2.3 Decarbonization modeling and fuels infrastructure analysis

STUDY APPROACH & METHODOLOGY

2.3 Decarbonization modeling and fuels infrastructure analysis

Demand-side and supply-side models with high temporal, sectoral, and spatial49 resolution were integrated in this study to provide an economy-wide view on potential decarbonization pathways for California. This pair of models produces energy, cost, and emissions data over the 30‐year study period, 2020 – 2050. This modeling approach is similar in architecture to those used in other California decarbonization studies, such as the 2018 report by the CEC.50 Likewise, it is similar to the approach used in the 2020 CARB report51, while also employing a dedicated capacity expansion model for supply-side optimization, (see details in Appendix).

The demand-side model estimates fi nal energy demand in a bottom-up fashion, for each of the over sixty end-uses or subsectors of the economy, ranging from residential space heating to heavy-duty trucks. Demand estimates are based on user decisions about technology adoption and energy service activity levels. Energy effi ciency and end-use electrifi cation measures are incorporated in demand-side scenarios. The fi nal energy demand for fuels along with time‐varying (8760 hour52) electricity demand profi les are used as inputs to the supply-side model.

The supply-side model used for this analysis is a linear programming model that combines capacity expansion and sequential hourly operations to fi nd least‐cost supply‐side pathways. It optimizes annual investments for the electricity and fuels sectors to meet carbon targets and other constraints. It incorporates estimated fi nal energy demand in future years from the demand-side modeling, as well as the future technology and fuel options available (including their effi ciency, operating, and cost characteristics), and clean energy goals such as Renewable Portfolio Standards (RPS), Clean Energy Standards (CES), and carbon intensity.

This model is able to refl ect detailed interactions among sectors, represented by electricity generation, fuel production and consumption, and carbon capture. With high temporal granularity, the model allows for co‐optimized (electricity and fuels) supply‐side solutions while enforcing economy‐wide emissions constraints. This is important for accurate representation of the economics when electricity is used to produce fuels, for example when renewable over‐generation is used for hydrogen production.

The analysis then goes beyond what many other full decarbonization analyses have historically done, using the results of the economy-wide decarbonization modeling to assess the potential for investment in clean fuels infrastructure, additional potential costs associated with fuel-switching, and potential gas system decommissioning costs and savings.

49Spatial resolution refers to the model’s approach for projecting electric transmission expansion, as discussed in Section 2.1 (Overall Methodology), above. 50California Energy Commission, “Deep decarbonization in a high renewables future”, June 2018, available at: https://www.ethree.com/wp-content/uploads/2018/06/Deep_Decarbonization_in_a_High_Renewables_Future_CEC-500-2018-012.pdf. 51Energy+Environmental Economics, “Achieving Carbon Neutrality in California: Pathways scenarios developed for the California Air Resources Board”, October 2020, available at: https://ww2.arb.ca.gov/sites/default/fi les/2020-10/e3_cn_fi nal_report_oct2020_0.pdf. 52To cover all hours in a year.

STUDY APPROACH & METHODOLOGY

Five key dimensions of clean fuels infrastructure formed the basis for this analysis: hydrogen blending, pure hydrogen delivery, hydrogen storage, carbon management, and decommissioning. Along these dimensions, high-level answers to critical questions effectively created parameters within which the clean fuels network architecture was designed. These questions included, but were not limited to, the following and had to be answered differently across each scenario:

Hydrogen blending: To what extent can the existing SoCalGas infrastructure (e.g., transmission pipelines made of high tensile strength steel) handle hydrogen blends? How, where, and at what cost can new hydrogen infrastructure be used to minimize total system cost?

Pure hydrogen delivery: Where and at what cost could renewable energy resources be leveraged to economically connect green hydrogen supply to FCEV refueling stations, an end-use of pure hydrogen? How and where will natural gas and hydrogen be separated before reaching those customers who are connected to a blended pipeline but cannot tolerate a blend? What investment will be needed to deliver pure hydrogen to industrial customers or in “concentrated hydrogen hubs” where needed?

Hydrogen storage: Given hydrogen levels in specifi c areas of the system, where would storage ideally be located to minimize cost with adequate safety and reliability? What additional infrastructure, such as pipelines, would be required for the most feasible hydrogen storage options?

Carbon management: Where are the “sources” and “sinks” of carbon located? How could pipeline mileage be minimized to lower total costs of carbon pipelines? What investment is required to build those pipelines?

Decommissioning: What zones have the highest cost to serve, both for gas and electric? In what zones would electrifi cation be most benefi cial (e.g., most cost-effective) to California’s energy system? What are the full costs of decommissioning?

This analysis relied on historical SoCalGas data, research conducted by SoCalGas and by third parties (e.g., universities, national labs, other utilities, etc.), market forecasts from a range of sources, and learnings from other geographies. Whenever available, California-specifi c data were used to improve analytical accuracy; for example, global averages of pipeline costs would result in an underestimate of the total pipeline cost for California. More granular location-specifi c analysis is required for planning. Assumptions and methodology for calculating the associated infrastructure costs in this high-level analysis are included in Appendix B.

Finally, it is important to acknowledge that this modeling and the assumptions inherently involve conjecture, as they rely on projections over a 30-year time period of technology development, customer behaviors, and other large-scale trends. In addition, the chosen assumptions also are constructed to refl ect a range of potential scenarios, and thereby represent modeled corner cases. Therefore, the results of this modeling are not forecasts; they are meant to directionally inform policy-making and high-level strategic approaches for capital allocation and energy system decarbonization planning.