  It is anticipated that large-scale energy storage facility (ESF) will become
an essential part of the future energy markets to increase the penetration of
renewables. In this paper, a new optimization algorithm is developed to
participate in large-scale merchant ESF to deal with renewables fluctuations.
The ESF is an investor-owned and independently-operated, seeking to maximize
its total profit while hedging the system against net-load variation. To do so,
an exact computationally efficient bi-level mixed-integer linear programming
(MILP) is envisaged. The ESF operator tries to maximize its own profit at the
upper level while ISO seeks to clear the markets at the lower level. In
principle, the ESF will be able to construct its bidding/offering curves to
buy/sell from/to different markets. To maintain the tractability of the
problem, each lower level is replaced by its KKT optimality conditions, and
nonlinearities are converted into linear equivalents using strong duality
theory rendering the single-level MILP and consequently recast as an MPEC.
Additionally, the proposed framework is constructed according to the
information gap decision theory (IGDT) instrument to capture the adverse impact
of uncertainty on the profit of ESF.
