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 its 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 an 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 resulting market price and the energy block offering prices is modeled through a probabilistic forecasting tool: a Distributional Neural Network, which also allows us to generate stochastic scenarios for the sensibility of the market towards the GENCO strategy via a set of linear constraints. Finally, this predictive model is embedded in the stochastic optimization model employing a constraint learning approach. Results show how allowing the GENCO to deviate from its true marginal costs renders significant changes in its profits and the marginal price of the market. Additionally, these results have also been tested in an out-of-sample validation setting, showing how this optimal offering strategy can effective in a real-world market context.

中文翻译：

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基于市场价格反应学习的大型生产商日前最优报价策略


在基于统一边际定价的日前电力市场中，报价和投标曲线的微小变化可能会大大改变最终的市场结果。在这项工作中，我们解决了在数据驱动的背景下为规避风险、利润最大化的发电公司 (GENCO) 找到最佳发行曲线的问题。特别是，GENCO 的市场份额较大可能意味着其发行策略可以改变边际价格形成，从而可以用来增加利润。我们从一个新颖的角度来解决这个问题。首先，我们提出一种基于优化的方法，将每个 GENCO 的逐步供应曲线总结为代表性价格能源块的子集。然后，通过概率预测工具（分布式神经网络）对由此产生的市场价格与能源块发行价格之间的关系进行建模，该工具还允许我们通过一组线性约束。最后，该预测模型被嵌入到采用约束学习方法的随机优化模型中。结果表明，允许 GENCO 偏离其真实边际成本会如何导致其利润和市场边际价格发生重大变化。此外，这些结果还在样本外验证设置中进行了测试，展示了这种最佳产品策略如何在现实市场环境中发挥作用。