Patricia Hidalgo-Gonzalez

This paper presents a fast-acting, decentralized dynamic virtual inertial (VI) allocation method for frequency control in power grids with high penetration of inverter-connected resources under low and spatio-temporally varying inertia. The proposed decentralized method involves solving a local constrained convex optimization problem with implicit local frequency dynamics, enabling the use of projected gradient descent. The proposed controller stabilizes post-contingency frequency transients in milliseconds with less than 0.01% convergence error. Extensive simulations investigate the sensitivity of the cumulative and maximum frequency deviations, cumulative VI allocation, transient time and convergence error in frequency stabilization to varying objective function weights on phase angle and frequency deviation, penalty on control effort, and gradient step sizes. The impact of this work is to propose decentralized control schemes as new mechanisms that act before or in alignment with primary and secondary control to safely regulate the frequency in future power grids dominated by inverter-connected resources.

This paper reviews the burgeoning field of data-driven algorithms and their application in solving increasingly complex decision-making, optimization, and control problems within active distribution networks. By summarizing a wide array of use cases, including network reconfiguration and restoration, crew dispatch, Volt-Var control, dispatch of distributed energy resources, and optimal power flow, we underscore the versatility and potential of data-driven approaches to improve active distribution system operations. The categorization of these algorithms into four main groups-mathematical optimization, end-to-end learning, learning-assisted optimization, and physics-informed learning-provides a structured overview of the current state of research in this domain. Additionally, we delve into enhanced algorithmic strategies such as non-centralized methods, robust and stochastic methods, and online learning, which represent significant advancements in addressing the unique challenges of active distribution systems. The discussion extends to the critical role of datasets and test systems in fostering an open and collaborative research environment, essential for the validation and benchmarking of novel data-driven solutions. In conclusion, we outline the primary challenges that must be navigated to bridge the gap between theoretical research and practical implementation, alongside the opportunities that lie ahead. These insights aim to pave the way for the development of more resilient, efficient, and adaptive active distribution networks, leveraging the full spectrum of data-driven algorithmic innovations.

Abstract This study performs a detailed intercomparison of four open-source electricity capacity expansion models—Temoa, Switch, GenX, and USENSYS—to evaluate (1) how closely the results of these models align when inputs and configurations are harmonized, and (2) the degree to which varying model configurations affect outputs. We harmonize the inputs to each model using PowerGenome and use clearly defined scenarios (policy conditions) and configurations (model setup choices). This allows us to isolate how differences in model structure affect policy outcomes and investment decisions. Our framework allows each model to be tested on identical assumptions for policy, technology costs, and operational constraints, allowing us to focus on differences that arise from inherent model structures. Key findings highlight that, when harmonized, models produce very similar capacity portfolios under current policies and net-zero scenarios, with less than 1% difference in system costs for most configurations. This agreement among models allows us to focus on how configuration choices affect model results. For instance, configurations with unit commitment constraints or economic retirement yield different investments and system costs compared to simpler configurations. Our findings underscore the importance of aligning input data and transparently defining scenarios and configurations to provide robust policy insights.

Capacity expansion models have proven to be valuable for developing cost-optimized decarbonization pathways for grid systems. While air pollution impacts of these pathways have been investigated, challenges persist in modeling infrastructure and environmental systems together, and nuanced environmental justice dynamics are often neglected. This work attempts to address this gap in the literature by pairing an open-source capacity expansion model with pollutant dispersion modeling tools through an iterative procedure while exploring the challenges inherent to modeling emissions from future facilities. Using California as a case study, decarbonization pathways and proposed natural gas plants’ NOx and PM 2.5 emissions are modeled, with a statewide target of zero grid-related CO 2 emissions by 2046. The plant with the greatest environmental justice impacts is then restricted in the next iteration of capacity expansion modeling; an additional exploratory iteration investigates the impacts of restricting the facility with the greatest health burden per TWh generated. While inequities persist in the second iteration under each constraint, this methodology lays a foundation for applying environmental justice constraints in the development of equitable grid decarbonization pathways and highlights potential paths forward for more effective constraints.

Major government studies and policy reports project that substantial expansion of interregional transmission will be needed to integrate clean energy and ensure reliability in decarbonized power systems. Using the open-source Switch capacity expansion model with detailed representation of existing U.S. generation and transmission infrastructure, solar, wind, and storage resources, and hourly operations, we evaluate the role of interregional transmission across least-cost, carbon-priced, and zero-emissions scenarios for 2050. An optimal nationwide plan would more than triple interregional transmission capacity, yet this reduces the cost of a zero emissions system by only 7% relative to relying on existing interregional transmission, as storage, solar and wind siting, and nuclear generation serve as close substitutes. Regional cost and rent effects vary, with transmission generally favoring wind and hydrogen resources over solar and batteries. Sensitivity analysis shows diminishing returns: one-fifth of the benefits of full expansion can be achieved with one-twelfth of the added capacity, while cost reductions for batteries and hydrogen provide comparable or greater system savings than interregional transmission. Upgrading existing interregional corridors with advanced conductors roughly doubling capacity per link at half the cost of new builds reduces system costs by only 1.6%, suggesting that reconductoring benefits are modest and that realizing their full potential likely requires pairing with new connections on key corridors or complementary reductions in battery costs. These results suggest that while substantial transmission expansion is economically justified, a diverse set of flexibility resources can substitute for large-scale grid build out, and the relative value of transmission is highly contingent on technological and cost developments.

A wide variety of control schemes for Grid-Forming Inverters (GFMIs) have been developed to enable inverter-based resources (IBRs) to comply with evolving grid-code requirements and deliver essential support services such as frequency and voltage regulation. However, the diversity in these control structures presents substantial challenges for consistent modeling, analysis, and systematic comparison. Conventional approaches to modeling Grid-Forming Inverters typically focus on individual control strategies or isolated configurations, limiting their flexibility and scalability for broader applications. To overcome these limitations, this paper introduces a unified, modular framework utilizing state-space representation (SSR) and the Component Connection Method (CCM) for small-signal modeling of GFMIs. The proposed framework systematically accommodates four prevalent Active Power Control (APC) strategies—Droop control, Droop with Low-Pass Filter (LPF), Virtual Synchronous Generator (VSG), and Compensated Generalized Virtual Synchronous Generator (CGVSG). By establishing a comprehensive and modular modeling methodology, this paper facilitates efficient stability analysis, parameter sensitivity assessment, and performance optimization of diverse GFMI configurations under varying grid conditions. The effectiveness and accuracy of the proposed approach are demonstrated through detailed eigenvalue analyses and validated via time-domain simulations, illustrating significant implications for practical engineering design, grid code compliance, and operational stability in inverter-dominated power systems.

The electric sector simultaneously faces two challenges: decarbonization to mitigate, and adaptation to manage, the impacts of climate change. In many regions, these challenges are compounded by an interdependence of electricity and water systems, with water needed for hydropower generation and electricity for water provision. Here, we couple detailed water and electricity system models to evaluate how the Western Interconnection grid can both adapt to climate change and develop carbon-free generation by 2050, while accounting for interactions and climate vulnerabilities of the water sector. We find that by 2050, due to climate change, annual regional electricity use could grow by up to 2% from cooling and water-related electricity demand, while total annual hydropower generation could decrease by up to 23%. To adapt, we show that the region may need to build up to 139 GW of additional generating capacity between 2030 and 2050, equivalent to nearly thrice California’s peak demand, and could incur up to $150 billion (+7%) in extra costs. The authors link water and electricity system models to evaluate how the electric grid can both adapt to climate change impacts and decarbonize, while also accounting for dependencies and climate vulnerabilities of the closely coupled water sector.

This study performs a detailed intercomparison of four open-source electricity capacity expansion models - Temoa, Switch, GenX, and USENSYS - to evaluate 1) how closely the results of these models align when inputs and configurations are harmonized, and 2) the degree to which varying model configurations affect outputs. We harmonize the inputs to each model using PowerGenome and use clearly defined scenarios (policy conditions) and configurations (model setup choices). This allows us to isolate how differences in model structure affect policy outcomes and investment decisions. Our framework allows each model to be tested on identical assumptions for policy, technology costs, and operational constraints, allowing us to focus on differences that arise from inherent model structures. Key findings highlight that, when harmonized, models produce very similar capacity portfolios under current policies and net-zero scenarios, with less than 1% difference in system costs for most configurations. This agreement among models allows us to focus on how configuration choices affect model results. For instance, configurations with unit commitment constraints or economic retirement yield different investments and system costs compared to simpler configurations. Our findings underscore the importance of aligning input data and transparently defining scenarios and configurations to provide robust policy insights.

Natural gas (NG) is a substantial supplier of electri-cal power systems (EPSs) due to its flexible dispatch. However, the strong dependence of the EPS power generation on NG leads the EPS to experience high operation costs and high Locational Marginal Prices (LMPs) whenever the NG supply is rationed in the EPS. To mitigate this adverse impact on the EPS, this work demonstrates that when NG supply is rationed, the short-term EPS operation optimization including natural gas network (NGN) constraints results in dispatch decisions that better coordinate hydro and gas resources and yield lower LMPs compared to the traditional EPS operation optimization without inclusion of NGN operation modeling. First, we introduce a hydrothermal unit commitment model that includes NGN operation constraints via Mix Integer Second Order Cone Programming. Second, we develop a publicly available real-world test system that models Peru's integrated EPS and NGN systems with high spatial resolution. We simulate the Peruvian integrated EPS and NGN system under realistic gas rationing. Simulations demonstrate that the integrated EPS and N G N operation optimization reveals a more efficient use of hydro and gas resources and a 14.5 % reduction in LMPs compared to a traditional unit commitment model that does not include NGN operation modeling. Moreover, we demonstrate that the high altitude of Peru's Andes grants a 121 % gain of gas pressure to Peru's NGN that results in a 12 % and 14.8% reduction in compressors gas consumption and LMPs, respectively, relative to the integrated EPS and NGN operation optimization that neglects Peru's Andes altitudes.

Abstract Long-duration energy storage (LDES) is a key resource in enabling zero-emissions electricity grids but its role within different types of grids is not well understood. Using the Switch capacity expansion model, we model a zero-emissions Western Interconnect with high geographical resolution to understand the value of LDES under 39 scenarios with different generation mixes, transmission expansion, storage costs, and storage mandates. We find that a) LDES is particularly valuable in majority wind-powered regions and regions with diminishing hydropower generation, b) seasonal operation of storage becomes cost-effective if storage capital costs fall below US$5 kWh −1 , and c) mandating the installation of enough LDES to enable year-long storage cycles would reduce electricity prices during times of high demand by over 70%. Given the asset and resource diversity of the Western Interconnect, our results can provide grid planners in many regions with guidance on how LDES impacts and is impacted by energy storage mandates, investments in LDES research and development, and generation mix and transmission expansion decisions.

As the world races to decarbonize power systems to mitigate climate change, the body of research analyzing paths to zero emissions electricity grids has substantially grown. Although studies typically include commercially available technologies, few of them consider offshore wind and wave energy as contenders in future zero-emissions grids. Here, we model with high geographic resolution both offshore wind and wave energy as independent technologies with the possibility of collocation in a power system capacity expansion model of the Western Interconnection with zero emissions by 2050. In this work, we identify cost targets for offshore wind and wave energy to become cost effective, calculate a 17% reduction in total installed capacity by 2050 when offshore wind and wave energy are fully deployed, and show how curtailment, generation, and transmission change as offshore wind and wave energy deployment increase.

We consider the frequency regulation problem for a Virtual Power Plant (VPP) consisting of inverter-interfaced distributed energy resources connected to a power grid, modeled macroscopically, by a conventional generator connected to multiple time-varying loads. To improve the transient performance (settling time, overshoot, etc.) of the frequency response under load disturbances, we introduce a novel Droop Reset Integral Control (DRIC) law that synergistically combines resetting integrators with integral droop controllers (also referred to as proportional integral (PI) control in the literature). We prove the stability of the proposed control scheme, and its robustness to external disturbances, using conditions based on linear matrix inequalities (LMI) that can be numerically verified a priori. Furthermore, we validate the proposed approach using both learned voltage source inverter dynamics and a high-fidelity Simscape model developed by Sandia National Laboratories. Our results show that the DRIC algorithm is able to significantly reduce overshoot, induce zero steady-state error, and decrease settling times up to 7 times that of standard droop and PI control. We also provide heuristic tuning guidelines for the proposed controller, which can be particularly useful for system operators whenever a detailed model of the virtual power plant is unavailable.

—We propose a control scheme via a non-cooperative linear quadratic differential game to coordinate the inverter dynamics of Distributed Energy Resources (DERs) in a microgrid (MG). The MG can provide regulation services in support to the upper-level grid, in addition to serving its own load. The control scheme is designed for the MG to track a power reference, while each DER seeks to minimize its individual cost function subject to learned inverter dynamics and load perturbations. We use a nonlinear high-fidelity model developed by Sandia National Laboratories to learn inverter dynamics. We determine a Nash strategy for the DERs that uses state estimation of a Loop Transfer Recovery. Results show that the control scheme enables savings up to 9.3 to 208 times in the DERs objective cost functions and a time-domain response with no oscillations with up to 3 times faster settling times relative to using droop and PI control.

The increasing penetration of converter-based renewable generation has resulted in faster frequency dynamics, and low and variable inertia. As a result, there is a need for frequency control methods that are able to stabilize a disturbance in the power system at timescales comparable to the fast converter dynamics. This paper proposes a combined linear and neural network controller for inverter-based primary frequency control that is stable at time-varying levels of inertia. We model the time-variance in inertia via a switched affine hybrid system model. We derive stability certificates for the proposed controller via a quadratic candidate Lyapunov function. We test the proposed control on a 12-bus 3-area test network, and compare its performance with a base case linear controller, optimized linear controller, and finite-horizon Linear Quadratic Regulator (LQR). Our proposed controller achieves faster mean settling time and over 50% reduction in average control cost across $100$ inertia scenarios compared to the optimized linear controller. Unlike LQR which requires complete knowledge of the inertia trajectories and system dynamics over the entire control time horizon, our proposed controller is real-time tractable, and achieves comparable performance to LQR.

Electricity markets typically clear in two stages: a day-ahead market and a real-time market. In this paper, we propose market mechanisms for a two-stage multi-interval electricity market with energy storage, generators, and demand uncertainties. We consider two possible mixed bidding strategies: storage first bids cycle depths in the day ahead and then charge-discharge power bids in the real-time market for any last-minute adjustments. While the first strategy only considers day-ahead decisions from an individual participant's perspective as part of their individual optimization formulation, the second strategy accounts for both the market operator's and participants' perspectives. We demonstrate that the competitive equilibrium exists uniquely for both mechanisms. However, accounting for the day-ahead decisions in the bidding function has several advantages. Numerical experiments using New York ISO data provide bounds on the proposed market mechanism.

The increasing penetration of converter-based renewable generation has resulted in faster frequency dynamics, and low and variable inertia. As a result, there is a need for frequency control methods that are able to stabilize a disturbance in the power system at timescales comparable to the fast converter dynamics. This paper proposes a combined linear and neural network controller for inverter-based primary frequency control that is stable at time-varying levels of inertia. We model the time-variance in inertia via a switched affine hybrid system model. We derive stability certificates for the proposed controller via a quadratic candidate Lyapunov function. We test the proposed control on a 12-bus 3-area test network, and compare its performance with a base case linear controller, optimized linear controller, and finite-horizon Linear Quadratic Regulator (LQR). Our proposed controller achieves faster mean settling time and over 50% reduction in average control cost across 100 inertia scenarios compared to the optimized linear controller. Unlike LQR which requires complete knowledge of the inertia trajectories and system dynamics over the entire control time horizon, our proposed controller is real-time tractable, and achieves comparable performance to LQR.

We model frequency dynamics for power systems with inverters and provide analytical results that enable the development of a novel control scheme for frequency regulation using non-cooperative linear quadratic differential games (NLQGs). First, we prove for the first time that the model for frequency dynamics consisting of n synchronous generators (SGs), r sixth-order-model grid following inverters (GFLIs), and a Laplacian network matrix of a grid with n nodes is stabilizable. We leverage this analytical result and propose a compensator design to ensure the existence of a Nash equilibrium solution in a NLQG for frequency regulation that is mindful of networked inverter dynamics. In this NLQG, we reformulate the frequency dynamics model of an electrical grid considering that n SGs and r GFLIs jointly participate in frequency regulation. All agents are selfish such that each of them seeks to minimize its individual linear quadratic cost during the frequency regulation service. Simulations in a 12-bus network show that the proposed control scheme enables 30%-83% less overshoot and 56%-69% faster settling times than conventional frequency regulation. Moreover, the proposed control scheme is still able to steer frequency back to nominal value despite contingencies, e.g., disconnection of a transmission line or a SG.