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Optimal Operation Model of Micro-energy Network Considering Classification and Disposal of Biomass Waste

Authors :
Teng, Yun
Sun, Peng
Hui, Qian
Chen, Zhe
Source :
Teng, Y, Sun, P, Hui, Q & Chen, Z 2021, ' 考虑生物质废物分类处理的微能源网运行优化模型 ', Dianli Xitong Zidonghua/Automation of Electric Power Systems, bind 45, nr. 15, s. 55-63 . https://doi.org/10.7500/AEPS20200726003
Publication Year :
2021

Abstract

In view of the ever-increasing requirements for the micro-energy network autonomy and multiple operation modes in the background of zero-waste city and Energy Internet, an optimized model for the micro-energy network of electricity, heat, hydrogen and gas is established which contains the biomass waste disposal facilities. Firstly, different biomass waste classification and disposal methods are analyzed. The pyrolysis gasification power generation model is established for the residual waste, and the gas production model by fermentation is established for the food waste and manure. Secondly, a multi-objective operation optimization model is established with the objective of the lowest operation cost and the maximum ecological benefits of the micro-energy network. Taking into account the uncertainty of the multi-energy source and load of the micro-energy network and the uncertainty of the waste generation, a robust multi-objective optimization algorithm for the micro-energy network is proposed. Case studies verify that the proposed optimized operation model of the micro-energy network considering the energy supply of the biomass waste disposal equipment can improve the operation economy of the micro-energy network, while improving the urban waste disposal capacity and realizing the effective integration of the energy consumption and environmental governance.

Details

Database :
OpenAIRE
Journal :
Teng, Y, Sun, P, Hui, Q & Chen, Z 2021, ' 考虑生物质废物分类处理的微能源网运行优化模型 ', Dianli Xitong Zidonghua/Automation of Electric Power Systems, bind 45, nr. 15, s. 55-63 . https://doi.org/10.7500/AEPS20200726003
Accession number :
edsair.dedup.wf.001..acc84dc51fe0c9e79ac208fceda4975a
Full Text :
https://doi.org/10.7500/AEPS20200726003