In day-ahead electricity markets based on uniform marginal pricing, small variations in the offering and bidding curves may substantially modify the resulting market outcomes. In this work, we deal with the problem of finding the optimal offering curve for a risk-averse profit-maximizing generating company (GENCO) in a data-driven context. In particular, a large GENCO's market share may imply that her offering strategy can alter the marginal price formation, which can be used to increase profit. We tackle this problem from a novel perspective. First, we propose a optimization-based methodology to summarize each GENCO's step-wise supply curves into a subset of representative price-energy blocks. Then, the relationship between the market price and the resulting energy block offering prices is modeled through a Bayesian linear regression approach, which also allows us to generate stochastic scenarios for the sensibility of the market towards the GENCO strategy, represented by the regression coefficient probabilistic distributions. Finally, this predictive model is embedded in the stochastic optimization model by employing a constraint learning approach. Results show how allowing the GENCO to deviate from her true marginal costs renders significant changes in her profits and the market marginal price. Furthermore, these results have also been tested in an out-of-sample validation setting, showing how this optimal offering strategy is also effective in a real-world market contest.

翻译：在基于统一边际定价的日前电力市场中，报价曲线的微小变化可能显著影响最终市场结果。本文从数据驱动视角出发，研究风险规避型利润最大化发电公司（GENCO）的最优报价曲线问题。具体而言，大型发电商的市场份额意味着其报价策略可能改变边际价格的形成过程，从而可用于增加利润。我们从全新角度解决该问题。首先，提出基于优化的方法将每个发电商的阶梯式供应曲线归纳为若干代表性价格-能量区块子集；其次，通过贝叶斯线性回归方法建立市场价格与能量区块报价之间的关系模型，该方法同时可根据回归系数的概率分布生成市场对发电商策略敏感度的随机场景；最后，采用约束学习方法将该预测模型嵌入随机优化框架。结果表明，允许发电商偏离真实边际成本将显著改变其利润与市场边际价格。此外，基于样本外验证环境测试证实，该最优报价策略在实际市场环境中同样有效。