Study Of Uncertain Problem And Robust Optimization For Load Scheduling Of A Smart Home

To improve the energy efficiency of the whole society,the user side is gradually transformed from a single energy consumption unit into a set of energy generation,storage and consumption as one of the two-way flow of energy nodes.As the user side has been given more and more functional requirements,the issues of demand response become more important.This paper studies the optimal load scheduling problem in a smart home,which mainly includes three aspects.At first,the household electric water heater(HEWH)load scheduling based on demand response in real time pricing tariffs is studied.A type of first-order explicit equation is utilized to describe the thermal dynamic modes of the HEWH.With such background,the HEWH load scheduling is addressed using linear integer programming.Then,the energy costs of different indistinctive adjusting schemes of comfort zone are discussed.For comparison's sake,the selective adjusting schemes with synthetic weight ranking method based on multi-dimensional information are also analyzed.Secondly,the household load scheduling of a smart home with photovoltaic system under different pricing mechanisms is analyzed.After a series of mathematical transformations,this problem is formulated as a nonlinear mixed integer programming model.The energy costs of a smart home with photovoltaic system under different pricing mechanisms and without photovoltaic system under flat pricing are compared.Simulation results indicate that the proposed methods are able to household load scheduling problem accurately and quickly.At last,this paper proposes a robust optimization based on interval tolerance degree for constraint violation.The uncertain parameters in home energy management are addressed with interval number.Correspondingly,the interval model of household load scheduling is formulated,which could be transformed into a deterministic linear optimization problem by the introduction of tolerance degree.Simulation results indicate that this method could obtain the robust scheduling schemes reliably.