Optimal Control Theory And Application Of House Energy

House energy optimization is an important part of power system demand side management,and it is an important part of smart grid optimization.It can effectively promote the development of smart grid to energy saving,high efficiency,low carbon environmental protection and sustainable development.However,with the access of distributed generators,dynamic energy storage devices,and dynamic loads,and with the improving requirements of the optimizations from user,house energy optimization confronts new problems.On the one hand,the sampling period is shorten to raise the precision of model.This leads to the increase of control sequence,that is,the time dimension of control becomes high,which brings great pressure to optimization algorithms.On the other hand,because of the uncertainties of power demand from user,dynamic price,and the the external environment,house energy optimization contains a large number of uncertain parameters,and the dynamic model of house is difficult to describe precisely.The effectiveness of optimization result is dubiously in practical applications.Therefore,in order to find new methods to solve the new problems,this paper conducts the researches from multi-time scale optimization and adaptive dynamic programming with the energy characteristics of smart house are considered.The main contents of this paper are as follows:Firstly,a method of solving house energy optimization based on multi-time scale is studied.Aiming at the high dimension of time in house energy optimization,a multi-time scale method which transforms the high dimensional single-time scale optimization into low dimensional multi-time scale optimizations is studied to improve the speed of optimization.The studies include the model,performance index function,and constraint in multi-time scale,the equivalent conditions of optimization transformation,and a fast optimization algorithm without iteration for the transformed optimization.Secondly,a fast algorithm based on adaptive dynamic programming is studied to guarantee convergence precision with finite iterations.Adaptive dynamic programming avoids the pressure of computation caused by the high dimension of time,solves dynamic optimizations forward by time.It is a powerful tool to solve the above problems.In this paper,a adaptive dynamic programming algorithm based on value iteration is studied.The convergence condition of value iteration is given with convergence proof.Then,a cooperative optimization algorithm based on value iteration is proposed according to the condition.There are multiple value iterations in the algorithm,each value iteration updates its iterative performance index function through cooperative mechanism.Thirdly,the method of solving house energy optimization based on adaptive dynamic programming is studied.The model and performance index function are certain in current theory researches of adaptive dynamic programming.However,the performance index function and the model of house energy optimization may contain uncertain parameters.According to the characteristics of parameters in households,two house energy optimization are studied,dynamic models are given,a new adaptive dynamic programming algorithm is presented and studies of stability,convergence,and optimality are shown in the paper.Finally,the full text is summarized and the research direction in the future is prospected.