Intelligent voltage prediction of active distribution network with high proportion of distributed photovoltaics

Liu, Wei; Tang, Ping; Liu, Han; Zhao, Peizhi

doi:10.1016/j.egyr.2022.08.142

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…In each tree iteration, each leaf node's weight is multiplied by a reduction coefficient. This way, the impact of each tree will not be too large, leaving more space for optimization for the trees below (Wang et al, 2017 ; Liu et al, 2022 ). Another way is Column Subsampling, which is similar to random forest selection for tree construction.…”

Section: Methodsmentioning

confidence: 99%

Enhanced LSTM-based robotic agent for load forecasting in low-voltage distributed photovoltaic power distribution network

Zhang,

Wang,

Wang

et al. 2024

Front. Neurorobot.

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To ensure the safe operation and dispatching control of a low-voltage distributed photovoltaic (PV) power distribution network (PDN), the load forecasting problem of the PDN is studied in this study. Based on deep learning technology, this paper proposes a robot-assisted load forecasting method for low-voltage distributed photovoltaic power distribution networks using enhanced long short-term memory (LSTM). This method employs the frequency domain decomposition (FDD) to obtain boundary points and incorporates a dense layer following the LSTM layer to better extract data features. The LSTM is used to predict low-frequency and high-frequency components separately, enabling the model to precisely capture the voltage variation patterns across different frequency components, thereby achieving high-precision voltage prediction. By verifying the historical operation data set of a low-voltage distributed PV-PDN in Guangdong Province, experimental results demonstrate that the proposed “FDD+LSTM” model outperforms both recurrent neural network and support vector machine models in terms of prediction accuracy on both time scales of 1 h and 4 h. Precisely forecast the voltage in different seasons and time scales, which has a certain value in promoting the development of the PDN and related technology industry chain.

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Section: Methodsmentioning

confidence: 99%

Enhanced LSTM-based robotic agent for load forecasting in low-voltage distributed photovoltaic power distribution network

Zhang,

Wang,

Wang

et al. 2024

Front. Neurorobot.

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“…In theory, the above-mentioned multi-layer load collaborative prediction model can be regarded as an n-block distributed optimization problem with the goal of minimizing the total error of load prediction and the constraint condition of multilayer load aggregation consistency. At present, the commonly used distributed optimization algorithms include distributed gradient descent algorithm, second-order optimization algorithm, neighboring gradient algorithm, and alternating direction multiplier algorithm (ADMM) [20], each with its own advantages and disadvantages [21]. Among them, the ADMM algorithm is widely used by researchers to solve distributed optimization problems due to its fusion of dual decomposition and Lagrange multiplier method advantages, good convergence and strong robustness.…”

Section: Multi-layer Load Collaborative Prediction Model Based On Adm...mentioning

confidence: 99%

Research on deep-learning-based photovoltaic output estimation and multi-layer collaborative load prediction in distribution networks

Xu,

Lv,

Wang

et al. 2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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In recent years, the penetration rate of distributed photovoltaics in distribution networks has been continuously increasing. The coupling of photovoltaic output with actual load with randomness forms a generalized load with greater uncertainty, posing a serious challenge to the safe and stable operation of the distribution network. Accurate distributed photovoltaic output and load forecasting is the foundation for ensuring the safety and economy of distribution network operation. However, currently most distributed photovoltaics are installed after the electricity meter and lack proprietary metering devices, making photovoltaic output invisible to distribution network operators, greatly affecting photovoltaic output prediction. In addition, the load of the distribution network has a multi-layered structure from bottom to top, and existing load forecasting methods are difficult to meet the aggregation and consistency requirements of multi-layered loads, which increases the operational decision-making burden of the distribution network. In this context, this study applies deep learning techniques such as generative adversarial networks and long short-term memory neural networks to achieve accurate estimation of photovoltaic output and collaborative prediction of multi-layer loads in distribution networks containing distributed photovoltaics.

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“…The wide application of photovoltaic power generation and energy storage technology provides a more flexible and sustainable solution for energy supply [1]. However, due to the instability and intermittence of renewable energy, the operation and management of distributed distribution network are facing a series of challenges [2]. In this context, predictive and optimal control methods are increasingly concerned in distributed distribution networks.…”

Section: Introductionmentioning

confidence: 99%

Design of photovoltaic-energy storage-load forecasting and optimal control method for distributed distribution network based on reinforcement learning

Zhao,

Qin,

Wang

et al. 2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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The safe and stable operation of distribution network can improve the economic benefit and operational performance of power system. In order to improve the photovoltaic-energy storage-load effect of distribution network, a method of photovoltaic-energy storage-load prediction and optimal control of distributed distribution network based on reinforcement learning is proposed. Calculate the dynamic photovoltaic-energy storage-load and static photovoltaicenergy storage-load of distribution network, determine the change of the ratio of static and dynamic photovoltaic-energy storage-load of distribution network, and comprehensively predict the photovoltaic-energy storage-load of distribution network; Then, reinforcement learning is used to optimize the photovoltaic-energy storage-load control of distributed distribution network. The experimental results show that the proposed method shows better prediction accuracy and effect after optimization, and has higher control accuracy for photovoltaic output power, energy storage power and load power of distributed distribution network.

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Intelligent voltage prediction of active distribution network with high proportion of distributed photovoltaics

Cited by 9 publications

References 12 publications

Enhanced LSTM-based robotic agent for load forecasting in low-voltage distributed photovoltaic power distribution network

Enhanced LSTM-based robotic agent for load forecasting in low-voltage distributed photovoltaic power distribution network

Research on deep-learning-based photovoltaic output estimation and multi-layer collaborative load prediction in distribution networks

Design of photovoltaic-energy storage-load forecasting and optimal control method for distributed distribution network based on reinforcement learning

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