A comprehensive study on low-carbon impact of distributed generations on regional power grids: A case of Jiangxi provincial power grid in China

Cao, Yijia; Wang, Xifan; Tan, Yi; Xing, Jianbo; Fan, Ruixiang

doi:10.1016/j.rser.2015.09.008

Cited by 33 publications

(17 citation statements)

References 53 publications

(24 reference statements)

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“…However, despite this negative environmental impact from the non-renewable energy sources, DG could have positive impacts of integrating renewable energy sources into the electrical power network which will reduce the gaseous emission mentioned above. Various literature exists on the use of renewable energy sources to reduce gaseous emission see (M. Chen & Cheng, 2012;Liew et al, 2017;Di;Somma et al, 2016;Cao et al, 2016;Akorede et al, 2010;Abdallah & El-Shennawy, 2013). Table 3(a-b) are environmental standard value for pollutant and penalty emission control (M. Chen & Cheng, 2012) and Table 4, shows potential emission reduction of Carbon (IV) oxide and electricity emission to be achieved in 2030 ([PNNL] Pratt et al, 2010).…”

Section: A Environmental Aspectmentioning

confidence: 99%

Optimization techniques applied for optimal planning and integration of renewable energy sources based on distributed generation: Recent trends

2020

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Numerous potential advantages to the requirements and effectiveness of the supplied electricity can be accomplished by the installation of distributed generation units. In order to take full advantage of these benefits, it is essential to position the Distributed Generation (DG) units in appropriate locations. Otherwise, their installation may have an adverse effect on the quality of energy and system operation. Several optimization techniques have been created over the years to optimize distributed generation integration. Optimization techniques are therefore constantly changing and have been the main attention of many fresh types of research lately. This article evaluates cutting-edge techniques of optimizing the issue of positioning and sizing distributed generation units from renewable energy sources based on recent papers that have already been applied to distribution

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Section: A Environmental Aspectmentioning

confidence: 99%

Optimization techniques applied for optimal planning and integration of renewable energy sources based on distributed generation: Recent trends

2020

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“…A simulation study by Cao et al [63] indicated that DG incorporation into Jiangxi provincial power grid with wind power integration rate of 0.16% could reduce annual carbon emissions by up to 1.08 million tonnes. Optimised DG operation for catering Italian large-scale utility consumers was associated with carbon emissions reduction by 26% [64].…”

Section: Environmental Benefitsmentioning

confidence: 99%

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Theo

Lim

et al. 2017

Renewable and Sustainable Energy Reviews

225

105

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“…Although different types of electricity generation are present in China, some papers did not consider separate sources of energy and treated the electricity generation sector as a whole when analyzing the driving forces of CO 2 emissions in this sector [39][40][41][42][43]. This can be partly explained by the fact that the structure of the Chinese electricity generation sector had been rather simple prior to the expansion of the renewables.…”

Section: Literature Review On China's Thermal Electricity Generationmentioning

confidence: 99%

Energy-Related CO2 Emission in China’s Provincial Thermal Electricity Generation: Driving Factors and Possibilities for Abatement

et al. 2018

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China's electricity sector mainly relies on coal-fired thermal generation, thus resulting that nearly 50% of China's total CO 2 emissions coming from the electricity sector. This study focuses on the provincial CO 2 emissions from China's thermal electricity generation. Methodologically, Index Decomposition Analysis (IDA), facilitated by the Shapley Index, is applied to discover the driving factors behind CO 2 emission changes at the provincial level. In addition, the Slack-based Model (SBM) is used to identify which provincial power grids should be allocated with a higher (lower) CO 2 reduction burden. The IDA results indicate that economic activity pushed the CO 2 emissions up in all 30 provincial power grids, excluding Beijing and Shanghai; the carbon factor contributed to a decrease in the CO 2 emissions in all 30 provincial power grids, with the exception of Jilin, Guangdong, and Ningxia; though the effect of energy intensity varied across the 30 provinces, it played a significant role in the mitigation of CO 2 emissions in Beijing, Heilongjiang, Liaoning, Jilin, Shanghai, Anhui, and Sichuan. According to the SBM results, the lowest carbon shadow prices are observed in Yunnan, Shanghai, Inner Mongolia, Jilin, Qinghai, Guizhou, Anhui, and Ningxia. These provincial power grids, thus, should face higher mitigation targets for CO 2 emissions from thermal electricity generation.

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A comprehensive study on low-carbon impact of distributed generations on regional power grids: A case of Jiangxi provincial power grid in China

Cited by 33 publications

References 53 publications

Optimization techniques applied for optimal planning and integration of renewable energy sources based on distributed generation: Recent trends

Optimization techniques applied for optimal planning and integration of renewable energy sources based on distributed generation: Recent trends

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Energy-Related CO2 Emission in China’s Provincial Thermal Electricity Generation: Driving Factors and Possibilities for Abatement

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