Imitation Learning Based Fast Power System Production Cost Minimization Simulation

Hu, Qinran; Guo, Zishan; Li, Fangxing

doi:10.1109/tpwrs.2023.3237398

Cited by 8 publications

(1 citation statement)

References 8 publications

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“…In order to reduce the complexity of load modeling, literature (Zhang et al, 2021b) proposes a comprehensive load simplification model based on the dominant parameter selection, which transforms the induction motor model into a second-order equation of state; literature (Han et al, 2022) proposes a fast calculation method for the parameters of the comprehensive load model based on the sensitivity analysis; and some researchers use intelligent optimization algorithms (Wang et al, 2018;Hu et al, 2022) or machine learning algorithms (Cui et al, 2019;Bu et al, 2020;Hu et al, 2023) for the overall identification of the model parameters. These studies aim to reduce the parameter space of the load model or increase the parameter calculation rate to simplify the complexity of load modeling.…”

Section: Introductionmentioning

confidence: 99%

Online modeling method for composite load model including EVs and battery storage based on measurement data

Yin,

Zhong,

et al. 2024

Front. Energy Res.

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Load models have a significant influence on power system simulation. However, current load modeling approaches can hardly satisfy the diversity and time-varying characteristics of loads [including electric vehicles (EVs) and battery storage] in terms of model accuracy and computing efficiency. An online modeling method for composite load models based on measurement information is proposed in this paper. Firstly, the dominant factors in load model output are analyzed based on the active subspace of parameter space. Then the clustering algorithm is applied to cluster the large number of underlying loads based on the characteristics of load daily output curves. Finally, the underlying loads are equivalently aggregated from the low voltage levels to the high voltage levels to construct the composite load model. Simulation results obtained based on PSCAD/EMTDC demonstrate that the load model constructed by the proposed approach can accurately reflect the actual load characteristics of a power system.

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Section: Introductionmentioning

confidence: 99%

Online modeling method for composite load model including EVs and battery storage based on measurement data

Yin,

Zhong,

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

Artificial intelligence-based methods for renewable power system operation

Li,

Ding,

et al. 2024

Nat Rev Electr Eng

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Distributed flexible resource regulation strategy for residential communities based on deep reinforcement learning

Xu,

Chen,

Gao

et al. 2024

IET Generation Trans & Dist

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In an era characterized by the rapid proliferation of distributed flexible resources (DFRs), the development of customized energy management and regulation strategies has attracted significant interest from the field. The inherent geographical dispersion and unpredictability of these resources, however, pose substantial barriers to their effective and computationally tractable regulation. To address these impediments, this paper proposes a deep reinforcement learning‐based distributed resource energy management strategy, taking into account the inherent physical and structural constraints of the distribution network. This proposed strategy is modelled as a sequential decision‐making framework with a Markov decision process, informed by physical states and external information. In particular, targeting the community energy management system for critical public infrastructure and community holistic benefits maximization, the proposed approach proficiently adapts to fluctuations in resource variability and fluctuating market prices, ensuring intelligent regulation of distributed flexible resources. Simulation and empirical analysis demonstrate that the proposed deep reinforcement learning‐based strategy can improve the economic benefits and decision‐making efficiency of distributed flexible resource regulation while ensuring the security of distribution network power flow.

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Imitation Learning Based Fast Power System Production Cost Minimization Simulation

Cited by 8 publications

References 8 publications

Online modeling method for composite load model including EVs and battery storage based on measurement data

Online modeling method for composite load model including EVs and battery storage based on measurement data

Artificial intelligence-based methods for renewable power system operation

Distributed flexible resource regulation strategy for residential communities based on deep reinforcement learning

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