学习为最优需求响应定价电力

中文摘要 AI 辅助

利用时变电价来塑造消费者需求响应，并使能源需求与可再生能源生产更好地对齐，引起了人们的极大兴趣。然而，最优价格通常随时间变化，以响应复杂信号，如天气预报、日出/日落时间和星期模式；现有方法无法有效利用此类丰富的上下文信息。在此，我们提出一种基于神经网络的上下文能源定价算法，将定价建模为Stackelberg博弈，并利用Mehrabi等人（2024）的均值场解表示。该方法学习从上下文特征到可行价格信号的约束映射。我们通过模拟美国多个城市的能源电网来验证我们的方法，并表明纳入上下文信息可以显著提高需求响应计划的价值。

英文摘要

There is considerable interest in using time-varying electricity prices to shape consumer demand response, and better align energy demand with renewable production. However, optimal prices generally vary over time in response to complex signals such as weather forecasts, sunrise/sunset times, and day-of-week patterns; and existing methods are not able to make efficient use of such rich contextual information. Here, we propose a neural-network-based algorithm for contextual energy pricing, modeling pricing as a Stackelberg game and leveraging a mean-field solution representation from Mehrabi et al.(2024). The approach learns constrained mappings from contextual features to feasible price signals. We validate our approach by simulating the energy grid in several US cities, and show that incorporating contextual information can considerably increase the value of the demand response programs.

发表机构

• Stanford University（斯坦福大学）
• National Laboratory of the Rockies（落基山国家实验室）
• Prime Coalition（普莱姆联盟）
• Eaton Research Labs（伊顿研究实验室）

机构由 AI 辅助整理，请以论文原文为准。