Power ramp-rates of utility-scale PV systems under passing clouds: Module-level emulation with cloud shadow modeling

Chen, Xiaoyang; Du, Yang; Lim, Eng Gee; Wen, Huiqing; Yan, Ke; Kirtley, James L.

doi:10.1016/j.apenergy.2020.114980

Cited by 43 publications

(14 citation statements)

References 38 publications

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“…For instance, the solar forecasting horizon can be changed depending on the application. The dataset can be used to forecast the effect of the clouds in thermosolar energy generation systems such as concentrated solar power [13] , [14] .…”

Section: Value Of the Datamentioning

confidence: 99%

Girasol, a sky imaging and global solar irradiance dataset

et al. 2021

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The energy available in a microgrid that is powered by solar energy is tightly related to the weather conditions at the moment of generation. A very short-term forecast of solar irradiance provides the microgrid with the capability of automatically controlling the dispatch of energy. We propose a dataset to forecast Global Solar Irradiance (GSI) using a data acquisition system (DAQ) that simultaneously records sky imaging and GSI measurements, with the objective of extracting features from clouds and use them to forecast the power produced by a Photovoltaic (PV) system. The DAQ system is nicknamed the Girasol Machine (Girasol means Sunflower in Spanish). The sky imaging system consists of a longwave infrared (IR) camera and a visible (VI) light camera with a fisheye lens attached to it. The cameras are installed inside a weatherproof enclosure that it is mounted on a solar tracker. The tracker updates its pan and tilt every second using a solar position algorithm to maintain the Sun in the center of the IR and VI images. A pyranometer is situated on a horizontal mount next to the DAQ system to measure GSI. The dataset, composed of IR images, VI images, GSI measurements, and the Sun’s positions, has been tagged with timestamps.

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Section: Value Of the Datamentioning

confidence: 99%

Girasol, a sky imaging and global solar irradiance dataset

et al. 2021

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“…Moreover, unprecedented growth in DER, particularly small‐scale photovoltaic (PV) systems (<100 kW) and EVs, is resulting in greater uncertainty in generation and load demand profiles, which can require additional reserves in order to maintain grid stability, due to the possibility of temporarily losing GW‐level generation within a few minutes during uncertain (cloudy) weather conditions (X. Chen et al, 2020), changeable human behavior (Pratt & Erickson, 2020), and cascading DER trips initiated by transmission level faults (AEMC, 2019; AEMO, 2019a; National Grid ESO, 2019). In Germany, for example, 49 GW of solar PV generation capacity was installed by the end of 2019, with over 52% of the systems noted as small scale systems (< 100 kW), and more than 98% of them connected to the low‐voltage (LV) grid (Wirth, 2020).…”

Section: Introductionmentioning

confidence: 99%

Power system stability in the transition to a low carbon grid: A techno‐economic perspective on challenges and opportunities

Meegahapola

Mancarella

Flynn

et al. 2021

WIREs Energy & Environment

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Increasing power system stability challenges are being witnessed worldwide, while transitioning toward low‐carbon grids with a high‐share of power electronic converter (PEC)‐interfaced renewable energy sources (RESs) and distributed energy resources (DERs). Concurrently, new technologies and operational strategies are being implemented or proposed to tackle these challenges. Since electricity grids are deregulated in many jurisdictions, such technologies need to be integrated within a market framework, which is often a challenge in itself due to inevitable regulatory delays in updating grid codes and market rules. It is also highly desirable to ensure that an economically feasible optimal technology mix is integrated in the power system, without imposing additional burdens on electricity consumers. This article provides a comprehensive overview of emerging power system stability challenges posed by PEC‐interfaced RES and DER, particularly related to low inertia and low system strength conditions, while also introducing new technologies that can help tackle these challenges and discussing the need for suitable techno‐economic considerations to integrate them into system and market operation. As a key point, the importance of recognizing the complexity of system services to guarantee stability in low‐carbon grids is emphasized, along with the need to carefully integrate new grid codes and market mechanisms in order to exploit the full benefits of emerging technologies in the transition toward ultra‐low carbon futures. This article is categorized under: Energy Systems Economics > Economics and Policy Energy Systems Analysis > Systems and Infrastructure Energy and Development > Systems and Infrastructure

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“…The AM is used to adaptively perceive feature information from the time series data. Chen et al [25] proposed a cloud shadow model using computer vision techniques to forecast real cloud coverage nature for PV systems and enhanced the PV energy generation forecasting results consequently. Chang et al [26] introduced a virtual inertia control based on PV load forecasting results, showing real-world applications of the PV energy generation forecasting techniques.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Enhanced Solar Energy Forecasting with AI‐Driven IoT

Zhou

Liu

Yan

et al. 2021

Wireless Communications and Mobile Computing

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Short-term photovoltaic (PV) energy generation forecasting models are important, stabilizing the power integration between the PV and the smart grid for artificial intelligence- (AI-) driven internet of things (IoT) modeling of smart cities. With the recent development of AI and IoT technologies, it is possible for deep learning techniques to achieve more accurate energy generation forecasting results for the PV systems. Difficulties exist for the traditional PV energy generation forecasting method considering external feature variables, such as the seasonality. In this study, we propose a hybrid deep learning method that combines the clustering techniques, convolutional neural network (CNN), long short-term memory (LSTM), and attention mechanism with the wireless sensor network to overcome the existing difficulties of the PV energy generation forecasting problem. The overall proposed method is divided into three stages, namely, clustering, training, and forecasting. In the clustering stage, correlation analysis and self-organizing mapping are employed to select the highest relevant factors in historical data. In the training stage, a convolutional neural network, long short-term memory neural network, and attention mechanism are combined to construct a hybrid deep learning model to perform the forecasting task. In the testing stage, the most appropriate training model is selected based on the month of the testing data. The experimental results showed significantly higher prediction accuracy rates for all time intervals compared to existing methods, including traditional artificial neural networks, long short-term memory neural networks, and an algorithm combining long short-term memory neural network and attention mechanism.

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Power ramp-rates of utility-scale PV systems under passing clouds: Module-level emulation with cloud shadow modeling

Cited by 43 publications

References 38 publications

Girasol, a sky imaging and global solar irradiance dataset

Girasol, a sky imaging and global solar irradiance dataset

Power system stability in the transition to a low carbon grid: A techno‐economic perspective on challenges and opportunities

Deep Learning Enhanced Solar Energy Forecasting with AI‐Driven IoT

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