As distributed energy resources (DERs) proliferate, future power system will need new market platforms enabling prosumers to trade various electricity and grid-support products. However, prosumers often exhibit complex, product interdependent preferences and face limited cognitive and computational resources, hindering engagement with complex market structures and bid formats. We address this challenge by introducing a multi-product market that allows prosumers to express complex preferences through an intuitive format, by fusing combinatorial clock exchange and machine learning (ML) techniques. The iterative mechanism only requires prosumers to report their preferred package of products at posted prices, eliminating the need for forecasting product prices or adhering to complex bid formats, while the ML-aided price discovery speeds up convergence. The linear pricing rule further enhances transparency and interpretability. Finally, numerical simulations demonstrate convergence to clearing prices in approximately 15 clock iterations.

翻译：随着分布式能源资源（DERs）的广泛部署，未来的电力系统需要新的市场平台，使产消者能够交易各类电能及电网辅助服务产品。然而，产消者通常表现出复杂且产品相互依赖的偏好，同时面临有限的认知与计算资源，这阻碍了他们参与复杂的市场结构和投标格式。为应对这一挑战，本文通过融合组合时钟交易与机器学习（ML）技术，提出了一种多产品市场机制，允许产消者通过直观的格式表达复杂偏好。该迭代机制仅要求产消者在公布的价格下报告其偏好的产品组合包，无需预测产品价格或遵循复杂的投标格式，而机器学习辅助的价格发现过程则加速了收敛。线性定价规则进一步提升了透明度和可解释性。最后，数值模拟表明，该机制可在约15轮时钟迭代后收敛至出清价格。