  Decisions for a variable renewable resource generators commitment in the
energy market are typically made in advance when little information is
obtainable about wind availability and market prices. Much research has been
published recommending various frameworks for addressing this issue. However,
these frameworks are limited as they do not consider all markets a producer can
participate in. Moreover, current stochastic programming models do not allow
for uncertainty data to be updated as more accurate information becomes
available. This work proposes two decision-making frameworks for a wind energy
generator participating in day-ahead, intraday, reserve, and balancing markets.
The first framework is a two-stage stochastic convex optimization approach,
where both scenario-independent and scenario-dependent decisions are made
concurrently. The second framework is a series of four two-stage stochastic
optimization models wherein the results from each model feed into each
subsequent model allowing for scenarios to be updated as more information
becomes available to the decision-maker. In the simulation experiments, the
multi-phase framework performs better than the single-phase in every run, and
results in an average profit increase of 7%. The proposed optimization
frameworks aid in better decision-making while addressing uncertainty related
to variable resource generators and maximize the return on investment.
