Research On Energy Collaborative Optimization Method Of Smart Factory For Carbon Neutralization

At present,renewable energy,cogeneration technology and energy storage technology are widely used in carbon-neutral smart factory energy systems.However,the rigid coupling of electricity and heat brought by the operation of combined heat and power reduces the flexibility of energy coordination.The traditional operation mode of " determining electricity by heat " can no longer be applied to the current situation of high permeability of renewable energy,and the intermittence of renewable energy makes the stable operation of energy system impacted,resulting in the phenomenon of abandoning wind and light.In order to solve the above problems,this paper studies the coordinated operation of production capacity equipment,energy storage equipment,load side and inter-factory power coordination strategy.The main work is as follows :(1)Aiming at the uncertainty of electro-thermal coupling and renewable energy,gas-fired boilers and thermal energy storage equipment are introduced,and a day-ahead stochastic programming scheduling model for wind-solar-fuel-storage energy coordination is established based on scenario analysis technology.Multi-population Optimization Algorithm based on Migration Strategy(MAMPOA)is used to solve the problem.The algorithm uses Sobol sequence to generate uniformly distributed solutions,proposes a population migration strategy to improve population diversity,introduces nonlinear asymmetric factors and adaptive weights to balance the global and local search capabilities in the early and late stages.Experiments show that MA-MPOA has higher solution efficiency and convergence.The wind-solar-fuel-storage synergy can decouple the electro-thermal rigid coupling,improve the flexibility of the system,and reduce the low-carbon economic operation cost of energy synergy.(2)Aiming at the problem of low renewable energy consumption rate and stable operation caused by renewable energy fluctuation,a source-load collaborative multi-objective optimization model is established to minimize economic cost and environmental pollution cost and maximize renewable energy consumption rate.The improved non-dominated sorting genetic algorithm II based on Beetle Antennae Search(BAS-NSGA-II)is used to solve the problem.The algorithm introduces BAS to update NSGA-II,which can improve the ability of the population to jump out of the local optimum and ensure the global search ability of the algorithm in the later stage.Experiments show that BASNSGA-II has better convergence speed,and source-load coordination significantly improves the consumption rate of renewable energy and the flexibility of energy coordination.(3)Aiming at the problem that the overall renewable energy consumption capacity of multiple smart factories is low and it is difficult to achieve regional autonomy,an energy trading model of multiple smart factories is established based on periodic double auction.Aiming at the electric energy interaction between multiple smart factories,an auction model is established based on the constructed energy trading market architecture,a matching mechanism based on preference set is designed,a clearing strategy is determined,and the auction mechanism is verified to meet the economic attributes.Experiments show that the mechanism can achieve fair and efficient transactions between microgrids,and improve the consumption capacity of renewable energy and the economy of microgrid operation.