Proceedings of the PowerUp Conference

With the large-scale grid integration of inverter- based resources (IBRs), power systems are rapidly transitioning toward a converter-dominated paradigm. This trend motivates hybrid grid-following (GFL) and grid-forming (GFM) control to combine fast power regulation with improved voltage and frequency support. This paper reviews classical GFL-GFM foun- dations and provides a structured taxonomy of hybrid GFL-GFM strategies for both converters and systems, including switching- type, dual-source, loop-augmented, and system-level categories. A parallel-branch hybrid architecture, which was recently ap- proved by the Western Electricity Coordinating Council, is then introduced. In this model, the GFL and GFM branches operate concurrently under a coordinating plant controller. Both the REGFM C1 converter control model and the REPCGFM C1 plant control model have been implemented and made available in the open-source ANDES simulator. Case studies on Single- Machine Infinite-Bus system and the IEEE 14-bus system validate the effectiveness of the implementation by demonstrating rapid reference tracking and effective voltage and reactive power responses to disturbances.

We examine the joint investment and operational decisions of a prosumer, a customer who both consumes and generates electricity, under net energy metering (NEM) tariffs. Traditional NEM schemes provide temporally flat compensation at the retail price for net energy exports over a billing period. However, ongoing reforms in several U.S. states are introducing time-varying prices and asymmetric import/export compensation to better align incentives with grid costs. While prior studies treat PV capacity as exogenous and focus primarily on consumption behavior, this work endogenizes PV investment and derives the marginal value of solar capacity for a flexible prosumer under asymmetric NEM tariffs. We characterize optimal investment and show how optimal investment changes with prices and PV costs. Through this analysis, we identify a PV effect: changes in NEM pricing in one period can influence net demand and consumption in generating periods with unchanged prices through adjustments in optimal PV investment. The PV effect weakens the ability of higher import prices to increase prosumer payments, with direct implications for NEM reform. We validate our theoretical results in a case study using simulated household and tariff data derived from historical conditions in Massachusetts.

Small-signal instabilities, such as unforced sub- synchronous oscillations (SSOs), are increasingly observed in inverter-based resource (IBR) dominated grids. While decentral- ized stability certificates offer a scalable means to avoid instability onset, they are typically derived under restrictive network-state assumptions–such as small angle differences or negligible voltage drops–that cannot capture how departures from these conditions affect system stability. In this paper, we develop a network model and a decentralized analysis framework that explicitly characterizes how reactive power mismatches, line loading, and inverter control parameters jointly determine small-signal stability. We show that increased steady-state reactive power mismatches and line loading lead to more stringent conditions on admissible inverter droop gains. These results make decentralized stability certificates explicitly network-state dependent, showing how network stress shrinks the set of stabilizing local controller parameters.

Incentive-based load curtailment unlocks critical demand-side flexibility but is hindered by the limited knowledge of private user parameters and the inherent nonsmoothness of responses due to physical device constraints. We address this via a constrained bilevel optimization framework and propose the Bi-ZOL (Bilevel Zeroth-Order Learning) algorithm. Unlike conventional black-box methods, Bi-ZOL exploits the bilevel structure to decompose the hypergradient, integrating the exact analytical information of the SO’s objective with a zeroth-order estimate of the unknown response sensitivity. This structural decomposition-based learning method mathematically smoothes the nonsmooth response landscape and reduces hypergradient estimation error. We provide theoretical convergence guarantees to an approximate stationary point and demonstrate through simulations that Bi-ZOL achieves near-optimal performance.

The rapid growth of large, power electronics-rich data center loads (DCLs) is creating new operational challenges for bulk power systems. A key risk arises when a DCLs unin- terruptible power supply (UPS) disconnects the facility during voltage/frequency disturbances and then reconnects it while the bulk grid is still dynamically settling to a new equilibrium point. Poorly timed reconnection can amplify electromechanical oscillations, deepen frequency deviations, and lead to repeated connect-disconnect flapping. In this paper, we develop an an- alytical framework to characterize the safe reconnection time for large DCL after a disturbance-induced disconnection that avoids flapping. Using a model in the spirit of the classical single-machine infinite-bus system, we capture (i) swing dynamics during the disconnection interval and (ii) voltage-angle coupling at the load bus, which determines the electrical power step at re- connection under constant-power load assumptions. Using energy function method, we characterize the critical safe reconnection time such that for any reconnection time after the critical safe reconnection time, the post-reconnection trajectory is guaranteed to remain within operational limits (frequency/angle/voltage) and converge to the post-reconnection equilibrium, thereby prevent- ing flapping. Time-domain simulations validate the effectiveness of the proposed analytical approach. The results provide a simple, physics-informed criterion that can be used to bound reconnection windows for large DCL facilities and inform UPS reconnection logic.

The evolving landscape of power systems is leading to larger and more complex systems. Future power systems comprise more diverse components, exhibit faster dynamics, and are, in general, less predictable. As a result, time-domain simulations are becoming increasingly necessary for both offline studies and real-time stability assessment. This evolution renders dynamic simulations computationally more demanding, calling for improved simulation speed. Although classical solvers are reliable, their computational performance is limited by the need for iterative solution schemes. We present a modular method that allows Neural Network (NN) based surrogates of any power system component to be embedded within the decoupled Newton iterations of a time-domain solver. A case study integrates Physics-Informed Neural Networks (PINNs) into a solver and demonstrates the general applicability of the proposed approach. The results reveal specific challenges regarding PINN accuracy but highlight the promise of the framework and motivate further exploration using a wider variety of surrogate models.

Distribution utilities increasingly rely on data-driven methods for state estimation and forecasting as large-scale renewable energy integration causes rapid state changes, yet most learning-based approaches remain tied to specific feeder topologies and require retraining when networks are reconfig- ured. In practice, retraining models for every feeder and opera- tional configuration is infeasible due to limited data availability, modeling effort, and computational costs. This paper presents a physics-aware graph learning framework for three-phase un- balanced distribution networks that enables zero-shot transfer across unseen feeder reconfigurations, with potential extension to feeders sharing similar electrical and topological statistics. By combining scalar-weight graph convolutions grounded in power flow physics with adaptive graph pooling, the proposed model learns topology-invariant representations while accommodating variable network sizes. Case studies on the IEEE 123-bus unbalanced distribution feeder using AMI-based measurements demonstrate accurate state estimation and short-term forecasting under unseen reconfigurations, highlighting the promise of the approach for reconfigurable distribution systems.

While the rapid expansion of data centers poses challenges for power grids, it also offers new opportunities as flexible loads. Existing power system research often abstracts data centers as aggregate resources, while computer system research focuses on GPU energy efficiency and largely ignores grid impacts. To bridge this gap, we develop a GPU-to-Grid framework that couples device-level GPU control with power system objectives. We study distribution-level voltage regulation enabled by LLM inference flexibility, using batch size as a data- center-side control knob that trades off GPU power consumption, inference latency, and token throughput. We first formulate the problem as an optimization problem and then realize it as an online feedback optimization controller, implemented by the data center operator using its own empirical GPU power-performance model and real-time measurements from both the GPU and grid systems. Our key insight is that reducing GPU power alleviates lower-voltage violations, while increasing GPU power mitigates upper-voltage violations; this challenges the common belief that minimizing GPU power is always beneficial to power grids. 1

This paper addresses the generation expansion plan- ning (GEP) problem, formulated as a mixed-integer linear programming model with intertemporal storage constraints. Be- ing generally NP-hard, the problem’s computational complexity grows sharply with the planning horizon and the number of binary variables. While previous research has tackled this challenge using heuristic time series aggregation (TSA) methods, we propose a theoretically grounded marginal-cost-based TSA, designed to construct an aggregated model that preserves the active constraints of its full-scale counterpart, thereby explic- itly targeting exact temporal aggregation. This TSA method is embedded within solution algorithms that iteratively refine theoretically validated bounds on the maximum error introduced by the temporal aggregation relative to conventional full-scale optimization, thus offering a formal performance guarantee to the decision-maker. Numerical results highlight the computational advantages of the proposed algorithms, which notably recover tractability whereas full-scale optimization proves intractable.

We design statistical methodology to project the impact of climate change on residential load at the level of substations. To achieve high-resolution load projections, we combine the outputs of downscaled global climate models (GCMs) with a statistical additive regression model. To this end we design a bespoke Generalized Additive Model (GAM) that links cooling/heating loads at given substation and hour of day to daily meteorological covariates. Our models are trained on the ResStock outputs for a realistic building mix and illustrated on the entire 1898 substations in the CATS synthetic grid. The results visualize feature relevance and aggregate climate impacts on California residential loads.

Electricity markets often utilize the DC approxi- mation of the AC power flow equations to facilitate solving an otherwise complex nonconvex optimization problem. These DC power flow equations have analogies to DC circuit laws such as Kirchhoff’s Laws, resulting in an intuitive understanding of power flows under this model. Variables derived from the Lagrangian dual of the DC optimal power flow problem, such as locational marginal prices (LMPs) and congestion cost, are less intuitive without an understanding of optimization theory. Even with this understanding, LMP behavior, such as the conditions in which negative prices occur or the impact of individual congested lines on network-wide LMPs, remains somewhat mysterious. In this paper, we show that prices also obey DC circuit laws, which can help facilitate an intuitive understanding of their behav- ior and relationships throughout a network without explicitly understanding duality. In particular, prices can be modeled as voltages, and their differences can be modeled as flows, allowing for a physical interpretation of prices. This analogy also lends itself to the use of well-understood DC circuit concepts such as superposition and Kirchhoff’s Laws, which can further facilitate a clearer understanding of price behavior.

Large, spatially flexible electricity consumers such as data centers can reallocate demand across locations, influenc- ing dispatch and prices in wholesale electricity markets. While flexible load is often assumed to improve system efficiency, this intuition typically relies on price-taking behavior. We study price-anticipatory spatial load shifting by modeling a large flexible consumer as a Stackelberg leader interacting with DC optimal power flow (DC-OPF) based market clearing. We show that decentralized, cost-minimizing load shifting need not align with system operating cost minimization, and that misalignment arises at boundaries between DC-OPF operating regimes, where small changes in load can induce discrete changes in marginal generators or congestion patterns. We evaluate strategic load shifting on the 73-bus RTS-GMLC test system, where findings indicate reductions in system operating cost in most hours, but misalignment in a subset of cases that are driven by redispatch at merit-order discontinuities. We find that these outcomes are primarily redistributive relative to a price-taking benchmark, reducing generator profits while lowering electricity procurement costs for both flexible and inflexible consumers, even in cases where total system operating costs increase.

Transmission expansion planning (TEP) seeks to identify cost-effective investment strategies that enable power systems to reliably meet future demand, while accounting for the location, timing, and operational impacts of infrastructure upgrades. As demand grows and renewable penetration increases, there is growing interest in jointly modeling energy storage investments to enhance operational flexibility. This work presents a multiperiod formulation that co-optimizes transmission reconductoring and energy storage installation across multiple planning periods. To solve the resulting large-scale problem efficiently, a Benders decomposition with per-period trust-region stabilization and a relaxation-based warm-start procedure is proposed. The approach is demonstrated on a 2,000-bus Texas system, producing an investment plan spanning 2030–2045 with optimality gap below 1.0%, and is compared against a rolling- horizon approach to highlight the benefits of multiperiod coor- dination

Resource adequacy (RA) studies commonly repre- sent energy storage via economic dispatch, with the real-time state of charge (SOC) of storage units implicitly assumed to follow a predetermined schedule until scarcity conditions arise. This paper explores the implications of this assumption, by documenting how downward deviations in energy storage SOC in real-time operations affect reliability outcomes. Applying a probabilistic RA model to an ERCOT-like system with 5 GW of energy storage, we quantify the sensitivity of normalized expected unserved energy (NEUE) and marginal effective load carrying capability (ELCC) to SOC deviations across two-, four-, and eight-hour storage durations. We find that a downward SOC adjustment equal to 25% of maximum storage energy capacity increases system NEUE by a factor of 1.6–2.3 and reduces the marginal storage ELCC by 11–13 percentage points, both of which represent significant differences compared to reference cases. Metrics for shorter-duration storage technologies are most sensitive to SOC deviations on a relative basis, shaped by the nature of lost load within the specific test system. The results highlight the importance of accurately modeling energy storage operations when accrediting storage in RA modeling efforts to help ensure model outcomes are consistent with real-world operational behavior.

Energy storage is increasingly relied upon to meet short-term demand uncertainties from renewable variability and electrification. Unlike conventional generators, storage’s contri- bution to reliability is policy-dependent and balances near-term arbitrage against future scarcity risk. We study how demand uncertainty alters such dynamic storage operation and how these operating decisions propagate into long-run investment outcomes. We formulate storage operation as an average-cost Markov decision process and embed the resulting stationary policies into a stylized capacity expansion framework. Demand uncertainty induces a precautionary storage policy which hedges against stochastic scarcity, leading to materially different post-storage demand distributions relative to perfect-foresight benchmarks. We additionally demonstrate that the reliability externality char- acteristic of electricity markets interacts with uncertainty in a manner that uniquely distorts both storage operation and investment.

Power system outages expose market participants to significant financial risk unless promptly detected and hedged. We develop an outage identification method from public market signals grounded in the parametric quickest change detection (QCD) theory. Parametric QCD operates on stochastic data streams, distinguishing pre- and post-change regimes using the ratio of their respective probability density functions. To derive the density functions for normal and post-outage market signals, we exploit multi-parametric programming to decompose complex market signals into parametric random variables with a known density. These densities are then used to construct a QCD-based statistic that triggers an alarm as soon as the statistic exceeds an appropriate threshold. Numerical experiments on a stylized PJM testbed demonstrate rapid line outage identification from public streams of electricity demand and price data.

We formulate a method to co-optimize power system capacity planning decisions and policy investments that shape electricity load patterns. To this end, we leverage a gradient- based solution technique that enables the efficient solution of operation-aware planning models. To compute gradients with respect to the conditions that define daily electricity demand profiles, we introduce and formalize the concept of differentiable scenario generation and show that generative machine learning models satisfy the mathematical requirements needed to compute consistent gradients. We demonstrate the feasibility of the pro- posed approach through numerical experiments using a diffusion model–based scenario generator and a stylized generation and capacity expansion planning model.

The ability to assess and control approximation errors is key to the success of numerical methods. In contrast, learned approximations must ensure during training that error requirements are met, typically relying on statistical loss func- tions that do not account for the underlying physics. In this work, we argue that the assessment of learning errors in neural solvers should be aligned with numerical methods by evaluating errors in the residual norm of the governing equations. We show how this residual-based error measure can be integrated into the learning process through a physics-informed non-dimensionalisation of inputs and outputs, yielding a training objective that reflects physical sensitivities. The proposed construction reduces reliance on dataset-dependent statistics, improves robustness for limited training data, and provides an interpretable connection between training loss and engineering error measures. We demonstrate the approach on learning power flow solutions as an example of neural solvers for algebraic systems.

This paper establishes crucial cooperation criteria for the operation of Dynamic Virtual Power Plants (DVPPs). We propose a control design and reward allocation mechanism to enable and incentivize Distributed Energy Resources (DERs) to provide dynamic ancillary services (DAS). Our results illustrate how the cooperative aggregation of heterogeneous DERs lever- ages technical complementarities to outperform standalone DAS provision. The proposed reward allocation fulfills critical game- theoretic criteria, including individual rationality, coalitional stability, incentive compatibility, optimality, fairness and ex- post consistency. The control design and reward allocation are validated using a case study based on the Finnish power grid.

Flexible loads can enhance power system stability by providing reserves, but their limited energy capacity and uncer- tain availability distinguish them from conventional generators. To accommodate these characteristics, the Danish Transmission System Operator (TSO) recently introduced new reserve market rules that incorporate energy constraints and relax reliability requirements. In this context, the optimal reserve quantification becomes a joint chance-constrained reserve quantification prob- lem, which is difficult to solve. In this paper, we derive two analytical reformulations of this problem: an exact one when a reserve direction dominates and an approximate one, otherwise. Furthermore, when flexible loads must collectively satisfy a reliability requirement, we introduce the concept of an optimal portfolio of flexible loads: adding loads with similar expected values but different stochastic behaviors to an existing portfolio may change the total portfolio’s reserves. To support this idea, we theoretically study the marginal increase in reserves resulting from adding a load. Numerical results show that our analytical reformulations closely match the exact formulation, with a mean absolute error of 2.5%. Case studies further demonstrate the existence of optimal load groupings and our ability to predict the portfolio in which a load’s marginal value is highest, leveraging our theoretical analysis.

The rapid growth of distributed energy resources (DERs) is reshaping wholesale electricity markets, where aggre- gators coordinate large populations of prosumers, end users who own DERs and can both consume and produce electricity, to participate at scale. Existing models typically either optimize a single aggregator under exogenous prices or analyze a small number of strategic agents; neither captures the scale, structure, and price feedback that arise when large DER populations interact through wholesale market clearing. We address this gap with a hybrid mean field framework in which each aggregator coordinates a large prosumer population through its infinite agent limit, while a finite number of aggrega- tors participate in wholesale markets. Each aggregator maximizes the collective payoff of its prosumers, a cooperative structure rep- resented by mean field control. Across aggregators, interactions are non-cooperative; as a starting point, we model aggregators as non-strategic, price-responsive learners, while market prices remain endogenous through aggregate prosumer actions, leading to a mean field equilibrium. We establish conditions under which this equilibrium exists and is unique. To handle uncertainty in demand, renewable generation, and market prices, we design a two-phase reinforcement learning algorithm that enables aggregators to learn optimal control policies from price feedback. Numerical experiments on the Oahu power system show that the proposed framework reduces price volatility, flattens demand profiles, improves storage utilization, and lowers costs for both consumers and prosumers.

To ensure the security of power systems, operators solve security-constrained optimal power flow (SCOPF) problems to determine setpoints that satisfy operational constraints under credible contingencies. As the size of the contingency increases, the problem becomes computationally expensive, especially for very large systems with thousands of buses and lines. Solving SCOPF problems with large systems using the typical B-θ model can become intractable. Instead, a more tractable approach is to use a sensitivity-based model based on Power Transfer Distri- bution Factors (PTDFs). Sensitivity-based models solve SCOPF problems more efficiently when a subset of the constraints is active at optimal solutions. Although solving SCOPF with the PTDF has many advantages over the B-θ model, the PTDF matrix is very dense in real-world systems, resulting in slow computational performance. This paper presents a method for sparsifying the PTDF matrix without compromising solution quality. The proposed method is exact and yields faster computa- tion speed. We demonstrate the proposed sparsification method on large-scale test systems representing the eastern and western interconnections of North America, with up to 78,000 buses and 10,000 contingencies. The results show that the proposed method solves the SCOPF problem 3 to 7 times faster than the common PTDF model.

This paper presents a welfarist approach to fair active power curtailment in distribution grids with distributed photovoltaics. We address the lack of consistent axiomatic foun- dations in existing ad-hoc curtailment rules by modeling the decision as a social choice problem over feasible operating points and by deriving curtailment objectives from a set of foundational axioms that express principled stances on fairness and grid access rights. Rather than relying on the typically assumed full comparability of utilities, which can lead to undesirable outcomes in heterogeneous residential systems, we adopt a cardinal non- comparability stance on utilities. This approach requires far fewer assumptions about prosumers’ private preferences while providing a rigorous basis for fair social ranking. We then present a unified framework that demonstrates that existing curtailment schemes represent specific instances of the Kalai- Smorodinsky rule applied to different normative reference points. This perspective offers grid operators an auditable, axiomatic foundation for justifying fairness in local energy systems.

Financial Transmission Rights (FTRs) enable elec- tricity market participants to hedge congestion risk in Day Ahead Market (DAM) operations, but for the market to be solvent, Independent System Operators (ISOs) must ensure that FTR payouts do not exceed the collected DAM merchandising surplus that funds them. We show that FTR underfunding (or conversely, hedging efficiency) can arise structurally from misalignment between the network models used in the FTR auction and the DAM, independent of bidding behavior. We develop a geometric framework in which both DAM mer- chandising surplus and the maximum supportable FTR payout are expressed as support functions of network-feasible injection polytopes. The resulting dual representation assigns nonneg- ative weights to transmission element-contingency constraints, enabling constraint-level attribution of model misalignment. Using this framework, we derive sharp implications for canon- ical FTR network modeling choices like uniform transmission element derates, and for structural sources of underfunding like unplanned DAM outages. We further show that multi-interval FTR products impose an intrinsic hedging inefficiency when DAM shadow prices vary over time, even under perfect model alignment. These results provide ISOs with rigorous tools to diagnose underfunding and quantify the efficiency cost of conservative FTR network modeling choices at realized DAM outcomes. Extending these to ex ante design is future work.

This paper presents conservative probabilistic bounds for the spectrum of the admittance matrix and classical linear power flow models under uncertain network parameters; for example, probabilistic line contingencies. Our proposed approach imports tools from probability theory, such as concentration inequalities for random matrices. This provides a theoretical framework for understanding error bounds of common ap- proximations of the AC power flow equations under parameter uncertainty, including the DC and LinDistFlow approximations. Additionally, we show that the upper bounds scale as functions of nodal criticality. This network-theoretic quantity captures how uncertainty concentrates at critical nodes for use in contingency analysis. We validate these bounds on IEEE test networks, demonstrating that they correctly capture the scaling behavior of spectral perturbations up to conservative constants.

Recent research has shown that optimization proxies can be trained to high fidelity, achieving average optimality gaps under 1% for large-scale problems. However, worst-case analyses show that there exist in-distribution queries that result in orders of magnitude higher optimality gap, making it difficult to trust the predictions in practice. This paper aims at striking a balance between classical solvers and optimization proxies in order to enable trustworthy deployments with interpretable speed-optimality tradeoffs based on a user-defined optimality threshold. To this end, the paper proposes a hybrid solver that leverages duality theory to efficiently bound the optimality gap of predictions, falling back to a classical solver for queries where optimality cannot be certified. To improve the achieved speedup of the hybrid solver, the paper proposes an alternative training procedure that combines the primal and dual proxy training. Experiments on large-scale transmission systems show that the hybrid solver is highly scalable. The proposed hybrid solver achieves speedups of over 1000x compared to a parallelized simplex-based solver while guaranteeing a maximum optimality gap of 2%.

To ensure power system stability and security, it is vital to understand the complex nonlinear power system dy- namics related to converter-interfaced generators. For example, grid-forming (GFM) converters are expected to be a key asset for maintaining a strong and stable power system, but might cause wide-bandwidth stability issues with underlying mechanisms heretofore unseen or understudied, including sub-synchronous oscillations (SSOs). This paper details a continuation-based bi- furcation analysis of a GFM converter, revealing stability bounds with respect to operational conditions in addition to the time constant of the cascaded inner voltage and current controllers. We focus our analysis on the strong grid instability caused by an inner controller-related SSO, including continuation of the limit cycle past the Hopf bifurcation point, revealing rapid onset of unacceptably large oscillations. Furthermore, we investigate the impact of the circular current limiter, revealing spurious Hopf bifurcations in weak grids associated with the aforementioned SSO when adopting smooth approximations; this suggests the need for careful implementation of such approximations for GFMs, at least in bifurcation studies.

Small-signal instabilities, such as unforced sub- synchronous oscillations (SSOs), are increasingly observed in inverter-based resource (IBR) dominated grids. While decentral- ized stability certificates offer a scalable means to avoid instability onset, they are typically derived under restrictive network-state assumptions–such as small angle differences or negligible voltage drops–that cannot capture how departures from these conditions affect system stability. In this paper, we develop a network model and a decentralized analysis framework that explicitly characterizes how reactive power mismatches, line loading, and inverter control parameters jointly determine small-signal stability. We show that increased steady-state reactive power mismatches and line loading lead to more stringent conditions on admissible inverter droop gains. These results make decentralized stability certificates explicitly network-state dependent, showing how network stress shrinks the set of stabilizing local controller parameters.

Distribution utilities increasingly rely on data-driven methods for state estimation and forecasting as large-scale renewable energy integration causes rapid state changes, yet most learning-based approaches remain tied to specific feeder topologies and require retraining when networks are reconfig- ured. In practice, retraining models for every feeder and opera- tional configuration is infeasible due to limited data availability, modeling effort, and computational costs. This paper presents a physics-aware graph learning framework for three-phase un- balanced distribution networks that enables zero-shot transfer across unseen feeder reconfigurations, with potential extension to feeders sharing similar electrical and topological statistics. By combining scalar-weight graph convolutions grounded in power flow physics with adaptive graph pooling, the proposed model learns topology-invariant representations while accommodating variable network sizes. Case studies on the IEEE 123-bus unbalanced distribution feeder using AMI-based measurements demonstrate accurate state estimation and short-term forecasting under unseen reconfigurations, highlighting the promise of the approach for reconfigurable distribution systems.

Flexible interconnection agreements are increasingly used to streamline the distributed generation interconnection process by limiting real power exports and avoiding costly grid upgrades. Agrivoltaic systems–solar photovoltaic (PV) panels installed over agricultural land–can provide added value under these agreements by adjusting the PV panels away from sun tracking while increasing the sunlight available to crops. This technical note investigates the operation of agrivoltaics under flexible interconnection limits and evaluates their impact on both PV energy production and crop outcomes. We formulate an optimization problem that determines the time-varying tilt of a single-axis tracking agrivoltaic system to maximize energy production subject to a real power export limit over an entire growing season. The resulting PV operating schedules are then used to evaluate PV energy production and crop yield. In a case study, we demonstrate that agrivoltaic systems can comply with flexible interconnection agreements through operational adjustments that improve crop yield, distinguishing them from conventional PV systems that rely solely on inverter curtailment.

Current Flexible Ramping Product (FRP) procure- ment relies heavily on gas-fired generators, which incur signifi- cant inefficiencies due to the “over-commitment” problem caused by minimum generation constraints. While HVAC-based Virtual Batteries (VBs) offer a flexible alternative, their large-scale aggregation transforms them from price-takers to price-makers, creating a unique financial risk: the HVAC load increasing phase may coincide with self-induced price spikes, eroding ramping revenues. To address this, this paper proposes a two-stage stochastic market clearing framework that co-optimizes energy and ramping services. We explicitly model the distinct “Up- Down” and “Down-Up” load shifting of VBs to provide ramping capacities. Crucially, a financial risk management mechanism is integrated into the optimization to hedge against the price volatility caused by the VBs’ own load-shifting behavior. Nu- merical results demonstrate that the proposed method effectively mitigates the rigid commitment of gas units and ensures the economic viability of VB participation by shielding them from adverse price impacts.

Coordinate transformations significantly simplify power systems computations. Most notably, the classical Clarke and dq0 transformations are widely used in three-phase systems, as together they transform balanced abc quantities into constant- valued signals. However, during unbalanced operation, the utility of these transformations diminishes, since a null 0-coordinate cannot be ensured and oscillating signals emerge. While recently proposed transformations ensure a null 0-coordinate, they either do not lead to constant-valued signals in the dq0 domain or fail under various unbalanced scenarios. In this paper, we propose a Generalized Vector Locus (GVL) transformation that ensures both a null 0-coordinate and constant-valued signals. Moreover, we show that, in the balanced case, the classical amplitude- invariant Clarke transformation is an instance of the proposed GVL transformation.

This paper presents preliminary results toward a conceptual foundation for Next-Generation Grid Codes (NGGCs) based on decentralized stability and performance certification for dynamic ancillary services. The proposed NGGC framework targets two core outcomes: (i) guaranteed closed-loop stability and (ii) explicit performance assurances for power-system frequency and voltage dynamics. Stability is addressed using loop-shifting and passivity-based methods that yield local frequency-domain certificates for individual devices, enabling fully decentralized verification of the interconnected system. Performance is char- acterized by deriving quantitative bounds on key time-domain metrics (e.g., nadirs, rate-of-change-of-frequency (RoCoF), steady- state deviations, and oscillation damping) through frequency- domain constraints on local device behavior. The framework is non-parametric and model-agnostic, accommodating a broad class of device dynamics under mild assumptions, and provides an initial unified approach to stability and performance certification without explicit device-model parameterization. As such, these results offer a principled starting point for the development of future grid codes and control design methodologies in modern power systems.

Frequency dynamics in power systems reflect active power imbalance in real time, thereby providing an instantaneous signal to inform electricity pricing. However, existing real-time markets operate on much slower timescales and fail to exploit this signal. In this letter, we develop integrated market–frequency dynamics that enable online pricing directly from frequency measurements. Representing the real-time market as a dynamic price-discovery process, and integrating this process with the grid frequency dynamics, we derive an explicit price formation mech- anism from frequency measurements. This mechanism manifests as a distributed PID-like controller for each generator, where frequency response is driven and remunerated by electricity prices derived solely from local frequency measurements.

Transmission network reconfiguration is essential for congestion management, yet substation reconfiguration (bus splitting) is often overlooked due to its modeling complexity. This paper proposes a unified framework to jointly optimize line switching and bus splitting. To maintain computational tractabil- ity, we employ two complementary representations: a bus-branch representation that models bus splitting as a line outage and power transfer; and a more detailed bus-breaker representa- tion that captures flexibility from substation reconfigurations beyond the bus-branch representation. We demonstrate that the bus-branch optimal solutions effectively guide the modeling of branch-to-busbar assignment policies of the bus-breaker repre- sentation, further enhancing performance under high loading levels with tight switching budgets. Numerical experiments on IEEE test systems confirm that this joint approach provides significant flexibility compared to line switching alone.

Utilities are increasingly interested in power con- verters to increase the headroom of their assets by actively controlling power flows on their network. In this work we demonstrate that thermal limits of dc link capacitors can result in substantially diminished benefits of these converters under unbalanced operation, due to constraints on neutral current and double-line frequency power ripple. Considering nine voltage source converter topologies with varying ripple capabilities, the upper bound (in terms of additional headroom released) increases by more than 80% compared to a no-ripple case for the application of phase current unbalance mitigation.

This work formulates and solves optimization prob- lems to generate input points that yield high errors between a neural network’s predicted AC power flow solution and solutions to the AC power flow equations. We demonstrate this capability on an instance of the CANOS-PF graph neural network model, as implemented by the PF∆ benchmark library, operating on a 14-bus test grid. Generated adversarial points yield errors as large as 3.7 per-unit in reactive power and 0.08 per-unit in voltage magnitude. When minimizing the perturbation from a training point necessary to satisfy adversarial constraints, we find that the constraints can be met with as little as an 0.04 per-unit perturbation in voltage magnitude on a single bus. This work motivates the development of rigorous verification and robust training methods for neural network surrogate models of AC power flow.

This paper presents preliminary results toward a conceptual foundation for Next-Generation Grid Codes (NGGCs) based on decentralized stability and performance certification for dynamic ancillary services. The proposed NGGC framework targets two core outcomes: (i) guaranteed closed-loop stability and (ii) explicit performance assurances for power-system frequency and voltage dynamics. Stability is addressed using loop-shifting and passivity-based methods that yield local frequency-domain certificates for individual devices, enabling fully decentralized verification of the interconnected system. Performance is char- acterized by deriving quantitative bounds on key time-domain metrics (e.g., nadirs, rate-of-change-of-frequency (RoCoF), steady- state deviations, and oscillation damping) through frequency- domain constraints on local device behavior. The framework is non-parametric and model-agnostic, accommodating a broad class of device dynamics under mild assumptions, and provides an initial unified approach to stability and performance certification without explicit device-model parameterization. As such, these results offer a principled starting point for the development of future grid codes and control design methodologies in modern power systems.