The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather

Gao, Yajing; Hu, Cheng; Zhu, Jing; Liang, Haifeng

doi:10.3390/su8010071

Cited by 16 publications

(9 citation statements)

References 24 publications

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“…Energies 2018, 11, 528 5 of 22 (3). Air Temperature Air temperature has important influence not only on dry/wet state of aerosol particles, but also on the existential state of semi volatile components, boundary layer height, and atmospheric stability, thus affecting the relationship between the PM concentration and AOD.…”

Section: (2) Boundary Layer Heightmentioning

confidence: 99%

Ultra-Short-Term Forecast of Photovoltaic Output Power under Fog and Haze Weather

Liu

et al. 2018

Energies

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Fog and haze (F-H) weather has been occurring frequently in China since 2012, which affects the output power of photovoltaic (PV) generation dramatically by directly weakening solar irradiance and aggravating dust deposition on PV panels. The ultra-short-term forecast method presented in this study would help to fully reflect the dual effects of F-H on PV output power. Aiming at the weakening effect on solar irradiance, estimation models of atmospheric aerosol optical depth (AOD) based on particle matter (PM) concentration were established with machine learning (ML) method, and the total irradiance received by PV panels was calculated based on simplified REST2 model. Aiming at the aggravating effect on dust deposition on PV panels, sample set of "cumulative PM concentration-efficiency reduction" was constructed through special measurement experiments, then the efficiency reduction under certain dust deposition state was estimated with similar-day choosing method. Based on photoelectric conversion model, PM concentration prediction and weather forecast information, ultra-short-term forecast of PV output power was realized. Experimental results proved the validity and feasibility of the presented forecast method.

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Section: (2) Boundary Layer Heightmentioning

confidence: 99%

Ultra-Short-Term Forecast of Photovoltaic Output Power under Fog and Haze Weather

Liu

et al. 2018

Energies

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“…According to the latest data released by BP, in 2016, China had discharged 9123 million tons, accounting for 27.3% of the world's carbon emissions [4]. In fact, the burning of fossil fuels not only results in a surge in CO 2 , but also is responsible for approximately 70% of the domestic air pollution total [5][6].…”

Section: Introductionmentioning

confidence: 99%

Exploring Carbon Emissions in China’s Electric Power Industry for Low-Carbon Development: Drivers, Decoupling Analysis and Policy Implications

Li¹,

Wang²

2019

Pol. J. Environ. Stud.

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As an important single source to carbon emissions, China's power industry should bear social responsibility for mitigating climate change. To explore what low-carbon development means for the industry, a novel approach that combines the extended multilevel LMDI model with Tapio algorithm was conducted to study the drivers of carbon emissions in the power industry and whether CO 2 emissions from power output is out of sync with economic development, covering the period from 1996 to 2016. Our results come to the following: 1. Carbon emissions from electricity output are characterized by increases and volatility, with an average annual growth rate of 7.05%. The carbon emission factor of electricity, facilitating to compute CO 2 data, shows a decline. 2. The positive driving factors are economic activity effect (169.53%), population scale effect (9.29%), fuel mix structure effect (0.41%), and electricity trade effect (1.05%); the negative driving factors are electricity intensity effect (-46.38%), power generation efficiency effect (-24.93%), and power generation structure effect (-8.97%). 3. Weak decoupling and expansive decoupling are the main status during the research period. The electricity intensity effect is the main force to promote the decoupling process. 4. The market-oriented reform in the power industry in 2003 has a significant effect. The generationside competition mechanism successfully changes the historical developmental trend of the decoupling elastic index.

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“…Aggregation refers to the integration of a large number of dispersed resources into a polymer with large scheduling capacity, good regulation characteristics and simple regulation according to the external environment or the operation purpose, which is an important means for distributed resources management and coordination. Lots of studies have been done for the resource aggregation [6][7][8][9][10][11][12][13]. In [6], considering the complementarity of DERs, …”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique

et al. 2017

Self Cite

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Abstract:To fully investigate the characteristics and the complementarities of demand side resources (DSRs), and to achieve efficient utilization of resources, the aggregation of DSRs is studied in this paper. Considering the uncertainty of DSRs, the characteristics analysis and the selection of relevant daily feature corresponding to various types of DSR are carried out. Then a multi-scenario model based on quarter division and self-organizing map (SOM) neural network algorithm is proposed. In the model, the clustering feature vector is selected as the input vector of the SOM algorithm to perform DSR clustering analysis to get the different scenarios. In addition, to obtain the resource aggregation (RA) with good load characteristics, response characteristics and distributed generation (DG) consumption, a multi-scenario objective optimization aggregation model of DSR based on scenario partition is established, and an the model is solved by an improved niche evolutionary multi-objective immune algorithm. Finally, the case studies are given to verify the validity of the model.

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The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather

Cited by 16 publications

References 24 publications

Ultra-Short-Term Forecast of Photovoltaic Output Power under Fog and Haze Weather

Ultra-Short-Term Forecast of Photovoltaic Output Power under Fog and Haze Weather

Exploring Carbon Emissions in China’s Electric Power Industry for Low-Carbon Development: Drivers, Decoupling Analysis and Policy Implications

Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique

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