Papers

This paper studies whether ramp-product settlement compensates generators that absorb intertemporal ramp scarcity as effectively as look-ahead economic dispatch. We evaluate this question by comparing the lost opportunity cost (LOC) induced by each approach. Using rolling-horizon simulations on a 10-generator system and a modified RTS-GMLC system, we compare ramp-product settlement (RP-LMP), look-ahead settlement (LA-LMP), and temporal locational marginal pricing (TLMP). In the featured deterministic ramp stressed cases, aggregate LOC is higher under RP-LMP than under LA-LMP. The main contribution is a generator-level critical assessment for ramp-product settlement: we identify which units are left with uncompensated intertemporal opportunity cost under RP-LMP and how that burden changes under LA-LMP. RP-LMP LOC is concentrated on units with repeated ramp-binding exposure, while LA-LMP mainly relieves those same units and leaves smaller residual LOC on other units. Multi-day tests preserve this ordering under perfect foresight in the larger test system, but show that it need not hold under forecast error.

This paper presents a fully digital Programmable Ramp Generator (PRG) for reconfigurable frequency-ramp synthesizers used in Frequency-Modulated Continuous-Wave radar, spread-spectrum clock generation, and test and measurement applications. A novel modular architecture is proposed, integrating a phase-accumulator-based frequency sweep generator and Delta-Sigma Modulator (DSM) that drives a Multi-Modulus Divider (MMD), giving independent digital control over ramp slope, bandwidth, and duration. While prior works demonstrate chirp synthesis, the circuit-level constraints governing reliable PRG operation have not been jointly analyzed. This work identifies and analyzes three such constraints: (a) MMD octave-boundary behavior under dynamic updates, (b) divider-synchronous timing, and (c) DSM limits on step duration and achievable slope. Together, these constraints define the PRG design space, enabling a systematic algorithm that maps application-level specifications directly to hardware parameters. Following this methodology, a 16-bit PRG implemented in TSMC 65 nm CMOS is verified across 18 ramp profiles and hardware prototyping on a Xilinx Spartan-7 FPGA confirms real-time operation.

Recent years have witnessed the advanced development of topology inference research, which helps elucidate the interaction relationships of components in many biological networks. This paper focuses on inferring the topology of a group of immune cells, based on the collected data from cell-depletion based experiments. The problem is very challenging due to i) the lack of standard analytical models for the cell interactions, and ii) the restrictive data availability determined by the huge experiment and time costs. To address these issues, we first leverage certain common knowledge and observations on the experiments to characterize three properties on the cell amounts during the interaction process: state non-negativity, ratio-based convergence, and triple signs of topology weights. Then, we construct a new model with simple structure and analytical convenience, and obtain sufficient conditions for the model to accommodate all three properties. Finally, based on the constructed model, we propose a constrained quadratic programming method to infer the topology from limited number of data pairs. Validation on experiment data demonstrate the effectiveness of the proposed method.

Energy storage and flexible loads both help balance power systems with high shares of variable renewables, but they do so differently. A battery moves electricity from one period to another. A factory or data center instead moves production or computing activity, while meeting demand for its product or service. As batteries become cheaper, it is natural to ask whether demand flexibility is still needed. Many assessments treat that controllable load as already available and focus on enabling or participation costs. For a large load that normally runs near full capacity, however, shifting electricity use can require extra equipment, delay sales, tie up working capital, and disrupt the process. Here, we compare energy storage technologies with industrial and data-center load shifting on a levelized cost basis, measuring the incremental cost of flexible relative to baseline operation. We find that energy storage becomes more expensive per shifted kilowatt-hour as the flexibility period lengthens and assets are cycled less often. Load shifting does not share that rise in capacity cost under a fixed avoided-hour share, but new productive capacity can still be too expensive to justify. Under representative central technology costs, batteries compete well for short-cycle shifting but are more than 80 times more expensive than load shifting of aluminum smelting when providing seasonal flexibility. The answer therefore depends on the timescale, the load, and whether the needed capacity is new or already sunk.

Data centers are among the power grid's fastest-growing loads. Since data center servers are sensitive electronic components, they need to be protected against the grid's voltage disturbances during grid faults. While disconnecting from the grid achieves this, it can further destabilize the power system if many data centers trip at once. To address this emerging concern, voltage ride-through (VRT) grid codes have been proposed to standardize data center behavior. They require a data center to stay connected for a period of time through the disturbance, hold an active power floor, and recover its draw within a deadline upon restoration. However, systematically designing and certifying controllers that satisfy these coupled temporal and operational requirements remains challenging. We propose SolVRT, a system that synthesizes a grid-code-compliant VRT controller for a given data center using formal methods. We develop a specification language that expresses a grid code in Signal Temporal Logic (STL) as the basis for formal reasoning. Our encoding algorithm takes the specification, along with a model of the data center's power topology, and translates the constraints into a controller synthesis problem. This step produces a correct-by-construction controller if a solution can be found, or a proof that no such controller exists. For the latter case, SolVRT provides a diagnostic step: it traces the facility's "conflict frontier," isolates the conflicting clauses that led to non-compliance, and computes the smallest hardware or workload change that would enable compliance. We evaluate SolVRT through closed-loop simulations of a 200 MW data center connected to a 140-bus transmission system. The results demonstrate that SolVRT can synthesize compliant VRT controllers, certify infeasibility when compliance is unattainable, and identify targeted modifications that enable compliance.

Coupled electrical and thermal infrastructures need controllers that respond to market prices and still solve fast enough to run online. This paper presents a unified Economic Model Predictive Control (EMPC) framework for the coordinated operation of integrated thermal and electrical energy networks. Building on cyclic-terminal EMPC, the proposed approach incorporates hybrid thermal-electrical dynamics, network constraints, and time-varying economic signals within a single mixed-integer state-space representation, jointly optimizing combined heat and power units, large-scale heat pumps, thermal energy storage, batteries, and grid interactions under a convex economic stage cost. Computational tractability is ensured by reduced-order models of district heating networks and DC power flow grids. The framework is demonstrated on a campus-scale energy system under time-varying prices and demand profiles. A joint sweep of the prediction horizon against the terminal penalty weight shows that the two act as substitutes rather than as independent tuning knobs. Without terminal anchoring, the closed-loop cost approaches the periodic-reference average-performance bound only once the horizon spans several diurnal cycles. With a sufficiently large terminal weight, the bound is attained essentially tightly at every tested horizon, including the shortest one, so the horizon ceases to be a performance-critical parameter and becomes a purely computational one. The result reproduces on a second, independent price week. Beyond the weight at which the soft terminal constraint activates, closed-loop behavior is insensitive to the weight over a wide multi-decade plateau; below activation, cost and storage tracking both degrade markedly. A residual receding-horizon drift of the cost-neutral thermal-storage state is also documented and interpreted.

Modern AI is no longer a single model but an ecosystem: classical ML predictors, deep and multimodal models, large language models, and agents, each trained and tuned over different data sources and each producing outputs at scale that become inputs to the others. Operating such an ecosystem is fundamentally a data integration problem - the knowledge it depends on is fragmented across dozens of heterogeneous, independently governed sources that must be reconciled and continually maintained. Yet integration alone is not enough. The predictions these systems make are shaped by many interacting factors, and the events, decisions, and variables that drive an outcome are routinely entangled with the ones that merely accompany it; treated as a basis for action, such correlational signals invite confounded decisions. This becomes acute once agents act autonomously: to be trustworthy and reliable, an agent must anticipate the consequences of its actions, not merely extrapolate from what has co-occurred before. Causal reasoning is what closes this gap, distinguishing the drivers of an outcome from its correlates, and enabling prescriptive and counterfactual analysis over the ecosystem's data. We therefore argue that the integrated ecosystem needs an explicit causal layer, and we propose to build it as a shared, persistent, queryable Causal World System (CWS).

Integrating renewable energy sources into weak grids presents significant challenges for maintaining grid stability. Several essential services are required to ensure a stable and secure grid, including voltage support, fault current injection, system strength support, frequency control, and inertia support. Synchronous condensers, with their inherent inertia and fault current capabilities, have been traditional solutions for grid stability. Recent advancements in power electronics and control structures have led to the development of various technologies aimed at enhancing power grid stability. Static synchronous compensators (STATCOM) and Enhanced STATCOM, which can provide active and reactive power support, show considerable promise. These technologies offer grid-forming (GFM) capabilities that can significantly improve the stability of weak grids with high renewable penetration. Advancements in STATCOM and E-STATCOM technologies offer enhanced control tunability, controllable damping, and immediate response to grid perturbations. This paper investigates the comparative performance of synchronous condensers versus STATCOM and Enhanced STATCOM with grid-forming capability in providing services and enhancing the stability of weak grids with high renewable penetration.

Qualitative transitions in nonlinear dynamical systems (e.g., loss of stability, onset of oscillations, emergence of multistability) delimit operating regimes and can arise as implicit constraints in robust analysis and design under parametric uncertainty. When parameters are inferred from data, admissible values are naturally represented as uncertainty sets, motivating certified tests for the presence or absence of regime-transition candidates. We propose a validated interval workflow that encodes saddle-node and Hopf candidate conditions as square augmented algebraic systems and applies the Krawczyk operator to certify, over a prescribed state-parameter box, either (i) existence and local uniqueness of a candidate solution or (ii) certified absence. Numerical experiments on uncertain synthetic gene-network ODE models yield locally certified saddle-node candidate enclosures on a two-parameter slice for a bistable circuit and certified Hopf candidate enclosures for a three-state oscillator using a Routh-Hurwitz specialization. The resulting certificates are intended to support regime-aware analysis and design under bounded uncertainty by complementing non-validated, pointwise baselines (e.g., Newton method solves at discrete parameter values) and sampling-based workflows with rigorous presence/absence guarantees on user-specified parameter slices.

This paper presents a novel trajectory optimization method for nonlinear l1-optimal control problems in which the control horizon is treated as a free variable, allowing both the control duration and the l1-norm of the control input to be evaluated. Unlike traditional approaches that minimize the l1-norm of the input under a fixed control horizon, the proposed method treats both the control input and control horizon as design variables, enabling joint optimization of energy and temporal efficiency. The method extends a Newton-based algorithm to accommodate this objective, leveraging gradient information with respect to both variables. This formulation enables one to find high-fidelity optimal trajectories without pre-specifying the control horizon. The effectiveness and robustness of the method are demonstrated through numerical simulations, in which the proposed algorithm recovers the analytical solution to the spacecraft Hohmann transfer, and determines an optimal control horizon for an Earth-Moon transfer.

Equilibrium points define operating conditions for nonlinear dynamical and control systems. Their existence, multiplicity, and stability under parametric uncertainty determine feasible operating regimes and the validity of robustness claims. With parameters constrained to a bounded set, one can (i) compute equilibria at sampled parameter values, (ii) trace equilibria along a prescribed path in parameter space, or (iii) identify states in a given operating domain that are equilibria for at least one admissible parameter realization. We compare standard pointwise workflows-direct simulation, numerical continuation, residual minimization, and a multistart Newton-Raphson method-with validated interval-analysis-based workflows. The latter (a) provide formal certificates of exclusion, existence, and uniqueness of equilibria for fixed parameters and, under parametric inclusion conditions, uniformly over entire parameter boxes, and (b) construct rigorous outer enclosures in state space that provably contain all equilibria associated with the full admissible parameter set. Biomolecular circuit models governed by nonlinear ODEs serve as a representative application domain. We benchmark three canonical architectures across four levels of parameter uncertainty, including a genetic toggle switch near a symmetry-breaking bifurcation. Sampling- and slice-based approaches can miss or underrepresent multistability, whereas interval-based outer enclosures yield mathematically rigorous bounds on the equilibrium set induced by parametric uncertainty.

If an automated vehicle is no longer capable of handling the dynamic driving tasks, regulations require that a minimal risk manouvre is initiated aimed at achieving minimum risk condition. Details on measures and actions to resolve this standstill position are missing. This article provides clues for closing this gap and addresses various states in which the human intervention operator must understand the current situation and decide whether it is more acceptable to maintain the stay, e.g. on road-shoulder, or to move the vehicle back into traffic flow or 'limp it' to another position, depending on the outcome of a health check. Our investigations, based on a list of nearly 30 reasons and a subsequent development of 'scenario trees' prove the complexity of the situation and the heavy difficulties faced by the operator to handle in a correct manner. We also discuss how the technical automated driving system can be enhanced so that it stronger supports the operator or allows to even dismiss human intervention. This deems technically feasible but limits became obvious, so that assistance from out side appears indispensable at least for the ramp-up phase of respective automated vehicle. The non-trivial problem of transitioning the 'stranded' vehicle needs to be addressed by further regulation for which this paper may provide useful input.

Developing nonlinear models that are both expressive and computationally efficient remains a challenge in machine learning and nonlinear system identification. Tensor network kernel machines (TNKM) address this challenge by combining nonlinear feature representations with compact low-rank tensor-network parameterizations. However, practical and extensible software frameworks for developing TNKM models remain limited. In this work, we introduce "tnkm", an open-source Python library for constructing and training TNKM models using JAX. The library provides a unified interface for combining different feature maps, tensor-network architectures, and optimization strategies, including alternating least squares and gradient-based methods. We demonstrate the capabilities of "tnkm" on nonlinear benchmark problems, showing that the implemented models achieve competitive prediction accuracy while retaining compact parameterizations and efficient training. The proposed framework facilitates reproducible development and application of tensor-network-based learning methods.

This paper studies linear discrete-time systems affected by independent and identically distributed (i.i.d.) multiplicative uncertainties and additive noise. It establishes the main links between covariance recursions, the spectral properties of associated Kronecker-based matrices, and mean-square stability, and exploits these links to derive tractable conditions for controller synthesis. We first derive a deterministic covariance recursion within the tube-based Stochastic Model Predictive Control (SMPC) framework using a Kronecker product based matrix augmentation. For linear stochastic systems with multiplicative uncertainty and without additive noise, we show that the full-space matrix representation arising from the covariance recursion has the same spectral radius as its symmetric-space counterpart. Combined with the existing symmetric-space characterization, this establishes that Schur stability of the full-space augmented matrix is equivalent to mean-square stability. For state-feedback design, we propose new sufficient Linear Matrix Inequality (LMI) conditions that are numerically more tractable owing to their reduced size compared with the conventional necessary and sufficient conditions. Numerical tests illustrate the usefulness of the covariance characterization for recursively estimating the covariance without relying on sampling-based methods. We also assess the computational burden of the proposed LMI conditions and their conservatism relative to the necessary and sufficient ones.

Although remarkable capabilities have been demonstrated by Large Language Models (LLMs) across scientific domains, feedback controller design remains underexplored. Existing benchmarks focus mainly on linear single-Degree-of-Freedom (DoF) systems and large API-hosted models, leaving performance on complex controller-design tasks and feasibility for edge deployment unclear. To address these limitations, we introduce the Complex Dynamics-to-Control Benchmark for Large Language Models (CoDyControlBench), comprising 132 system configurations across five evaluation dimensions: number of DoF, system type, coupling level, damping regime, and controller type. Six state-of-the-art LLMs were evaluated over three independent runs, including three commercial models (GPT, Gemini, and Claude) and three open-source models (GLM, DeepSeek, and Qwen). GPT achieved the highest design success rate at 94.8%, whereas Qwen showed the lowest rate at 50.0%. Across the benchmark dimensions, DoF and controller type exhibited the largest model-averaged variations in design success, with success-rate ranges of 36.3% and 17.6%, respectively, exceeding those associated with system type, coupling level, and damping regime. Comparison of GPT and Qwen showed that their performance gap arose mainly from the control-design knowledge, particularly gain selection and the use of transient-limiting mechanisms. For edge deployment, a specialized 1.5B-parameter model was developed through reasoning distillation. The reasoning-distilled model outperformed the answer-distilled and base model on CoDyControlBench, maintained stable performance across 1-6 DoFs, and achieved successful traget tracking in all three physical trials on a pneumatic-artificial-muscle-driven robotic arm. These results establish a benchmark baseline and highlight the potential of lightweight, edge-deployable controller-design models.

Bidirectional propellers are often treated as signed thrust sources, but their thrust is a signed-quadratic function of rotor speed.Thus, thrust reversal necessarily occurs through zero rotor speed, where the ability of a bounded motor torque to change thrust collapses.This work formalizes this obstruction by studying exact thrust-trajectory reproducibility under bounded motor inputs with prescribed smoothness.We derive a normalized thrust-coordinate model with vanishing input gain at zero thrust, and prove necessary and sufficient reproducibility conditions in terms of the zero-crossing order of the desired thrust.Generic reversals, in which thrust crosses zero with nonzero slope, require unbounded motor input; the resulting conditions provide direct design rules for shaping thrust reversals that avoid singular motor commands.We also derive the corresponding current and voltage regularity requirements for a DC motor driving a bidirectional propeller.Experiments on a motor-propeller setup validate the predicted reversal-order effects, showing localized current/voltage peaks and thrust-tracking degradation for linear reversals, but not for higher-order reversals.These results expose an intrinsic actuator-level limitation that must be considered in force, acceleration, and interaction-control references for aerial robots.

This article applies dynamic integral quadratic constraints (IQCs) to the analysis and synthesis of accelerated stochastic gradient algorithms. We consider composite objective functions whose gradient can be approximated via mini-batch sampling and we model the resulting stochastic gradient oracle as a feedback nonlinearity in the spirit of Lur'e systems from robust control. Our first main contribution is a family of IQCs that characterize the second-order statistics of mini-batch gradients, extending the classical Zames–Falb multipliers. Our second contribution is a semidefinite-program for certifying exponential convergence rates of stochastic gradient algorithms, and a complementary condition for bounding the asymptotic variance caused by non-vanishing gradient noise. Our third contribution is a convex synthesis procedure that identifies mini-batch gradient algorithms with the smallest certifiable convergence rate.

This work proposes a new adaptive longitudinal platooning strategy in the framework of concurrent learning. Adaptive refers to vehicles facing uncertainty in powertrain parameters via on-line estimation; concurrent learning refers to using both current and past data in the estimation. The proposed platooning strategy advances existing ones since convergence to the true powertrain parameters is guaranteed without imposing persistence of excitation on the vehicle behavior: it suffices the presence of a single non-zero data sample. Meanwhile, the concurrent learning proof we give advances existing ones since it takes into account an extra unknown gain in the error dynamics.

In longitudinal platooning, some key sources of uncertainty are the powertrain time constants of the vehicles. Because such time constants appear in the input matrix of the platooning dynamics, their correct estimation is either impractical with methods requiring persistence of excitation, or impossible with methods requiring the input matrix to be known. This work proposes a novel adaptive longitudinal platooning method with correct estimation of the powertrain time constants. To achieve correct estimation, the composite adaptive control framework and its stability analysis are suitably modified to handle the time constant uncertainty in the design of the adaptive law. The result is a platooning protocol that guarantees convergence of the estimated time constants to their true values without the need for persistence of excitation: it is sufficient the derivative of the acceleration to be nonzero over a possibly short transient, an extremely relaxed excitation condition. Comparisons with state-of-the-art platooning solutions reveal advantages such as no required measurements of acceleration derivative nor collection of past data. The robustness and practicality of the proposed design is also verified with CarSim-based platooning experiments.

This paper aims to contextualize the requirements for Quantum Computing (QC) algorithms to achieve a quantum advantage in solving the alternating current power flow (ACPF) problem, with a focus on runtime complexity. First, we establish a benchmark for a QC iterative solver to demonstrate an advantage over the classical Newton-Raphson Load Flow (NRLF) algorithm. Next, we derive a baseline expression for the end-to-end runtime complexity of any Gate-based QC algorithm as $\Omega(N \kappa/\varepsilon),$ reflecting dependence on system size $N$, condition number $\kappa$, and error tolerance $\varepsilon$. Finally, we highlight key areas where QC algorithms may offer potential benefits over NRLF in addressing the standard ACPF problem.

In this paper, we introduce the approximate strong upper alternating simulation (ASUAS), a new behavioral relation for transition systems. Building on this relation, we construct upper- and lower-sparse abstractions for monotone systems that together form a complete abstraction pair: any controller synthesized for the upper-sparse abstraction can be refined into a controller for the original system, and the absence of a controller for the lower-sparse abstraction implies the absence of a controller for the original system. A key feature of our approach is the ability to provably tune the conservativeness gap between the two abstractions by tuning the space-discretization parameter. We further extend these results, beyond the model-based setting, to data-driven systems, where the abstraction is constructed directly from finite sampled data, without requiring an explicit system model. The theoretical results are illustrated through simulations.

The rapid growth of AI-driven data centers is introducing load behaviors that are not explicitly represented in conventional composite load models. This paper presents DC-CLM, a workload-aware extension of the WECC composite load model that incorporates UPS-supported IT demand, mixed motor/VFD cooling loads, auxiliary demand, and training, inference, and idle workload profiles. A rule-based supervisory state machine represents grid, battery, and diesel operating states and captures temporary IT-load isolation and workload-dependent restoration following voltage recovery. The model is implemented in MATLAB/Simulink using positive-sequence phasor-domain simulation and evaluated under fault-induced delayed voltage recovery (FIDVR) conditions. For a study system with a 100~MW conventional composite load and a 200~MW data-center load, the long-duration voltage recovery is governed mainly by conventional stalled-motor thermal dynamics, while the first 100~ms after fault clearance is strongly workload-dependent. Training and inference produce approximately 50–52~mpu voltage variation and 751–755~MW active-power variation, compared with approximately 36~mpu and 654~MW for idle operation. The results demonstrate the value of incorporating data-center-specific load dynamics into transmission-level stability studies while highlighting the need for future measurement-based validation and more detailed converter-level modeling.

We introduce the Theory of Certified Information Persistence Systems (CIPS), a universal mathematical framework for computing the maximal certified persistence of information in cyber-physical systems (CPS). CIPS provides an axiomatic foundation that separates the continuous evolution of state validity from discrete, memoryless control interventions. By accommodating digital sampling and execution latency through robust set contraction, the framework mathematically isolates a system's maximal certified persistence horizon — a strict theoretical upper bound on safe autonomous operation relative to the system's defined metric growth bounds. Our central representation theorem proves that CIPS provides a universal representation framework: every empirically safe scheduling policy, is structurally isomorphic to a conservative surrogate evaluation within a canonical CIPS. By dynamically targeting this latency-compensated canonical horizon, the framework minimizes conservatism relative to the bounding assumptions, achieving an optimal certified scheduling policy, minimizing computational and network interventions while mathematically guaranteeing continuous physical safety.

Motivated by the rapid growth of data centers, we develop a model to evaluate bringyour-own-capacity (BYOC) mandates and flexibility accreditation in capacity markets for new large loads with shared supply-chain constraints. With efficient pricing, BYOC mainly reallocates procurement between grid-built and self-built capacity and therefore has little welfare effect, while flexibility delivers a modest gain by reducing the effective capacity requirement. Under administrative price caps, mandates can improve static welfare by forcing data centers to internalize the full cost of capacity. The welfare ranking of the two instruments depends on supply-chain stress. At low or moderate stress, only the flexibility instrument raises welfare. Under severe stress with capped prices, the welfare gain from the BYOC obligation can exceed the gross flexibility benefit. The two instruments differ in their effects on a neighboring market: a unilateral BYOC mandate can crowd out its capacity investment, while flexibility produces essentially no spillover at our calibrated benchmark. Finally, applying current capacity non-performance penalties to flexible loads may lead to financial incentives that are too weak to induce truthful flexibility reporting.

Data center power systems are increasingly exposed to weak-grid conditions due to the rapid growth of converter-dominated networks and highly dynamic artificial intelligence (AI) workloads. In centralized uninterruptible power supply (UPS) architectures, the front-end rectifier continuously processes the incoming facility power, making its dynamic performance critical for ensuring stable operation and reliable power delivery to information technology (IT) equipment. Under weak-grid conditions, conventional phase-locked loop (PLL)-based proportional-integral (PI) rectifier controllers may exhibit instability due to strong interactions between converter control dynamics and grid impedance. This paper investigates the stability of centralized UPS data center systems operating under weak-grid conditions using a detailed switching-level model developed in MATLAB/Simulink and validated in real time using an OPAL-RT platform. To enhance weak-grid stability and improve converter-grid interaction, a voltage-modulated direct power control (VM-DPC) strategy with adaptive reactive power support is applied to the front-end rectifier. The proposed approach directly regulates active and reactive power without PLL synchronization while dynamically supporting the point of common coupling (PCC) voltage during rapid IT load variations. Results demonstrate that conventional PI-based rectifier control becomes unstable under SCR<=2 conditions, leading to dc-link oscillations and degradation of downstream power delivery. In contrast, the proposed VM-DPC strategy restores stable operation, improves system damping, and maintains reliable power transfer to highly dynamic IT loads under weak-grid operation.

Increasing penetration of inverter-based resources in today's power systems requires substitution of the contribution from synchronous generators to dynamic voltage stability and inertial response. However, established approaches for power system planning are sequential, as stabilising measures are only considered at a later stage of the planning process. We investigate the advantages of an integrated approach for power system planning, where stabilising measures are considered simultaneously with the expansion of generation, transmission, and storage systems via simplified stability constraints on inertia and voltage stability. We find that system costs are reduced with the integrated approach and that the dual-use option of grid-forming battery energy storage systems is favoured over other stabilising measures like static synchronous compensators.

High shares of distributed energy resources (DERs) transform distribution systems into active participants in integrated transmission and distribution (ITD) operations. Linear models enable scalable distribution-level flexibility aggregation but can misclassify AC feasible operating points, whereas direct nonlinear aggregation becomes costly, especially in multiperiod ITD coordination. This paper reformulates transmission-distribution coordination within a hierarchical optimization framework and introduces a non-iterative predictor-corrector aggregation method. By leveraging path-following techniques from real-time optimal control, the approach achieves tractable computation with guaranteed error bounds. Across 24 radial distribution-network cases and seven meshed variants, including the real KIT Campus North grid, the proposed method yields substantially lower sampled false- and lost-flexibility rates than linear surrogates and a convex relaxation. On two 24-period ITD testcases, the formulation reduces end-to-end wall-clock time by factors of 6 relative to the corresponding centralized formulation, primarily through dimensionality reduction.

Despite advances in artificial intelligence (AI) across multiple sectors, today's AI tools, including deep learning and generative AI, still fail when embedded into physical systems, such as robots and vehicles operating under real-world physical laws. This stems from their inability to maintain reliable world models for long-horizon planning under uncertainty and generalize to unseen scenarios. In this context, wireless networks, through pervasive sensing and communication, can orchestrate physical intelligence. However, current architectures optimize throughput, latency, and reliability and cannot support real-time physical AI coordination, requiring agents to maintain shared spatiotemporal context. To address these challenges, a network of holonic digital twins (HDT-Nets) framework is proposed to deliver real-time physical AI inference through holonic agents that actively reason about their environment rather than passively mirror physical assets. Each HDT is realized as a hierarchical structure spanning the physical agent and network edge, reasoning autonomously at the local level while cooperating with neighboring HDTs to form collectively intelligent units. In HDT-Net, causal Markov blankets spanning sensing, communication, and control determine which agents must coordinate and enable counterfactual reasoning over multi-domain interventions. Active inference within these boundaries unifies perception, action, and learning by minimizing expected free energy while deciding which beliefs to transmit based on their cognitive value to the receiver. Category theory ensures that transmitted beliefs preserve semantic structure across heterogeneous agents with incompatible representations. Finally, integrated information theory quantifies when collective intelligence exceeds independent operation and how network intelligence evolves through coordinated learning and information exchange.

The distribution system is becoming a fundamental part for enabling the energy transition. The fault causes may be several and of different nature, embracing a variety of specializations. This review aims at providing an integrated overview of moisture effects across underground Medium Voltage (MV) cable systems and their accessories. The study collects both literature contributions and practical experiences derived from distribution system operators and research projects on the topic. The findings highlight that the choice of the insulation material is fundamental for mitigating the various consequences of the moisture ingress, linked to chemical and physical processes, as well as mechanical, thermal, and electrical factors, all of which are analysed in detail in the paper. The paper examines the effects of moisture on different types of materials, both traditional and innovative, and discusses diagnostic techniques to detect moisture ingress from the surrounding environment as well as internally generated moisture. The path towards increasing MV distribution reliability, reducing outages, and support the energy transition with more robust distribution infrastructure, is hence achievable through improving moisture management across design, installation, monitoring, and maintenance.

Orchestrated collectives of large language model (LLM) agents that debate and vote are an emerging form of computational intelligence: the intelligent behaviour resides in the interaction, not in any single agent. They improve task accuracy, yet remain black boxes at the system level: there is no principled test of convergence, no bound on the rounds needed, and no faithful account of what drove a decision. This paper develops a novel framework based on Koopman operator theory and validates its theoretical guarantees on multi-agent consensus dynamics. Treating the collective as one nonlinear dynamical system on a communication graph, we read its essential behaviour off the spectrum of its Koopman transfer operator, an exact linear representation of the nonlinear dynamics estimated from interaction traces. The spectrum yields three machine-checkable certificates: the sub-dominant eigenvalue $\lambda_2$ fixes the intrinsic timescale of reasoning and yields a convergence deadline computable before the debate runs; its eigenvector names the coherent factions the collective reasons in, and $|\lambda_2|$ certifies when that explanation is valid; and the leading spectral coordinates form a compressed, auditable message basis. On an attention-consensus model, the deadline tracks observed convergence with log–log correlation $0.93$ and bounds it in 96% of 24 configurations; attribution is exact whenever the spectrum certifies metastability; eight of 32 coordinates preserve the decision at 99.7% fidelity; and a certificate learned from 15 debates held on 60/60 held-out debates. The study runs in minutes on a CPU, making spectral certification a practical layer for trustworthy collective reasoning.