Purpose of Review Dividing the electric grid into the functions of generation, transmission, anddistribution enabled the drawing of jurisdictional lines and the application of the U.S.Constitution’s f...

Purpose of Review The Bonneville Power Administration (BPA) provides a unique window into how long-standing public agencies adapt to rapidly evolving governance challenges in the energy transition. Th...

Energy-system models (ESMs) are used often to support policymakers, system operators, or investors, but much less for offering guidance to technology …

Purpose of Review This review examines the emerging Book & Claim mechanism as a potential market and accounting framework to accelerate decarbonization in the cement and concrete industries. The paper...

Purpose of Review Integrated Transmission and Distribution (ITD) systems for electric power are complex systems encompassing strongly coupled physical, economic, and legal processes. This review ident...

Purpose of Review: As carbon dioxide removal (CDR) becomes an increasingly important component of net-zero strategies, understanding how scientific collaboration is structured globally is essential. T...

Purpose of review Carbon Dioxide Removal (CDR) is deemed critical to achieve the climate targets set by the Paris Agreement, underscoring the need for robust and scalable CDR markets. This review expl...

Electric vehicles are increasingly adopted in the USA, with concurrent expansion of charging infrastructure and electricity demand. This Review details these trends and discusses their drivers and bro...

Purpose of Review Utilities and regulators must weigh the benefits of electricity resilience projects against their costs, which would be passed on to customers. Cost-benefit analysis (CBA) is an appr...

Purpose of Review This review aims to systematically synthesize recent developments in artificial intelligence (AI)-driven power system planning under the low-carbon transition. It focuses on how AI methodologies address increasing system complexity and uncertainty by improving scenario generation, model optimization, and decision support transparency. Recent Findings Recent advances demonstrate that deep learning, reinforcement learning, and generative models significantly enhance input accuracy and optimization efficiency for large-scale, non-convex planning problems. Explainable AI techniques, such as SHAP and LIME, have been increasingly integrated to improve the interpretability and credibility of planning outcomes. However, challenges persist in ensuring physical consistency in scenario generation, modelling realistic multi-agent interactions, and developing trustworthy AI frameworks. Summary AI technologies are reshaping power system planning by offering intelligent, robust, and sustainable solutions aligned with high renewable penetration and evolving market dynamics. Future research must address key gaps in physical feasibility, behavioural realism, and explainability to fully leverage AI’s potential for supporting low-carbon power system transitions.

Purpose of Review The global transition from fossil fuels to renewable energy creates a wide array of challenges that call for new models and analytical methods. This review identifies ten mathematica...

This chapter compares the performance of two solar electricity-generation technologies with complementary energy-storage technologies—concentrating solar power (CSP) with thermal energy storage ...

Purpose of Review The share of asynchronous inverter-based resources is increasing in many electricity systems, displacing synchronous generators. This leads to a decreasing level of system inertia, w...

Purpose of Review Non-firm (flexible) grid connection agreements are emerging as a fast, lower-cost alternative to traditional firm access for integrating renewable and distributed energy resources (D...

This paper reviews the existing definitions of grid resilience and its metrics at distribution and transmission levels, while investigating resilience requirements and assessment methods. The paper al...