A General Real-time OPF Algorithm Using DDPG with Multiple Simulation Platforms

Nie, Huanhuan; Chen, Ying; Song, Yankan; Huang, Shaowei

doi:10.1109/isgt-asia.2019.8881174

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…This training continues thoroughly under the off policy so that the result may be slightly different from MAT-POWER. But still, it matches almost 100% even if the load variation is severe compared to other methods in [6], [7].…”

Section: Discussionmentioning

confidence: 86%

“…As such, it is a challenging task to obtain general real-time ACOPF solutions from existing algorithms. Therefore, ACOPF solutions with DNN (Deep Neural Network) and DRL (Deep Reinforcement Learning) were introduced to speed up the calculation process [6], [7], [8]. It has been noted that DNN and DRL approaches can solve the ACOPF problem of complex, larger dimensions, and continuous state space.…”

Section: Introductionmentioning

confidence: 99%

“…rate limits. Since TD3, which policy gradient methods used, is well known as a model to avoid the curse of dimensionality, although their units are not the same, TD3 algorithm does not need a procedure of state discretization like DDPG[6].The load changes would potentially lead to changes on terminal voltages of the generator side. Thus, the constant terminal voltage of the generation side is preferred to ensure reliability of the system.…”

mentioning

confidence: 99%

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Real-Time Optimal Power Flow Using Twin Delayed Deep Deterministic Policy Gradient Algorithm

Woo

Park

et al. 2020

IEEE Access

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The general concept of AC Optimal Power Flow (ACOPF) refers to the economic dispatch planning under electric network constraints. Moreover, each instance with the entire network must be solved in real-time (i.e., every five minutes) to ensure cost-effective power system operation while satisfying power balance equation. As the operation of power systems penetrated with intermittent renewable energy becomes more complicated, this paper proposes Deep Neural Network (DNN) and Levenberg-Marquardt backpropagation-based Twin Delayed Deep Deterministic Policy Gradient (TD3) approach to improve computational performance of ACOPF. Specifically, because the ACOPF model shall consider prevailing constraints of the power system, including power balance equation, we set the appropriate reward vector in the training process to build our own policy. Furthermore, we add random Gaussian noise to individual net loads for representing uncertainty characteristics introduced by renewable energy sources. Finally, the proposed model is compared with the MAT-POWER solution on the IEEE 118-bus system to demonstrate its efficacy and robustness.

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Section: Discussionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Real-Time Optimal Power Flow Using Twin Delayed Deep Deterministic Policy Gradient Algorithm

Woo

Park

et al. 2020

IEEE Access

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“…Research on artificial intelligence (AI) has achieved a growth spurt in the past few years. AI algorithms such as graph neural networks (GNNs) and reinforcement learning (RL) have been applied to a variety of power system studies such as measurement enhancement [14], dynamic component modeling [15], parameter inference [16], optimization and control [17], and stability assessment [18]. AI models can learn and approximate any functions with enough samples [19].…”

Section: Introductionmentioning

confidence: 99%

Exploration of Artificial-intelligence Oriented Power System Dynamic Simulators

Xiao

Chen

Wang

et al. 2023

Journal of Modern Power Systems and Clean Energy

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With the rapid development of artificial intelligence (AI), it is foreseeable that the accuracy and efficiency of dynamic analysis for future power system will be greatly improved by the integration of dynamic simulators and AI. To explore the interaction mechanism of power system dynamic simulations and AI, a general design for AI-oriented power system dynamic simulators is proposed, which consists of a high-performance simulator with neural network supportability and flexible external and internal application programming interfaces (APIs). With the support of APIs, simulation-assisted AI and AIassisted simulation form a comprehensive interaction mechanism between power system dynamic simulations and AI. A prototype of this design is implemented and made public based on a highly efficient electromechanical simulator. Tests of this prototype are carried out in four scenarios including sample generation, AI-based stability prediction, data-driven dynamic component modeling, and AI-aided stability control, which prove the validity, flexibility, and efficiency of the design and implementation for AI-oriented power system dynamic simulators.

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Leveraging AI for Enhanced Power Systems Control: An Introductory Study of Model-Free DRL Approaches

Zhou,

et al. 2024

IEEE Access

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A General Real-time OPF Algorithm Using DDPG with Multiple Simulation Platforms

Cited by 7 publications

References 6 publications

Real-Time Optimal Power Flow Using Twin Delayed Deep Deterministic Policy Gradient Algorithm

Real-Time Optimal Power Flow Using Twin Delayed Deep Deterministic Policy Gradient Algorithm

Exploration of Artificial-intelligence Oriented Power System Dynamic Simulators

Leveraging AI for Enhanced Power Systems Control: An Introductory Study of Model-Free DRL Approaches

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