The Green Mirage: Impact of Location- and Market-based Carbon Intensity Estimation on Carbon Optimization Efficacy

TL;DR

The paper addresses the problem that concurrent use of location-based ($CI_{lb}$) and market-based ($CI_{mb}$) carbon attribution can distort carbon-reduction estimates. It analyzes three carbon-optimization techniques (spatial load shifting, temporal load shifting, and resource autoscaling) under both attribution methods across 123 regions, quantifying discrepancies and potential emission increases. Key findings show up to $55.1 hicksim ext{%}$ overestimation for consumers without PPAs and up to $28.2 hicksim ext{%}$ lower savings when evaluated with MB signals, with optimizer behavior changing significantly when PPAs are accounted for. The work highlights the need for real-time residual grid data and integrated attribution to ensure reliable carbon accounting and effective decarbonization strategies in practice.

Abstract

In recent years, there has been an increased emphasis on reducing the carbon emissions from electricity consumption. Many organizations have set ambitious targets to reduce the carbon footprint of their operations as a part of their sustainability goals. The carbon footprint of any consumer of electricity is computed as the product of the total energy consumption and the carbon intensity of electricity. Third-party carbon information services provide information on carbon intensity across regions that consumers can leverage to modulate their energy consumption patterns to reduce their overall carbon footprint. In addition, to accelerate their decarbonization process, large electricity consumers increasingly acquire power purchase agreements (PPAs) from renewable power plants to obtain renewable energy credits that offset their "brown" energy consumption. There are primarily two methods for attributing carbon-free energy, or renewable energy credits, to electricity consumers: location-based and market-based. These two methods yield significantly different carbon intensity values for various consumers. As there is a lack of consensus which method to use for carbon-free attribution, a concurrent application of both approaches is observed in practice. In this paper, we show that such concurrent applications can cause discrepancies in the carbon savings reported by carbon optimization techniques. Our analysis across three state-of-the-art carbon optimization techniques shows possible overestimation of up to 55.1% in the carbon reductions reported by the consumers and even increased emissions for consumers in some cases. We also find that carbon optimization techniques make different decisions under the market-based method and location-based method, and the market-based method can yield up to 28.2% less carbon savings than those claimed by the location-based method for consumers without PPAs.

Figures (18)

• Figure 1: Carbon intensity of electricity varies spatially and temporally, with regions and periods with more renewable energy having lower carbon intensity values.
• Figure 2: An illustration of the Power Purchase Agreement (PPA) commonly used to meet carbon-free energy targets. Solid lines denote the physical flow of electricity; dashed lines denote the transactional flows.
• Figure 3: Average increase in $CI_{res}$ across 123 regions worldwide when all solar and wind energy is contracted out.
• Figure 4: Weekly trace showing how $CI_{res}$ in California differs from $CI_{lb}$ as more and more renewables are contracted out.
• Figure 5: In California, the difference between $CI_{res}$ and $CI_{lb}$ varies seasonally, with spring and summer months showing more difference when there is more solar energy.