Dynamic Droop Control in Low-Inertia Power Systems

Yan, Jing-Jou; Pates, Richard; Mallada, Enrique

doi:10.1109/tac.2020.3034198

Cited by 32 publications

(17 citation statements)

References 30 publications

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“…4 shows the minimum virtual inertia constant requirement m v,min for eliminating the Nadir as a function of the inverse storage droop α b . Both the exact solution from (7) and the linear approximation from (8) are shown, thus demonstrating the minimal difference between the two. One thing to note is that the m v,min required has rather high values, the equivalent of more than 30 times of the actual system inertia (under the high renewable penetration scenario).…”

Section: A Nadir Elimination Via Virtual Inertiamentioning

confidence: 96%

“…At the same time, this also reduces the requirements on both power rating and energy capacity of the storage units. In this paper, we show that a novel dynamic droop control strategy -called iDroop [5]- [7] -outperforms both droop control and virtual inertia, while still being a rather simple first-order control. We demonstrate that by properly choosing the iDroop parameters, it is possible to completely eliminate the frequency Nadir.…”

Section: Introductionmentioning

confidence: 91%

“…In fact, to achieve Nadir elimination, the storage control parameters (α b , m v ) should satisfy the following relation [7]:…”

Section: A Nadir Elimination Via Virtual Inertiamentioning

confidence: 99%

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Dynamic Droop Approach for Storage-based Frequency Control

Jiang,

Cohn,

Vorobev

et al. 2019

Preprint

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Transient frequency dips that follow sudden power imbalances -frequency Nadir-represent a big challenge for frequency stability of low-inertia power systems. Since low inertia is identified as one of the causes for deep frequency Nadir, virtual inertia, which is provided by energy storage units, is said to be one of the solutions to the problem. In the present paper, we propose a new method for frequency control with energy storage systems (ESS), called dynamic droop control (iDroop), that can completely eliminate frequency Nadir during transients. Nadir elimination allows us to perform frequency stability assessment without the need for direct numerical simulations of system dynamics. We make a direct comparison of our developed strategy with the usual control approaches -virtual inertia (VI) and droop control (DC)-and show that iDroop is more effective than both in eliminating the Nadir. More precisely, iDroop achieves the Nadir elimination under significantly lower gains than virtual inertia and requires almost 40% less storage power capacity to implement the control. Moreover, we show that rather unrealistic control gains are required for virtual inertia in order to achieve Nadir elimination.

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Section: A Nadir Elimination Via Virtual Inertiamentioning

confidence: 96%

Section: Introductionmentioning

confidence: 91%

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Dynamic Droop Approach for Storage-based Frequency Control

Jiang,

Cohn,

Vorobev

et al. 2019

Preprint

Self Cite

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“…The literature proposes various ways to support the frequency of AC systems such as wind power-HVDC systems [16]- [18], multi-terminal HVDC grids [19]- [21], and fast frequency reserve sources [22], [23]. Furthermore, assuming advanced communication infrastructure and adaptive control capabilities, interesting control methods were assessed in [24]- [27]. However, in accordion with [15], TSOs must favor robustness and security of HVDC frequency support over optimality.…”

Section: Introductionmentioning

confidence: 99%

Frequency Dynamics of the Northern European AC/DC Power System: A Look-Ahead Study

Dijokas

Obradović

Misyris

et al. 2022

IEEE Trans. Power Syst.

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In many power systems, the increased penetration of inverter-based renewable generation will cause a decrease in kinetic energy storage, leading to higher frequency excursions after a power disturbance. This is the case of the future Nordic Power System (NPS). The look-ahead study reported in this paper shows that the chosen units participating in Frequency Containment Reserves (FCR) cannot keep the frequency above the prescribed threshold following the outage of the largest plant. This analysis relies on a detailed model of the Northern European grid. The latter is compared to the classical singlemass equivalent, and the impact of voltage-dependent loads is assessed in some detail. Next, the paper focuses on emergency power control of the HVDC links that connect the NPS to the rest of the European grid, which can supplement or even replace part of the FCR. The proper tuning of that control is discussed. Finally, the analysis is extended to the HVDC links connecting the future North Sea Wind Power Hub under two configurations, namely low and zero inertia. The impact of outages in the latter sub-system is also assessed. The material to simulate the system with industrial software is made publicly available.

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“…Transient stability in power systems refers to the ability of a system to converge to an acceptable steady-state after a disturbance [1], [2]. With the increased penetration of renewable energy sources (RES), power systems have reduced inertia and transient stability is becoming increasingly important [3]. Meanwhile, RES are connected to the grid via electronic interfaces and can be controlled freely by inverters to implement almost arbitrary control laws.…”

Section: Introductionmentioning

confidence: 99%

Lyapunov-Regularized Reinforcement Learning for Power System Transient Stability

Cui

Zhang

2021

Preprint

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Transient stability of power systems is becoming increasingly important because of the growing integration of renewable resources. These resources lead to a reduction in mechanical inertia but also provide increased flexibility in frequency responses. Namely, their power electronic interfaces can implement almost arbitrary control laws. To design these controllers, reinforcement learning (RL) has emerged as a powerful method in searching for optimal non-linear control policy parameterized by neural networks.A key challenge is to enforce that a learned controller must be stabilizing. This paper proposes a Lyapunov regularized RL approach for optimal frequency control for transient stability in lossy networks. Because the lack of an analytical Lyapunov function, we learn a Lyapunov function parameterized by a neural network. The losses are specially designed with respect to the physical power system. The learned neural Lyapunov function is then utilized as a regularization to train the neural network controller by penalizing actions that violate the Lyapunov conditions. Case study shows that introducing the Lyapunov regularization enables the controller to be stabilizing and achieve smaller losses.

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Dynamic Droop Control in Low-Inertia Power Systems

Cited by 32 publications

References 30 publications

Dynamic Droop Approach for Storage-based Frequency Control

Dynamic Droop Approach for Storage-based Frequency Control

Frequency Dynamics of the Northern European AC/DC Power System: A Look-Ahead Study

Lyapunov-Regularized Reinforcement Learning for Power System Transient Stability

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