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Electric power systems face heightened risks from climate change, on top of existing challenges like aging infrastructure, regulatory shifts, and cybersecurity threats. This paper explores how advanced technologies, including smart grids, artificial intelligence (AI), and machine learning, (ML), enhance the resilience of power systems against climate-driven extreme weather events. Drawing insights from resilience theory, the paper presents a state-of-the-art review of the literature on power system resilience, highlighting the escalating vulnerabilities of energy systems to weather-related disruptions. Although utilities currently use technologies like automated meter reading and advanced metering infrastructure to collect vital grid performance data, the lack of strategic collaboration often impedes effective data governance and sharing, thus undermining efficient responses to climate threats. The paper underscores the significance of distributed energy resources, long-duration energy storage, microgrids, and demand-side management. It further illustrates how AI and ML optimize smart grids to support these strategies. Proactive integration of smart grids with advanced technologies could significantly reduce climate-related costs compared to non-adaptive methods. Such proactive grid resilience strategies not only climate-proof energy infrastructure against climatic changes but also herald a modern, placed-based industrial transformation. Climate change exacerbates challenges in our energy systems, from aging infrastructure and a constantly shifting regulatory environment to cybersecurity risks and diversifying energy portfolios. Addressing these issues requires strategic investment in modern infrastructure, particularly smart grids enhanced by advanced technologies like artificial intelligence (AI) and machine learning (ML). These technologies are vital for enhancing power system resilience against climate impacts. Automated systems such as automated meter infrastructure (AMI) and supervisory control and data acquisition (SCADA) provide real-time data crucial for managing extreme weather events. AI and ML contribute to predictive maintenance, preventing failures and blackouts. They also forecast grid loads during severe weather, facilitating proactive power distribution management to prevent blackouts. This comprehensive improvement in situational awareness promotes economic growth in the energy sector and supports sustainable, climate-resilient transformation. AI and ML not only improve energy distribution and efficiency but also promote conservation efforts and ensure reliable energy amidst a changing climate. Collaboration among utility managers, regulators, and governments is key, focusing on data access, verification, and adaptability. Strategies should be tailored to each utility's unique challenges. Moreover, establishing technical standards is critical for enhancing power grid resilience against climate-induced extreme weather events. [ABSTRACT FROM AUTHOR]

Electricity price forecasting is crucial for grid management, renewable energy integration, power system planning, and price volatility management. However, poor accuracy due to complex generation mix data and heteroskedasticity poses a challenge for utilities and grid operators. This paper evaluates advanced analytics methods that utilize principal component analysis (PCA) to improve forecasting accuracy amidst heteroskedastic noise. Drawing on the experience of the California Independent System Operator (CAISO), a leading producer of renewable electricity, the study analyzes hourly electricity prices and demand data from 2016 to 2021 to assess the impact of day‐ahead forecasting on California's evolving generation mix. To enhance data quality, traditional outlier analysis using the interquartile range (IQR) method is first applied, followed by a novel supervised PCA technique called robust PCA (RPCA) for more effective outlier detection and elimination. The combined approach significantly improves data symmetry and reduces skewness. Multiple linear regression models are then constructed to forecast electricity prices using both raw and transformed features obtained through PCA. Results demonstrate that the model utilizing transformed features, after outlier removal using the traditional method and SAS Sparse Matrix method, achieves the highest forecasting performance. Notably, the SAS Sparse Matrix outlier removal method, implemented via proc RPCA, greatly contributes to improved model accuracy. This study highlights that PCA methods enhance electricity price forecasting accuracy, facilitating the integration of renewables like solar and wind, thereby aiding grid management and promoting renewable growth in day‐ahead markets. This article is categorized under:Energy and Power Systems > Energy ManagementEnergy and Power Systems > Distributed GenerationEmerging Technologies > Digitalization [ABSTRACT FROM AUTHOR]

Carbon Finance: How Carbon and Stock Markets are Affected by Energy Prices and Emissions Regulations by SVETLANA VITEVA and YULIA VELD-MERKOULOVA (Springer International Publishing, Switzerland, 2016). 134 pages. Hardcover. ISBN [...]

Expansion of distributed solar photovoltaic (PV) and natural gas‐fired generation capacity in the United States has put a renewed spotlight on methods and tools for power system planning and grid modernization. This article investigates the impact of increasing natural gas‐fired electricity generation assets on installed distributed solar PV systems in the Pennsylvania–New Jersey–Maryland (PJM) Interconnection in the United States over the period 2008–2018. We developed an empirical dynamic panel data model using the system‐generalized method of moments (system‐GMM) estimation approach. The model accounts for the impact of past and current technical, market and policy changes over time, forecasting errors, and business cycles by controlling for PJM jurisdictions‐level effects and year fixed effects. Using an instrumental variable to control for endogeneity, we concluded that natural gas does not crowd out renewables like solar PV in the PJM capacity market; however, we also found considerable heterogeneity. Such heterogeneity was displayed in the relationship between solar PV systems and electricity prices. More interestingly, we found no evidence suggesting any relationship between distributed solar PV development and nuclear, coal, hydro, or electricity consumption. In addition, considering policy effects of state renewable portfolio standards, net energy metering, differences in the PJM market structure, and other demand and cost‐related factors proved important in assessing their impacts on solar PV generation capacity, including energy storage as a non‐wire alternative policy technique. This article is categorized under:Photovoltaics > Economics and PolicyFossil Fuels > Climate and EnvironmentEnergy Systems Economics > Economics and Policy [ABSTRACT FROM AUTHOR]

Economics of Unconventional Shale Gas Development: Case Studies and Impacts, edited by WILLIAM E. HEFLEY and YONGSHENG WANG. (Springer International Publishing Switzerland 2015). 294 pages. ISBN 978-3-319-11498-9 Policymakers maintain that [...]

This study uses a spatial Durbin error model (SDEM) approach to analyze adoption trends for residential energy-efficiency measures (EEMs) in New York state. Model results are based on socioeconomic, building, and household demographic characteristics during the 2012–2016 period. Our study's results confirm that a positive correlation exists between EEM uptake and multifamily buildings, gas-heated homes, education effects, and spatial spillover effects among neighboring ZIP codes. The results show that building attributes hold a relatively high explanatory power over EEM adoption compared with socioeconomic characteristics. Our results show that energy-efficiency policies can create positive and significant neighborly effects in promoting EEM adoption. The developed SDEM methodological framework provides useful insights in identifying energy-efficiency opportunities that exist in rural, suburban, and urban communities, highlighting the need to review policy incentives periodically to address underlying changes in the built environment and spatial disparities in energy-efficiency investments. [ABSTRACT FROM AUTHOR]

The U.S. shale boom has exerted downward pressure on natural gas prices nationally, widened oil-to-gas price spreads, and accelerated coal-to-gas fuel substitution. One concern is the impact of the rising production of shale gas on further development of a domestic solar photovoltaic ( PV) market. Specifically, will lower natural gas prices slow or even reverse the current rapid growth in the solar market? Using the Phillips-Sul convergence test, this paper investigates whether the levelized cost of energy ( LCOE) of solar PV and natural gas electricity generation in the United States have converged. Using weekly Henry Hub-linked natural gas spot prices and utility PV system prices from 2010 to 2015, empirical tests for convergence are applied to examine the extent of spot market integration and the speed with which market forces move the two energy prices toward equilibrium. The paper also assesses the link between the MAC Solar Energy Index ( SUNIDX) and the S&P GSCI natural gas index spot prices for evidence of market integration during 2007-2015. We conclude that PV and natural gas prices are not converging, and the two markets are not integrated nationally, but some level of integration could exist at regional and state levels that will need to be tested in future research. We also conclude that complementary use of the technologies is likely; while price convergence is not likely to occur soon, distinctive complementary benefits of each resource compared to each other (e.g., fast-start capabilities for gas and low price volatility for PV) will offer opportunities that expand market demand for both. WIREs Energy Environ 2017, 6:e238. doi: 10.1002/wene.238 For further resources related to this article, please visit the . [ABSTRACT FROM AUTHOR]