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Patricia Hidalgo-Gonzalez

Publications and source records attributed to Patricia Hidalgo-Gonzalez.

5 recordsLinked to original sources

Process and Policy Insights from an Intercomparison of Open Electricity System Capacity Expansion Models

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.

econ.GN↗

Stability-Constrained Learning for Frequency Regulation in Power Grids with Variable Inertia

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.

eess.SY↗

A Market Mechanism for a Two-stage Settlement Electricity Market with Energy Storage

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.

math.OC↗

Distributed Frequency Control in Power Grids with Low and Time-Varying Inertia

This paper presents a distributed frequency control method for power grids with high penetration of inverter-connected resources under low and time-varying inertia due to renewable energy (RE). We provide a distributed virtual inertia (VI) allocation method using the distributed subgradient algorithm. We implement our distributed control strategy under full and sparse communication architectures. The distributed full and sparse communication controllers achieve comparable performance to a centralized controller and stabilize the test system within 6~s. We study the sensitivity of the controller performance to varying objective function weights on phase angle versus frequency deviation, gradient step sizes, and allowed rate of change of inertia (RoCoI) coefficients. We observe that the settling time of the states and the controller performance and effort are susceptible to changes in the gradient step size and objective weights on the frequency and angle deviation. While a higher objective weight on angle versus frequency deviations positively affects their settling times, it negatively impacts controller performance and control effort. The impact of this work is to propose distributed 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.

eess.SY↗

Learning Invariant Stabilizing Controllers for Frequency Regulation under Variable Inertia

Declines in cost and concerns about the environmental impact of traditional generation have boosted the penetration of renewables and non-conventional distributed energy resources into the power grid. The intermittent availability of these resources causes the inertia of the power system to vary over time. As a result, there is a need to go beyond traditional controllers designed to regulate frequency under the assumption of invariant dynamics. This paper presents a learning-based framework for the design of stable controllers based on imitating datasets obtained from linear-quadratic regulator (LQR) formulations for different switching sequences of inertia modes. The proposed controller is linear and invariant, thereby interpretable, does not require the knowledge of the current operating mode, and is guaranteed to stabilize the switching power dynamics. We also show that it is always possible to stabilize the switched system using a communication-free local controller, whose implementation only requires each node to use its own state. Simulations on a 12-bus 3-region network illustrate our results.

eess.SY↗