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| 244 | INPUTS, |
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| 245 | model.TIMESTEPS, |
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| 246 | rule=_constraint_input_rule, |
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| 247 | ), |
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| 248 | ) |
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| 249 | |||
| 250 | View Code Duplication | def _inputs_tsam(): |
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| 251 | INPUTS = po.Set(initialize=input_levels.keys()) |
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| 252 | setattr(model, f"{name}_INPUTS", INPUTS) |
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| 253 | |||
| 254 | inactive_input = po.Var( |
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| 255 | INPUTS, |
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| 256 | model.TIMEINDEX_TYPICAL_CLUSTER_OFFSET, |
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| 257 | domain=po.Binary, |
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| 258 | bounds=(0, 1), |
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| 259 | ) |
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| 260 | setattr(model, f"{name}_active_input", inactive_input) |
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| 261 | |||
| 262 | constraint_name = f"{name}_input_active_constraint" |
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| 263 | |||
| 264 | def _input_active_rule(m): |
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| 265 | r""" |
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| 266 | .. math:: |
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| 267 | \hat{y}_n \ge (E(t) - E_n) / E_{max} |
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| 268 | """ |
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| 269 | for p, i, g in m.TIMEINDEX_CLUSTER: |
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| 270 | k = m.es.tsa_parameters[p]["order"][i] |
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| 271 | t = m.get_timestep_from_tsam_timestep(p, k, g) |
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| 272 | tk = m.get_timestep_from_tsam_timestep(p, k, g) |
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| 273 | for inp in input_levels: |
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| 274 | getattr(m, constraint_name).add( |
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| 275 | (inp, p, i, g), |
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| 276 | ( |
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| 277 | m.GenericStorageBlock.intra_storage_delta[ |
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| 278 | storage_component, p, k, g + 1 |
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| 279 | ] |
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| 280 | + m.GenericStorageBlock.inter_storage_content[ |
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| 281 | storage_component, i |
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| 282 | ] |
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| 283 | * (1 - storage_component.loss_rate[t]) |
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| 284 | ** (g * m.timeincrement[tk]) |
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| 285 | ) |
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| 286 | / storage_component.nominal_storage_capacity |
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| 287 | - input_levels[inp] |
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| 288 | <= inactive_input[inp, p, k, g], |
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| 289 | ) |
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| 290 | |||
| 291 | setattr( |
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| 292 | model, |
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| 293 | constraint_name, |
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| 294 | po.Constraint( |
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| 295 | INPUTS, |
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| 296 | model.TIMEINDEX_CLUSTER, |
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| 297 | noruleinit=True, |
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| 298 | ), |
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| 299 | ) |
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| 300 | setattr( |
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| 301 | model, |
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| 302 | constraint_name + "build", |
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| 303 | po.BuildAction(rule=_input_active_rule), |
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| 304 | ) |
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| 305 | |||
| 306 | # Define constraints on the input flows |
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| 307 | def _constraint_input_rule(m, i, p, k, g): |
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| 308 | t = m.get_timestep_from_tsam_timestep(p, k, g) |
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| 309 | return ( |
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| 310 | m.flow[i, multiplexer_bus, p, t] |
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| 311 | / m.flows[i, multiplexer_bus].nominal_capacity |
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| 312 | <= 1 - inactive_input[i, p, k, g] |
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| 313 | ) |
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| 314 | |||
| 315 | setattr( |
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| 316 | model, |
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| 317 | f"{name}_input_constraint", |
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| 318 | po.Constraint( |
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| 319 | INPUTS, |
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| 320 | model.TIMEINDEX_TYPICAL_CLUSTER, |
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| 321 | rule=_constraint_input_rule, |
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| 322 | ), |
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| 323 | ) |
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| 324 | |||
| 325 | if not model.TSAM_MODE: |
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| 326 | _inputs() |
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| 327 | else: |
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| 328 | _inputs_tsam() |
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| 329 |