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1. Leader–follower equilibria to examine investment decisions in grid resilience‐enhancing measures

   https://doi.org/10.1111/risa.13921  

   Lo Prete, Chiara ; Radhakrishnan, Ashish ( February 2023 , Risk Analysis)

   Large‐area, long‐duration power outages are increasingly common in the United States, and cost the economy billions of dollars each year. Building a strategy to enhance grid resilience requires an understanding of the optimal mix of preventive and corrective actions, the inefficiencies that arise when self‐interested parties make resilience investment decisions, and the conditions under which regulators may facilitate the realization of efficient market outcomes. We develop a bi‐level model to examine the mix of preventive and corrective measures that enhances grid resilience to a severe storm. The model represents a Stackelberg game between a regulated utility (leader) that may harden distribution feeders before a long‐duration outage and/or deploy restoration crews after the disruption, and utility customers with varying preferences for reliable power (followers) who may invest in backup generators. We show that the regulator's denial of cost recovery for the utility's preventive expenditures, coupled with the misalignment between private objectives and social welfare maximization, yields significant inefficiencies in the resilience investment mix. Allowing cost recovery for a higher share of the utility's capital expenditures in preventive measures, extending the time horizon associated with damage cost recovery, and adopting a storm restoration compensation mechanism shift the realized market outcome toward the efficient solution. If about one‐fifth of preventive resilience investments is approved by regulators, requiring utilities to pay a compensation of $365 per customer for a 3‐day outage (about seven times the level of compensation currently offered by US utilities) provides significant incentives toward more efficient preventive resilience investments. 

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2. Learning and flexibility for water supply infrastructure planning under groundwater resource uncertainty

   https://doi.org/10.1088/1748-9326/ab4664  

   Fletcher, Sarah ; Strzepek, Kenneth ; Alsaati, Adnan ; de Weck, Olivier ( November 2019 , Environmental Research Letters)

   Water supply infrastructure planning in groundwater-dependent regions is often challenged by uncertainty in future groundwater resource availability. Many major aquifer systems face long-term water table decline due to unsustainable withdrawals. However, many regions, especially those in the developing world, have a scarcity of groundwater data. This creates large uncertainties in groundwater resource predictions and decisions about whether to develop alternative supply sources. Developing infrastructure too soon can lead to unnecessary and expensive irreversible investments, but waiting too long can threaten water supply reliability. This study develops an adaptive infrastructure planning framework that applies Bayesian learning on groundwater observations to assess opportunities to learn about groundwater availability in the future and adapt infrastructure plans. This approach allows planners in data scarce regions to assess under what conditions a flexible infrastructure planning approach, in which initial plans are made but infrastructure development is deferred, can mitigate the risk of overbuilding infrastructure while maintaining water supply reliability in the face of uncertainty. This framework connects engineering options analysis from infrastructure planning to groundwater resources modeling. We demonstrate a proof-of-concept on a desalination planning case for the city of Riyadh, Saudi Arabia, where poor characterization of a fossil aquifer creates uncertainty in how long current groundwater resources can reliably supply demand. We find that a flexible planning approach reduces the risk of over-building infrastructure compared to a traditional static planning approach by 40% with minimal reliability risk (<1%). This striking result may be explained by the slow-evolving nature of groundwater decline, which provides time for planners to react, in contrast to more sudden risks such as flooding where tradeoffs between cost and reliability risk are heightened. This Bayesian approach shows promise for many civil infrastructure domains by providing a method to quantify learning in environmental modeling and assess the effectiveness of adaptive planning.

    

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3. Applying the framework to study climate-induced extremes on food, energy, and water systems (C-FEWS): The role of engineered and natural infrastructures, technology, and environmental management in the United States Northeast and Midwest

   https://doi.org/10.3389/fenvs.2023.1070144  

   Vörösmarty, Charles J. ; Melillo, Jerry M. ; Wuebbles, Donald J. ; Jain, Atul K. ; Ando, Amy W. ; Chen, Mengye ; Tuler, Seth ; Smith, Richard ; Kicklighter, David ; Corsi, Fabio ; et al ( March 2023 , Frontiers in Environmental Science)

   Change to global climate, including both its progressive character and episodic extremes, constitutes a critical societal challenge. We apply here a framework to analyze Climate-induced Extremes on the Food, Energy, Water System Nexus (C-FEWS), with particular emphasis on the roles and sensitivities of traditionally-engineered (TEI) and nature-based (NBI) infrastructures. The rationale and technical specifications for the overall C-FEWS framework, its component models and supporting datasets are detailed in an accompanying paper (Vörösmarty et al., this issue). We report here on initial results produced by applying this framework in two important macro-regions of the United States (Northeast, NE; Midwest, MW), where major decisions affecting global food production, biofuels, energy security and pollution abatement require critical scientific support. We present the essential FEWS-related hypotheses that organize our work with an overview of the methodologies and experimental designs applied. We report on initial C-FEWS framework results using five emblematic studies that highlight how various combinations of climate sensitivities, TEI-NBI deployments, technology, and environmental management have determined regional FEWS performance over a historical time period (1980–2019). Despite their relative simplicity, these initial scenario experiments yielded important insights. We found that FEWS performance was impacted by climate stress, but the sensitivity was strongly modified by technology choices applied to both ecosystems (e.g., cropland production using new cultivars) and engineered systems (e.g., thermoelectricity from different fuels and cooling types). We tabulated strong legacy effects stemming from decisions on managing NBI (e.g., multi-decade land conversions that limit long-term carbon sequestration). The framework also enabled us to reveal how broad-scale policies aimed at a particular net benefit can result in unintended and potentially negative consequences. For example, tradeoff modeling experiments identified the regional importance of TEI in the form wastewater treatment and NBI via aquatic self-purification. This finding, in turn, could be used to guide potential investments in point and/or non-point source water pollution control. Another example used a reduced complexity model to demonstrate a FEWS tradeoff in the context of water supply, electricity production, and thermal pollution. Such results demonstrated the importance of TEI and NBI in jointly determining historical FEWS performance, their vulnerabilities, and their resilience to extreme climate events. These infrastructures, plus technology and environmental management, constitute the “policy levers” which can actively be engaged to mitigate the challenge of contemporary and future climate change. 

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4. Optimizing Transmission Infrastructure Investments to Support Line De-energization for Mitigating Wildfire Ignition Risk

   Kody, Alyssa ; Pianksy, Ryan ; Molzahn ; Daniel K ( July 2022 , IREP Symposium on Bulk Power System Dynamics and Control-XI. A 100% Renewable Energy Source Bulk Power Grid: Opportunities and Challenges)

   Wildfires pose a growing risk to public safety in regions like the western United States, and, historically, electric power systems have ignited some of the most destructive wildfires. To reduce wildfire ignition risks, power system operators preemptively de-energize high-risk power lines during extreme wildfire conditions as part of "Public Safety Power Shutoff" (PSPS) events. While capable of substantially reducing acute wildfire risks, PSPS events can also result in significant amounts of load shedding as the partially de-energized system may not be able to supply all customer demands. In this work, we investigate the extent to which infrastructure investments can support system operations during PSPS events by enabling reduced load shedding and wildfire ignition risk. We consider the installation of grid-scale batteries, solar PV, and line hardening or maintenance measures (e.g., undergrounding or increased vegetation management). Optimally selecting the locations, types, and sizes of these infrastructure investments requires considering the line de-energizations associated with PSPS events. Accordingly, this paper proposes a multi-period optimization formulation that locates and sizes infrastructure investments while simultaneously choosing line de-energizations to minimize wildfire ignition risk and load shedding. The proposed formulation is evaluated using two geolocated test cases along with realistic infrastructure investment parameters and actual wildfire risk data from the US Geological Survey. We evaluate the performance of investment choices by simulating de-energization decisions for the entire 2021 wildfire season with optimized infrastructure placements. With investment decisions varying significantly for different test cases, budgets, and operator priorities, the numerical results demonstrate the proposed formulation's value in tailoring investment choices to different settings. 

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5. Willingness to delay charging of electric vehicles

   Daziano, R.A. ( July 2020 , 25th Annual Conference of the European Association Environmental and Resource Economists)

   null (Ed.)

   Electrification of vehicles is becoming one of the main avenues for decarbonization of the transportation market. To reduce stress on the energy grid, large-scale charging will require optimal scheduling of when electricity is delivered to vehicles. Coordinated electric-vehicle charging can produce optimal, flattened loads that would improve reliability of the power system as well as reduce system costs and emissions. However, a challenge for successful introduction of coordinated deadline-scheduling of residential charging comes from the demand side: customers would need to be willing both to defer charging their vehicles and to accept less than a 100% target for battery charge. Within a coordinated electric-vehicle charging pilot run by the local utility in upstate New York, this study analyzes the necessary incentives for customers to accept giving up control of when charging of their vehicles takes place. Using data from a choice experiment implemented in an online survey of electric-vehicle owners and lessees in upstate New York (N=462), we make inference on the willingness to pay for features of hypothetical coordinated electric-vehicle charging programs. To address unobserved preference heterogeneity, we apply Variational Bayes (VB) inference to a mixed logit model. Stochastic variational inference has recently emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of discrete choice models. Our results show that individuals negatively perceive the duration of the timeframe in which the energy provider would be allowed to defer charging, even though both the desired target for battery charge and deadline would be respected. This negative monetary valuation is evidenced by an expected average reduction in the annual fee of joining the charging program of $2.64 per hour of control yielded to the energy provider. Our results also provide evidence of substantial heterogeneity in preferences. For example, the 25% quantile of the posterior distribution of the mean of the willingness to accept an additional hour of control yielded to the utility is $5.16. However, the negative valuation of the timeframe for deferring charging is compensated by positive valuation of emission savings coming from switching charging to periods of the day with a higher proportion of generation from renewable sources. Customers also positively valued discounts in the price of energy delivery. 

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