Kin Cheong Sou

Scheduling smart home appliances using mixed integer linear programming

This paper considers the minimum electricity cost scheduling problem of smart home appliances. Operation characteristics, such as expected duration and peak power consumption of the smart appliances, can be adjusted through a power profile signal. The optimal power profile signal minimizes cost, while satisfying technical operation constraints and consumer preferences. Constraints such as enforcing uninterruptible and sequential operations are modeled in the proposed framework using mixed integer linear programming (MILP). Several realistic scenarios based on actual spot price are considered, and the numerical results provide insight into tariff design. Computational issues and extensions of the proposed scheduling framework are also discussed.

Electric power network security analysis via minimum cut relaxation

In this paper an efficient computation scheme for analyzing the security of power transmission networks is presented. In order to strategically allocate protection devices in the network, the problem of finding the sparsest stealthy false data attack to the state estimator is studied. While the attack search problem is traditionally solved as a generic constrained cardinality minimization problem, this paper exploits the problem structure intrinsic to the power network application to obtain a polynomial time approximate algorithm based on a minimum cut relaxation. Experiment results from realistic test cases are presented to demonstrate the efficiency and accuracy of the proposed algorithm.

Network-layer protection schemes against stealth attacks on state estimators in power systems

The power system state estimator is an important application used to calculate optimal power flows, to maintain the system in a secure state, and to detect faulty equipment. Its importance in the operation of the smart grid is expected to increase, and therefore its security is an important concern. Based on a realistic model of the communication infrastructure used to deliver measurement data from the substations to the state estimator, in this paper we investigate the vulnerability of the power system state estimator to attacks performed against the communication infrastructure. We define security metrics that quantify the importance of individual substations and the cost of attacking individual measurements. We provide efficient algorithms to calculate these metrics, and use the metrics to show how various network layer and application layer mitigation strategies can be used to decrease the vulnerability of the state estimator. We illustrate the efficiency of the algorithms on the IEEE 118 and 300 bus benchmark power systems.

A singular value decomposition based closed loop stability preserving controller reduction method

In this paper a controller reduction method which preserves closed loop stability is described. A Lyapunov inequality based sufficient condition is proposed in the search of the reduced controller. The reduced controller leads to a stable closed loop system with guaranteed approximation quality. Furthermore, the proposed problem can be formulated as a matrix approximation problem which can be solved efficiently using singular value decomposition. Numerical application examples are shown in the end to evaluate the generality of the proposed reduction method.

Detection and identification of data attacks in power system

In this paper a power system state estimator cyber-attack detection and identification scheme is presented. The proposed scheme considers the information from both the active power measurements and the reactive power measurements. Under the scenario that the network operator can take multiple different samples of measurements and the attackers can attack only once, the proposed scheme can detect the presence of what has been described as stealth (or unobservable) attacks. The detection is provably correct. Furthermore, if an attack is present, the proposed scheme can identify exactly the attacked transmission lines.

Controller reduction via minimum rank matrix approximation

In this paper a controller reduction method for discrete-time linear time-invariant systems is described. Using the bounded-real lemma, the proposed method generates reduced controllers with closed loop stability and H~ norm performance guarantee. Information of the full controller is used as a basis for reduction using singular value decomposition. This is different from traditional model reduction schemes such as weighted balanced truncation. Numerical assessment of the proposed method is given in the end of the paper.

Controller reduction with closed loop performance guarantee

This paper describes a modification of a singular value decomposition (SVD) based controller reduction method recently proposed in [13]. Instead of formulating a ℋ2 norm characterizing generalized controllability Gramian inequality as in the previous case, the current method applies the bounded-real lemma to certify the closed loop performances in ℋ∞ norm. In addition, unlike the previous method, the current one does not suffer from the lack of symmetry that all the data from the plant is not utilized. Yet, the current method inherits the same merit as the previous one that the formulated problem can be solved as a generalized minimum rank matrix approximation problem which can be solved efficiently using SVD. Extensions and numerical examples are shown in the end.

Frequency domain model reduction method for parameter-dependent systems

In this paper a modification of a recently proposed model simplification method for linear time invariant parameterized models is presented. It is able to obtain models with explicit parameter dependence. The method is based on convex optimization with semidefinite constraints. Computation of the reduced model requires only the frequency samples of the full one, hence it is possible to apply the methods to large-scale models. Numerical examples showing the drawbacks and the advantages of the method are also presented.