Streamline investment periods

examples/generic_invest_limit/example_generic_invest.py

@@ -74,7 +74,7 @@ def main(optimize=True):
     data = [0, 15, 30, 35, 20, 25, 27, 10, 5, 2, 15, 40, 20, 0, 0]
 
     # create an energy system
-    idx = solph.create_time_index(2020, number=len(data))
+    idx = solph.create_time_index(2020, number_of_intervals=len(data))
     es = solph.EnergySystem(timeindex=idx, infer_last_interval=False)
 
     # Parameter: costs for the sources

examples/invest_nonconvex_flow_examples/diesel_genset_nonconvex_investment.py

@@ -101,7 +101,7 @@ def main(optimize=True):
 
     # Create the energy system.
     date_time_index = solph.create_time_index(
-        number=n_days * 24, start=start_date
+        number_of_intervals=n_days * 24, start=start_date
     )
     energysystem = solph.EnergySystem(
         timeindex=date_time_index, infer_last_interval=False

examples/investment_with_minimal_invest/minimal_invest.py

@@ -41,7 +41,7 @@ def main(optimize=True):
     data = [0, 15, 30, 35, 20, 25, 27, 10, 5, 2, 15, 40, 20, 0, 0]
 
     # create an energy system
-    idx = solph.create_time_index(2017, number=len(data))
+    idx = solph.create_time_index(2017, number_of_intervals=len(data))
     es = solph.EnergySystem(timeindex=idx, infer_last_interval=False)
 
     bus_0 = solph.Bus(label="bus_0")

examples/min_max_runtimes/min_max_runtimes.py

@@ -42,7 +42,7 @@ def main(optimize=True):
         demand_el[n] = 1
 
     # create an energy system
-    idx = solph.create_time_index(2017, number=24)
+    idx = solph.create_time_index(2017, number_of_intervals=24)
     es = solph.EnergySystem(timeindex=idx, infer_last_interval=False)
 
     # power bus and components

examples/result_object/result_object.py

@@ -122,7 +122,9 @@ def main(optimize=True):
     )
 
     logging.info("Initialize the energy system")
-    date_time_index = create_time_index(2012, number=number_of_time_steps)
+    date_time_index = create_time_index(
+        2012, number_of_intervals=number_of_time_steps
+    )
 
     # create the energysystem and assign the time index
     energysystem = EnergySystem(

examples/simple_dispatch/simple_dispatch.py

@@ -69,7 +69,7 @@ def main(optimize=True):
 
     # Create an energy system and optimize the dispatch at least costs.
     # ####################### initialize and provide data #####################
-    datetimeindex = create_time_index(2016, number=len(data))
+    datetimeindex = create_time_index(2016, number_of_intervals=len(data))
     energysystem = EnergySystem(
         timeindex=datetimeindex, infer_last_interval=False
     )

examples/start_and_shutdown_costs/startup_shutdown.py

@@ -63,7 +63,7 @@ def main(optimize=True):
         0,
     ]
     # create an energy system
-    idx = solph.create_time_index(2017, number=len(demand_el))
+    idx = solph.create_time_index(2017, number_of_intervals=len(demand_el))
     es = solph.EnergySystem(timeindex=idx, infer_last_interval=False)
 
     # power bus and components

examples/storage_investment/v1_invest_optimize_all_technologies.py

@@ -111,7 +111,9 @@ def main(optimize=True):
 
     logger.define_logging()
     logging.info("Initialize the energy system")
-    date_time_index = solph.create_time_index(2012, number=number_timesteps)
+    date_time_index = solph.create_time_index(
+        2012, number_of_intervals=number_timesteps
+    )
     energysystem = solph.EnergySystem(
         timeindex=date_time_index, infer_last_interval=False
     )

examples/storage_investment/v2_invest_optimize_only_gas_and_storage.py

@@ -114,7 +114,9 @@ def main(optimize=True):
 
     logger.define_logging()
     logging.info("Initialize the energy system")
-    date_time_index = solph.create_time_index(2012, number=number_timesteps)
+    date_time_index = solph.create_time_index(
+        2012, number_of_intervals=number_timesteps
+    )
 
     energysystem = solph.EnergySystem(
         timeindex=date_time_index, infer_last_interval=False

examples/storage_investment/v3_invest_optimize_only_storage_with_fossil_share.py

@@ -113,7 +113,9 @@ def main(optimize=True):
 
     logger.define_logging()
     logging.info("Initialize the energy system")
-    date_time_index = solph.create_time_index(2012, number=number_timesteps)
+    date_time_index = solph.create_time_index(
+        2012, number_of_intervals=number_timesteps
+    )
 
     energysystem = solph.EnergySystem(
         timeindex=date_time_index, infer_last_interval=False

examples/storage_investment/v4_invest_optimize_all_technologies_with_fossil_share.py

@@ -110,7 +110,9 @@ def main(optimize=True):
 
     logger.define_logging()
     logging.info("Initialize the energy system")
-    date_time_index = solph.create_time_index(2012, number=number_timesteps)
+    date_time_index = solph.create_time_index(
+        2012, number_of_intervals=number_timesteps
+    )
 
     energysystem = solph.EnergySystem(
         timeindex=date_time_index, infer_last_interval=False

examples/time_index_example/simple_time_step_example.py

@@ -49,7 +49,9 @@ def main(optimize=True):
     solver = "cbc"  # 'glpk', 'gurobi',...
     solver_verbose = False  # show/hide solver output
 
-    date_time_index = solph.create_time_index(2000, interval=0.25, number=8)
+    date_time_index = solph.create_time_index(
+        2000, interval_length=0.25, number_of_intervals=8
+    )
 
     energy_system = solph.EnergySystem(
         timeindex=date_time_index, infer_last_interval=False

examples/tsam/invest_optimize_all_technologies_using_mp_and_tsam.py

@@ -229,9 +229,7 @@ def main():
         outputs={
             bel: solph.Flow(
                 fix=wind_profile,
-                nominal_capacity=solph.Investment(
-                    ep_costs=epc_wind, lifetime=10
-                ),
+                nominal_capacity=solph.Investment(ep_costs=epc_wind),
             )
         },
     )
@@ -254,9 +252,7 @@ def main():
                     ],
                     ignore_index=True,
                 ),
-                nominal_capacity=solph.Investment(
-                    ep_costs=epc_pv, lifetime=10
-                ),
+                nominal_capacity=solph.Investment(ep_costs=epc_pv),
             )
         },
     )
@@ -298,7 +294,7 @@ def main():
         invest_relation_output_capacity=1 / 6,
         inflow_conversion_factor=1,
         outflow_conversion_factor=0.8,
-        nominal_capacity=solph.Investment(ep_costs=epc_storage, lifetime=10),
+        nominal_capacity=solph.Investment(ep_costs=epc_storage),
     )
 
     energysystem.add(excess, gas_resource, wind, pv, demand, pp_gas, storage)

examples/tuple_as_labels/tuple_as_label.py

@@ -157,7 +157,9 @@ def main(optimize=True):
 
     logging.info("Initialize the energy system")
     energysystem = EnergySystem(
-        timeindex=create_time_index(2012, number=number_of_time_steps),
+        timeindex=create_time_index(
+            2012, number_of_intervals=number_of_time_steps
+        ),
         infer_last_interval=False,
     )
 

examples/variable_chp/variable_chp.py

@@ -130,7 +130,9 @@ def main(optimize=True):
     logging.info("Initialize the energy system")
 
     # create time index for 192 hours in May.
-    date_time_index = solph.create_time_index(2012, number=len(data))
+    date_time_index = solph.create_time_index(
+        2012, number_of_intervals=len(data)
+    )
 
     energysystem = solph.EnergySystem(
         timeindex=date_time_index, infer_last_interval=False

src/oemof/solph/_energy_system.py

@@ -51,16 +51,10 @@
         a 'freq' attribute that is not None. The parameter has no effect on the
         timeincrement parameter.
 
-    periods : list or None
-        The periods of a multi-period model.
-        If this is explicitly specified, it leads to creating a multi-period
-        model, providing a respective user warning as a feedback.
-
-        list of pd.date_range objects carrying the timeindex for the
-        respective period;
-
-        For a standard model, periods are not (to be) declared, i.e. None.
-        A list with one entry is derived, i.e. [0].
+    investment_times : list or None
+        The points in time investments can be made. Defaults to timeindex[0].
+        If multiple times are specified, it leads to creating a multi-period
+        model.
 
     tsa_parameters : list of dicts, dict or None
         Parameter can be set in order to use aggregated timeseries from TSAM.
@@ -76,10 +70,6 @@
         will be adapted. Note that timeseries for components have to
         be set up as already aggregated timeseries.
 
-    use_remaining_value : bool
-        If True, compare the remaining value of an investment to the
-        original value (only applicable for multi-period models)
-
     kwargs
     """
 
@@ -88,9 +78,8 @@
         timeindex=None,
         timeincrement=None,
         infer_last_interval=False,
-        periods=None,
+        investment_times=None,
         tsa_parameters=None,
-        use_remaining_value=False,
         groupings=None,
     ):
         # Doing imports at runtime is generally frowned upon, but should work
@@ -130,20 +119,18 @@
                 )
                 warnings.warn(msg, FutureWarning)
             else:
-                if periods is None:
-                    msg = (
-                        "Specifying the timeincrement and the timeindex"
-                        " parameter at the same time is not allowed since"
-                        " these might be conflicting to each other."
-                    )
-                    raise AttributeError(msg)
-                else:
-                    msg = (
-                        "Ensure that your timeindex and timeincrement are "
-                        "consistent."
-                    )
-                    warnings.warn(msg, debugging.ExperimentalFeatureWarning)
+                msg = (
+                    "The timeincrement is infered from the given timeindex."
+                    " As both parameters might be conflicting to each other,"
+                    " you cannot define both at the same time."
+                    " Please give only a timeindex."
+                )
+                raise AttributeError(msg)
 
+        elif timeindex is None and timeincrement is not None:
+            timeindex = pd.Index(
+                np.append(np.array([0]), np.cumsum(timeincrement))
+            )
         elif timeindex is not None and timeincrement is None:
             if tsa_parameters is not None:
                 pass
@@ -188,8 +175,8 @@
                 tsa_parameters = [tsa_parameters]
 
             # Construct occurrences of typical periods
-            if periods is not None:
-                for p in range(len(periods)):
+            if investment_times is not None:
+                for p in range(len(investment_times)):
                     tsa_parameters[p]["occurrences"] = collections.Counter(
                         tsa_parameters[p]["order"]
                     )
@@ -211,92 +198,22 @@
         self.tsa_parameters = tsa_parameters
 
         timeincrement = self._init_timeincrement(
-            timeincrement, timeindex, periods, tsa_parameters
+            timeincrement, timeindex, investment_times, tsa_parameters
         )
         super().__init__(
             groupings=groupings,
             timeindex=timeindex,
             timeincrement=timeincrement,
         )
 
-        self.periods = periods
-        if self.periods is not None:
-            msg = (
-                "CAUTION! You specified the 'periods' attribute for your "
-                "energy system.\n This will lead to creating "
-                "a multi-period optimization modeling which can be "
-                "used e.g. for long-term investment modeling.\n"
-                "Please be aware that the feature is experimental as of "
-                "now. If you find anything suspicious or any bugs, "
-                "please report them."
-            )
-            warnings.warn(msg, debugging.ExperimentalFeatureWarning)
-            self._extract_periods_years()
-            self._extract_periods_matrix()
-            self._extract_end_year_of_optimization()
-            self.use_remaining_value = use_remaining_value
-        else:
-            self.end_year_of_optimization = 1
-
-    def _extract_periods_years(self):
-        """Map years in optimization to respective period based on time indices
-
-        Attribute `periods_years` of type list is set. It contains
-        the year of the start of each period, relative to the
-        start of the optimization run and starting with 0.
-        """
-        periods_years = [0]
-        start_year = self.periods[0].min().year
-        for k, v in enumerate(self.periods):
-            if k >= 1:
-                periods_years.append(v.min().year - start_year)
-
-        self.periods_years = periods_years
-
-    def _extract_periods_matrix(self):
-        """Determines a matrix describing the temporal distance to each period.
-
-        Attribute `periods_matrix` of type list np.array is set.
-        Rows represent investment/commissioning periods, columns represent
-        decommissioning periods. The values describe the temporal distance
-        between each investment period to each decommissioning period.
-        """
-        periods_matrix = []
-        period_years = np.array(self.periods_years)
-        for v in period_years:
-            row = period_years - v
-            row = np.where(row < 0, 0, row)
-            periods_matrix.append(row)
-        self.periods_matrix = np.array(periods_matrix)
-
-    def _extract_end_year_of_optimization(self):
-        """Extract the end of the optimization in years
-
-        Attribute `end_year_of_optimization` of int is set.
-        """
-        duration_last_period = self.get_period_duration(-1)
-        self.end_year_of_optimization = (
-            self.periods_years[-1] + duration_last_period
-        )
-
-    def get_period_duration(self, period):
-        """Get duration of a period in full years
-
-        Parameters
-        ----------
-        period : int
-            Period for which the duration in years shall be obtained
-
-        Returns
-        -------
-        int
-            Duration of the period
-        """
-        return (
-            self.periods[period].max().year
-            - self.periods[period].min().year
-            + 1
-        )
+        # bare system to load pickled data
+        if self.timeindex is not None:
+            if investment_times is None:
+                self.investment_times = [self.timeindex[0]]
+            else:
+                self.investment_times = sorted(
+                    set([self.timeindex[0]] + investment_times)
+                )
 
     @staticmethod
     def _init_timeincrement(timeincrement, timeindex, periods, tsa_parameters):