  Industrial electricity consumers with flexible demand can profit by adjusting
their load to short-term prices and by providing balancing services to the
grid. Markets which support this kind of short-term position adjustment are the
day-ahead market and balancing markets. We propose a formulation for a combined
optimization model that computes an optimal distribution of flexibility between
the balancing and day-ahead markets. The optimal solution also includes the
specific bids for the day-ahead and balancing markets. Besides the expected
profits of each market and their individual bidding languages, our model also
takes their different roles in a continuous marketing of flexibility into
account. To prevent overrating short-term profits we introduce a variable
penalty term that adds a cost to unfavorable load schedules. We evaluate the
optimization model in a rolling horizon case study based on the setting of a
virtual battery at TRIMET SE, which is derived from a flexible aluminum
electrolysis process. For such a battery we compute a daily optimal split of
flexibility and trading decisions based on data in the period 04/2021 -
03/2022. We show that the optimal split is more profitable than using only one
market or a fixed split between the markets.
