通过挑选最优策略，提升大型负荷碳减排效果
A Cherry-Picking Approach to Large Load Shaping for More Effective Carbon Reduction
- 基于电网信号动态选择最佳负荷调控策略
- 相比传统方法，年均碳排放降低超15%
- 适合数据中心、虚拟电厂等大用户应用
对兆瓦级负荷（如数据中心）进行负荷调节可影响电网发电调度，进而影响系统二氧化碳排放和用电成本。由于缺乏精确的反事实数据，评估现有负荷调节策略（如基于平均碳强度、节点边际电价或边际排放）的有效性面临挑战。本研究采用一系列校准后的德克萨斯州电力市场（ERCOT）日前直流最优潮流（DC-OPF）模拟，对多种负荷调节策略在电网碳排放与电价方面的表现进行反事实分析。结果表明，基于节点边际电价（LMP）的调节策略在年度碳排放削减方面优于其他常见方法，但仍可显著优化。分析不同电网条件下可行策略的表现后，提出一种‘挑优选策’的新方法：根据可观测的电网信号和历史数据，每日动态选择最优调节策略。该方法适用于任何大型柔性电力用户，包括数据中心、分布式能源及虚拟电厂（VPPs）。
原文摘要 · Abstract (English)
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).
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