Inverter-Data-Driven Second-Level Power Forecasting for Photovoltaic Power Plant

Meng, Xiangjian; Gao, Feng; Xu, Tao; Zhou, Kangjia; Li, Wei; Wu, Qiang

doi:10.1109/tie.2020.3005098

Cited by 22 publications

(4 citation statements)

References 35 publications

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“…As another example, ref. [73] introduced an ANN-based strategy to predict the output power of PVs using multi-layer feed-forward. The proposed method of [73] is a two-level strategy and it considers the shading condition.…”

Section: Pv Output Predictionmentioning

confidence: 99%

“…[73] introduced an ANN-based strategy to predict the output power of PVs using multi-layer feed-forward. The proposed method of [73] is a two-level strategy and it considers the shading condition. Furthermore, in [74], a gray-box neural network-based strategy has been proposed, which could be used to forecast the output power of PVs.…”

Section: Pv Output Predictionmentioning

confidence: 99%

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Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

et al. 2023

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Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids.

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Section: Pv Output Predictionmentioning

confidence: 99%

Section: Pv Output Predictionmentioning

confidence: 99%

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

et al. 2023

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“…The extensive incorporation of PV into distribution networks has introduced several challenges, including voltage levels, reverse power flow, and complications in configuring protective relays. Numerous approaches have been proposed to address the operational challenges in distribution networks that incorporate PV units and keep the service voltage within the American National Standards Institute range [2]. Based on previous literature, controlling the PV smart inverter might solve these problems.…”

Section: Introductionmentioning

confidence: 99%

Centralized Volt-VAR-Watt Optimization for Real Distribution Grids

Hasan,

Paudyal,

Padullaparti

et al. 2024

Preprint

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“…Among renewable energy sources, distributed photovoltaics (DPVs) have the advantages of flexible installation and a short construction period, which has attracted much attention. However, the output of DPVs is random due to a variety of factors such as installation location and weather [2][3][4]. With the increasing penetration rate of DPVs, problems such as power backward transmission and voltage overruns have occurred in some areas, bringing challenges to the safe operation of the distribution network [5].…”

Section: Introductionmentioning

confidence: 99%

Distributed Photovoltaic Generation Aggregation Approach Considering Distribution Network Topology

Tang,

Sun,

Zhao

et al. 2024

Energies

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Distributed photovoltaics (DPVs) are widely distributed and the output is random, which brings challenges to the safe operation of the distribution network, so the construction of photovoltaic aggregations can effectively participate in the flexible regulation of the power system. At present, the extraction of DPV clustering features is not sufficient, only considering the output characteristics of PVs. Certain PVs under some nodes may have a more pronounced regulation effect, but they may be ignored in the clustering process. To address the above problems, this paper proposes a DPV aggregation approach considering the distribution network topology. It combines the voltage sensitivity and the power curve and regards them as clustering features to form the DPV aggregation with the highest average voltage sensitivity participating in voltage regulation. The simulation on the IEEE 33-node system verifies that the proposed aggregation approach can select DPV aggregation more suitable for voltage regulation, and make full use of the aggregation to realize the optimal voltage regulation effect.

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Inverter-Data-Driven Second-Level Power Forecasting for Photovoltaic Power Plant

Cited by 22 publications

References 35 publications

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

Centralized Volt-VAR-Watt Optimization for Real Distribution Grids

Distributed Photovoltaic Generation Aggregation Approach Considering Distribution Network Topology

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