Given the vital role that smart meter data could play in handling uncertainty in energy markets, data markets have been proposed as a means to enable increased data access. However, most extant literature considers energy markets and data markets separately, which ignores the interdependence between them. In addition, existing data market frameworks rely on a trusted entity to clear the market. This paper proposes a joint energy and data market focusing on the day-ahead retailer energy procurement problem with uncertain demand. The retailer can purchase differentially-private smart meter data from consumers to reduce uncertainty. The problem is modelled as an integrated forecasting and optimisation problem providing a means of valuing data directly rather than valuing forecasts or forecast accuracy. Value is determined by the Wasserstein distance, enabling privacy to be preserved during the valuation and procurement process. The value of joint energy and data clearing is highlighted through numerical case studies using both synthetic and real smart meter data.

翻译：鉴于智能电表数据在应对能源市场不确定性方面可发挥关键作用，数据市场已被提议作为提升数据可获取性的一种途径。然而，现有文献大多将能源市场与数据市场分开考量，忽略了两者之间的相互依存关系。此外，现有数据市场框架依赖于可信实体进行市场出清。本文提出一个联合能源与数据市场，聚焦于具有不确定需求的日前零售商能源采购问题。零售商可向消费者购买差分隐私保护的智能电表数据以降低不确定性。该问题被建模为一个集成预测与优化的模型，提供了一种直接评估数据价值（而非评估预测结果或预测精度）的方法。数据价值通过Wasserstein距离确定，从而在估值与采购过程中保持隐私性。通过使用合成及真实智能电表数据的数值案例研究，本文凸显了能源与数据联合出清的价值。