A Comparative Analysis of a Power System Stability with Virtual Inertia

Vetoshkin, Lavr; Müller, Zdeněk

doi:10.3390/en14113277

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…The introduction and advancement of VI technologies in microgrids marks a significant step towards accommodating the growing share of RES in power systems while maintaining system stability and reliability [34]. As power systems continue to evolve towards a more sustainable and renewable-centric model, the role of VI in ensuring smooth and stable operation becomes increasingly crucial [35].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement-Learning-Based Virtual Inertia Controller for Frequency Support in Islanded Microgrids

Afifi,

Marei,

Mohamad

2024

Technologies

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As the world grapples with the energy crisis, integrating renewable energy sources into the power grid has become increasingly crucial. Microgrids have emerged as a vital solution to this challenge. However, the reliance on renewable energy sources in microgrids often leads to low inertia. Renewable energy sources interfaced with the network through interlinking converters lack the inertia of conventional synchronous generators, and hence, need to provide frequency support through virtual inertia techniques. This paper presents a new control algorithm that utilizes the reinforcement learning agents Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG) to support the frequency in low-inertia microgrids. The RL agents are trained using the system-linearized model and then extended to the nonlinear model to reduce the computational burden. The proposed system consists of an AC–DC microgrid comprising a renewable energy source on the DC microgrid, along with constant and resistive loads. On the AC microgrid side, a synchronous generator is utilized to represent the low inertia of the grid, which is accompanied by dynamic and static loads. The model of the system is developed and verified using Matlab/Simulink and the reinforcement learning toolbox. The system performance with the proposed AI-based methods is compared to conventional low-pass and high-pass filter (LPF and HPF) controllers.

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Section: Introductionmentioning

confidence: 99%

Reinforcement-Learning-Based Virtual Inertia Controller for Frequency Support in Islanded Microgrids

Afifi,

Marei,

Mohamad

2024

Technologies

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“…The various characteristics of the energy storage systems were also explained. Vetoshkin et al in [14] carried out a comparative analysis of the stability of the grid using different virtual inertia control techniques. Findings of their work reveal that virtual inertia control techniques can be used to improve the stability of the modern power grid, however, generation expansion planning in the presence of ESUs was not considered in their model analysis.…”

Section: Introductionmentioning

confidence: 99%

Optimal planning of Renewable energy generators in modern power grid for enhanced system inertia

Ayamolowo

Manditereza²,

Kusakana³

2022

Technol Econ Smart Grids Sustain Energy

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In recent times renewable energy sources have become an integral part of the modern power grid. As a result, the overall system inertia of the grid has been reduced, thus leading to frequency instability issues such as fast rate of change of frequency. Thus, to compensate for the declining inertia, it is important to carefully select renewable energy generators (REGs) and energy storage systems (ESS) in order to ensure the stability of the power grid, while also controlling greenhouse gases emissions in line with environmental standards. Therefore, this paper proposes an optimal planning model of REGs and ESS, considering the inertia requirement of the grid. The objective function is formulated to minimize the cost of operation, emissions, and investment in new REGs and energy storage units while maximizing the system inertia. The model was developed as a mixed integer linear programming problem and solved using CPLEX solver in GAMS. Finally, the model was validated using a modified IEEE 9-bus system and compared under three scenarios. The results show that in scenario 3 where system inertia is considered in the presence of REGs and ESS, higher system inertia of 8.776 s was achieved at minimal emission and cost, which justifies the aim of the study.

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“…The DGRs' integration into the power system is also strongly dependent on the overall power system's transient stability. As a result, determining the intensity of transients and qualitative parameters in the range of the observed period is challenging [7,8]. In many cases, DGRs are installed at low voltage levels in the distribution system, which might increase the amount of fault current [9].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Prosumer Behavior in the Electrical Network

et al. 2021

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This article deals with the prosumer behavior, specifically on an on-grid electrical network that is connected to a larger synchronous electrical network, as well as an off-grid system. In the Simulink (Matlab) application, two models were constructed for this purpose. The modeling of the operation of the electrical network’s on-grid system takes place in one of the models. The simulation of the operation of the electrical network’s off-grid system takes place in the other. We examined the model’s behavior in the provided simulated period from the standpoint of transient states and qualitative indicators of electrical energy under various connection configurations in both systems. The simulations resulted in the possibility of incorporating new sources of energy accumulation, such as pumped storage hydropower plants based on energy storage systems (ESSs), and modifying the model to the user’s needs.

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A Comparative Analysis of a Power System Stability with Virtual Inertia

Cited by 7 publications

References 20 publications

Reinforcement-Learning-Based Virtual Inertia Controller for Frequency Support in Islanded Microgrids

Reinforcement-Learning-Based Virtual Inertia Controller for Frequency Support in Islanded Microgrids

Optimal planning of Renewable energy generators in modern power grid for enhanced system inertia

Analysis of Prosumer Behavior in the Electrical Network

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