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  1. Given the increasing occurrence of high-impact low-probability (HILP) contingencies in existing power systems, strengthening the resilience of these systems has become of paramount importance. Enhancing the resilience of power systems is not solely a technical issue but also a socio-economic and policy concern. Therefore, improving the performance of power systems greatly relies on the guidance provided by energy policies. While the decarbonization response, supported by these policies to mitigate climate change, influences the adoption of energy technologies, its impact on the resilience of the system remains uncertain. To uncover the interactions between technologies, policies, and economics concerning power systems resilience, this study focuses on constructing resilience-oriented networked microgrid systems. It develops a two-stage stochastic programming model by integrating a method for selecting power outage scenarios identified by users, in the presence of emissions policies. The results confirm the contributions of integrated systems in enhancing resilience, but they also reveal that low-carbon emissions policies play an inhibiting role by increasing the financial costs associated with resilience planning and operations. Nevertheless, a 30% emissions reduction threshold can still be achieved from the integrated network, facilitating the dual benefits of maximizing emissions reduction and minimizing the burden of emissions taxes. The study's contributions are threefold: firstly, it incorporates techno-economic incentives and regulations simultaneously; secondly, it quantifies the unintended consequences of policies on resilience; and thirdly, it provides constructive guidance for future energy policymaking, particularly in maintaining system resilience. 
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    Free, publicly-accessible full text available January 1, 2025
  2. Climate change mitigation measures are often projected to reduce anthropogenic carbon dioxide concentrations. Yet, it seems there is ample evidence suggesting that we have a limited understanding of the impacts of these measures and their combinations. For example, the Inflation Reduction Act (IRA) enacted in the U.S. in 2022 contains significant provisions, such as the electric vehicle (EV) tax credits, to reduce CO2 emissions. However, the impact of such provisions is not fully understood across the U.S., particularly in the context of their interactions with other macroeconomic systems. In this paper, we employ an Integrated Assessment Model (IAM), the Global Change Assessment Model (GCAM), to estimate the future CO2 emissions in the U.S. GCAM is equipped to comprehensively characterize the interactions among different systems, e.g., energy, water, land use, and transportation. Thus, the use of GCAM-USA that has U.S. state-level resolution allows the projection of the impacts and consequences of major provisions in the IRA, i.e., EV tax credits and clean energy incentives. To compare the performance of these incentives and credits, a policy effectiveness index is used to evaluate the strength of the relationship between the achieved total CO2 emissions and the overarching emission reduction costs. Our results show that the EV tax credits as stipulated in the IRA can only marginally reduce carbon emissions across the U.S. In fact, it may lead to negative impacts in some states. However, simultaneously combining the incentives and tax credits improves performance and outcomes better than the sum of the individual effects of the policies. This demonstrates that the whole is greater than the sum of the parts in this decarbonization approach. Our findings provide insights for policymakers with a recommendation that combining EV tax credits with clean energy incentives magnifies the intended impact of emission reduction. 
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    Free, publicly-accessible full text available December 1, 2024
  3. There is a general consensus about the performance of photovoltaic plants particularly on their efficiency benefits. However, it is not clear to what extents such efficiencies correlate with the efficient frontier of performance when such plants are evaluated under varying geospatial environmental factors and over intertemporal periods. This study carries out a performance benchmarking exercise on photovoltaic power stations. It employs a non-parametric modelling technique in the form of Data Envelopment Analysis to evaluate the performance over time of three photovoltaic power plants within an electric utility. It presents an optimization modelling approach for performance benchmarking over time under situations where there are a limited number of decision-making units. Specifically, the study introduces a multi-period modeling approach which employs real data and captures actual variabilities in environmental factors that influence the output of photovoltaic power plants over time. In comparing the deterministic approach often employed in the extant literature with the multi-period model, the results reveal that the deterministic model overestimates the efficiency values and underestimates the output targets relative to a unit operating on the efficient frontier. The study further employs non-parametric statistical techniques and post-hoc tests to validate the findings. 
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    Free, publicly-accessible full text available September 1, 2024
  4. This study employs a Data Envelopment Analysis (DEA) modeling technique to investigate the efficiency and productivity of renewable energy (RE) adoption across technologically diverse electricity-generating utilities. By employing metrics capturing policy effects, the study evaluates the RE adoption efficiency and productivity using a dynamic DEA model and the Malmquist DEA technique. First, the findings reveal that RE adoption is not significantly different across regional electricity markets. Second, the study revealed that RE adoption increased over the last three years. The total mean productivity change over the entire study period showed a mean improvement of 4.8%. 
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    Free, publicly-accessible full text available June 1, 2024
  5. While extant research explores the impact of Electric Vehicle (EV) incentives on EV market shares, less is known about how such policies and other socioeconomic factors interact that ultimately affect the goal of transportation emission reductions. The study summarized herein employed a sample of 510 state-year CO 2 emissions data sets in the transportation sector spanning a decade (2010-2019) in a multiple linear regression model. Going beyond earlier studies, we find that, while a higher number of EV incentives would significantly contribute to transportation emission reductions, this effect could be dampened by population growth. In addition, we find that, while higher electricity prices may weaken the effectiveness of EV incentives, a high count of EV incentives is more effective in reducing CO 2 emissions than a low count of EV incentives when the electricity price is low. This finding implies that having multiple EV incentives can be effective in reducing transportation carbon emissions even in the face of rising prices of electricity. The study also examines the effectiveness of promoting the density of charging stations and alternative fuel incentives in advancing carbon emission reductions. 
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  6. Ellis, K ; Ferrell, W. ; Knapp, J. (Ed.)
    Failure identification and prediction in a power system are essential components that are prerequisites for optimizing the maintenance of the system. The incidences of power system failures have increased dramatically in recent times due to the uncertainties inherent in the advent of both man-made and natural disasters. This problem is further exacerbated due to the increasing demand for higher operational efficiency in power systems. Currently, there is a paucity of studies that predict and identify failure in a distribution power system. In this paper, we propose an integrated methodology for selecting the optimal maintenance plan based on predicting and identifying failure modes with the aid of Hidden Markov Models (HMM) and a probabilistic decision-making tool. While the model parameters of previous studies were determined utilizing observable prior knowledge, the use of HMM offers a different approach especially in the absence of such observable prior distributions. Thus, we determine the status of health of a power system by using an HMM to capture the relationship between unobservable degradation state and observed parameters. The preliminary outcome is instructive for the management of power systems especially in response to fortifying the system against aging and degradation. 
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  7. Ellis, K. ; Ferrell, W. ; Knapp, J. (Ed.)
    There is no doubt that there is an increase in the penetration of electrical energy into the operation of high-speed railway systems (HSR). This is even more pronounced with the increasing trends in smart electric multiple units (EMU). The operational speed serves as a metric for punctuality and safety, as well as a critical element to maintain the balance between energy supply and consumption. The speed-based regenerative energy from EMU’s braking mode could be utilized in the restoration of system operation in the aftermath of a failure. This paper optimizes the system resiliency with respect to the operational speed for the purpose of restoration by minimizing the total cost of implementing recovery measures. By simultaneously valuating the dual-impact of any given fault on the speed deterioration level from the railway operation systems (ROS) side and the power supply and demand unbalance level from the railway power systems (RPS) side, this process develops an adaptive two-dimension risk assessment scheme for prioritizing the handling of different operational zones that are cascaded in the system. With the aid of an integrated speed-based resilience cost model, we determine the optimal resilience time, speed modification plan, and energy allocation strategy. The outcome from implementing this routine in a real-world HSR offers a pioneering decision-making strategy and perspective on optimizing the resilience of an integrated system. 
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  8. null (Ed.)
    The widespread presence of contingent generation, when coupled with the resulting volatility of the chronological net-load (i.e., the difference between stochastic generation and uncertain load) in today's modern electricity markets, engender the significant operational risks of an uncertain sufficiency of flexible energy capacity. In this article, we address several operational flexibility concerns that originate from the increase in generation variability captured within a security-constrained unit commitment (SCUC) formulation in smart grids. To quantitatively assess the power grid operational flexibility capacity, we first introduce two reference operation strategies based on a two-stage robust SCUC, one through a fixed and the other via an adjustable uncertainty set, for which the state-of-the-art techniques may not be always feasible, efficient, and practical. To address these concerns and to account for the effects of the uncertainty cost resulting from dispatch limitations of flexible resources, a new framework centered on the adjustable penetration of stochastic generation is proposed. Our hypothesis is that if the SCUC is scheduled with an appropriate dispatch level of stochastic generation, the system uncertainty cost will decrease, and subsequently, the system's ability to accommodate additional uncertainty will improve. Numerical simulations on a modified IEEE 73-bus test system verify the efficiency of the suggested assessment techniques. 
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