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Decisions for a variable renewable resource generators commitment in theenergy market are typically made in advance when little information isobtainable about wind availability and market prices. Much research has beenpublished recommending various frameworks for addressing this issue. However,these frameworks are limited as they do not consider all markets a producer canparticipate in. Moreover, current stochastic programming models do not allowfor uncertainty data to be updated as more accurate information becomesavailable. This work proposes two decision-making frameworks for a wind energygenerator 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 madeconcurrently. The second framework is a series of four two-stage stochasticoptimization models wherein the results from each model feed into eachsubsequent model allowing for scenarios to be updated as more informationbecomes available to the decision-maker. In the simulation experiments, themulti-phase framework performs better than the single-phase in every run, andresults in an average profit increase of 7%. The proposed optimizationframeworks aid in better decision-making while addressing uncertainty relatedto variable resource generators and maximize the return on investment.

Two-Stage Stochastic Optimization Frameworks to Aid in Decision-Making  Under Uncertainty for Variable Resource Generators Participating in a  Sequential Energy Market