In order to improve the energy efficiency of the electric heating system

a particle swarm optimization (PSO

Particle Swarm Optimization)-based operation optimization strategy for the direct storage combined electric heating system is proposed.A mathematical model of influencing factors inside and outside the walls of electric heating buildings is established

and the simulink toolbox in matlab is used to build the overall system under the premise of determining the quantity of electric heating.Combining demand response ideas

the objective function is to establish the minimum heating and electricity cost of the user

and different sub-modules are selected to form the control module to achieve simulation verification

and the inverse cosine method is used to update the improved particle swarm algorithm to update the learning factor to solve the set objective function.Finally

through a calculation example of electricity consumption data of an enterprise in Jinan

Shandong

comparing energy consumption and economy can be obtained: the total energy consumption throughout the day is lower than the actual energy consumption

and the electricity bill is reduced by 17.16% compared with the unoptimized time.