This comment reacts to Ruhnau and Schiele’s (2023) assessment of the cost and emissions impacts of electrolytic hydrogen production operating under different green hydrogen certification requirements in the EU. We critique the paper’s use of short-run marginal emissions rates to estimate emissions impacts, a methodology which the literature has shown to be inadequate for assessing the full lifecycle emissions impacts of electricity sector interventions. We hope that our response clarifies the need to consider induced structural change when assessing the greenhouse gas emissions impacts of electricity sector decisions at all scales.
Targeting one of the largest CO2-emitting sectors, the US Environmental Protection Agency (EPA) finalized new regulations on power plant emissions in 2024. However, the regulations are complex, with multiple mitigation options for compliance, making it difficult to understand their consequential effects on total CO2 emissions. We evaluate these effects by enhancing a capacity expansion model via incorporating new detailed operational constraints tailored to different technologies based on the EPA’s new rules. We show that the new rules could nearly double power sector CO2 emissions reductions through 2040, bringing emissions to about 51% below the 2022 level at low average cost per ton avoided, driven primarily by coal retirements. However, the rules omit regulations on existing natural gas generators, encouraging greater use of inefficient older gas plants. We find that emissions could be cost-effectively driven to 81%–88% below 2022 levels if the EPA’s rules were applied equally to all gas generators, regardless of vintage.
Expanding transmission capacity is likely a bottleneck that will restrict variable renewable energy (VRE) deployment required to achieve ambitious emission reduction goals. Interconnection and inter-zonal transmission buildout may be displaced by the optimal sizing of VRE to grid connection capacity and by the co-location of VRE and battery resources behind interconnection. However, neither of these capabilities is commonly captured in macro-energy system models. We develop two new functionalities to explore the substitutability of storage for transmission and the optimal capacity and siting decisions of renewable energy and battery resources through 2030 in the Western Interconnection of the United States. Our findings indicate that modeling optimized interconnection and storage co-location better captures the full value of energy storage and its ability to substitute for transmission. Optimizing interconnection capacity and co-location can reduce total grid connection and shorter-distance transmission capacity expansion on the order of 10% at storage penetration equivalent to 2.5%–10% of peak system demand. The decline in interconnection capacity corresponds with greater ratios of VRE to grid connection capacity (an average of 1.5–1.6 megawatt (MW) PV:1 MW inverter capacity, 1.2–1.3 MW wind:1 MW interconnection). Co-locating storage with VREs also results in a 9%–13% increase in wind capacity, as wind sites tend to require longer and more costly interconnection. Finally, co-located storage exhibits higher value than standalone storage in our model setup (up to ∼43%–45%). Given the coarse representation of transmission networks in our modeling, this outcome likely overstates the real-world importance of storage co-location with VREs. However, it highlights how siting storage in grid-constrained locations can maximize the value of storage and reduce transmission expansion.
Early markets for advanced clean energy technologies can spur substantial technological learning, leading to cost reductions and unlocking greenhouse gas savings far beyond the reduction of emissions directly associated with initial investments. Similarly to the feed-in tariffs and renewable portfolio standards that created a demand pull for wind and solar PV in the past, commitments to 24/7 CFE matching and other advanced market commitments by private sector stakeholders can accelerate the development of FOAK and early commercial projects of innovative energy technologies. A proactive private sector contribution can complement governmental support and reduce pressure on tight fiscal budgets. The virtuous system dynamics we describe can be activated by a handful of companies and governments committed to timely action, thereby fostering rapid innovation and making climate solutions more accessible and affordable for everyone.
The US Inflation Reduction Act (IRA) subsidizes the deployment of clean electricity, hydrogen production, and carbon capture and storage (CCS), which could enable additional actions by other federal, state, and local policy-makers to reduce emissions. Power plant rules finalized by the Environmental Protection Agency (EPA) in 2024 are one such example of complementary policies. The rules establish emissions intensity standards, not technology mandates, meaning power plant owners can choose from a range of technologies and control options provided that emissions standards are met. This flexibility makes electricity systems modeling important to understand the potential effects of these regulations. We report below a multimodel analysis of the EPA power plant rules that can provide timely information, including for other countries and states, on emissions impacts, policy design for electricity decarbonization, power sector investments and retirements, cost impacts, and load growth. We also discuss related technical, political, and legal uncertainties.
We consider electricity capacity expansion models, which optimize investment and retirement decisions by minimizing both investment and operation costs. In order to provide credible support for planning and policy decisions, these models need to include detailed operations and time-coupling constraints, consider multiple possible realizations of weather-related parameters and demand data, and allow modeling of discrete investment and retirement decisions. Such requirements result in largescale mixed integer optimization problems that are intractable with off-the-shelf solvers. Hence, practical solution approaches often rely on carefully designed abstraction techniques to find the best compromise between reduced computational burden and model accuracy. Benders decomposition offers scalable approaches to leverage distributed computing resources and enable models with both high resolution and computational performance. In this study, we implement a tailored Benders decomposition method for large-scale capacity expansion models with multiple planning periods, stochastic operational scenarios, time-coupling policy constraints, and multi-day energy storage and reservoir hydro resources. Using multiple case studies, we also evaluate several level-set regularization schemes to accelerate convergence. We find that a regularization scheme that selects planning decisions in the interior of the feasible set shows superior performance compared to previously published methods, enabling high-resolution, mixed integer planning problems with unprecedented computational performance.
Direct air capture (DAC) of carbon dioxide (CO2) is energy intensive given the low concentration ( 0.1%) of CO2 in ambient air, but offers relatively strong verification of removals and limited land constraints to scale. Lower temperature solid sorbent based DAC could be coupled on-site with low carbon thermal generators such as nuclear power plants. Here, we undertake a unique interdisciplinary study combining process engineering with a detailed macro-energy system optimization model to evaluate the system-level impacts of such plant designs in the Texas electricity system. We contrast this with using grid power to operate a heat pump to regenerate the sorbent. Our analysis identifies net carbon removal costs accounting for power system impacts and resulting indirect CO emissions from DAC energy consumption. We find that inefficient configurations of DAC at a nuclear power plant can lead to increases in power sector emissions relative to a case without DAC, at a scale that would cancel out almost 50% of the carbon removal from DAC. Net removal costs for the most efficient configurations increase by roughly 18% once indirect power system-level impacts are considered, though this is comparable to the indirect systems-level emissions from operating grid-powered heat pumps for sorbent regeneration. Our study therefore highlights the need for DAC energy procurement to be guided by consideration of indirect emission impacts on the electricity system. Finally, DAC could potentially create demand pull for zero carbon firm generation, accelerating decarbonization relative to a world without such DAC deployment. We find that DAC operators would have to be willing to pay existing or new nuclear power plants roughly $30–80/tCO2 or $150–400/tCO2 respectively, for input energy, to enable nuclear plants to be economically competitive in least cost electricity markets that do not have carbon constraints or subsidies for nuclear energy.
Multi-day energy storage (MDS), a subset of long-duration energy storage, may become a critical technology for the decarbonization of the power sector, as current commercially available Lithium-ion battery storage technologies cannot cost-effectively shift energy to address multi-day or seasonal variability in demand and renewable energy availability. MDS is difficult to model in existing energy system planning models (such as electricity system capacity expansion models (CEMs)), as it is much more dependent on an accurate representation of chronology than other resources. Techniques exist for modeling MDS in these planning models; however, it is not known how spatial and temporal resolution affect the performance of these techniques, creating a research gap. In this study we examine what spatial and temporal resolution is necessary to accurately capture the full value of MDS, in the context of a continent-scale CEM. We use the results to draw conclusions and present best practices for modelers seeking to accurately model MDS in a macro-energy systems planning context. Our key findings are: (1) modeling MDS with linked representative periods is crucial to capturing its full value, (2) MDS value is highly sensitive to the cost and availability of other resources, and (3) temporal resolution is more important than spatial resolution for capturing the full value of MDS, although how much temporal resolution is needed will depend on the specific model context.
As the availability of weather-dependent, zero marginal cost resources such as wind and solar power increases, a variety of flexible electricity loads, or ‘demand sinks’, could be deployed to use intermittently available low-cost electricity to produce valuable outputs. This study provides a general framework to evaluate any potential demand sink technology and understand its viability to be deployed cost-effectively in low-carbon power systems. We use an electricity system optimization model to assess 98 discrete combinations of capital costs and output values that collectively span the range of feasible characteristics of potential demand sink technologies. We find that candidates like hydrogen electrolysis, direct air capture, and flexible electric heating can all achieve significant installed capacity (>10% of system peak load) if lower capital costs are reached in the future. Demand sink technologies significantly increase installed wind and solar capacity while not significantly affecting battery storage, firm generating capacity, or the average cost of electricity.
As decarbonisation agendas mature, macro-energy systems modelling studies have increasingly focused on enhanced decision support methods that move beyond least-cost modelling to improve consideration of additional objectives and tradeoffs. One candidate is modelling to generate alternatives (MGA), which systematically explores new objectives without explicit stakeholder elicitation. This paper provides comparative testing of four existing MGA methodologies and proposes a new Combination vector selection approach. We examine each existing method’s runtime, parallelizability, new solution discovery efficiency, and spatial exploration in lower dimensional (N ⩽ 100) spaces, as well as spatial exploration for all methods in a three-zone, 8760 h capacity expansion model case. To measure convex hull volume expansion, this paper formalizes a computationally tractable high-dimensional volume estimation algorithm. We find random vector provides the broadest exploration of the near-optimal feasible region and variable Min/Max provides the most extreme results, while the two tie on computational speed. The new Combination method provides an advantageous mix of the two. Additional analysis is provided on MGA variable selection, in which we demonstrate MGA problems formulated over generation variables fail to retain cost-optimal dispatch and are thus not reflective of real operations of equivalent hypothetical capacity choices. As such, we recommend future studies utilize a parallelized combined vector approach over the set of capacity variables for best results in computational speed and spatial exploration while retaining optimal dispatch.