Satellite cloud image data plays an important role in the ultra-short-term photovoltaic（PV） power forecasting. By calculating the cloud displacement vectors of two adjacent cloud images, it is possible to calculate ...
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Satellite cloud image data plays an important role in the ultra-short-term photovoltaic（PV） power forecasting. By calculating the cloud displacement vectors of two adjacent cloud images, it is possible to calculate the cloud cluster’s occlusion of the PV power plant for a period of time in the future, thereby forecasting the PV power. The satellite cloud image data provided by FY-4 A Satellite has large and unequal sampling time intervals, which brings difficulties to cloud displacement vector calculation. This paper proposes a method for calculating the displacement vector of the cloud edge block based on the cosine similarity and the adaptive adjustment of the matching spatial scale. The parameter equation is used to establish the relationship between the relative position of the cloud cluster and the PV plant over time, and to predict the future position. On this basis, this paper proposes an ultra-short-term forecasting method of PV power based on cloud displacement vector. The performance of the proposed model is verified through case analysis.
Attention to efficient and cost-effective utilize of energy has become a common view in the energy sector. A variety of microgrid energy management systems are developed and installed to achieve the goal. However, the...
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This paper investigates a consensus-based nodal pricing mechanism for incenting automated demand response in the deregulated market environment. These small-scale customers are autonomously managed by automated demand...
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The wind farm has only one NWP data point, which cannot accurately reflect the wind resources inside the wind farm. To solve the problem of a poor match between short-term wind power prediction numerical weather forec...
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The wind farm has only one NWP data point, which cannot accurately reflect the wind resources inside the wind farm. To solve the problem of a poor match between short-term wind power prediction numerical weather forecast and wind farm measured weather. Put forward a kind of based on clustering and temporal characteristics of the modified liters of scale wind power prediction method, first of all, using EOF orthogonal decomposition method to extract the features of each fan, using hierarchical analysis method of the characteristics of the data matrix clustering, and then, each cluster sample selected representative of a fan, using the error transfer characteristic of the NWP data sequence is modified. Finally, The CNN prediction model is used for power prediction, and the weight is determined according to the cluster sample size. A wind farm in northern Hebei, China, is used to verify the effectiveness of the proposed method.
Renewable energy sources exhibit significant variability and uncertainty, showing numerous distinct scenarios. In the context of generation planning, it's imperative to employ scenario reduction techniques to sele...
Renewable energy sources exhibit significant variability and uncertainty, showing numerous distinct scenarios. In the context of generation planning, it's imperative to employ scenario reduction techniques to select representative scenarios that capture the characteristics of renewable energy outputs. However, traditional scenario reduction methods often fail to adequately preserve the extreme output scenarios of these renewables. This paper improves the simultaneous backward reduction method (SBR), enhancing its capability to effectively capture the extreme output scenarios of renewable sources. As a result, the generation planning becomes more economically viable and more reliable. The efficacy of the improved SBR method is demonstrated using data from the Qinhuangdao region in China.
Introduction of nanoparticles (NPs) in the solvent was treated an efficient route to enhance mass and heat transfer, therefore, it was a promising strategy to boost carbon dioxide (CO2) absorption and desorption, espe...
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Batteries, with support of governments and industry, have flourished in recent years, which appears to be one of the most promising power sources in the global transportation field. In addition to being widely used in...
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In recent years, electrification has become a major trend in the transportation domain, where the electrification of forklifts is even significant. In 2016, the annual growth of electric forklift production reached ne...
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With the large-scale integration of distributed generations (DGs), a centralized control approach is being challenged concerning communication efficiency, resiliency to communication failure, privacy, and scalability....
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ISBN:
(纸本)9798350399677
With the large-scale integration of distributed generations (DGs), a centralized control approach is being challenged concerning communication efficiency, resiliency to communication failure, privacy, and scalability. An autonomous decentralized control for DGs in distribution networks is proposed to ensure privacy while also reducing the computational burden. Meanwhile,a multi-agent reinforcement learning method considering privacy protection constraints is developed to solve the autonomous decentralized control problem. The effectiveness of the proposed control method is verified by the numerical example of the modified 141-bus distribution system test_feeder.
Cleanliness of energy system requires both NOx reduction due to urgent air pollution control and CO2 cutting due to long-term climate change pressure. In the urban heating sector, quick-win plans use to be popular tha...
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