Fast frequency response of inverter-based resources and its impact on system frequency characteristics

Su, Lining; Qin, Xiaohui; Zhang, Shang; Zhang, Yantao; Jiang, Yu; Han, Yi

doi:10.1016/j.gloei.2020.11.007

Cited by 21 publications

(15 citation statements)

References 14 publications

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“…Unlike inertial FFCAS, controlled electrical power contribution is not proportional to RoCoF in non-inertial FFCAS, and not necessarily be injected at the PCC of generating unit. The following may be some non-inertial FFCAS [7,9] .…”

Section: Ffcasmentioning

confidence: 99%

Decentralized reinforce‐control of renewable dynamic virtual power plant enabling it to offer ancillary services: An attempt towards net‐zero targets

Kumar

Ahmed

Naidoo

et al. 2022

IET Renewable Power Gen

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This article presents internal model control (IMC) based decentralized reinforce-control of renewable dynamic virtual power plant (DVPP) so that, it can be integrated into the power system as a substitution of fuel-based conventional generators. Such grid integration towards net-zero targets could not be possible without providing additional ancillary service (AS) to the power system, as the traditional AS would fall short with the retirement/substitution of conventional generators. The theory of DVPP from a technical perspective (i.e., TDVPP) is presented in a detailed and simplified manner, including the formulation of a generalized control objective (desired specification) for DVPP integration. The solution approach includes two steps: (1) disaggregation of desired specification and (2) decentralized reinforce-control to match the disaggregated specification. The theory and solution approach for DVPP integration is presented in a generalized manner enabling the DVPP to offer multiple ASs, but the case study is limited only to frequency control AS (FCAS) in this article. The study is performed on the 'western system coordinating council (WSCC)' test system, in which an attempt is made towards net-zero targets by substituting the largest thermal generator with renewable DVPP ensuring the grid's operation or dynamics safe.

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Section: Ffcasmentioning

confidence: 99%

Decentralized reinforce‐control of renewable dynamic virtual power plant enabling it to offer ancillary services: An attempt towards net‐zero targets

Kumar

Ahmed

Naidoo

et al. 2022

IET Renewable Power Gen

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show abstract

“…Photovoltaic generation is at the same time a problem and a potential solution for power grid frequency reserves. In fact, increased photovoltaic penetration without VPPs is a part of the problem, as it decreases the grid inertia Su et al (2020), Zhang et al (2020) and increases the demand for frequency reserves (Karbouj et al, 2019). This increased demand is specifically due to the short-term variability of photovoltaic generation (Sinsel et al, 2020).…”

Section: Nowcasting For Vpp With Photovoltaic Generationmentioning

confidence: 99%

Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks

et al. 2021

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The rapid penetration of photovoltaic generation reduces power grid inertia and increases the need for intelligent energy resources that can cope in real time with the imbalance between power generation and consumption. Virtual power plants are a technology for coordinating such resources and monetizing them, for example on electricity markets with real-time pricing or on frequency reserves markets. Accurate short-term photovoltaic generation forecasts are essential for such virtual power plants. Although significant research has been done on medium- and long-term photovoltaic generation forecasting, the short-term forecasting problem requires special attention to sudden fluctuations due to the high variability of cloud cover and related weather events. Solar irradiance nowcasting aims to resolve this variability by providing reliable short-term forecasts of the expected power generation capacity. Sky images captured in proximity to the photovoltaic panels are used to determine cloud behavior and solar intensity. This is a computationally challenging task for conventional computer vision techniques and only a handful of Artificial Intelligence (AI) methods have been proposed. In this paper, a novel multimodal approach is proposed based on two Long Short-Term Memory Networks (LSTM) that receives a temporal image modality of a stream of sky images, a temporal numerical modality of a time-series of past solar irradiance readings and cloud cover readings as inputs for irradiance nowcasting. The proposed nowcasting pipeline consists of a preprocessing module and an irradiance augmentation module that implements methods for cloud detection, Sun localization and mask generation. The complete approach was empirically evaluated on a real-world solar irradiance case study across the four seasons of the northern hemisphere, resulting in a mean improvement of 39% for multimodality.

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“…Traditionally, the inertial response from the synchronous generator is an inherent characteristic, and it is not treated as an ancillary service. However, with the increase in penetration of RES-DG, the grid operators in several power systems have identified inertia as an ancillary service [46]. From the grid operator's perspective, the grid can be categorized as a high and low inertia grid depending on the penetration level of RES-DG.…”

Section: System Security and Inertia Constant Modelling 21 Res-dg Units And Time Changing Inertiamentioning

confidence: 99%

An Online Security Prediction and Control Framework for Modern Power Grids

2021

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The proliferation of renewable energy sources distributed generation (RES-DG) into the grid results in time-varying inertia constant. To ensure the security of the grid under varying inertia, techniques for fast security assessment are required. In addition, considering the high penetration of RES-DG units into the modern grids, security prediction using varying grid features is crucial. The computation burden concerns of conventional time-domain security assessment techniques make it unsuitable for real-time security prediction. This paper, therefore, proposes a fast security monitoring model that includes security prediction and load shedding for security control. The attributes considered in this paper include the load level, inertia constant, fault location, and power dispatched from the renewable energy sources generator. An incremental Naïve Bayes algorithm is applied on the training dataset developed from the responses of the grid to transient stability simulations. An additive Gaussian process regression (GPR) model is proposed to estimate the load shedding required for the predicted insecure states. Finally, an algorithm based on the nodes’ security margin is proposed to determine the optimal node (s) for the load shedding. The average security prediction and load shedding estimation model training times are 1.2 s and 3 s, respectively. The result shows that the proposed model can predict the security of the grid, estimate the amount of load shed required, and determine the specific node for load shedding operation.

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Fast frequency response of inverter-based resources and its impact on system frequency characteristics

Cited by 21 publications

References 14 publications

Decentralized reinforce‐control of renewable dynamic virtual power plant enabling it to offer ancillary services: An attempt towards net‐zero targets

Decentralized reinforce‐control of renewable dynamic virtual power plant enabling it to offer ancillary services: An attempt towards net‐zero targets

Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks

An Online Security Prediction and Control Framework for Modern Power Grids

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