The open-source Python package pyam provides a suite of features and methods for the analysis, validation and visualization of reference data and scenario results generated by integrated assessment models, macro-energy tools and other frameworks in the domain of energy transition, climate change mitigation and sustainable development. It bridges the gap between scenario processing and visualisation solutions that are "hard-wired" to specific modelling frameworks and generic data analysis or plotting packages. The package aims to facilitate reproducibility and reliability of scenario processing, validation and analysis by providing well-tested and documented methods for working with timeseries data in the context of climate policy and energy systems. It supports various data formats, including sub-annual resolution using continuous time representation and "representative timeslices". The pyam package can be useful for modelers generating scenario results using their own tools as well as researchers and analysts working with existing scenario ensembles such as those supporting the IPCC reports or produced in research projects. It is structured in a way that it can be applied irrespective of a user's domain expertise or level of Python knowledge, supporting experts as well as novice users. The code base is implemented following best practices of collaborative scientific-software development. This manuscript describes the design principles of the package and the types of data which can be handled. The usefulness of pyam is illustrated by highlighting several recent applications.

This study presents a methodology to solve simultaneously the alternating current (AC) transmission network expansion and reactive power planning problems, considering multiple stages and operating conditions. A mixed‐integer non‐linear programming model for the proposed planning problem is presented and rewritten with semidefinite structures. Then, the generalised Benders decomposition is used to separate the overall problem into an upper‐level (master) problem and several lower‐level (slaves) problems. The master problem is a mixed‐integer linear programming problem that optimises the investment cost and constraints of the multistage expansion. Each slave problem minimises the operating costs associated with each stage and operating condition (normal operation or contingency), considering the AC power flow via semidefinite relaxation. With the proposed methodology, the global optimality of generalised Benders decomposition can be preserved due to the use of semidefinite relaxation in each slave problem. Garver's 6‐bus system and an IEEE 118‐bus system are used to show the precision and convergence to near‐global optimal solutions with small relaxation gaps through the proposed approach.

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 (DERs). This review examines: Which types of non-firm agreements exist, how are they implemented, and what lessons can be drawn from recent regulatory pilots? Recent Findings: Literature and pilot projects reveal four predominant models of non-firm grid connections: capacity-limited, time-limited, dynamic operating envelopes, and fully flexible based access. These models are being formalised through legislation or regulatory guidance in the United Kingdom, Australia, Spain, and in various countries of the European Union. Their implementation is increasingly supported by digital monitoring and growing links to local flexibility markets. Summary: Flexible connections can reduce connection costs by up to 80% and halve leadtimes, while safely expanding hosting capacity. Key challenges are curtailment predictability, compensation, and alignment with emerging market platforms. Clear, standardised regulatory frameworks and interoperable digital solutions are essential for scaling these agreements and accelerating DER integration.

The open-source Python package pyam provides a suite of features and methods for the analysis, validation and visualization of reference data and scenario results generated by integrated assessment models, macro-energy tools and other frameworks in the domain of energy transition, climate change mitigation and sustainable development. It bridges the gap between scenario processing and visualisation solutions that are "hard-wired" to specific modelling frameworks and generic data analysis or plotting packages. The package aims to facilitate reproducibility and reliability of scenario processing, validation and analysis by providing well-tested and documented methods for timeseries aggregation, downscaling and unit conversion. It supports various data formats, including sub-annual resolution using continuous time representation and "representative timeslices". The code base is implemented following best practices of collaborative scientific-software development. This manuscript describes the design principles of the package and the types of data that can be handled. The usefulness of pyam is illustrated by highlighting several recent applications.