Shaping multi-megawatt loads, such as data centers, impacts generator dispatch on the electric grid, which in turn affects system CO2 emissions and energy cost. Substantiating the effectiveness of prevalent load shaping strategies, such as those based on grid-level average carbon intensity, locational marginal price, or marginal emissions, is challenging due to the lack of detailed counterfactual data required for accurate attribution. This study uses a series of calibrated granular ERCOT day-ahead direct current optimal power flow (DC-OPF) simulations for counterfactual analysis of a broad set of load shaping strategies on grid CO2 emissions and cost of electricity. In terms of annual grid level CO2 emissions reductions, LMP-based shaping outperforms other common strategies, but can be significantly improved upon. Examining the performance of practicable strategies under different grid conditions motivates a more effective load shaping approach: one that "cherry-picks" a daily strategy based on observable grid signals and historical data. The cherry-picking approach to power load shaping is applicable to any large flexible consumer on the electricity grid, such as data centers, distributed energy resources and Virtual Power Plants (VPPs).

翻译：对数据中心等多兆瓦级负荷进行整形会影响电网的发电机调度，进而影响系统二氧化碳排放和能源成本。由于缺乏准确归因所需的详细反事实数据，证实基于电网级平均碳强度、节点边际电价或边际排放等主流负荷整形策略的有效性具有挑战性。本研究使用一系列经过校准的ERCOT日前直流最优潮流精细化仿真，对广泛负荷整形策略在电网二氧化碳排放和电力成本方面进行反事实分析。在年度电网级二氧化碳减排方面，基于节点边际电价的整形策略优于其他常见策略，但其性能仍有显著提升空间。通过考察可行策略在不同电网条件下的表现，本文提出一种更有效的负荷整形方法：基于可观测电网信号和历史数据，每日"择优选择"一种策略。这种电力负荷整形的择优选择方法适用于电网上的任何大型柔性消费者，例如数据中心、分布式能源资源和虚拟电厂。