线性离散系统最小信息损失动态状态估计原理及其在热网中的应用

Minimal Information Loss Estimation for the Discrete-time Linear Dynamic System and Its Application in the District Heating Network

综合能源系统作为未来能源系统的发展趋势,可以有效地助力我国“双碳”目标的实现。状态估计作为能量管理系统的基础核心,在综合能源系统领域变得日益重要。无论是热网还是气网,其动态过程的建模最后都可以转化为线性离散系统的形式。因此,该文从信息科学的视角对线性离散系统动态状态估计的理论基础进行研究。根据最小信息损失(minimuminformationloss,MIL)决策原理,提出通用的线性离散系统MIL动态状态估计新原理,建立MIL动态状态估计模型,分析不同时刻噪声相关性、不同噪声概率分布类型对该模型的影响,在理论上证明全信息滚动时域估计、多断面加权最小二乘、多断面加权最小绝对值等常用估计方法均为MIL动态状态估计在一定假设下的特例。进一步以热网为例对该原理进行验证,建立热网状态空间模型和热网MIL动态状态估计模型,对该模型在考虑不同噪声概率分布类型、不同时刻噪声相关性、预测信息等方面的普适性进行验证。

Integrated energy systems(IES)are the future shape of energy systems and play an important role in China’s aim of carbon neutrality.As the basic function of energy management systems,state estimation has become increasingly important in the research and the industrial field of IESs.The dynamics of the district heating networks(DHNs)and the natural gas networks can be transformed into a discrete-time linear model based on the finite difference method(FDM).Therefore,this paper explores the dynamic state estimation of discrete-time linear systems from the perspective of information science.According to the principle of minimum information loss(MIL)decision-making theory,a novel general MIL dynamic state estimation(DSE)model for discrete linear systems is proposed.The extended version under different assumptions of the measurement noise correlation and the probability distribution is deduced.It is proved that the moving horizon estimation,the multi-snapshot weighted least square state estimation,and the multi--snapshot weighted absolute value state estimation are special cases of the proposed MIL state estimation.Taking the DSE for the DHN as an example,the proposed MIL state estimation model is verified,and the generality of the model is tested.