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

TL;DR

This paper introduces two advanced stochastic optimization frameworks for wind energy generators to improve decision-making across multiple energy markets under uncertainty, leading to increased profits and better resource management.

Contribution

It presents a novel multi-phase decision-making framework that updates uncertainty data dynamically, outperforming traditional single-phase models in profit maximization.

Findings

Multi-phase framework yields 7% higher average profit.

Framework effectively incorporates multiple energy markets.

Dynamic updates improve decision accuracy.

Abstract

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.