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DOE OSTI2024

Inertia Estimation Under High Penetration of Inverter-based Resources

Many types of renewable energy sources, especially solar and wind generation, are connected to the electric grid through power-electronic-based interfaces (inverters). These inverter-based resources (IBRs) are mechanically decoupled from the grid, which reduces system inertia and thus may compromise its stability and reliability. In this study, we examine the impact of high penetration of IBRs on the power grid’s inertia. To achieve this, we intentionally introduce disturbances into a simulation case study, such as a step load change, to observe and record the system’s frequency responses. This study offers a nuanced understanding of how the integration of IBRs affects grid stability, and it provides essential guidance for future grid management and resilience strategies.

Yadav, Ajay↗

DOE OSTI2024

Measurement-Based Approach for Inertia-Trend Analysis of the US Western Interconnection

Rising deployment of inverter-based resources (IBRs), characterized by a lack of rotating mass, is decreasing the total inertia of the system. This can lead to an increased Rate of Change of Frequency (RoCoF) during the disturbance and false activation of protective devices. There is a need to assess the inertia over the past decade amidst the evolving landscape of renewable energy sources to develop strategies for integrating energy storage, enhancing resilience measures, and ensuring the stable and reliable operation of the grid. Therefore, a realistic assessment of the inertia trend using a measurement-based approach that addresses the limitations of existing models is proposed. An inertia study of the Western Interconnection in the United States is performed utilizing the data from 2013 to 2022, obtained from FNET/ GridEye network. The three-second RoCoF time window is chosen for the study as it showed an optimum balance between a strong correlation with the power imbalance (ΔP) and minimum inclusion of primary response from governor. The obtained inertia trend result shows a small percentage declination of inertia over the decade. By examining the result alongside a generation mix graph, insights are gained into the dynamic interplay between shifting energy landscape and system inertia.

Dulal, Saurav↗

DOE OSTI2021

Transient Simulations with a Large Penetration of Converter-Interfaced Generation: Scientific Computing Challenges and Opportunities

Current trends in energy systems point to renewable energy sources (RESs) and battery energy storage systems (BESSs) becoming prevalent in power system operations. As of writing this article, the United States has more than 37 GW of utility-scale solar capacity and an additional 112 GW under development. With the rapidly declining capital costs of many of these technologies, we can expect significant deployments in the coming years.

analytical models↗

DOE OSTI2020

Developing More Efficient Wind Turbines: A Survey of Control Challenges and Opportunities

Wind energy has received considerable attention during the past two decades, owing to its vital role in battling both the energy crisis and global climate change. Wind energy has grown to become the most significant nonconventional renewable energy source. Today, modern wind turbines (WTs) are controlled to offer several functionalities that improve their flexibility at a level similar to conventional power sources. However, until now, there have been few reports in the literature summarizing and listing WT control concepts to encourage industry and academia to pursue improvements in developing more efficient WTs and bridge the gap between businesses and universities. Hence, this article focuses on a thoughtful investigation into developing better and more efficient WTs, detailing different issues involved in wind energy generation, such as WT components, control systems, operational modes, and, especially, new research and development trends.

17 WIND ENERGY↗

DOE OSTI2021

Probabilistic Voltage Sensitivity based Preemptive Voltage Monitoring in Unbalanced Distribution Networks

With increasing penetration of renewable energy and active consumers, control and management of power distribution networks has become challenging. Renewable energy sources can cause random voltage fluctuations as their output power depends on weather conditions. Conventional voltage control schemes such as tap changers and capacitor banks lack the foresight required to quickly alleviate voltage violations. Thus, there is an urgent need for effective approaches for predicting and mitigating voltage violations as a result of random fluctuations in power injections. This work proposes a novel voltage monitoring approach based on low-complexity, data-driven probabilistic voltage sensitivity analysis. The usefulness of this work is not only in predicting voltage violations in unbalanced distribution grids, but also in opening up the door for optimal voltage control. Using system data and forecasts, the proposed approach predicts the distribution of system node voltages which is then used to to identify nodes that may violate the nominal operational limits with high probability. The method is tested on the IEEE 37 node distribution system considering integrated distributed solar energy sources. The method is validated against the classic load flow based method and offers over 95% accuracy in predicting voltage violations.

Abujubbeh, Mohammad↗

DOE OSTI2023

Integrated Resource and System Plannning Based on Energy Costs and Emission Analysis for a Sustainable Future

Efficient integration of renewable energy sources (RES) - such as wind, solar, geothermal, tidal, and wave - into the electric power grids can help improve energy security and contribute toward a sustainable energy future. To date, however, electric power grids are critically dependent on conventional sources, such as fossil fuels. Given the increasing penetration of RES and dynamic loads, there is a need to thoroughly reanalyze the performance of power grids in terms of cost, efficiency, and energy security. This paper presents a comprehensive analysis in terms of energy costs and emissions estimates of dynamic power grids incorporating various power-generating sources.

distributed energy resources↗

DOE OSTI2025

Optimal Complementarity Analysis of Potential Floating Solar Co-Located With Existing Hydropower Assets Across the Contiguous United States

The U.S. is expected to double its rate of renewable capacity from 2024 to 2030. However, the stochastic nature of renewable energy poses challenges to the operation and reliability of our power grid. The combined generation from renewable energy sources, with dispatchable sources (such as hydropower) operating as a hybrid energy plant, could mitigate this variability. In this paper, the complementarity analysis of selected U.S. reservoirs with existing hydropower assets (EHAs) and potential floating photovoltaics (FPVs) is conducted for the continuous U.S. (CONUS). The optimal FPV capacity for each site is determined by minimizing the variability of the combined output, while adhering to the FPV potential. Our results indicate that over 50% of the analyzed reservoirs achieve a stability coefficient exceeding 0.5, leading to a less-variable output after optimization. Finally, we analyze the complementary hydro-FPV hybrid reservoirs by considering both the Pearson correlation coefficient and the stability coefficient on daily, monthly, and yearly scales. Summaries are included of locations of theoretical FPVs co-located with hydropower plants that exhibit high complementarity based on the selected metrics.

13 - HYDRO ENERGY↗

DOE OSTI2021

DSP Implementation of a Novel Recurrent Neural Network Controller into a TI Solar Microinverter

Single-phase grid-tied inverters are widely used to integrate small-scale renewable energy sources and distributed generations to the utility grid. A novel Recurrent Neural Network (RNN) current controller is introduced to solve the resonant problem associated with LCL based inverters. The well-trained RNN controller was validated through a Texas Instruments (TI) LCL filter based solar microinverter kit that contains a C2000 TI microcontroller. The inverter closed current loop test without the grid connection was conducted in a laboratory setup to verify the RNN contoller functionality. The laboratory waveform results verified that the RNN current controller produced the strong tracking of the measured inverter AC current to its reference value even at a low sampling frequency of 5KHz. The proposed RNN current controller also proved to perform better than the built-in three-pole-three-zero (3P3Z) current controller provided by TI and produce lower values of Total Harmonic Distortion (THD).

digital signal processor↗

DOE OSTI2021

A Cooperative Game Theory-based Approach to Under-frequency Load Shedding Control

This paper proposes a cooperative game theory-based under-frequency load shedding (UFLS) approach for frequency stability and control in power systems. UFLS is a crucial factor for frequency stability and control especially in power grids with high penetration of renewable energy sources and restructured power systems. Conventional UFLS methods, most of which are off-line, usually shed fixed amounts of predetermined loads based on a predetermined schedule which can lead to over or under curtailment of load. This paper presents a co-operative game theory-based two-stage strategy to effectively and precisely determine locations and amounts of loads to be shed for UFLS control. In the first stage, the total amount of loads to be shed, also referred to as deficit in generation or the disturbance power, is computed using the initial rate of change of frequency (ROCOF) referred to the equivalent inertial center. In the second stage, the Shapley value, one of the solution concepts of cooperative game theory, is used to determine load shedding amounts and locations. The proposed method is implemented on the reduced 9-bus 3-machine Western Electricity Coordinating Council (WECC) system and simulated on Real-time Digital Simulators (RTDS). The results show that the proposed UFLS approach can effectively return the system to normal state after disturbances.

42 ENGINEERING↗

DOE OSTI2024

A Model for Hybrid Systems for Production Cost Modeling Studies Considering Ancillary Services

This paper introduces a model for simulating hybrid plants participating in energy and ancillary services for bulk power system studies. The model considers a hybrid plant comprised of a renewable energy source, a thermal power unit, a storage unit, fixed power loads, or any combination of these technologies. The model focuses on Production Cost Modeling (PCM) studies under the assumption of centralized dispatch. We present an example case study to illustrate the use of the model in a single-stage production cost model similar to those conducted by planning agencies. We explore the allocation of behind-the-meter ancillary services products and total energy participation to hybrid plant sub-assets and the resulting impacts on the system's ancillary service allocation. The model is implemented and simulated in a unit commitment problem in the RTS test system, which was modified to include a hybrid plant asset.

ancillary services↗

DOE OSTI2024

Early Exploration of a Flexible Framework for Efficient Quantum Linear Solvers in Power Systems

The rapid integration of renewable energy resources presents formidable challenges in managing power grids. While advanced computing and machine learning techniques offer some solutions for accelerating grid modeling and simulation, there remain complex problems that classical computers cannot effectively address. Quantum computing, a promising technology, has the potential to fundamentally transform how we manage power systems, especially in scenarios with a higher proportion of renewable energy sources. One critical aspect is solving linear systems of equations, crucial for power system applications like power flow analysis, for which the Harrow-Hassidim-Lloyd (HHL) algorithm is a well-known quantum solution. However, HHL quantum circuits often exhibit excessive depth, making them impractical for current Noisy-Intermediate-Scale-Quantum (NISQ) devices. In this paper, we introduce a versatile framework, powered by NWQSim, that bridges the gap between power system applications and quantum linear solvers available in Qiskit. This framework empowers researchers to efficiently explore power system applications using quantum linear solvers. Through innovative gate fusion strategies, reduced circuit depth, and GPU acceleration, our simulator significantly enhances resource efficiency. Power flow case studies have demonstrated up to a eight-fold speedup compared to Qiskit Aer, all while maintaining comparable levels of accuracy.

quantum computing, Harrow-Hassidim-Lloyd, high-per↗

DOE OSTI2024

On the Use of Tidal Power for Enhancing Power System Resilience with Networked Microgrids

The resilience of a power system to withstand a high-impact low-frequency event is a growing concern, especially in coastal communities that tend to be densely populated and very prone to extreme events such as storms and flooding. In such areas, marine renewable energy can be integrated with the existing renewable energy sources to improve generation diversity and power system resiliency. Using networked microgrids as an example, this paper proposes an empirical study to investigate the impact of marine resources, in particular, tidal energy on the power system resiliency. A set of quantitative metrics with intuitive physical interpretations is used to evaluate the resiliency of a power system that is enhanced with distributed generation in the form of rooftop photovoltaic systems and tidal generators. The effectiveness of resource diversification on the power system resiliency is demonstrated through simulations on a model of a real-world coastal distribution feeder located in Sequim, Washington.

Peerzada, Aaqib A.↗

DOE OSTI2024

Impacts of Control, Penetration, and Distribution of Embedded Storage Network in Bulk Power System

The current shift in generation mix from fossil fuel plants towards variable and intermittent renewable energy sources is poised to create a future grid with reduced physical inertia and mismatch between generation and demand. Embedded storage, which is a concept of a coordinated network of storage units sited at the interface between the transmission and distribution system, is proposed as a mechanism to provide a buffer between generation and demand. This paper proposes an automated framework to model and integrate embedded storage in large-scale power systems with industry-grade grid-following (GFL) and grid-forming (GFM) control technologies. More importantly, the developed framework is used to explore the impacts of embedded storage control, penetration, location, and capacity in providing fast frequency response to the grid under contingency events such as generator trips and faults. The framework and study are conducted using the transient-stability simulation tool PSS/E and a realistic model of the Puerto Rico grid as a chosen test system. The simulation results show that GFL and GFM embedded storage, distributed throughout the system, with sufficient penetration and capacity, can effectively improve primary frequency response of the system under the studied contingency events.

Battery Energy Storage, embedded storage, grid-for↗

DOE OSTI2023

Multi-Agent Reinforcement Learning for Distribution System Critical Load Restoration

Grid resilience has become a critical topic recently because of the increasing occurrence of extreme events and the growing integration of intermittent renewable energy sources. To build a resilient distribution system, this paper develops a multiagent reinforcement learning-based (MARL) method to coordinate distribution energy resources (DERs) dispatch, load pickup, and network reconfiguration for load restoration after a system outage. With the help of two types of control agents, namely critical load restoration (CLR) and coordination (COR) agents, system loads can be restored efficiently, given available resources. The effectiveness and superiority of the proposed algorithm are demonstrated through simulations and comparative studies on a real distribution feeder in Western Colorado.

distribution system↗

DOE OSTI2025

Distributed Control and Optimization of Virtual Inertia in Power Systems

The integration of renewable energy sources into power grids through inverter-based resources (IBRs) reduces system inertia, leading to faster frequency dynamics and potential grid instability. This paper proposes a distributed, consensus-based approach for the real-time control and optimization of inertia sources during system disturbances, enhancing both system stability and economic performance. Furthermore, we integrate this approach with the traditional inertia placement problem, demonstrating its suitability for existing frameworks. We validate its effectiveness in a 4-bus test system and a two-area 8-bus test system, showcasing its potential for scalable, dynamic inertia management in low-inertia grids.

Yadav, Ajay [ORNL] (ORCID:000000016111881X)↗

DOE OSTI2025

Using Artificial Intelligence to Improve Reliability and Operational Efficiency of Small-Scale Hydroelectric Distributed Generation

Reliability and resilience are critical concerns for distributed generation (DG) at the rural electric level. The integration of renewable energy sources, such as small-scale hydroelectric distributed generators (hydro DGs), introduces operational challenges, particularly regarding aging infrastructure and grid stability. Artificial Intelligence (AI)-driven Machine Learning (ML) models and applications of Large Language Models (LLMs) offer promising solutions for optimizing DG operations and enhancing resilience. This paper explores AI-based models for improving efficiency, fault resolution, and outage mitigation in small-scale hydro DGs. Furthermore, it highlights the development of a centralized, AI-powered information portal for rural electric cooperatives and municipalities. The research evaluates hydro DG plant models and discusses the applicability of AI-powered question-answering tools for real-time operations, focusing on statistical data, load flow, voltage regulation, and generation power. The findings demonstrate AI’s potential to transform DG management to ensure greater stability and resilience in rural electric grids.

Bhattacharyya, Arjun [ORNL] (ORCID:000900060976046↗

DOE OSTI2023

Co-Simulation Model for Optimal Wind-Hydro Coordination Using Wind Farm Control Dynamics

The growing share of Variable Renewable Energy sources (VRES) in power systems presents challenges for regula- tors, grid operators and energy producers. The VRES’ operation has limited flexibility in their operations, as they are highly dependent on ambient environments. To address these challenges, decision-makers must consider multiple objectives, among these are revenue, power system services and mechanical load on wind turbines. Coordinated operation of power plants and different wind farm control strategies are examples of measures that can benefit these objectives. This study proposes a Multi-Objective Linear Programming (MOLP) model to simulate the optimum operation of wind and hydropower plants that share limited transmission capacity. Further, wind farm control dynamics are included to obtain realistic output power and accumulated damage. From this, a case study based on a relevant location in Norway is presented to analyze the improved effect of wind- hydro coordination and wind farm control in achieving the objectives of accumulated wind turbine damage and total revenue of the hybrid power system. In addition, the study considers the potential advantages of adding a variable-speed pump to the hydropower plant. The results demonstrate that by considering multiple objectives in the optimization, one may achieve better overall performance of the objectives. By utilizing the flexibility of hydro storage, the decision maker may adjust to obtain the most desired outcome. Moreover, the added flexibility of utilizing a pump for hydro storage shows great improvements for the combined revenue of the power plants and reduced curtailment of the wind farm. However, less impact is observed from using a variable speed pump compared to a fixed speed pump.

13 HYDRO ENERGY↗

DOE OSTI2025

Calculating Critical Inertia of a Power System

The increasing integration of renewable energy sources in modern power systems has led to a decline in system inertia, raising concerns about frequency stability following large disturbances. Determining critical inertia is essential to prevent excessive frequency decline and ensure grid stability. This paper evaluates four different methods for calculating critical inertia, using the Electric Reliability Council of Texas (ERCOT) system as a test case. The results highlight the importance of employing multiple methodologies to capture the full spectrum of inertia requirements.

Hakim sneha, Fariha [University of Tennessee, Knox↗