Research On Industrial Energy Optimization Scheduling And Power Supply Capacity Planning For Green Production

Saving energy and reducing greenhouse gas emissions have become the consensus of all mankind to solve energy shortages and environmental degradation problems.China is a big manufacturing country.It is very urgent and very meaningful to explore how to achieve energy conservation and reduce carbon emissions in the manufacturing sector.Energy dispatching,especially for industrial enterprises,is a key to achieving green production and energy saving targets.Considering that industrial enterprises adopt both power supply and self-generation,industrial enterprises can rationally plan the production arrangements and flexibly adjust the power supply ratio of self-generation and power grid,so as to achieve the purpose of energy saving,emission reduction and green production.This paper studies the energy optimization scheduling problems of big iron and steel enterprises.By simultaneously optimizing production scheduling and energy supply scheduling,it is expected to lower down the energy costs and cut down the emissions on the premise of finishing all tasks on time.Here are the main works of this paper:In view of the local optimal results of single scheduling,this paper proposes to combine them to achieve unified optimization scheduling.Under the time-of-use electricity policy,the peak-to-valley price difference can be used to rationally arrange production tasks and energy scheduling,so as to reduce electricity costs.Besides,with the tradable carbon emission permits,the model is established to minimize the carbon trading cost and energy cost.The model can works well with small scale problems.For the large scale problems,an optimization algorithm is proposed to solve the problem quickly and efficiently.Considered in the long run,the capacity allocation of energy system is studied to ensure the economy and environmental friendliness of the system.As the experiments show,compared to the single scheduling,the model we proposed can lower the electricity costs and cut down carbon emissions with consideration of carbon trading.The algorithm we adopted can solve the model quickly and get better results.At last,the study on capacity planning also indicates that proper capacity of energy system can help lowering energy costs.