Search

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]

In the pursuit of a sustainable energy future, this study employs multicriteria decision analysis (MCDA) to present a concise and comprehensive strategy for onshore wind project cost analysis, addressing the critical consideration of structural stability under survival wind loads. Navigating the challenges of the contemporary industrial economy, including escalating energy costs, supply uncertainties, and environmental concerns, the research explores turbine selection based on power factor considerations, economic evaluations, and a meticulous wind resource assessment using the robust Weibull distribution. Recognizing the economic advantages of proximity between power generation sources and load centers, the study introduces the groundbreaking E-115 model, a cost-effective wind turbine. Evaluation metrics, including the levelized cost of energy (LCOE), simple payback period (SPP), net present value (NPV) per kW, benefit-to-cost ratio (BCR), internal rate of return (IRR), and cost of turbine per kW, underscore the robust viability and economic advantages of the E-115 model. Beyond economic considerations, the research emphasizes structural stability for resilience under survival wind loads and highlights the potential of wind turbines to reduce greenhouse gas emissions by 97.26% compared to coal power plants. Positioned as a central force in steering nations toward responsible and eco-conscious development, wind energy emerges as a transformative and imperative component of the sustainable energy landscape. The study's implications extend beyond numerical revelations, providing a foundation for future research and policy decisions, shaping the discourse around renewable energy, and offering insights that transcend mere statistics to chart a course toward a greener, more resilient future. [ABSTRACT FROM AUTHOR]