The Research Of Home Energy Management Considering Harmonic Detection

Home energy management is the expansion of demand side response,real-time price is the most effective adjustment lever of home energy management.We can use the improved particle swarm algorithm to solve the optimal electricity planning,which can guide the users reduce electricity costs at a higher price period,while helping grid company to peak load shifting.There is a problem of over-harmonics in residential electricity consumption,and it is necessary to improve the accuracy of harmonic detection to analyze the harmonic of household appliances and reduce the risk of harmonic over-standard.A new window function: Hanning self-multiplying window,is proposed to solve the harmonic problem generated by household load and photovoltaic power generation.Combining four-spectrum-line interpolation algorithm,a four-spectrumline interpolation FFT algorithm based on Hanning self-multiplying window is proposed.Comparision for simulated harmonic signals with other algorithms and this algorithm are carried out by simulation.The harmonic analysis of thermoelectric secondary current in Nanjing Chemical Industry Park shows that the algorithm can improve the accuracy of harmonic detection by 0.5%.This article makes improvements about particle swarm optimization algorithm,and the improved particle swarm optimization algorithm,the firefly algorithm,cuckoo algorithm and VS algorithm are used for 55 test function optimization test,and conclude that the algorithm has a stronger optimization ability;for household electricity management problems,we have defined the importance of household load,and the mathematical model of household electricity consumption management with and without consideration of user's electric comfort are established.The improved particle swarm algorithm is used to solve the optimal electricity consumption scheme of the two models respectively.The simulation results show that the average household electricity consumption can be reduced by 18.1% by using the improved particle swarm algorithm to solve the household electricity consumption management model.