Thrust 4: Strategize Pathways for Net-Zero Transition

The Goal

To characterize the “deep uncertainty” associated with net-zero transition that arises on the 100% renewable energy grid (T100RE) and economy-wide sides, while also devising cross-infrastructure solutions to facilitate natural gas, hydrogen, power integration and their engineering-economic linkages and multi-energy systems (MES) resiliency.

The Theory

While the climate pledges are stated clearly, they can be achieved in numerous ways, each of which is driven by a combination of global, national and regional factors. As there is no single “right” pathway, navigating and choosing among these pathways requires, among other things, understanding fundamental trade-offs between economics, reliability, and broader societal impacts of the T100RE power grid. While Thrusts 1-3 fill critical knowledge gaps, a simple compilation of these models, algorithms, and tools will be insufficient to tackle economy-wide decarbonization. There is also a need to characterize these tradeoffs in a broadened yet nuanced manner that is robust and adaptive to both uncertain objectives and environment of the T100RE power grid, and also engages actors and perspectives outside the electric power sector. To successfully decarbonize the “last 20%”, power grid operators and regulators will have to align measures within the power sector with society-scale decarbonization goals. Therefore, translating climate pledges into pathways for net-zero transition must consider not only the grid’s economic and reliability performance but also how the availability and allocation of clean-energy resources can help catalyze the technology adoption, social outcomes, and, ultimately, institutions that are needed to drastically reduce emissions outside the electricity sector.

The Methods

Modeling and endogenizing deep uncertainty for long-run resource and grid planning

There are several sources of short-run risks and long-run deep uncertainties for the T100RE power grid. The latter are defined as uncertainties that could profoundly affect power grid performance but for which meaningful probabilities cannot be assigned because of a lack of mechanistic understanding and historical data. Usually, but not always, the term refers to uncertainties that will unfold over time scales of decades.

Short-run risks of variable renewable energy resources (VREs) are well recognized. However, weather patterns not only affect the output of VREs, but being distributed and networked, those uncertainties can change power system dynamics, thus motivating new control means for VRE-rich power grids (see Thrust 2). In the long-term, planners must cope with uncertain availability and mixes of VREs climate-relevant timescales, as well as risks associated with economics, technology evolution, and policy. Short- and long-run uncertainty factors are tightly related. For example, climate change has been shown to impact availability of hydro- and wind power resources and, thus, their contributions to power production; this affects the reliability and economic benefits of further investments in those sources. Further, climate change can increase the variability of weather and the frequency and severity of extreme events, and will therefore drive the need for enhanced T100RE grid resiliency.

In addition to climate-related factors, deep uncertainty manifests itself in the evolution of technology, policy, and economics, whose social natures make them especially difficult to project. Some technological factors that could greatly influence decarbonization pathways are techno-economic characteristics of VREs and storage resources which are not yet deployed at scale; the ability to effectively aggregate small-scale DERs with high accuracy, power grid operation and market visibility, and controllability; and whether modular nuclear technology will turn out to be a competitive means of mitigating emissions. In turn, future choices of policy instruments can dramatically affect the attractiveness and mixes of alternative resource investments. Furthermore, policy uncertainty arises around carbon emissions, capture, and decarbonization pledges, which prescribe end goals but do not inform specific implementation routes.

This research direction will develop a multi-scenario modeling framework based upon best practices utilized in time-series forecasting models. These models would build upon the Vector Autoregression and Vector Error-Correction Models (VAR and VECM) models used for environmental market analysis to model joint distributions of key model inputs such as fuel prices and demand growth, while including structural uncertainties concerning climate, technology development, and policy evolution. Statistically rigorous multi-variate forecasts can then be combined with planning models to produce statistically rigorous estimates of distributions of key outcomes such as costs. They are also well suited for environments where the objectives are based upon probabilistic criteria, such as minimizing the probability of a transmission contingency or supply deficiency, or minimizing expected resource and transmission cost in multi-scenario stochastic programs, which will be co-developed with Thrust 2 group. As a demonstration of the value of such methods, sets of scenarios developed by the above methods will be used for two types of analyses central to planning the energy transition. First, they will be integrated into long-run transmission planning models that the investigators have used in the UK, US, and Australia, and the benefits of doing so will be compared with traditional methods. Second, building on earlier sensitivity analyses of transition pathways, we will derive rigorous uncertainty characterizations for the cost, technology trajectories, and emissions from energy transition models, which are widely used for policy analyses but usually in a deterministic or sensitivity analysis mode. The characterizations will be fully probabilistic, providing likelihood information that is more useful for risk management, research planning, and valuation of hedges and options, instead of simple sensitivities.

Economic principles and tools for hydrogen-power system integration as a springboard for deep decarbonization of the economy

Decarbonization of the entire economy, and especially the difficult “last 20%”, will require firstly a T100RE power grid and unprecedented electrification of transportation, buildings, and industrial processes, alongside the use of other zero-carbon energy carriers such as hydrogen. Recently, hydrogen has been viewed as both the means to decarbonize some economic sectors (e.g. cement, steel, chemical manufacturing) and to provide additional flexibility to the T100RE power grid as a controllable, no-carbon resource. As needed analyses for short- and long-run management, we consider three crucial
issues for managing the linkages between the T100RE power and hydrogen sectors.

The first issue to be analyzed is the benefit to the T100RE power grid of production of hydrogen via electrolysis, which creates a flexible demand for electricity and offers the possibility of long-duration energy storage, both essential for the difficult final stage in decarbonizing the power sector. The resulting production of green hydrogen provides a zero-emission fuel for other hard-to-decarbonize areas of the economy, particularly in heavy industry. Its cost-effectiveness, however, is hindered by a fundamental trade-off around the load factors with which electrolyzers are operated. Future T100RE power grid scenarios have many hours of very low wholesale prices, but restricting electrolysis to these hours raises the fixed cost that must be recovered per kg produced. On the other hand, electrolysers could provide several ancillary services most useful to support stability of the T100RE power grid. The possibility of operating flexible electrolysers to participate in and revenue-stack across multiple markets could thus create new business case opportunities and decrease the net cost of hydrogen production. Hence, the need to thoroughly explore the economics of this trade-off, since the shape and variability of the electricity price-duration curve will drive electrolyzer costs, especially under the effect of underlying T100RE uncertainty. The second issue to be explored and modeled is the design of government support schemes for green hydrogen production, addressing a key tradeoff: what is the optimal degree of risk reduction provided by such policies when intervention can blunt market signals for efficiency of the power-hydrogen system? The third issue is an engineering-economic comparison of alternative mixes of electricity-to-gas and gas-to-electricity technologies. We will model and analyze the ways in which demand response by electrolyzers and generation from different gas-to-power technologies can support deep decarbonization of the power sector, the likely cost of this compared to other options for decarbonization, and what policies might be required to develop those technologies.

Transformation for resilient MES for a net-zero society

We will pursue the cross-sectoral integration of energy infrastructures – that is, natural gas, hydrogen and electricity – needed to efficiently and reliably supply clean energy to global communities, while modeling tradeoffs with broader social contexts and impacts. This cross-sectoral integration improves technical, economic and environmental performance; however, interdependent infrastructures might increase vulnerabilities to increasingly severe and frequent weather-driven extreme events, whose impacts can cascade to multiple economic sectors (e.g., the Texas 2021 freeze affecting electricity-dependent industries). Therefore, our priority will be to use the T100RE models (e.g., from Thrust 1) for assessing optimal performance-resiliency-emissions trade-offs from multi-energy integrations, considering the ability of these interactions to facilitate electrification, reduce asset redundancy, improve efficiency, and either decrease or exacerbate vulnerabilities. Using the resulting improved understanding of the benefits and costs (broadly interpreted) of the natural gas, hydrogen, and electric power linkages, we will devise metrics and develop relevant optimization models (in collaboration with Thrust 1), based on the grid management models developed in other RDs to capture in appropriate detail the emerging operational MES complexity. These models will allow for quantification and optimization of robustness, adaptability and flexibility in the face of both short- and long-term uncertainties (RD 4.1); controlling of investment risks associated with different plans; and assessment of option values arising from operational flexibility provided by technologies such as modular DERs, hydrogen electrolyzers, and distributed MES that can be quickly deployed to change their setpoints and provide ancillary services (see Thrust 3). Finally, we will model and quantify resilience benefits that MES can provide under various types of extreme events, as well as climate and other deep uncertainties. The goal of this modeling will be to identify suitable architectures for T100RE power grids in the MES context that allow for both techno-economic efficiency and resiliency. In collaboration with G-PST, we will aim to systematically compare, for the first time, alternative hydrogen-power system designs and operations on real-world grid models. The final aim is to move beyond (already incredibly challenging) traditional deterministic approaches to whole-system planning to models that are suited to address the deep uncertainties discussed in Section 1 with large-scale MES applications.