Decarbonizing China’s energy system – Modeling the transformation of the electricity, transportation, heat, and industrial sectors

Burandt, Thorsten; Xiong, Bobby; Löffler, Konstantin

doi:10.1016/j.apenergy.2019.113820

Cited by 124 publications

(64 citation statements)

References 62 publications

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“…The current version of the model covers a time horizon until 2050 with annual time steps until 2025 followed by 5-year time steps afterwards. For further information about the mathematical formulation of the model we refer to [4][5][6][7][8].…”

Section: Model Framework and Analysis Methodologymentioning

confidence: 99%

Cost-optimal configuration of a renewable-based Australian power system

Aboumahboub¹,

Brecha²,

Shrestha³

et al. 2020

REPQJ

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Proposed emission reduction targets and the scarcity of fossil fuel resources make a transition of the energy system towards an emission-free electricity supply necessary. Australia represents an interesting case for energy system transformation modelling. While it currently has a power system dominated by fossil fuels, and specifically with a heavy coal component, there is a vast potential for expansion and use of renewable energy, in particular solar and wind energy. However, integrating high shares of such variable renewable energy sources challenges the power system due to their temporal fluctuations and geographical dispersion. This paper applies a state-resolved energy system model for Australia, based on linear optimization. We investigate the cost-optimal configuration of a renewable-based Australian power system and its transformation pathway inline with the ambitious proposed climate targets. We particularly analyze the implications of storage and power transmission grid extensions in a prospective, highly renewable Australian power system. Spatial smoothening effects of a powerful transmission grid reduces the required backup and renewable capacities and thus contributes to further reduction of the total system costs.

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Section: Model Framework and Analysis Methodologymentioning

confidence: 99%

Cost-optimal configuration of a renewable-based Australian power system

Aboumahboub¹,

Brecha²,

Shrestha³

et al. 2020

REPQJ

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“…For those industry-oriented regions similar to Changxing, we should give priority to optimizing the energy structure, especially by eliminating dependence on fossil fuels for power generation, to reduce CO 2 emissions. Electricity decarbonization has been explored in the recent research [47,48]. On the other hand, the promotion of low-carbon technology is equally important.…”

Section: Relationship Between Co 2 Emissions and Economic Developmentmentioning

confidence: 99%

Analysis of CO2 Drivers and Emissions Forecast in a Typical Industry-Oriented County: Changxing County, China

et al. 2020

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Decomposing main drivers of CO2 emissions and predicting the trend of it are the key to promoting low-carbon development for coping with climate change based on controlling GHG emissions. Here, we decomposed six drivers of CO2 emissions in Changxing County using the Logarithmic Mean Divisia Index (LMDI) method. We then constructed a model for CO2 emissions prediction based on a revised version of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and used it to simulate energy-related CO2 emissions in five scenarios. Results show that: (1) From 2010 to 2017, the economic output effect was a significant, direct, dominant, and long-term driver of increasing CO2 emissions; (2) The STIRPAT model predicted that energy structure will be the decisive factor restricting total CO2 emissions from 2018 to 2035; (3) Low-carbon development in the electric power sector is the best strategy for Changxing to achieve low-carbon development. Under the tested scenarios, Changxing will likely reach peak total CO2 emissions (17.95 million tons) by 2030. Measures focused on optimizing the overall industrial structure, adjusting the internal industry sector, and optimizing the energy structure can help industry-oriented counties achieve targeted carbon reduction and control, while simultaneously achieving rapid economic development.

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“…We used the current version of OSeMOSYS originally coded in GNU MathProg while incorporating model enhancements introduced in previous studies [32,33] as well as new additions considered most relevant for addressing the questions of the present study.…”

Section: Model Formulationmentioning

confidence: 99%

“…The remainder of the model formulation is well described in previous studies [26,[32][33][34]. Model equations as well as a list of symbols used are provided in Appendix A.…”

Section: Of 39mentioning

confidence: 99%

Decarbonization of Australia’s Energy System: Integrated Modeling of the Transformation of Electricity, Transportation, and Industrial Sectors

et al. 2020

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To achieve the Paris Agreement’s long-term temperature goal, current energy systems must be transformed. Australia represents an interesting case for energy system transformation modeling: with a power system dominated by fossil fuels and, specifically, with a heavy coal component, there is at the same time a vast potential for expansion and use of renewables. We used the multi-sectoral Australian Energy Modeling System (AUSeMOSYS) to perform an integrated analysis of implications for the electricity, transport, and selected industry sectors to the mid-century. The state-level resolution allows representation of regional discrepancies in renewable supply and the quantification of inter-regional grid extensions necessary for the physical integration of variable renewables. We investigated the impacts of different CO2 budgets and selected key factors on energy system transformation. Results indicate that coal-fired generation has to be phased out completely by 2030 and a fully renewable electricity supply achieved in the 2030s according to the cost-optimal pathway implied by the 1.5 °C Paris Agreement-compatible carbon budget. Wind and solar PV can play a dominant role in decarbonizing Australia’s energy system with continuous growth of demand due to the strong electrification of linked energy sectors.

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Decarbonizing China’s energy system – Modeling the transformation of the electricity, transportation, heat, and industrial sectors

Cited by 124 publications

References 62 publications

Cost-optimal configuration of a renewable-based Australian power system

Cost-optimal configuration of a renewable-based Australian power system

Analysis of CO2 Drivers and Emissions Forecast in a Typical Industry-Oriented County: Changxing County, China

Decarbonization of Australia’s Energy System: Integrated Modeling of the Transformation of Electricity, Transportation, and Industrial Sectors

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