Power systems are being reshaped by decarbonization, digitalization, and high shares of renewables. At the same time, increasingly severe extreme conditions expose the limits of traditional reliability frameworks, calling for risk-aware, resilience-oriented approaches to address high-impact, low-probability (HILP) events. In this context, this paper presents a comprehensive overview of the foundations of power system resilience. It revisits the transition from reliability to resilience, formalizes key concepts and metrics, and introduces advanced approaches for resilience assessment, including fragility-based modeling, cascading failure analysis, and tail-risk indicators. The paper further examines resilience-oriented investment planning, operational strategies across all event phases, and the role of distributed energy resources, microgrids, and cybersecurity. The analysis highlights that resilience extends reliability by focusing on extreme conditions, fundamentally reshaping decision-making and requiring coordinated strategies across infrastructure, operation, and governance.

Transmission expansion planning (TEP) plays a critical role in ensuring power system reliability and facilitating the integration of renewable energy resources. However, this process requires planners to constantly deal with significant uncertainty. While multistage stochastic TEP models provide a robust framework for identifying investment plans under uncertainty, the rapid growth in problem size hinders their computational tractability. To address this challenge, this paper develops a hybrid machine learning-optimisation framework for stochastic TEP. The proposed approach uses investment decisions and uncertainty scenarios as input features to train surrogate neural networks, which are then reformulated as mixed-integer linear constraints and embedded within an optimisation model. The surrogate model approximates expected operational costs to inform TEP decisions, reducing the burden arising from large operational problems. Case study applications on IEEE test systems demonstrate that, after training, the proposed approach achieves near-optimal investment costs while reducing total computational time by up to a factor of around 13 compared to a single full-optimisation stochastic formulation. This enables performing extensive multi-scenario analysis and stress testing that would otherwise be computationally prohibitive at scale.

The growing interdependence between integrated electricity and gas systems (IEGS) calls for planning methods that capture their coupled long-term uncertainties and operational interactions. This paper develops a multi-stage stochastic framework for assessing the interplay of integrated electricity and gas systems (IEGS) in expansion planning under uncertainty, leveraging a scalable Column Generation and Sharing (CG-S) solution strategy to tackle computational challenges. The model jointly optimises investments in electricity transmission and generation, as well as natural gas (NG) processing facilities, under uncertainty in NG availability and prices, electricity demand growth, and capital costs. It integrates detailed hourly power system operations and daily gas flows, preserving realistic temporal granularity. Case studies on the Australian East Coast Energy System demonstrate that the CG-S algorithm reduces convergence times by up to 45% while maintaining stable memory use. The proposed planning framework enables the identification of a coherent pathway for strategic, coordinated investments across integrated electricity and gas systems, while uncoordinated deterministic practices risk oversizing early generation investments by up to 55% due to the inability to leverage cross-system synergies.

This work presents a techno-economic framework to explicitly integrate energy and ancillary service markets into the design phase of local multi-energy systems (MES) under uncertain future market conditions. The methodology combines scenario-based stochastic design optimisation with operational evaluation to identify cost-optimal system configurations. A local energy community is used as a case study, demonstrating that accounting for ancillary service revenues during the design phase significantly influences optimal component sizing. While current energy arbitrage alone cannot economically justify a community battery energy storage system (BESS), incorporating reserve market revenues makes the BESS investment clearly profitable, thus outweighing the associated investment costs. However, as reserve prices decline and electricity price volatility increases, the economic value of BESS shifts from reserve provision to energy arbitrage. Although solar PV on its own is susceptible to volatile prices, its combination with BESS remains profitable across all evaluated scenarios. In contrast, oversized thermal components have not proven economically prudent under the assessed market conditions. Finally, slightly smaller BESS and solar PV provide a balanced trade-off between economic performance and minimising investment risks. These findings highlight the importance of explicitly accounting for market revenues during the design phase, as reserve markets in particular significantly drive investment decisions in local MES.

Integrating resilience into system expansion planning models requires reckoning with extreme event risks, which are multifaceted, uncertain, and irreducible to expected costs. We propose a unified resilience and capacity expansion planning model that optimizes generation, transmission, and hardening investments by treating events as probability-free, multi-criteria stress tests that prevent unacceptable unmet demand and damage outcomes in extreme conditions while operating costs are optimized over ordinary conditions. Each event can contain multiple hazards that can disrupt operations, damage assets, and increase demand. Resilience can be improved with various hardening options that protect against some hazard impacts and by changing the timing, location, or technology of conventional investments. The model's solution is the least-cost, long-term expansion plan that meets all resilience and conventional planning criteria, such as emissions reduction targets. Resilience criteria are then varied to find Pareto-optimal cost and resilience tradeoffs. We execute the method on a large-scale 4,894-node synthetic Texas system with extreme heat, winter storm, and hurricane events, modeled as a linear program. The representative case study results show that a mix of hardening options and changes to capacity decisions relative to the baseline plan can improve system resilience while limiting cost increases to about 9%. The optimal adaptations de pend on both emissions targets and resilience criteria thresholds, which underscores the need for integrated models to meet the real-world requirements of planners and policy makers.

The proliferation of distributed energy resources is increasing the prevalence of both overvoltages and undervoltages in low-voltage (LV) distribution networks. Smart inverter functionalities, such as Volt-VAr and Volt-Watt control, can regulate voltage at the consumer level but are challenging to capture in optimization models due to their nondifferentiability. This paper proposes three nonlinear models for four-wire unbalanced optimal power flow that incorporate these nonsmooth functions without binary or integer variables. The first model encodes these nonsmooth functions directly as user-defined functions using control flow, a feat enabled by many state-of-the-art algebraic modeling languages. The second and third introduce bespoke smooth approximations with tunable approximation errors to address potential numerical issues arising from nondifferentiability, improving reliability while maintaining accuracy. All three methods are evaluated on real four-wire unbalanced LV networks with varying rooftop solar adoption levels, including a 539-bus system with 302 smart inverters. This paper is not only the first to demonstrate accurate, reliable, and tractable Volt-Var-Watt optimization (VVWO) on real four-wire unbalanced LV network models, but it also establishes that optimization-based methods offer superior reliability compared to commonly used incremental (quasi-steady-state) approaches.

Community Batteries (CBs) are emerging as critical assets for energy communities, enabling shared access to storage, facilitating renewable integration, and enhancing grid resilience. A techno-economic framework is developed to evaluate CBs across multiple markets, combining a comparative review of battery technologies with an optimization model applied to an Australian case study. The analysis considers lithium-ion chemistries (NMC, LFP, LTO) alongside vanadium redox flow batteries, highlighting how their technical properties align with community-scale operational requirements. Results indicate that vanadium redox flow batteries, with their scalability and long lifetimes, achieve cost-effectiveness across a wide range of conditions, whereas high-power lithium-ion technologies become viable only under favorable ancillary service prices. Participation in frequency control ancillary service markets emerges as the dominant factor shaping economic outcomes, with high-power CBs capable of unlocking large annual revenues in specific contexts. Sensitivity analysis confirms that profitability is influenced far more by market and price conditions than by moderate cost variations. These findings provide actionable insights for communities, policymakers, and regulators on the design of supportive frameworks for CBs deployment.

Power grids face significant threats from severe disturbances, often triggered by extreme weather, leading to widespread cascading power outages. Although intentional controlled islanding (ICI) is an effective last-resort operational mitigation strategy employed by system operators worldwide to prevent complete cascading blackouts, the impact of large-scale disturbances, particularly weather-induced cascading outages, on when and where to implement the ICI, is neither adequately considered nor reflected in current operational decision-making standards and procedures. This paper proposes a holistic cascading-driven ICI framework that seamlessly integrates advanced weather-related event modelling and cascading risk quantification of high-impact low-probability (HILP) (or tailrisk) events by using a novel ICI based on decision-making mechanism for enhancing the power grid operational resilience. The proposed framework provides a portfolio of mitigation actions proportional to cascading impacts, differentiating between tail-risk events and expected (average) events typically addressed in reliability-oriented studies and current industry practices, while being tailored to both near-real-time operations and short-term operational planning. The proposed framework involves system splitting around black-start units while forming stable and self-sufficient islands, thereby enhancing reliability and resilience. Studies on the IEEE 39-bus and IEEE 118-bus systems demonstrate the effectiveness with a significant improvement in served demand across all simulated initiating events, including up to $N-6$ contingencies.

The increasing integration of distributed energy resources (DER) offers new opportunities for distribution system operators (DSO) to improve network operation through flexibility services. To utilise flexible resources, various DER flexibility aggregation methods have been proposed, such as the concept of aggregated P-Q flexibility areas. Yet, many existing studies assume perfect coordination among DER and rely on single-phase power flow analysis, thus overlooking barriers to flexibility aggregation in real unbalanced systems. To quantify the impact of these barriers, this paper proposes a three-phase optimal power flow (OPF) framework for P-Q flexibility assessment, implemented as an open-source Julia tool 3FlexAnalyser.jl. The framework explicitly accounts for voltage unbalance and imperfect coordination among DER in low voltage (LV) distribution networks. Simulations on an illustrative 5- bus system and a real 221- bus LV network in the U.K. reveal that over 30% of the theoretical aggregated flexibility potential can be lost due to phase unbalance and lack of coordination across phases. These findings highlight the need for improved flexibility aggregation tools applicable to real unbalanced distribution networks.

This article provides valuable insights into the principles, challenges, opportunities, and use cases associated with the operational resource scheduling in multienergy systems from the perspective of energy system integration at different scales—from buildings to districts and communities (relevant, for example, to electricity-heat-gas district energy systems)—to regions and countries (relevant to electricity-gas-hydrogen networks and markets).

Massive theoretical and applied research is underway worldwide to assess the viability of transporting natural gas-hydrogen blends in pipelines. For the first time, this work derives simplified but closed-form equations that describe how changes in gas properties due to hydrogen blending at different volumes map to specific changes in pressure drop, compressor power, and linepack. These first-of-their-kind equations, which are extensively validated against transient gas flow models, enabled three unprecedented and unique findings. The first finding, which quantifies how a change in demand maps to a change in delay and swing on the supply side, reveals that pressure swings increase monotonically with an increase in hydrogen blending volume, translating into an increase in pipeline fatigue and risk of failure. The second finding crucially shows that pressure drop does not monotonically increase with an increase in hydrogen blending volume; in fact, it is highest at around 85 % hydrogen volume, not at 100 %. The third finding shows that the decrease in linepack, as a result of an increase in hydrogen volume, is not only related to the gross calorific value of the gas mixture, but also to the pressure-tocompressibility factor ratio, suggesting that smaller parallel pipelines can offset this linepack reduction compared to a single larger pipeline.

This paper studies the relationship between the operational demand-side flexibility provided by the centralised coordination of distributed energy resources (DER) and the mitigation of economic risks in long-term transmission planning. DER coordination could represent an alternative to utility-scale network investments, offering increased operational flexibility, faster deployment, and fewer social licence issues. However, significant uncertainty surrounds DER uptake due to regulation and challenging consumer engagement. A multi-stage Conditional Value-at-Risk (CVaR) scenario-based formulation is introduced to study the techno-economic value of DER coordination across uncertain scenarios and planners' risk aversion preferences. Using a multi-stage decision tree, we endogenously incorporate long-term planning uncertainties, including DER and renewable energy uptake, retirement of coal generation, fuel and capital costs, as well as lead times. Case studies using real scenarios from Australia's National Electricity Market (NEM) reveal that DER coordination could reduce expected total system costs by up to 11 % and CVaR by 14 %. Coordination could also lead to narrower investment portfolios across several scenarios, with transmission capacity reductions of up to 4 GW in later stages. In contrast, a system lacking DER coordination faces poorer economic performance and a heightened risk of committing to inefficient investment paths, with reliance on high-capital investments increasing by up to 50 % of installed capacity.

The decommissioning of large conventional generators leads to growing challenges for the power system in maintaining local reactive power provision for voltage stability. Rather than installing expensive compensation devices, local multi-energy systems could provide reactive power on a local level. Therefore, this work assesses the impact of reactive power provision on the optimal design of a local multi-energy system with electricity and heat. Our analysis considers a local reactive power market, motivated by regulatory developments in Germany, under varying prices and temporal requirements, adopting an exact iterative second-order cone power flow formulation. Our results show that accounting for reactive power remuneration increases investment in the community battery energy storage system by up to 19.1% and the solar PV arrays by up to 4.3%, as additional revenue from reactive power provision and improved energy arbitrage outweighs higher investment cost. Heat pumps and thermal storage remain largely unaffected by increasing remuneration despite their reactive power provision. These findings highlight the importance of incorporating future reactive power markets into early-stage planning, as regulatory frameworks facilitate their market participation.

Planning options based on flexibility provision by distributed energy resources (DER) could play a transcendental role in future active distribution systems. In some cases, these investment options could serve as suitable alternatives to delaying or deferring costly network reinforcements. However, current distribution planning approaches, typically single-scenario, fail to capture long-term uncertainties. Modelling these uncertainties is essential for properly valuing operational and investment flexibility. Ignoring them can skew cost-benefit analyses and thus distort decision-making. Hence, this paper presents a methodological framework that endogenises the modelling of long-term uncertainties into distribution system planning to address the optionality of DER-based alternatives. The proposed uncertainty-aware approach, grounded on decision theory principles, enables planners to conduct multi-criteria assessments, ranking and selecting the most suitable planning options from a predefined set. The framework is applied in an illustrative case study, demonstrating the usability and showcasing the conditions under which DER options can be advantageous.

Declining system inertia is introducing significant challenges for system operators worldwide. In this context, this paper investigates for the first time the potential value of inertia measurements, with an application to the National Electricity Market of Australia. Analysis of data from Reactive Technologies was carried out to compare inertia measurements with other approaches to estimate system inertia. The results indicate the presence of significant "residual" inertia, often overlooked or "hidden" on the demand side. An estimate of this residual inertia was incorporated in a unit commitment model formulated with a novel convex combination approach to approximate to sufficient accuracy the non-linear nadir constraints while retaining computational tractability. Techno-economic studies were then conducted, suggesting that inertia measurements could lead to significant operational savings in current systems, with even greater savings in future systems. Additionally, the results highlight potential planning savings by deferring or reducing investments driven by inertia requirements.

Green hydrogen (H 2) produced from renewable energy sources (RES) through electrolysis offers a promising so lution to decarbonize hard-to-abate sectors, paving the way for H 2 hubs. The agility of electrolyzers, especially proton-exchange membrane (PEM) technology, can be leveraged to provide flexibility to future integrated electricity and H 2 systems. More flexibility can be unlocked by optimizing the designs of H 2 hubs, which generally consist of electrolyzers, H 2 storage tanks, H 2 liquefiers, and battery energy storage systems (BESSs). This paper introduces a generic optimization framework for finding the least-cost designs of H 2 hubs that also minimize system operating costs under arbitrary H 2 demand profiles. The proposed electrolyzer model incorporates a variable efficiency to avoid overestimating the power consumption and the true size of electrolyzers. In RES-rich countries like Australia, envisaged H 2 export demand may constitute a significant source of demand flexibility. The proposed framework is therefore demonstrated on a case study involving the Australian National Electricity Market (NEM) under a future large-scale green H 2 export scenario, assessing the impact of three different H 2 export profile assumptions on H 2 hub investment costs, system operating costs, and system flexibility. These profiles include: (a) a realistic one based on historical liquefied natural gas (LNG) ship schedules and a pilot H 2 export project, (b) an inflexible constant demand across the year, and (c) a flexible monthly target without intraday and interday restrictions. Numerical analysis demonstrates that the optimal H 2 hub designs obtained under the more realistic H 2 export profile assumptions enjoy the lowest system operating costs and the highest flexibility, the latter of which is evidenced by a substantial increase in availability of reserves.-

System Strength (SS) is becoming increasingly important while more and more inverter-based resources (IBRs) get connected to weak parts of the grid. There is still, however, a level of ambiguity with respect to formal SS definition. It is to a large extent legacy from conventional systems dominated by synchronous generators, often only simplistically associated with fault level. To overcome these issues and encourage discussion that will lead to more formal SS definition, this paper proposes a new inclusive concept of SS and technical framework to clarify its fundamental technical aspects. Starting from a comprehensive review of existing definitions, assessment metrics, and observed real-life challenges and solutions, the paper identifies the gaps between the technical fundamentals of the SS concept and its legacy perception. This is followed by identification of the key SS components relevant for assessment of both steady-state and dynamic system performance. The paper proposes a new SS classification where the SS "umbrella concept" is broken down into small-signal strength, large-signal strength, and short-circuit strength with relevant SS factors, which may also be used in the context of ancillary services market developments. We indeed highlight how the overall SS can be influenced by a combination of factors, not only by fault level, and recommend that a "voltage-behind-an-impedance" parametrization and model may be more effective in defining SS in many circumstances. Finally, the capabilities of different types of IBRs in providing SS products are discussed along with relevant complexity and limitations.