Probabilistic sizing of a low-carbon emission power system considering HVDC transmission and microgrid clusters

2021 ◽  
Vol 304 ◽  
pp. 117760
Author(s):  
Bei Li ◽  
Jiangchen Li
2021 ◽  
Vol 252 ◽  
pp. 03060
Author(s):  
Lu Yu ◽  
Shi Shengyao ◽  
Zhang Dachi ◽  
Feng Shunqiang ◽  
Zhang Yuanmei ◽  
...  

In the context of low carbon economy, introducing carbon trading and developing low-carbon energy generation is an important means to realize low-carbon development of the power system. Because gas power generation has the advantages of high efficiency, low carbon emission and strong peak load capability, the gas generator unit is added to the planning plan and a low carbon power planning model based on carbon trading is established. The goal of the model is to minimize the cost of the system integration. The cost includes investment operation cost and carbon transaction cost. And the natural gas supply constraints and carbon trading constraints are increased in the constraint condition. Finally, the discrete bacterial colony chemotaxis algorithm is adopted to solve this model. Through the model comparison and sensitivity analysis, it is concluded that the addition of gas turbine unit and carbon trading mechanism can optimize the power supply structure, promote the construction of low carbon unit. and realize the conclusion of low carbon emission reduction of power system. And the results verify the effectiveness of the proposed power planning model.


2021 ◽  
Author(s):  
Homeyra Akter ◽  
Harun Or Rashid Howlader ◽  
Ahmed Y. Saber ◽  
Ashraf M. Hemeida ◽  
Hiroshi Takahashi ◽  
...  

2020 ◽  
Vol 12 (19) ◽  
pp. 8118
Author(s):  
Tu Peng ◽  
Xu Yang ◽  
Zi Xu ◽  
Yu Liang

The sustainable development of mankind is a matter of concern to the whole world. Environmental pollution and haze diffusion have greatly affected the sustainable development of mankind. According to previous research, vehicle exhaust emissions are an important source of environmental pollution and haze diffusion. The sharp increase in the number of cars has also made the supply of energy increasingly tight. In this paper, we have explored the use of intelligent navigation technology based on data analysis to reduce the overall carbon emissions of vehicles on road networks. We have implemented a traffic flow prediction method using a genetic algorithm and particle-swarm-optimization-enhanced support vector regression, constructed a model for predicting vehicle exhaust emissions based on predicted road conditions and vehicle fuel consumption, and built our low-carbon-emission-oriented navigation algorithm based on a spatially optimized dynamic path planning algorithm. The results show that our method could help to significantly reduce the overall carbon emissions of vehicles on the road network, which means that our method could contribute to the construction of low-carbon-emission intelligent transportation systems and smart cities.


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