State Estimation Methodology For Large-Scale Cyber-Physical Energy Systems

Cyber-physical systems(CPSs)are central to the "Made in China 2025" plan.With the advent of microsensor,communications,computer and computing,and information process-ing technologies,controlling and managing CPSs have recently gained substantial research interest.In particular,to intelligently sense the possibly dynamical physical process for CPSs in real time,advanced state estimation approaches and theory become a crucial prerequisite The Northern American power grids are regarded as the greatest engineering achievement in the 20th century,and is arguably the largest CPS on the earth,which is also known as a cyber-physical energy system,or CPES.Accurate monitoring of the grid's state is central to several system control and optimization tasks.This thesis puts forth a algorithmic framework for static and dynamic state estimation of cyber-physical energy systemsThe supervision control and data acquisition(SCADA)system installed in current CPESs can provide nonlinear measurements of the system state,which renders the state estimation(SE)problem nonconvex,and generally difficult to solve.Beginning with the static SE,this thesis develops several fast,scalable,yet efficient solvers,as well as the fundamental Cramer-Rao bound(CRB)under the additive white Gaussian noise(AWGN)model.The lat-ter can be broadly invoked to benchmark the performance of all unbiased algorithms.Further,dynamic SE approaches under modeled and unmodeled dynamics are discussed,and subop-timal solvers are pursued.Given the bulk real-time data communicated via a network,an event-triggered transmission scheme is devised to mitigate the network congestion(i.e.,time delay,data drop).Under both linear and nonlinear dynamic models,(suboptimal)estimators are proposed,and the effect of event-triggered transmission together with the data drops on stability of the estimator is characterized.Specifically,the ensuring topics are pursuedIn the envisioned CPES context,the present thesis first revisits the least-absolute-value SE from the vantage point of composite optimization.A couple of efficient yet robust SE solvers relying on the proximal-linear method are proposed.By leveraging the sparsity nat-ural to energy systems,acceleration is made possible by means of carefully mini-batching the SCADA data.Under suitable conditions,the accelerated approach enjoys only O(1)complexity independent of the system size,hence well-suited for large-scale CPESsUnder the AWGN model,leveraging the Wirtinger's calculus for complex analysis,this paper derives the fundamental CRB,which bridges the gap of missing judicious performance evaluation criterion between different SE algorithms in the literature.The established theory applies to and serves as a benchmark for all unbiased estimators.Building upon recent advances in nonconvex optimization,a new iterative algorithm called feasible point pursuit is advocated for nonlinear state estimation of CPESsDue to high penetration of renewables and wide participation of demand response pro-grams,future CPESs become increasingly uncertain and stochastic,rendering static SE insuf-ficient for tracking the fast-evolving system states.In this context,dynamic SE approaches are studied.With unmodeled dynamics,an online convex optimization based dynamic SE scheme is developed,which comes with strong performance guarantees.By explicitly mod-eling system dynamics,a moving-horizon estimation based SE method is devisedTo mitigate the network congestion in CPESs,this paper devises an event-triggered data transmission protocol for a linear approximate model.Intuitively,the "important" data will be reliably sent to the control center,while "less important" ones are sent with certain(or zero)probability.To ensure the scalability,a sequential Kalman filter variant is developed,whose near-optimality is proved under standard Gaussian approximation assumptions.Suffi-cient and necessary conditions ensuring the stability of the developed estimator are derivedTo capture the nonlinearity of CPESs,state estimation of nonlinear dynamic systems with data drops is investigated.A new extended filter is suggested,which trades of the stability and performance among existing approaches.By analyzing a suitably constructed Lyapunov function,the stochastic stability of the estimation error and the error covariance matrix is establishedComprehensive simulated tests corroborate the merits of the developed SE approaches as well as the correctness of the established theory.At last,this thesis is concluded with meaningful future directions.