Can Carbon-Aware Electric Load Shifting Reduce Emissions? An Equilibrium-Based Analysis

An increasing number of electric loads, such as hydrogen producers or data centers, can be characterized as carbon-sensitive, meaning that they are willing to adapt the timing and/or location of their electricity usage in order to minimize carbon footprints. However, the emission reduction efforts of these carbon-sensitive loads rely on carbon intensity information such as average carbon emissions, and it is unclear whether load shifting based on these signals effectively reduces carbon emissions. To address this open question, we design a carbon-aware equilibrium model, which expands the commonly used equilibrium model for standard (carbon-agnostic) electricity market clearing to include carbon-sensitive consumers that adapt their consumption based on average carbon emission signals and carbon costs. This analysis represents an idealized situation for carbon-sensitive consumers, where their carbon preferences are reflected directly in the market clearing, and contrasts with current practice, where carbon emission signals only become known to consumers a posteriori (i.e., after the market has already been cleared). Furthermore, we extend our model to consider temporal load shifting and time-varying maximum renewable generations. We employ illustrative three-bus examples and numerical simulations on the IEEE RTS-GMLC system to reveal the limitations of the widely adopted average carbon emission signal for guiding carbon emission reduction. Our model offers a novel perspective for evaluating the effectiveness of different carbon signals and contributes to new carbon signal design.

💡 Research Summary

This paper investigates a critical question in the decarbonization of the power sector: does load shifting by carbon-sensitive consumers (e.g., data centers, hydrogen producers), based on widely available average carbon intensity signals, actually lead to a reduction in overall system-wide carbon emissions? The authors argue that the current practice, where carbon signals are calculated after market clearing (a posteriori), creates a sequential and potentially suboptimal process. Consumers adjust their demand based on historical signals without seeing how their collective actions would change the generation dispatch and the carbon signals themselves in a new market clearing.

To address this, the paper’s primary contribution is the development of a novel “carbon-aware equilibrium model.” This model expands the standard equilibrium (game-theoretic) framework for electricity market clearing to incorporate carbon-sensitive consumers in a co-optimized manner. The model includes four types of agents: power generators, consumers, transmission owners, and the Independent System Operator (ISO). The key innovation lies in the consumers’ optimization problem and the ISO’s clearing conditions. Consumers maximize their utility, which now includes a term for carbon cost (λ * c_D,d), where λ is the average carbon emission signal. Simultaneously, the ISO determines not only the nodal electricity prices to balance supply and demand but also the average carbon signal (λ), defined as total system emissions divided by total demand. This formulation represents an idealized scenario where consumers’ carbon preferences are directly internalized into the market clearing process, contrasting with the sequential reality.

The authors further extend this base model to consider two important real-world aspects: 1) time-varying maximum availability of renewable generation (like wind and solar), and 2) temporal load shifting, where consumers can shift their consumption across time periods while keeping their total energy demand fixed.

The theoretical implications of the model are explored using an illustrative three-bus example. More substantially, numerical simulations are conducted on a modified IEEE RTS-GMLC 24-bus test system. The results reveal a significant and counterintuitive finding: load shifting guided by the average carbon intensity signal does not guarantee a reduction in overall carbon emissions and can sometimes even lead to an increase. The paper explains that this occurs because the average signal is a poor proxy for the marginal impact of consumption changes. It fails to account for network constraints and the merit order of generators. For instance, reducing demand in response to a high average signal might inadvertently cause a reduction in clean, marginal renewable generation elsewhere in the system rather than reducing output from carbon-intensive baseload plants.

In conclusion, the study demonstrates that the commonly adopted average carbon emission signal has fundamental limitations as a guide for effective emission reduction through demand-side flexibility. It may not align individual consumer actions with the system-wide least-carbon dispatch. Therefore, the paper advocates for the design of new, more sophisticated carbon signals—such as locational marginal carbon emissions—that better reflect the physical and economic realities of the power grid. This equilibrium-based analysis provides a novel and rigorous framework for evaluating the effectiveness of different carbon accounting signals and paves the way for improved signal design to truly harness the potential of carbon-aware load shifting.