Optimization Input¶

How to access aggregation results for energy system optimization models.

This notebook shows how to access:

• Cluster weights (occurrence counts)
• Cluster assignments (period ordering)
• Typical period data

Import pandas and the relevant time series aggregation class

In [1]:

%load_ext autoreload
%autoreload 2

import pandas as pd
import plotly.express as px
import plotly.io as pio

pio.renderers.default = "notebook_connected"

import warnings

import tsam
from tsam import ClusterConfig, ExtremeConfig

# Added to every example notebook: silence the v3 column-order
# FutureWarning in the rendered docs (tsam v4 returns result columns in
# input order; see migration guide).
warnings.filterwarnings(
    "ignore", category=FutureWarning, message=".*sorted alphabetically.*"
)

%load_ext autoreload %autoreload 2 import pandas as pd import plotly.express as px import plotly.io as pio pio.renderers.default = "notebook_connected" import warnings import tsam from tsam import ClusterConfig, ExtremeConfig # Added to every example notebook: silence the v3 column-order # FutureWarning in the rendered docs (tsam v4 returns result columns in # input order; see migration guide). warnings.filterwarnings( "ignore", category=FutureWarning, message=".*sorted alphabetically.*" )

Input data¶

Read in time series from testdata.csv with pandas

In [2]:

raw = pd.read_csv("testdata.csv", index_col=0)

raw = pd.read_csv("testdata.csv", index_col=0)

Transform the index to a datetime index

In [3]:

raw.index = pd.to_datetime(raw.index)

raw.index = pd.to_datetime(raw.index)

Plot raw data

In [4]:

fig = px.line(
    raw, facet_row="variable", labels={"index": "Time", "value": ""}
).update_yaxes(matches=None)
fig.show()

fig = px.line( raw, facet_row="variable", labels={"index": "Time", "value": ""} ).update_yaxes(matches=None) fig.show()

Aggregate the data¶

Aggregate to typical weeks, including days with minimum temperature and maximum load as extreme periods.

In [5]:

result = tsam.aggregate(
    raw,
    n_clusters=5,
    period_duration=24 * 7,  # Weekly periods
    cluster=ClusterConfig(method="hierarchical"),
    extremes=ExtremeConfig(
        method="new_cluster",
        min_value=["T"],
        max_value=["Load"],
    ),
)

result = tsam.aggregate( raw, n_clusters=5, period_duration=24 * 7, # Weekly periods cluster=ClusterConfig(method="hierarchical"), extremes=ExtremeConfig( method="new_cluster", min_value=["T"], max_value=["Load"], ), )

Create the typical periods

In [6]:

cluster_representatives = result.cluster_representatives
cluster_representatives.head()

cluster_representatives = result.cluster_representatives cluster_representatives.head()

Out[6]:

		GHI	Load	T	Wind
	timestep
0	0	0.0	416.893227	0.304081	0.312325
	1	0.0	409.367248	0.090194	0.000000
	2	0.0	403.570841	-0.123693	0.000000
	3	0.0	404.420754	-0.016750	0.208217
	4	0.0	410.034429	-0.123693	0.312325

Show the resulting order of aggregated periods¶

Calculates how the original index is represented by the old index

In [7]:

# Advanced: Access the internal aggregation object for features not exposed in the public API.
# Note: The _aggregation attribute is internal and may change in future versions.
index_matching = result._aggregation.indexMatching()
index_matching.head()

# Advanced: Access the internal aggregation object for features not exposed in the public API. # Note: The _aggregation attribute is internal and may change in future versions. index_matching = result._aggregation.indexMatching() index_matching.head()

Out[7]:

	PeriodNum	TimeStep
2009-12-31 23:30:00	3	0
2010-01-01 00:30:00	3	1
2010-01-01 01:30:00	3	2
2010-01-01 02:30:00	3	3
2010-01-01 03:30:00	3	4

Plot the appearance of the 5+2 aggregated periods in the original timeframe

In [8]:

visualization_df = pd.DataFrame(
    0, index=index_matching.index, columns=result.period_index
)
for col in visualization_df.columns:
    visualization_df.loc[index_matching["PeriodNum"] == col, col] = 1

visualization_df = pd.DataFrame( 0, index=index_matching.index, columns=result.period_index ) for col in visualization_df.columns: visualization_df.loc[index_matching["PeriodNum"] == col, col] = 1

In [9]:

fig = px.area(
    visualization_df,
    labels={"index": "Time", "value": "Occurrence"},
    color_discrete_sequence=px.colors.sample_colorscale(
        "Viridis", len(visualization_df.columns)
    ),
)
fig.show()

fig = px.area( visualization_df, labels={"index": "Time", "value": "Occurrence"}, color_discrete_sequence=px.colors.sample_colorscale( "Viridis", len(visualization_df.columns) ), ) fig.show()

Get input for potential energy system optimization¶

i. cluster_weights - The occurrence count of each typical period for weighting in the objective function.

Note: Period three is only partially evaluated since its appearance at the end of the year exceeds the original time series.

In [10]:

result.cluster_weights

result.cluster_weights

Out[10]:

{np.int64(0): np.int64(10),
 np.int64(1): np.int64(17),
 np.int64(2): np.int64(7),
 np.int64(3): np.float64(4.142857142857142),
 np.int64(4): np.int64(8),
 np.int64(5): np.int64(2),
 np.int64(6): np.int64(4)}

In [11]:

weights = pd.Series(result.cluster_weights)
fig = px.bar(
    x=weights.index,
    y=weights.values,
    labels={"x": "Period index", "y": "Number of occurence"},
)
fig.show()

weights = pd.Series(result.cluster_weights) fig = px.bar( x=weights.index, y=weights.values, labels={"x": "Period index", "y": "Number of occurence"}, ) fig.show()

ii. Accessing period data by index
Access aggregated time series values using period and time step indices. This uses internal API methods that may change in future versions.

In [12]:

# Advanced: Access internal dictionary-style data access.
# Note: The _aggregation attribute and its methods are internal and may change in future versions.
agg = result._aggregation
agg.clusterPeriodDict["GHI"][(agg.clusterPeriodIdx[3], agg.stepIdx[12])]

# Advanced: Access internal dictionary-style data access. # Note: The _aggregation attribute and its methods are internal and may change in future versions. agg = result._aggregation agg.clusterPeriodDict["GHI"][(agg.clusterPeriodIdx[3], agg.stepIdx[12])]

Out[12]:

132.49920893072988

Alternatively this is given as data frame

In [13]:

result.cluster_representatives.head()

result.cluster_representatives.head()

Out[13]:

		GHI	Load	T	Wind
	timestep
0	0	0.0	416.893227	0.304081	0.312325
	1	0.0	409.367248	0.090194	0.000000
	2	0.0	403.570841	-0.123693	0.000000
	3	0.0	404.420754	-0.016750	0.208217
	4	0.0	410.034429	-0.123693	0.312325

iii. cluster_assignments
The order of the typical periods to represent the original time series, e.g., to model seasonal storage.

In [14]:

result.cluster_assignments

result.cluster_assignments

Out[14]:

array([3, 2, 6, 6, 5, 2, 6, 6, 3, 0, 0, 0, 4, 0, 0, 4, 1, 4, 4, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 1, 4, 4, 0, 0, 3, 0, 0,
       0, 2, 2, 2, 2, 5, 2, 3, 3])