(631e) An Integrated Long-Term Modeling Strategy for Decarbonizing the Road Mobility and Power Sectors in the Presence of Carbon Capture and Green Hydrogen

Where should nations allocate their investments to reduce carbon emissions at minimal costs? There is a whole portfolio of promising alternatives to decarbonize the two sectors with the most significant carbon footprint–the power and transportation sectors. Although it is well known that a mix of alternatives is required, it is crucial to analyze how the leading candidate technologies interact in scenarios with fixed emission targets.

Renewable generation and energy storage are paramount to decarbonizing the power sector [1]. However, the inherent fluctuation of renewables and the current high costs of stationary batteries are cumbersome drawbacks [2]. It is, therefore, essential to analyze how the environmental benefits of a given renewable capacity share balance with the added complexity and potential costs associated.

On the other hand, electric vehicles (EVs) stand out as the leading alternative for decoupling carbon emissions from the transportation sector [3]. However, with the inclusion of EVs, the relationship between the power and transportation sectors becomes intricate [4]. Thus, it is imperative to assess whether EVs represent the most efficient long-term solution for decarbonization, considering the resolved investments in transmission, distribution, power generation capacity, and incentives they require.

Despite the substantial growth in EV sales in recent years, conventional vehicles still represent more than 98% of the total vehicle fleet worldwide and 85% of the total sales in 2022 [3]. Carbon capture and the production of alternative fuels are other prominent technologies that can contribute to reducing emissions cost-effectively but may conflict with the large-scale deployment of renewables and EVs [5,6]. The most prominent fuel alternatives are green hydrogen, biofuels, and e-fuels [1,6].

Planning optimal investment decisions in the overall power system and fuel alternatives is crucial to achieving long-term global emission reduction goals. The decisions need to be assessed based on the cost and effectiveness of reducing emissions in an integrated perspective.

Methodology

We propose a capacity expansion problem modeled as a stochastic mixed-integer linear program (MILP) that minimizes the investment and operation costs of a power system interconnected with the road transportation sector under fixed annual carbon dioxide emission limits. A two-stage decision framework is assumed to consider the uncertainty related to the operation stage. The first-stage variables involve the decision on capital investment for generation technologies, battery storage units and transmission lines, with integer variables used to establish investment in transmission expansion over the years. The second-stage continuous variables define the generation and load of each technology for multiple possible scenarios that follow a discrete probability distribution. Reserve balances and ramping limits are included in the formulation given their relevance in the scenarios where EVs are included.

The attached flowchart illustrates the generation and storage units considered in the model and their interrelation. On-flare carbon capture, direct air capture, and the production of hydrogen and e-fuels complete the portfolio of alternatives in the model. The model is solved using an extensive formulation, and multiple instances are solved in parallel.

Case Study and Results

The proposed model is applied to the study of the power system and transportation sector in Saudi Arabia. It is a notable case study for countries with high solar power potential and oil-based economies with a specific carbon-based energy matrix [8]. For instance, in 2022 more than 99% of the power capacity in Saudi Arabia relied on fossil fuels [9]. Moreover, Saudi Arabia has limited potential for other renewable sources, such as wind or hydropower, to balance solar power variability. Consequently, the investments associated with decarbonizing the grid may escalate significantly, particularly when reconciling the disparities in generation between day and night.

The model divides Saudi Arabia into four nodes, and the capacity expansion model is solved with multiple carbon emission caps and EV penetrations (Figure 2). Attention was focused to a case with a carbon cap of 25 million tons per year (or around 15% of the annual net emissions in 2020) and an EV penetration of 10%. Figure 3 illustrates a representative generation and demand profile for this case.

The results obtained over multiple parameters lead to the following conclusions:

1. 1. Regardless of the control level, an EV penetration of 10% reduces the total cost of a power system subject to emission constraints.
   2. Uncontrolled EV penetration of 10% reduces the total cost by up to 17%. With the inclusion of EVs, the installed capacity of fossil-based generation (represented by combined cycle gas turbines, CCGT) declines the most, followed by storage and renewables. However, more than 70% of the total cost reduction is due to the reduction in storage in all cases.
   3. The inclusion of electric vehicles further facilitates the penetration of renewable generation from 56% to up to 63% in a cost-optimal scenario with a stringent total carbon emission cap.
   4. The larger load caused by EVs is balanced with a higher power output from thermal units (higher capacity factor). The additional emissions from CCGT are offset by the emissions saved through vehicles electrification. Thus, investment costs decline, and operational costs increase but at a smaller pace.
   5. Managed charging and vehicle to grid capabilities(V2G) have the potential to reduce the total cost by an additional 8%. The main cost-reduction driver is a smaller investment in BES. The installed capacity of CCGT decreases, but their total energy increases considerably. In consequence, renewable capacity

Further analysis indicates that including stationary carbon capture as a potential technology, the renewable generation capacity share declines from 61-63% to 44-56%. Moreover, including carbon capture for $100/ton induces a massive reduction in the total costs of 70%, mainly due to the favorability of gas turbines with carbon capture over higher shares of renewables and battery storage. Regarding hydrogen, the break-even cost of hydrogen production is calculated to be $2.3/kg. This price, however, does not include the costs of compression and transportation.

Conclusions

The proposed capacity expansion model with a two-stage stochastic programming approach has proven helpful in investigating the existing interdependence and competition behaviors of multiple decarbonization alternatives. The integration of the transportation sector in the optimization model modifies the optimal power mix required to satisfy a carbon emission limit. Despite the added electricity load from EVs, the total cost of the power system declines by up to 17% with an EV penetration of 10%. With the incorporation of carbon capture in fossil-based generation, the model dispenses with the investment in costly battery storage, further reducing the total costs of a system by 70% for an annual carbon emission cap of 25 million tons.

References
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[8] J. Krane and W. S. Wilson, “Energy Governance in Saudi Arabia: An Assessment of The Kingdom’s resources, Policies, and Climate Approach,” tech. Rep., Rice University’s Baker Institute for Public Policy, 2019.

[9] Water and Electricity Regulatory Authority of Saudi Arabia, “Annual Statistical Booklet for Electricity Electricity Generation 2022,” tech. Rep., 2022., Retrieved from: https://wera.gov.sa/en/media-center/agency-publications.