fixup! Implement initial storage support

Author: Peque

docs/sphinx/source/user_guide/storage.rst

@@ -60,14 +60,17 @@ In order to work with time series pvlib relies on pandas and pytz to handle
 time and time zones. See "Time and time zones" section for a brief
 introduction.
 
-Also, when dealing with storage systems and energy flow, you need to take into
+Also, when dealing with storage systems and power flow, you need to take into
 account the following conventions:
 
-- Timestamps are associated to the beginning of the interval
+- Timestamps are associated to the beginning of the interval, as opposed to
+  other pvlib series where timestamps are instantaneous
 
 - The time series frequency needs to be well defined in the time series
 
-- Values represent power throughout the interval, in W
+- Values represent a constant power throughout the interval, in W (power flow
+  simplifies calculations if you want to be able to model different time step
+  lengths)
 
 - Positive values represent power provided by the storage system (i.e.:
   discharging), hence negative values represent power into the storage system
@@ -232,6 +235,9 @@ cycles:
    @suppress
    plt.close()
 
+
+.. ipython:: python
+
    @savefig sam_soc.png
    results["SOC"].plot(ylabel="SOC (%)")
    @suppress
@@ -241,10 +247,7 @@ cycles:
 Power flow
 ----------
 
-TODO: We are assuming power flow instead of energy flow in this module. Should we change that? It seems SAM works with power flow, maybe for a good reason? Or maybe it is more common? It seems to me like energy flow is more intuitive, but... :shrug: Or maybe we could rename it to "Power balance" instead of "Power flow" (and leave the flow for "Energy flow")
-
-With pvlib you can simulate power/energy flow for different scenarios and use
-cases.
+With pvlib you can simulate power flow for different scenarios and use cases.
 
 Self consumption
 ****************
@@ -302,7 +305,7 @@ self-consumption use case:
 
 .. ipython:: python
 
-   from pvlib.flow import self_consumption
+   from pvlib.powerflow import self_consumption
 
    power_flow = self_consumption(generation, load)
    power_flow.head()
@@ -313,7 +316,7 @@ from grid to load/system:
 
 .. ipython:: python
 
-   from pvlib.flow import self_consumption
+   from pvlib.powerflow import self_consumption
 
    @savefig power_flow_self_consumption_load.png
    power_flow.groupby(power_flow.index.hour).mean()[["System to load", "Grid to load"]].plot.bar(stacked=True, xlabel="Hour", ylabel="Power (W)", title="Average power flow to load")
@@ -367,7 +370,7 @@ function:
 
 .. ipython:: python
 
-   from pvlib.flow import self_consumption_ac_battery
+   from pvlib.powerflow import self_consumption_ac_battery
 
    battery = fit_sam(parameters)
    state, flow = self_consumption_ac_battery(generation, load, battery, sam)

pvlib/flow.py renamed to pvlib/powerflow.py

@@ -1,5 +1,5 @@
 """
-This module contains functions for simulating power/energy flow.
+This module contains functions for simulating power flow.
 """
 from pandas import DataFrame
 
@@ -66,19 +66,19 @@ def self_consumption_ac_battery(
         The resulting power flow provided by the system, the grid and the
         battery into the system, grid, battery and load. [W]
     """
-    ac_dc_loss = 1 - ac_dc_loss / 100
-    dc_ac_loss = 1 - dc_ac_loss / 100
+    ac_dc_efficiency = 1 - ac_dc_loss / 100
+    dc_ac_efficiency = 1 - dc_ac_loss / 100
     df = self_consumption(generation, load)
-    charging = df["System to grid"] * ac_dc_loss
-    discharging = df["Grid to load"] / dc_ac_loss
+    charging = df["System to grid"] * ac_dc_efficiency
+    discharging = df["Grid to load"] / dc_ac_efficiency
     dispatch = discharging - charging
     final_state, results = model(battery, dispatch)
     df["System to battery"] = -results["Power"].loc[results["Power"] < 0]
-    df["System to battery"] /= ac_dc_loss
+    df["System to battery"] /= ac_dc_efficiency
     df["System to battery"] = df["System to battery"].fillna(0.0)
     df["System to grid"] -= df["System to battery"]
     df["Battery to load"] = results["Power"].loc[results["Power"] > 0]
-    df["Battery to load"] *= dc_ac_loss
+    df["Battery to load"] *= dc_ac_efficiency
     df["Battery to load"] = df["Battery to load"].fillna(0.0)
     df["Grid to load"] -= df["Battery to load"]
     df["Grid"] = df[["Grid to system", "Grid to load"]].sum(

pvlib/tests/test_flow.py renamed to pvlib/tests/test_powerflow.py

@@ -7,8 +7,8 @@
 
 from pvlib.battery import boc
 from pvlib.battery import fit_boc
-from pvlib.flow import self_consumption
-from pvlib.flow import self_consumption_ac_battery
+from pvlib.powerflow import self_consumption
+from pvlib.powerflow import self_consumption_ac_battery
 
 
 @mark.parametrize(