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chore: eosdash improve plan display (#739)
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* chore: improve plan solution display Add genetic optimization results to general solution provided by EOSdash plan display. Add total results. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * fix: genetic battery and home appliance device simulation Fix genetic solution to make ac_charge, dc_charge, discharge, ev_charge or home appliance start time reflect what the simulation was doing. Sometimes the simulation decided to charge less or to start the appliance at another time and this was not brought back to e.g. ac_charge. Make home appliance simulation activate time window for the next day if it can not be run today. Improve simulation speed. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> --------- Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
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Bobby Noelte
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@@ -54,8 +54,21 @@ color_palette = {
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"pink-500": "#EC4899", # pink-500
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"rose-500": "#F43F5E", # rose-500
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}
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# Color names
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colors = list(color_palette.keys())
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# Colums that are exclude from the the solution card display
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# They are currently not used or are covered by others
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solution_excludes = [
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"date_time",
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"_op_mode",
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"_fault_",
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"_outage_supply_",
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"_reserve_backup_",
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"_ramp_rate_control_",
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"_frequency_regulation_",
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]
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# Current state of solution displayed
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solution_visible: dict[str, bool] = {
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"pv_energy_wh": True,
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@@ -122,7 +135,9 @@ def SolutionCard(solution: OptimizationSolution, config: SettingsEOS, data: Opti
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instruction_columns = [
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instruction
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for instruction in solution_columns
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if instruction.endswith("op_mode") or instruction.endswith("op_factor")
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if instruction.endswith("op_mode")
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or instruction.endswith("op_factor")
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or instruction.startswith("genetic_")
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]
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solution_columns = [x for x in solution_columns if x not in instruction_columns]
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@@ -140,13 +155,26 @@ def SolutionCard(solution: OptimizationSolution, config: SettingsEOS, data: Opti
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prediction_columns_to_join = prediction_df.columns.difference(df.columns)
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df = df.join(prediction_df[prediction_columns_to_join], how="inner")
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# Remove time offset from UTC to get naive local time and make bokey plot in local time
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# Exclude columns that currently do not have a value
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excludes = solution_excludes
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for instruction in instruction_columns:
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if instruction.endswith("op_mode") and df[instruction].eq(0).all():
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# Exclude op_mode and op_factor if all op_mode is 0
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excludes.append(instruction)
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excludes.append(f"{instruction[:-4]}factor")
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# Make bokey plot in local time at location
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# Determine daylight saving time change
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dst_offsets = df.index.map(lambda x: x.dst().total_seconds() / 3600)
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# Determine desired timezone
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if config.general is None or config.general.timezone is None:
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date_time_tz = "Europe/Berlin"
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else:
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date_time_tz = config.general.timezone
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df["date_time"] = pd.to_datetime(df["date_time"], utc=True).dt.tz_convert(date_time_tz)
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# Ensure original date_time is parsed as UTC and convert to local time
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df["date_time_local"] = (
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pd.to_datetime(df["date_time"], utc=True).dt.tz_convert(date_time_tz).dt.tz_localize(None)
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)
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# There is a special case if we have daylight saving time change in the time series
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if dst_offsets.nunique() > 1:
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@@ -241,21 +269,12 @@ def SolutionCard(solution: OptimizationSolution, config: SettingsEOS, data: Opti
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# Create line renderers for each column
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renderers = {}
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# Have an index for the colors of predictions, solutions and instructions.
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prediction_color_idx = 0
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solution_color_idx = int(len(colors) * 0.33) + 1
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instruction_color_idx = int(len(colors) * 0.66) + 1
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for i, col in enumerate(sorted(df.columns)):
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# Exclude some columns that are currently not used or are covered by others
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excludes = [
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"date_time",
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"_op_mode",
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"_fault_",
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"_forced_discharge_",
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"_outage_supply_",
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"_reserve_backup_",
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"_ramp_rate_control_",
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"_frequency_regulation_",
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"_grid_support_export_",
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"_peak_shaving_",
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]
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# excludes = ["date_time"]
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if any(exclude in col for exclude in excludes):
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continue
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if col in solution_visible:
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@@ -265,73 +284,85 @@ def SolutionCard(solution: OptimizationSolution, config: SettingsEOS, data: Opti
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solution_visible[col] = visible
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if col in solution_color:
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color = solution_color[col]
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elif col == "pv_energy_wh":
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color = "yellow-500"
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solution_color[col] = color
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elif col == "elec_price_amt_kwh":
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color = "red-500"
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solution_color[col] = color
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else:
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color = colors[i % len(colors)]
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if col in prediction_columns:
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color = colors[prediction_color_idx % len(colors)]
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prediction_color_idx += 3
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elif col in solution_columns:
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color = colors[solution_color_idx % len(colors)]
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solution_color_idx += 3
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else:
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color = colors[instruction_color_idx % len(colors)]
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instruction_color_idx += 3
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# Remember the color of this column
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solution_color[col] = color
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if col in prediction_columns:
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line_dash = "dotted"
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else:
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line_dash = "solid"
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if visible:
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if col == "pv_energy_wh":
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r = plot.vbar(
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x="date_time",
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top=col,
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source=source,
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width=BAR_WIDTH_1HOUR * 0.8,
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legend_label=col,
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color=color_palette[color],
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level="underlay",
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)
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elif col.endswith("energy_wh"):
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if col.endswith("energy_wh"):
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r = plot.step(
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x="date_time",
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x="date_time_local",
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y=col,
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mode="before",
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mode="after",
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source=source,
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legend_label=col,
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color=color_palette[color],
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line_dash=line_dash,
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)
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elif col.endswith("soc_factor"):
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r = plot.line(
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x="date_time_local",
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y=col,
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source=source,
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legend_label=col,
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color=color_palette[color],
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line_dash=line_dash,
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y_range_name="factor",
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)
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elif col.endswith("factor"):
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r = plot.step(
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x="date_time",
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x="date_time_local",
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y=col,
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mode="before",
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mode="after",
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source=source,
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legend_label=col,
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color=color_palette[color],
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line_dash=line_dash,
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y_range_name="factor",
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)
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elif col.endswith("mode"):
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r = plot.step(
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x="date_time",
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x="date_time_local",
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y=col,
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mode="before",
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mode="after",
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source=source,
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legend_label=col,
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color=color_palette[color],
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line_dash=line_dash,
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y_range_name="factor",
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)
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elif col.endswith("amt_kwh"):
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r = plot.step(
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x="date_time",
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x="date_time_local",
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y=col,
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mode="before",
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mode="after",
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source=source,
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legend_label=col,
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color=color_palette[color],
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line_dash=line_dash,
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y_range_name="amt_kwh",
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)
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elif col.endswith("amt"):
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r = plot.step(
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x="date_time",
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x="date_time_local",
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y=col,
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mode="before",
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mode="after",
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source=source,
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legend_label=col,
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color=color_palette[color],
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line_dash=line_dash,
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y_range_name="amt",
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)
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else:
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@@ -430,7 +461,16 @@ def SolutionCard(solution: OptimizationSolution, config: SettingsEOS, data: Opti
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)
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return Grid(
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Bokeh(plot),
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Grid(
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Bokeh(plot),
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Card(
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P(f"Total revenues: {solution.total_revenues_amt}"),
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P(f"Total costs: {solution.total_costs_amt}"),
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P(f"Total losses: {solution.total_losses_energy_wh / 1000} kWh"),
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P(f"Fitness score: {solution.fitness_score}"),
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),
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cols=1,
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),
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Checkbox,
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cls="w-full space-y-3 space-x-3",
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)
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