Remove deprecated dependency

Full Changelog: 0.5.0...0.5.1

Rename package to 'annuity' and automate publication on conda

The package can now be installed with conda install annuity -c jnettels

Include cost database example

An example Excel file that demonstrates the use of a cost database as an input is now included. The included data are only examples, a user must still validate their own cost assumptions. New input arguments allow more customization, e.g. in the column names of the cost database.

Further generalize the input for annual demands and proceeds

Changelog

Rename VDI2067.system()

Due to fixing some pylint warnings, the class VDI2067.system() is now called VDI2067.System().

Change input for sys.calc_annuities()

The approach for calling the primary method sys.calc_annuities() has changed, to make its use more flexible.

Prior, a call would have looked like this. Specific DataFrames had to be prepared for the three cost types "demand", "proceeds" and "other", and their respective price change factors r_V, r_E, r_S were arguments of calc_annuities(). This meant that one could not simply use a larger amout of different price change factors for different cost types.

df_V1 = pd.DataFrame(
        {'quantity': [5410.9, 3954.4, 92],
         'price': [20, 30, 237.4]},
        index=['Demand 1', 'Demand 2', 'Demand 3'])
df_E1 = pd.DataFrame(
        {'quantity': [494, 1811, 8098, 3954, 1852],
         'price': [100, 146, 60, 30, 220]},
        index=['Proceed 1', 'Proceed 2', 'Proceed 3', 'Proceed 4', 'Proceed 5'])

# Calculate the annuity of the energy system
q = 1.03  # interest factor
T = 20  # observation period
sys.calc_annuities(T=T, q=q,
                   df_V1=df_V1,
                   r_V=1.03,
                   df_E1=df_E1,
                   r_E=1.03,
                   )  # Get Series of annuities

Now, you need to prepare one DataFrame with all the cost types combined, including the price change factor for each. You can construct the DataFrame anyway you like, but:

• demand costs must have a negative price (while proceeds are defined positive)
• a column named "r" with the price change factors must be included

df_V1 = pd.DataFrame(
        {'quantity': [5410.9, 3954.4, 92],
         'price': [-20, -30, -237.4]},
        index=['Demand 1', 'Demand 2', 'Demand 3'])
df_E1 = pd.DataFrame(
        {'quantity': [494, 1811, 8098, 3954, 1852],
         'price': [100, 146, 60, 30, 220]},
        index=['Proceed 1', 'Proceed 2', 'Proceed 3', 'Proceed 4', 'Proceed 5'])

df_V1['r'] = 1.03  # set same price change factor for all entries
df_E1['r'] = 1.05  # set same price change factor for all entries
df_VSE = pd.concat([df_V1, df_E1],
                   keys=['Demand-related costs', 'Proceeds'])
                                       
# Calculate the annuity of the energy system
q = 1.03  # interest factor (which is an interest rate of 3 %)
T = 20  # observation period
sys.calc_annuities(T=T, q=q, df_VSE=df_VSE)  # Series of annuities

One advantage is that the table of costs and yields can now be freely structured, including the use of a MultiIndex (created by using concat()). The index is preserved, and the manipulated DataFrame can be accessed like so:

print(sys.df_VSE)

With this result: (quantity, price and r are preserved, the other columns are filled in)

                                    quantity  price         a          b        product     T     q     r
Demand-related costs Demand 1         5410.9  -20.0  0.067216  19.417476 -141241.736791  20.0  1.03  1.03
                     Demand 2         3954.4  -30.0  0.067216  19.417476 -154833.666478  20.0  1.03  1.03
                     Demand 3           92.0 -237.4  0.067216  19.417476  -28505.724786  20.0  1.03  1.03
Proceeds             Proceed 1         494.0  100.0  0.067216  23.453330   77875.750594  20.0  1.03  1.05
                     Proceed 2        1811.0  146.0  0.067216  23.453330  416818.131813  20.0  1.03  1.05
                     Proceed 3        8098.0   60.0  0.067216  23.453330  765956.876491  20.0  1.03  1.05
                     Proceed 4        3954.0   30.0  0.067216  23.453330  186996.387358  20.0  1.03  1.05
                     Proceed 5        1852.0  220.0  0.067216  23.453330  642301.534859  20.0  1.03  1.05

"Product" (the annuity) is the product of quantity, price, a (annuity factor) and b (cash value factor). T, q and r are stored to preserve all input data.