Reinforcement Learning based Approach for Virtual Inertia Control in Microgrids with Renewable Energy Sources

Skiparev, Vjatseslav; Belikov, Juri; Petlenkov, Eduard

doi:10.1109/isgt-europe47291.2020.9248777

Cited by 19 publications

(14 citation statements)

References 12 publications

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“…The considered microgrid was adopted from several recent publications [16,[33][34][35][36][37] and is depicted in Figure 2. The addressed scenario includes simplified residential/industrial loads, energy sources (thermal power plant, wind farm, and solar power plant), and energy storage systems [11,38,39].…”

Section: Modeling Of a Low-inertia Microgridmentioning

confidence: 99%

“…Reinforcement learning (RL) is an agent-based and model-free machine learning algorithm [84]. The main approach of RL optimization is based on trial and error, which allows direct validation of the artificial neural network (ANN)-based controller with the control object and prediction of negative consequences [37,[84][85][86]. The benefit of this method is mandatory data-driven optimization, which is naturally designed for online learning.…”

Section: Reinforcement Learning-based Controllermentioning

confidence: 99%

“…The benefit of this method is mandatory data-driven optimization, which is naturally designed for online learning. In [37], RL was compared with H ∞ , producing slightly better performance in terms of frequency stability in scenarios with 100%, 80%, and 40% inertia and connection of wind, solar, and thermal plants during the launch of industrial and residential loads, and 20% RES penetration. Since the algorithm uses a deep neural network, it requires strong computational hardware and is relatively complex for implementation.…”

Section: Reinforcement Learning-based Controllermentioning

confidence: 99%

“…The method requires selection of an optimal action a(t) * at each step s(t) and takes a long time. For RL, it is necessary to design a proper reward system and to choose the right training strategy, which may differ [37,[87][88][89]. For example, in previous works [87,88], the RL optimization for frequency support was performed by approximated dynamic programming.…”

Section: Reinforcement Learning-based Controllermentioning

confidence: 99%

“…For example, in previous works [87,88], the RL optimization for frequency support was performed by approximated dynamic programming. In contrast, Skiparev et al [37] used the deep deterministic policy gradient to train an RL-based controller for virtual inertia emulation. The optimization mechanism using the RL algorithm is summarized in Algorithm 3.…”

Section: Reinforcement Learning-based Controllermentioning

confidence: 99%

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Virtual Inertia Control Methods in Islanded Microgrids

et al. 2021

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Although the deployment and integration of isolated microgrids is gaining widespread support, regulation of microgrid frequency under high penetration levels of renewable sources is still being researched. Among the numerous studies on frequency stability, one key approach is based on integrating an additional loop with virtual inertia control, designed to mimic the behavior of traditional synchronous machines. In this survey, recent works related to virtual inertia control methods in islanded microgrids are reviewed. Based on a contextual analysis of recent papers from the last decade, we attempt to better understand why certain control methods are suitable for different scenarios, the currently open theoretical and numerical challenges, and which control strategies will predominate in the following years. Some of the reviewed methods are the coefficient diagram method, H-infinity-based methods, reinforcement-learning-based methods, practical-swarm-based methods, fuzzy-logic-based methods, and model-predictive controllers.

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