Python and pandas would do that quite easily. This code will output as many csv files as needed regardless of your actual number of variables, each file with 10 variables (except the latest which could have between 1 and 10). Each will be named like eplusout_0.csv, eplusout_1.csv, etc.

import pandas as pd
import numpy as np

df = pd.read_csv('eplusout.csv', index_col=0)
# Chunck size
n = 10
for i in range(0, df.shape[1], n):
    df_chunck = df.iloc[:, i:i+n]
    df_chunck.to_csv('eplusout_{}.csv'.format(i))

Note: If you wanted, you do a bit better to properly parse the Date/Time CSV column as a valid DateTimeIndex.

I'll allow myself to go into a small rant here: the time conventions used by E+ as a MAJOR pain. The Date/Time index doesn't include the year, but more importantly it uses end of timestep conventions AND labels midnight as "24:00". Try to parse that in Python, you'll see.

In [1]
def dateparse(x):          
    return pd.datetime.strptime("2005/{}".format(x.strip()), '%Y/%m/%d %H:%M:%S')

df = pd.read_csv('eplusout.csv', index_col=0, parse_dates=True, date_parser=dateparse)

Out[1]:
ValueError: time data '2005/01/01  24:00:00' does not match format '%Y/%m/%d %H:%M:%S'

This is because datetime logically expects 0-23 for hour.

In [2]: import datetime
        datetime.datetime(2005, 1, 1, 24, 0, 0)

Out[2]: ValueError: hour must be in 0..23

I would import the csv file into Excel and then separate whatever I need into different worksheets.

That should be quiet easy to do. You could write simple macro script to automate that.

You could also run simulation 10 times each time with different output list but that is inefficient.

Re Julien's rant above, regarding the poor E+ time conventions. Here is a snippet of python code that might be useful:

def read_eplusout(datafile, year=2018):

ep_datas={}
if os.path.exists(datafile):
    with open(datafile,mode='r') as inf:
        hline=inf.readline()             ##Reads the line of headers

        for hhead in hline.split(','):
            hhead=hhead.rstrip()
            ##Make sure that we read a variable that we want
            ep_datas[hhead]=[]

        ##Creates a huge data structure
        for i, lline in enumerate(inf):
            for hhead,dd in zip(hline.split(','),lline.split(',')):
                hhead=hhead.rstrip()
                if hhead=='Date/Time':
                    tyy,thh=dd.split()
                    mm,dd=tyy.split('/')
                    hh,mmm,ss=thh.split(':')
                    if hh=='24':
                        ddt=ep_datas[hhead][-1]+(ep_datas[hhead][-1]-ep_datas[hhead][-2])
                    else:
                        ddt=datetime.datetime(year,int(mm),int(dd),int(hh),int(mmm),int(ss))

                    ep_datas[hhead].append(ddt)
                    continue

                if dd=="":
                    dd=0.0

                ep_datas[hhead].append(float(dd))

    data_frame = pd.DataFrame(ep_datas)
    data_frame = data_frame.set_index(pd.DatetimeIndex(ep_datas['Date/Time']))

    return data_frame