Research On Steel Industry Demand Management System Based On Situation Awareness And Leading

Many active electricity price policies have adopted by the electricity market,Encourage consumers electricity consumption patterns,to reduce grid operation and construction costs.with the rapid development of information technology,research on building user participation in power demand side management has become a hot area in recent years.Industrial users,represented by the steel industry,have been large consumers of electricity all the time.It is also urgent to innovate energy management systems to improve production efficiency,reduce energy consumption,and save costs.However,the current lack of research on the participation of industrial users in demand-side management has caused the steel industry users to face a difficult situation of outdated energy management technology and lack of research on key technologies.Therefore,for the steel industry that implements the new two-part tariff policy,the demand management system is adopted in this paper as the object,and the concept of an intelligent demand control system was designed in steel industry,so the research and application of advance information technology in energy management have been carried out.The main contents include:(1)Theresearch status of the current operating mode?load characteristics and demand-side management for industrial users is analyzed.The system composition of the steel industry and the processes of typical continuous casting processes is summarized by this article.Furthermore,the operating mode of the energy network and the load characteristics of the main equipment in each production link is analyzed by this paper,and the simply operating load characteristics mold of the ladle furnace has been build.At the same time,based on the time-of-use electricity price background,the day-to-day planning and scheduling of continuous casting process multi-run production was carried out by traditional methods.(2)The situational awareness and guidance theory is adopted in this paper,an intelligent demand management system framework is proposed,and deep learning is applied to demand situational awareness.To solve the problem of insufficient dynamic autonomy of traditional systems,an intelligent demand management system based on situational awareness and guidance is proposed.The vertical information interaction mode of the traditional system has been changed,and developed the information horizontal interaction framework based on the demand management system.Then the long-term and short-term memory network of deep learning(LTSM)is applied in the situation understanding and perception of ultra-short-term demand in the steel industry.The auto-correlation characteristics of demand and other data correlations is analyzed,and the loss function defined by using the peak value.The demand data of a steel plant is used to verified the multivariate LSTM demand forecasting model.(3)A demand optimization method based on conditional flexible load is proposed in the context of two-part electricity prices.A demand management method based on the flexible load in a typical continuous casting process based on the two-part electricity price background is proposed by this paper.The characteristics of main production load is analyzed qualitatively.Based on the establishment of the process flow Resource Task Network(RTN)model,fully consider the margins,power control constraints,equipment physical constraints,and scheduling costs under multiple power control modes for flexible loads,a method was designed in optimizing demand for flexible ladle furnace load.The effectiveness of the method is verified based on an example of a steel plant.It can effectively solve the problem of excessive peak demand,the economy is obvious,and it has no impact on refining tasks and other major production equipment other than scheduled tasks.(4)A practical application method of demand management system based on load behavior analysis modeling is proposed.This paper completes the behavior analysis and load modeling of the ladle furnace.Based on the established load model and behavior recognition method,combined with the theoretical research of this paper,the actual application of the demand management system was realized in Iron and Steel Plant.It realizes the identification of the operating characteristics of the load and the effective and reliable demand regulation of the ladle furnace.From the experimental results and actual operating effects of the various schemes,the advanced demand management system studied in this article can achieve the expected goals and achieve.With the realization of the theoretical research in this paper,the limitations of the outdated framework of the existing demand management system scheme have been broken.