import matplotlib.pyplot as plt
ax = plt.gca()

# d is a dictionary from which dataframe is constructed
df = pd.DataFrame(data=d)

# to_plot (a function argument) is a dictionary of boolean values to decide what to plot, e.g.
to_plot = {'A':True,
           'B':True,
           'C':False,
           'D':True}

if to_plot['A']:
    pl = df['A'].plot(kind='bar', xticks=df.index, title=storage, ax=ax, color='deepskyblue', legend=True)

if to_plot['B']:
    pl = df['B'].plot(drawstyle='steps-post', xticks=df.index, title=storage, color='lightgreen',
                            linewidth=3, ax=ax, legend = True)
if to_plot['C']:
    pl = df['C'].plot(drawstyle='steps-post', xticks=df.index, title=storage, color='green',
                            linestyle='--', linewidth=2, ax=ax, legend=True)
if to_plot['D']:
    pl = df['D'].plot(drawstyle='steps-post', xticks=df.index, title=storage, color='orange',
                            linewidth=3, ax=ax, legend=True)

if to_plot['A'] or to_plot['B'] or to_plot['C'] or to_plot['D'] :
    plt.legend(loc='best')
    pl.set_xlabel("Time")
    pl.set_ylabel("Storage state(in %)")
    plt.show()

fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 1, 1])

UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  ax=fig.add_axes([0.1,0.1,1,1])
WARNING:matplotlib.legend:No handles with labels found to put in legend.