Data markets facilitate decentralized data exchange for applications such as prediction, learning, or inference. The design of these markets is challenged by varying privacy preferences as well as data similarity among data owners. Related works have often overlooked how data similarity impacts pricing and data value through statistical information leakage. We demonstrate that data similarity and privacy preferences are integral to market design and propose a query-response protocol using local differential privacy for a two-party data acquisition mechanism. In our regression data market model, we analyze strategic interactions between privacy-aware owners and the learner as a Stackelberg game over the asked price and privacy factor. Finally, we numerically evaluate how data similarity affects market participation and traded data value.

Forecasting is pivotal in energy systems, by providing fundamentals for operation at different horizons and resolutions. Though energy forecasting has been widely studied for capturing temporal information, very few works concentrate on the frequency information provided by forecasts. They are consequently often limited to single-resolution applications (e.g., hourly). Here, we propose a unified energy forecasting framework based on Laplace transform in the multi-resolution context. The forecasts can be seamlessly produced at different desired resolutions without re-training or post-processing. Case studies on both energy demand and supply data show that the forecasts from our proposed method can provide accurate information in both time and frequency domains. Across the resolutions, the forecasts also demonstrate high consistency. More importantly, we explore the operational effects of our produced forecasts in the day-ahead and intra-day energy scheduling. The relationship between (i) errors in both time and frequency domains and (ii) operational value of the forecasts is analysed. Significant operational benefits are obtained.

Energy market designs with non-merchant storage have been proposed in recent years, with the aim of achieving optimal integration of storage. In order to handle the time linking constraints that are introduced in such markets, existing works commonly make simplifying assumptions about the end-of-horizon storage level. This work analyses market properties under such assumptions, as well as in their absence. We find that, although they ensure cost recovery for all market participants, these assumptions generally lead to market inefficiencies. Therefore we consider the design of markets with non-merchant storage without such simplifying assumptions. Using an illustrative example, as well as detailed proofs, we provide conditions under which market prices in subsequent market horizons fail to reflect the value of stored energy. We show that this problem is essential to address in order to preserve market efficiency and cost recovery. Finally, we propose a method for restoring these market properties in a perfect-foresight setting.

Molslinjen, one of the world’s largest operators of fast-moving catamaran ferries, based in Denmark, adopted a focus on digitalization to profoundly change its operations and business practices. Molslinjen partnered with Halfspace, a data, analytics and AI company based in Copenhagen, Denmark, to support that transition. Halfspace and Molslinjen jointly developed and deployed a successful forecasting and revenue management toolbox for the data-driven operation of ferries in Denmark since 2020. This has resulted in $2.6-3.2 million yearly savings (and a total of $5 million savings as of December 2023), a significant reduction in the number of delayed departures and average delays, and a 3% reduction in fuel costs and emissions. This toolbox relies on some of the latest advances in machine learning for forecasting and in analytics approaches to revenue management. The potential for generalizing our toolbox to the global ferry industry is significant, with an impact on both revenues and environmental, societal, and governance criteria.