Fast Calculation of Probabilistic Power Flow: A Model-Based Deep Learning Approach

Yang, Yan; Yang, Zhifang; Yu, Jie; Zhang, Baosen; Zhang, Youqiang; Yu, Hongxin

doi:10.1109/tsg.2019.2950115

Cited by 90 publications

(33 citation statements)

References 36 publications

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“…The performance differences among neural networks with various structures are compared, and three kinds of power bus systems are used for evaluation benchmark. Compared with the pure datadriven deep learning method, the proposed method can comprehensively improve the approximate accuracy and training speed [26]. Compared with the current situation that learning-based approaches are mostly proposed to identify and evaluate system situations, Su proposes a power system control method with deep belief network [27].…”

Section: Related Workmentioning

confidence: 99%

Power flow adjustment for smart microgrid based on edge computing and multi-agent deep reinforcement learning

Wang

Cao

et al. 2021

J Cloud Comp

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In current power grids, a massive amount of power equipment raises various emerging requirements, e.g., data perception, information transmission, and real-time control. The existing cloud computing paradigm is stubborn to address issues and challenges such as rapid response and local autonomy. Microgrids contain diverse and adjustable power components, making the power system complex and difficult to optimize. The existing traditional adjusting methods are manual and centralized, which requires many human resources with expert experience. The adjustment method based on edge intelligence can effectively leverage ubiquitous computing capacities to provide distributed intelligent solutions with lots of research issues to be reckoned with. To address this challenge, we consider a power control framework combining edge computing and reinforcement learning, which makes full use of edge nodes to sense network state and control power equipment to achieve the goal of fast response and local autonomy. Additionally, we focus on the non-convergence problem of power flow calculation, and combine deep reinforcement learning and multi-agent methods to realize intelligent decisions, with designing the model such as state, action, and reward. Our method improves the efficiency and scalability compared with baseline methods. The simulation results demonstrate the effectiveness of our method with intelligent adjusting and stable operation under various conditions.

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Section: Related Workmentioning

confidence: 99%

Power flow adjustment for smart microgrid based on edge computing and multi-agent deep reinforcement learning

Wang

Cao

et al. 2021

J Cloud Comp

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“…To quantifying the impact of the correlation among multi-dimensional wind farms on the power system, Zhu proposes a probabilistic power flow calculation framework with learning-based distribution estimation approach [16]. Yang et al propose a model-based deep learning approach to quickly solve the power flow equations, with the main application of speeding up probabilistic power flow calculations [17]. Compared with the pure data-driven deep learning method, the proposed method can comprehensively improve the approximate accuracy and training speed.…”

Section: Related Workmentioning

confidence: 99%

Power Flow Adjustment for Smart Microgrid Based on Edge Computing and Deep Reinforcement Learning

Jiao

Cao

et al. 2021

Preprint

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As one of the core components that improve transportation, generation, delivery, and electricity consumption in terms of protection and reliability, smart grid can provide full visibility and universal control of power assets and services, provide resilience to system anomalies and enable new ways to supply and trade resources in a coordinated manner. In current power grids, a large number of power supply and demand components, sensing and control devices generate lots of requirements, e.g., data perception, information transmission, business processing and real-time control, while existing centralized cloud computing paradigm is hard to address issues and challenges such as rapid response and local autonomy. Specifically, the trend of micro grid computing is one of the key challenges in smart grid, because a lot of in the power grid, diverse, adjustable supply components and more complex, optimization of difficulty is also relatively large, whereas traditional, manual, centralized methods are often dependent on expert experience, and requires a lot of manpower. Furthermore, the application of edge intelligence to power flow adjustment in smart grid is still in its infancy. In order to meet this challenge, we propose a power control framework combining edge computing and machine learning, which makes full use of edge nodes to sense network state and power control, so as to achieve the goal of fast response and local autonomy. Furthermore, we design and implement parameters such as state, action and reward by using deep reinforcement learning to make intelligent control decisions, aiming at the problem that flow calculation often does not converge. The simulation results demonstrate the effectiveness of our method with successful dynamic power flow calculating and stable operation under various power conditions.

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“…Some studies have also explored embedding physical characteristics within deep learning methods. A physics-guided neural network approach was proposed in [63] to calculate probabilistic power flow. The training process was improved by the grid characteristics, and the case study showed a great computation speedup.…”

Section: Category 3 Surrogate Modelmentioning

confidence: 99%

Review of Learning-Assisted Power System Optimization

Ruan,

Zhong,

Zhang

et al. 2020

Preprint

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Machine learning, with a dramatic breakthrough in recent years, is showing great potential to upgrade the power system optimization toolbox. Understanding the strength and limitation of machine learning approaches is crucial to answer when and how to integrate them in various power system optimization tasks. This paper pays special attention to the coordination between machine learning approaches and optimization models, and carefully evaluates to what extent such data-driven analysis may benefit the rule-based optimization. A series of typical references are selected and categorized into four kinds: the boundary parameter improvement, the optimization option selection, the surrogate model and the hybrid model. This taxonomy provides a novel perspective to understand the latest research progress and achievements. We further discuss several key challenges and provide an in-depth comparison on the features and designs of different categories. Deep integration of machine learning approaches and optimization models is expected to become the most promising technical trend.

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Fast Calculation of Probabilistic Power Flow: A Model-Based Deep Learning Approach

Cited by 90 publications

References 36 publications

Power flow adjustment for smart microgrid based on edge computing and multi-agent deep reinforcement learning

Power flow adjustment for smart microgrid based on edge computing and multi-agent deep reinforcement learning

Power Flow Adjustment for Smart Microgrid Based on Edge Computing and Deep Reinforcement Learning

Review of Learning-Assisted Power System Optimization

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