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Multi-Objective Optimization for China’s Power Carbon Emission Reduction by 2035

Low carbon transformation plays an important role in promoting the energy production and consumption revolution. Currently, the power sector of China still faces a series of challenges, such as the overcapacity of coal-fired power, the renewable energy consumption, the new constraints of carbon-emissions, and fragmented power planning. This study develops a multi-objective optimization model to predict the future trend of China power structure by 2035. The key factors such as network, power, load and storage are taken into account. Besides, the technical feasibility, economic rationality and social acceptable constraints are also fully considered. Through planning and optimization, the premise of low carbon transformation is to ensure the continuity of existing policies for removing inefficient assets, and the core is to develop and utilize non-fossil energy on a large scale. Specifically, the capacity of coal-fired power will be attained in the peak in 2025, and the factor will also transfer from main power supplier to main power and energy supplier. Before 2025, the clean replacement of incremental power installation will be completed. In 2035, 92% of new investment comes from non-fossil energy. The economy and competitiveness of wind power and PV (Photovoltaic) power generation are continuously increasing. By 2020, the coal-fired power and the wind power in eastern of China will be parity firstly. In 2025, the cost of PV and wind power will be the same. Furthermore, the evaluation dimension of modern power system with clean, low-carbon, safety and high efficiency are innovatively constructed, and the index system target of 2035 is quantitatively analyzed and prospected.
- Ningbo University China (People's Republic of)
- Hubei University China (People's Republic of)
- Ningbo University of Technology China (People's Republic of)
- Xi’an Jiaotong-Liverpool University China (People's Republic of)
- Chinese Academy of Sciences China (People's Republic of)
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).27 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
