How to accommodate curtailed wind power: A comparative analysis between the US, Germany, India and China

Zhang, Huiming; Yang, Jiayun; Ren, Xianqiang; Wu, Qin; Zhou, Dequn; Elahi, Ehsan

doi:10.1016/j.esr.2020.100538

Cited by 26 publications

(11 citation statements)

References 33 publications

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“…he growing penetration of wind power in power system triggers the decline of the power system stability. To address this problem, incentive policies have been introduced around the world [1], especially in China [2]. These policies require new wind farms to install energy storage systems (ESSs) with 10%-30% of wind farms' installed capacities.…”

Section: Introductionmentioning

confidence: 99%

Self-Dispatch of Wind-Storage Integrated System: A Deep Reinforcement Learning Approach

Wei

Xiang

et al. 2022

IEEE Trans. Sustain. Energy

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The uncertainty of wind power and electricity price restrict the profitability of wind-storage integrated system (WSS) participating in real-time market (RTM). This paper presents a self-dispatch model for WSS based on deep reinforcement learning (DRL). The designed model is able to learn the integrated bidding and charging policy of WSS from the historical data. Besides, the maximum entropy and distributed prioritized experience replay frame, known as Ape-X, is used in this model. The Ape-X decouples the acting and learning in training by a central shared replay memory to enhance the efficiency and performance of the DRL procedures. Besides, the maximum entropy framework enables the designed agent to explore various optimal possibilities, thus the learned policy is more stable considering the uncertainty of wind power and electricity price. Compared with traditional methods, this model brings more benefits to wind farms while ensuring robustness. Index terms-wind farm; energy storage system; electricity market; deep reinforcement learning; distributed prioritized experience replay; maximum entropy.

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Section: Introductionmentioning

confidence: 99%

Self-Dispatch of Wind-Storage Integrated System: A Deep Reinforcement Learning Approach

Wei

Xiang

et al. 2022

IEEE Trans. Sustain. Energy

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“…As the population grows, it causes a surge in the number of vehicles and fuel demands, which accelerates CO 2 E. The PDN not only enhances human activities but also causes an increase in the growth of household income (Peterson, 2017). A larger populace needs a large amount of energy in the form of coal, natural gas, and electricity for continual living and work (Peng et al, 2020;Zhang et al, 2020;Tahir et al, 2021;Peng et al, 2022). Because of South Asia's rising populace in the last decades, the region has become a high CO 2 emitting region.…”

Section: Introductionmentioning

confidence: 99%

Population Density: An Underlying Mechanism Between Road Transportation and Environmental Quality

Din

Ming

Vega-Muñoz

et al. 2022

Front. Environ. Sci.

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Mounting degradation in the environmental quality (EQL), specifically from the transport industry, is a big threat and challenge for sustainable development. The transport sector’s emission has gained researchers’ attention on climate change and transportation because of its increasing share in global emission. This study, thus, aims to analyze the links among road infrastructure (RIN), road transport energy consumption (RTEC), and environmental quality with the moderating role of population density (PDN). The study has used a dataset of five South Asian countries from 1971 to 2014. The study applies the Breusch–Pagan LM test to identify the issue of cross-sectional dependence. CIPS (second-generation unit root test) is applied to check the stationarity properties of the data, whereas the Westerlund (Oxf. Bul. Econ. Stat., 2007, 69 (6), 709–748) co-integration test is used to confirm the long-run association among the variables. Moreover, a fully modified ordinary least square (FMOLS) model is applied to analyze the effect that road transportation has on environmental quality. The study finds a positive effect of road infrastructure, road density (RDN), energy intensity (EIN), and road transport energy consumption on transport-generated emissions, which indicates that road transportation is harmful to environmental quality. Our results confirm the significant moderating role of population density in strengthening the relations of road infrastructure, road transport energy consumption, and environmental quality. It is concluded that population density works as a bridge between road infrastructure, road transport energy consumption, and environmental quality, which helps capture a strong impact of road transportation. We offer the planners of road transportation with a novel and practical approach to examine population density changes policy in the growing countries to analyze the environmental quality.

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“…To study carbon emission, it is necessary to identify the main influencing factors of carbon emissions, and on this basis, put forward relevant suggestions for carbon emission reduction (Peng et al, 2018;Peng et al, 2020;Zhang et al, 2020;Zhong et al, 2020;Gu et al, 2020a;Wu et al, 2021;Zhao et al, 2021). First, the continuous advancement of new urbanization has an important impact on China's carbon emissions (Gu et al, 2019;Gu et al, 2020b;Zhao et al, 2020;Wang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Empirical Decomposition and Forecast of Carbon Neutrality for High-End Equipment Manufacturing Industries

Elahi

Sun

2022

Front. Environ. Sci.

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The Chinese government focuses on the high-end equipment manufacturing industry to achieve a target of carbon neutrality. This study takes China’s Bohai Rim as a case study. First, the Tapio decoupling model was used to analyze the carbon emission status of the high-end equipment manufacturing industry in the Bohai Rim. Second, LMDI was used to determine the main factors of carbon emission. Similarly, the Monte Carlo simulation predicted the time of carbon neutrality. The results found that the relationship between carbon emission and the development of the high-end equipment manufacturing industry is that of strong decoupling, but there is still a risk of “recoupling.” The scale effect is the primary driving force for carbon emission reduction in the equipment manufacturing industry, followed by a structural effect and a carbon emission intensity effect. In the baseline scenario, low-carbon scenario, and technological breakthrough scenario, carbon neutrality will be achieved before 2060. The results of the study suggest that China should improve energy utilization efficiency and encourage green innovation.

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How to accommodate curtailed wind power: A comparative analysis between the US, Germany, India and China

Cited by 26 publications

References 33 publications

Self-Dispatch of Wind-Storage Integrated System: A Deep Reinforcement Learning Approach

Self-Dispatch of Wind-Storage Integrated System: A Deep Reinforcement Learning Approach

Population Density: An Underlying Mechanism Between Road Transportation and Environmental Quality

Empirical Decomposition and Forecast of Carbon Neutrality for High-End Equipment Manufacturing Industries

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