KEYWORDS: Carbon, System integration, Solar energy, Power grids, Photovoltaics, Systems modeling, Atmospheric modeling, Wind energy, Mathematical optimization
The article establishes a integrated energy system optimization and scheduling model that considers the participation of P2G in the carbon trading market. Firstly, a integrated energy system operation framework was established based on the system load characteristics; Secondly, considering the background of carbon trading mechanism and the incentive effect of P2G on carbon trading mechanism, a comprehensive carbon trading cost model is established; Finally, a low-carbon scheduling model for the integrated energy system was established with the objective functions of minimizing operating costs and maximizing new energy consumption, and the effectiveness of the proposed model was verified through scenario comparison. Research has found that considering the comprehensive carbon trading cost model, setting a reasonable carbon price can effectively promote the low-carbon economic operation of the system.
The thesis proposes a new methodology of cost dynamic management to realize the whole process of power transmission and transformation project cost dynamic management. Based on historical cost data to modify expected cost control, building cost goal deviation classified warning system, using BP artificial neural network method for the cost of the deviation of the dynamic monitoring, power transmission and transformation project cost target deviation classified warning model is established. In the case of a substation project, to make a validation method. The results show that the proposed method is feasible and can achieve dynamic monitoring and early warning of power transmission and transformation project cost target deviation.
As an important implementation subject of the national energy strategy, the power grid undertakes the important responsibility of optimizing the allocation of energy resources and serving economic and social development. With the rapid development of China's national economy, the power demand of the whole society is gradually increasing. Under the background of building a new power system, the investment demand of China's power grid is increasing. Accurate and effective prediction of power grid investment demand can not only help to coordinate the funds, reasonably arrange the investment of power grid construction funds and reduce economic risks, but also effectively improve the operation status of power grid enterprises and improve the investment income. Based on the internal and external environment and development status of power grid investment, this paper puts forward five influencing factors affecting investment demand, and uses SSA-LSSVM algorithm to predict the investment trend of h power grid from 2021 to 2023. The results show that the investment in h power grid will increase steadily year by year after 2020.
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