A copula-based flexible-stochastic programming method for planning regional energy system under multiple uncertainties: A case study of the urban agglomeration of Beijing and Tianjin

In this paper, we apply an indicator-based approach to measure the resilience of energy regions in transition to a case study region in Austria. The indicator-based approach allows to determine the resilience of the transition of regional energy systems towards higher shares of renewables and potentially overall higher sustainability. The indicators are based on two core aspects of resilience, diversity and connectivity. Diversity is thereby operationalized by variety, disparity and balance, whereas connectivity is operationalized by average path length, degree centrality and modularity. In order to get a full picture of the resilience of the energy system at stake throughout time, we apply the measures to four distinct moments, situated in the pre-development, take-off, acceleration and stabilization phase of the transition. By contextually and theoretically embedding the insights in the broader transitions context and empirically applying the indicators to a specific case, we derive insights on (1) how to interpret the results in a regional context and (2) how to further develop the indicator-based approach for future applications.

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A scenario-based interval-stochastic basic-possibilistic programming method for planning sustainable energy system under uncertainty: A case study of Beijing, China

Journal of Cleaner Production ◽

10.1016/j.jclepro.2018.06.248 ◽

2018 ◽

Vol 197 ◽

pp. 1454-1471 ◽

Cited By ~ 11

Author(s):

L. Yu ◽

Y.P. Li ◽

B.G. Shan ◽

G.H. Huang ◽

L.P. Xu

Keyword(s):

Sustainable Energy ◽

Energy System ◽

Programming Method ◽

Possibilistic Programming ◽

Sustainable Energy System ◽

Beijing China

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Analysis of regional energy economic efficiency and its influencing factors: A case study of Yangtze river urban agglomeration

Sustainable Energy Technologies and Assessments ◽

10.1016/j.seta.2020.100784 ◽

2020 ◽

Vol 41 ◽

pp. 100784

Author(s):

Zhaoqiang Zhong ◽

Benhong Peng ◽

Lu Xu ◽

Awuah Andrews ◽

Ehsan Elahi

Keyword(s):

Influencing Factors ◽

Economic Efficiency ◽

Yangtze River ◽

Urban Agglomeration ◽

Regional Energy

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Energy-Water-Carbon Nexus Optimization for the Path of Achieving Carbon Emission Peak in China Considering Multiple Uncertainties: A Case Study in Inner Mongolia

Energies ◽

10.3390/en14041067 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1067

Author(s):

Yuan Liu ◽

Qinliang Tan ◽

Jian Han ◽

Mingxin Guo

Keyword(s):

Inner Mongolia ◽

Water Policy ◽

Solar Power ◽

Chinese Government ◽

Air Cooling ◽

Fuel Price ◽

Regional Energy ◽

Co2 Emissions Mitigation ◽

Multiple Uncertainties

The Chinese government has launched a guideline for carbon emissions up to the peak (CEUP) in the 2030 target. The electric power sector has to make its own contributions to the national CO2 emissions mitigation target. In this study, a patron–client interactive optimized (PCIO) model is proposed to investigate the regional energy–water–carbon nexus optimization under the policy background of the CEUP target. Inner Mongolia, the largest energy base in China, which is also facing the prominent contradiction including the energy production and serious environmental problems, is chosen as a case study. Multiple uncertainties, including the fuel price uncertainty and output of the wind and solar power, are considered to make the optimization process more realistic. Results show that coal-fired power will gradually be substituted by the gas, wind, and solar power in Inner Mongolia to reach the CEUP target. The CO2 capture and storage technology and air-cooling systems will play important roles, especially under the strict water policy scenario. However, the achievement of the CEUP and water-saving target will be at the expense of high system costs. The PCIO model makes it possible for the decision-maker to make flexible strategies to balance the CEUP target and saving system costs. The results have demonstrated the validity of the PCIO model in addressing the hierarchical programming problems.

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