Frequency Response Analysis of Load Effect on Dynamics of Grid-Forming Inverter Matias Berg , Tuomas Messo, Teuvo Suntio Laboratory of Electrical Energy Engineering, Tampere University of Technology, Tampere, Finland

Frequency Response Analysis of Load Effect on Dynamics of Grid-Forming Inverter Matias Berg, Tuomas Messo, Teuvo Suntio Laboratory of Electrical Energy Engineering, Tampere University of Technology, Tampere, Finland 

Abstract—The grid-forming mode of the voltage source inverters (VSI) is applied in uninterruptible power supplies and micro-grids to improve the reliability of electricity distribution. During the intentional islanding of an inverterbased micro-grid, the grid-forming inverters (GFI) are responsible for voltage control, similarly as in the case of uninterruptible power supplies (UPS). The unterminated model of GFI can be developed by considering the load as an ideal current sink. Thus, the load impedance always affects the dynamic behavior of the GFI. This paper proposes a method, to analyze how the dynamics of GFI and the controller design are affected by the load. Particularly, how the frequency response of the voltage loop gain changes according to the load and, how it can be used to the predict time-domain step response. The frequency responses that are measured from a hardware-in-the-loop simulator are used to verify and illustrate explicitly the load effect.

 SEE FULL ARTICLE : https://researchportal.tuni.fi/fi/publications/frequency-response-analysis-of-load-effect-on-dynamics-of-grid-fo/

Impedance Measurement of Megawatt-Level Renewable Energy Inverters using Grid-Forming and Grid-Parallel Converters Matias Berg1 , Tuomas Messo1, Tomi Roinila2, Henrik Alenius2 1 Electrical Energy Engineering, Tampere University of Technology, Tampere, Finland 2 Hydraulics and Automation, Tampere University of Technology, Tampere, Finland

 Impedance Measurement of Megawatt-Level Renewable Energy Inverters using Grid-Forming and Grid-Parallel Converters Matias Berg1, Tuomas Messo1, Tomi Roinila2, Henrik Alenius2 1 Electrical Energy Engineering, Tampere University of Technology, Tampere, Finland 2 Hydraulics and Automation, Tampere University of Technology, Tampere, Finland 

 Abstract—Harmonic resonance and power quality problems have been reported in grid-connected photovoltaic and wind power systems. The AC-side impedance of threephase converter is an important characteristic, which can be effectively used as a design parameter to avoid instability and excessive harmonics. A number of methods to measure the three-phase AC impedance have been reported. However, solutions for high power applications such as wind and photovoltaic converters with a power rating of several megawatts, have not been discussed. This paper introduces a new method to measure impedance from high power threephase converter. The impedance is identified by perturbing the converter first by voltage-type injection utilizing highpower grid-forming inverter, and subsequently by currenttype injection by utilizing low-power grid-parallel converter. The main benefit of the proposed setup is the possibility to measure the converter impedance online in its natural operating point both at high and low frequencies. The paper presents a proof-of-concept by validating the method using a switching model.

Medição de impedância de inversores de energia renovável de nível megawatt usando conversores formadores de rede e conversores paralelos à rede. Mathias BergThomas Messo, Tomi RoinilaHenrik Alenius 

Resumo -Problemas de ressonância harmônica e qualidade de energia têm sido relatados em sistemas de energia fotovoltaica e eólica conectados à rede. A impedância do lado CA do conversor trifásico é uma característica importante, que pode ser efetivamente utilizada como parâmetro de projeto para evitar instabilidade e harmônicos excessivos. Diversos métodos para medir a impedância CA trifásica foram relatados. No entanto, soluções para aplicações de alta potência, como conversores eólicos e fotovoltaicos com potência nominal de vários megawatts, não foram discutidas. Este artigo apresenta um novo método para medir a impedância de conversores trifásicos de alta potência. A impedância é identificada perturbando o conversor inicialmente por injeção de tensão utilizando um inversor formador de rede de alta potência e, posteriormente, por injeção de corrente utilizando um conversor paralelo à rede de baixa potência. O principal benefício da configuração proposta é a possibilidade de medir a impedância do conversor online em seu ponto de operação natural, tanto em altas quanto em baixas frequências. O artigo apresenta uma prova de conceito validando o método por meio de um modelo de chaveamento.

SEE FULL ARTICLE: https://researchportal.tuni.fi/fi/publications/impedance-measurement-of-megawatt-level-renewable-energy-inverter/

Overview on Grid-Forming Inverter Control Methods Peter Unruh * , Maria Nuschke *, Philipp Strauß * and FriedrichWelck-Power System Stability and Converter Technology Division, Fraunhofer Institute for Energy Economics and Energy System Technology IEE, 34119 Kassel, Germany

Concept Paper Overview on Grid-Forming Inverter Control Methods Peter Unruh * , Maria Nuschke *, Philipp Strauß * and FriedrichWelck Power System Stability and Converter Technology Division, Fraunhofer Institute for Energy Economics and Energy System Technology IEE, 34119 Kassel, Germany; friedrich.welck@sma.de

 Abstract: In this paper, dierent control approaches for grid-forming inverters are discussed and compared with the grid-forming properties of synchronous machines. Grid-forming inverters are able to operate AC grids with or without rotating machines. In the past, they have been successfully deployed in inverter dominated island grids or in uninterruptable power supply (UPS) systems. It is expected that with increasing shares of inverter-based electrical power generation, grid-forming inverters will also become relevant for interconnected power systems. In contrast to conventional current-controlled inverters, grid-forming inverters do not immediately follow the grid voltage. They form voltage phasors that have an inertial behavior. In consequence, they can inherently deliver momentary reserve and increase power grid resilience 

 SEE FULL ARTICLE : https://www.mdpi.com/1996-1073/13/10/2589

Painel de armazenamento de energia no Energy Week Unicamp-CEPETRO - Centro de Estudos de Energia e Petróleo da Universidade Estadual de Campinas

 

 

 Painel de Armazenamento de Energia no Energy Week organizado pelo CEPETRO - Centro de Estudos de Energia e Petróleo da Universidade Estadual de Campinas . Participação luiz carlos silva (Unicamp) , David Noronha (Energy Source), Roberto De Luca (UCB Power), @Renato Florence (Marcopolo S.A.).

 Perguntas do painel: 

 1) Pra começar: quem é você, que empresa representa e como vocês usam baterias para armazenamento de energia hoje? Quais as principais aplicações ou mercados atendidos? 

 2) Na sua visão, qual é hoje a maior dor do setor de armazenamento de energia com baterias no Brasil (tecnologia, custo, regulação, reciclagem, cadeia de suprimentos etc.) e como isso aparece no dia a dia da sua empresa? 

3) Pensando no Brasil: como as soluções que vocês desenvolvem podem fortalecer a indústria nacional? Onde estão as maiores oportunidades em fábricas de baterias, grandes BESS, microrredes e pequenas instalações? 

 4) E olhando para P&D: que tipo de projeto de pesquisa, desenvolvimento e inovação mais poderia ajudar a destravar essas oportunidades e reduzir as dores que você comentou?

Challenges and Solutions for Power Grid Stability with the Expansion of AI Data Centers

 

Challenges and Solutions for Power Grid Stability with the Expansion of AI Data Centers

Eng. Armando Cavero Miranda (UPS Specialist)

▶AI data centers experience extreme power fluctuations on the scale of milliseconds to minutes.Due to the synchronization characteristics of hundreds of thousands of GPUs during checkpoints, synchronization delays, and training completion, resulting in a greater amplitude of variation.10 times larger compared to the traditional cloud.In peak and trough situations, the total load can drop drastically, for example, from 100 (normalized base) to 42, representing a direct risk to system stability.

▶These Sudden load fluctuations are difficult to match with the response speed (MW/min) of existing generators.When combined with the decrease in system inertia due to the expansion of renewable energies, they can lead to the risk ofchain of blackoutsAccording to an analysis by ERCOT, there is a possibility of widespread voltage instability in the event of a simultaneous power outage exceeding 2.5 GW.

▶As countermeasures, hardware-based solutions are being implemented in conjunction, such as BESS (Battery Energy Storage System), grid-connected UPS (Uninterruptible Power Supply) and synchronous capacitors, software controls such as workload-aware smoothing and institutional measures such asmandatory LVRT (Voltage Sag Support) and conditional connection regulations.

1. Prospects for Accelerated Growth in Energy Demand from Global Data Centers

 The expansion of AI technology and the prospect of accelerated growth in energy demand from global data centers.

In 2024, data center energy consumption represented 1.5% (415 TWh) of global electricity consumption. It is expected to exceed 945 TWh by 2030, more than doubling. [1)]

The main reason for the increased energy demand in data centers is the growing demand for AI and digital services.

The US currently accounts for approximately 35-40% of the global data center market (based on GW).

 

2. Load Fluctuation Patterns of AI Data Centers

❏Load Fluctuation Characteristics of AI Data Centers

During GPU batch processing, power consumption spikes during array operations, and drops dramatically during data transfer and synchronization

• Checkpoint EventDuring the checkpoint process to save progress, the charge drops to near 'zero' for milliseconds, followed by a sharp increase as it instantly recovers.
• Synchronization DelayDuring parallel summation (AllReduce) operations on clusters of hundreds of thousands of GPUs, network transmission delay causes some devices to remain idle for a few seconds.
• End of TrainingFollowing a large-scale operation, if there is no immediate subsequent workload, gigawatt-scale loads can be disconnected simultaneously in a single event.

※Checkpoint: The process of saving intermediate AI learning results, allowing the execution of the same point to be resumed later.

※Parallel Sum Operation (AllReduce): A communication operation in distributed learning where the results calculated by each GPU (e.g., gradients from matrix operations) are summed collectively, and then the result is distributed equally to all GPUs. Because all devices must wait/synchronize simultaneously during this process, patterns of instantaneous load drops or peaks may occur.

※According to some data, based on Google Cloud data, it is reported that under specific conditions, AI workloads showed a load fluctuation approximately 10 times greater (1.5MW → 15MW) compared to the traditional cloud, but these are values ​​from individual cases and the proportion relative to total equipment was not disclosed.

3. Measures to Respond to Sudden Load Fluctuations

☐Hardware Solutions

❍Battery Energy Storage System (BESS)

• It acts as a physical "shock absorber" that absorbs abrupt fluctuations in AI load.
• It actively manages power quality with Fast Frequency Response (FFR) on a millisecond scale and contributes to improving LVRT capability.
• It goes beyond mere cost, transforming into a revenue asset through peak shaving, energy arbitrage, and participation in the ancillary services market.

❍Grid-Interactive Uninterruptible Power Supply (GIPS)

※An Uninterruptible Power Supply (UPS) functions to provide stable power for a certain period immediately when there is a momentary interruption in the

 power supply or voltage fluctuation. The power is drawn from the electrical grid and used.

• It evolves from a passive emergency power source to a Distributed Energy Resource (DER) that actively contributes to grid stabilization.
• It monitors the network frequency in real time, discharging when the frequency drops and charging when it rises, contributing to stabilization.
  ※It was marketed at Microsoft's Dublin data center, also serving as a backup power source.

❍Synchronous Capacitors and Other Equipment

• They provide the physical inertia of the electrical grid, which has been reduced due to the increased participation of renewable energies, ensuring frequency stability.
• They provide reactive power to dynamically support the voltage and increase the robustness of the system. [6)]
• STATCOM/SVCThey provide fast voltage support, and Grid-Forming Inverters provide virtual inertia, being used in a complementary way with BESS.

※STATCOM (Static Synchronous Compensator): A device that uses power electronics equipment to supply/absorb reactive power in real time, maintaining a stable voltage.

※SVC (Static Reactive Power Compensator): A device that controls the reactive power of the network to reduce voltage fluctuations. It has a slower response than STATCOM, but is cheaper and widely used.

※Grid-Forming Inverter: A device where distributed sources such as solar power and batteries create their own voltage/frequency reference, acting as a "mini power plant" to stabilize the grid.

New Challenges Presented by AI Data Centers

The AsExtreme load fluctuations on a millisecond-second-minute scale in AI data centers.They can fundamentally threaten the stability of the existing electrical grid.

·         They originate fromintrinsic characteristics of AI learning workloadswhere hundreds of thousands of GPUs operate in a synchronized manner, unlike the asynchronous workloads of the traditional cloud.

·         Due to unpredictable events such as checkpoints, synchronization delays, and training terminations, loads on the GW scale change abruptly in milliseconds.

·         THEThe response speed in minutes (MW/min) of existing generators is not capable of handlingwith this, and in addition, thereduction in system inertia due to increased renewable energyThis can further amplify the vulnerability.

OOsimultaneous tripping of large-scale loadscould emerge as a real risk ofchain blackout.

This is not merely a theoretical scenario, but follows a path similar to large-scale energy collapses that have already occurred.

·         The April 2025 event in the Iberian Peninsula (Spain, Portugal), where 2.2 GW of generation was lost and the entire grid collapsed in 27 seconds, is a representative example.

·         The isolated structure of the Texas power grid, lacking external interconnections, is similar to that of the Iberian Peninsula in Europe, exposing it to the same risk of chain reaction collapse.

References

-Donnellan, D., Lawrence, A., Bizo, D., and Judge, P., “Uptime Institute

Global Data Center Survey 2024”, Uptime Institute, 2024.

-Park Chan-guk, Assistant Professor, Faculty of Climate Change Convergence, Hankuk University

-Energytrackerasia, “AI Data Center Development in Japan and Clean Energy

Transition”, 2025.

-Entsoe, “Synchronous Condensers,” 2025a.

-[Paper Review] Power Stabilization for AI Training Datacenters