Strategic investment in electricity markets: Robust optimization versus stochastic programming
比较了战略型企业在电力市场扩张中使用风险规避的随机规划与鲁棒优化两种方法对投资决策和利润的影响，发现鲁棒优化更倾向投资可再生能源，而随机规划可能带来更差的利润表现。
Decarbonization policies have spurred the adoption of variable renewable energy (VRE) technologies such as wind and solar power. To enable flexible resources and accommodate VRE’s intermittency, electricity markets are shifting toward renewable-aware dispatch based on stochastic optimization. However, strategic firms may exploit such market structures to manipulate prices to their advantage. To complement the extant literature, we compare investment decisions as well as worst-case profits and losses in the context of generation expansion by a strategic firm that uses either risk-averse stochastic programming or robust optimization. The former is a bi-level optimization problem, whereas the latter is a tri-level problem. Our contributions are threefold in addressing policy and methodological challenges. First, we demonstrate that using robust optimization instead of stochastic programming generally leads to investment plans with a higher share of VRE because it serves as a hedge during undesirable realizations with low consumer willingness to pay and high marginal costs for conventional generation. Second, a regret analysis shows that the worst-case profit is significantly reduced if an investor uses expansion decisions from stochastic programming, highlighting the importance of selecting a methodology aligned with the main objective of the investor. The effect is especially pronounced if decisions stem from a social planner, thereby indicating how a conventional, centralized perspective may fail to reflect private incentives for generation expansion in evolving electricity markets. Third, the analysis of strategic behavior necessitates state-of-the-art decomposition techniques such as the constraint generation-based algorithm and the column-and-constraint generation algorithm for the bi- and tri-level problems, respectively.