• Energy Research

This paper considers a smart grid cyber-security problem analyzing the vulnerabilities of electric power networks to false data attacks. The analysis problem is related to a constrained cardinality minimization problem. The main result shows that an $l_1$ relaxation technique provides an exact optimal solution to this cardinality minimization problem. The proposed result is based on a polyhedral combinatorics argument. It is different from well-known results based on mutual coherence and restricted isometry property. The results are illustrated on benchmarks including the IEEE 118-bus and 300-bus systems.

In this paper, the minimum cost measurement placement problem guaranteeing power system observability is considered. Contrary to the mainstream studies predominately focused on phasor measurement unit (PMU) placements, this paper considers the placement with traditional units including line power flows and bus power injections. The result in this work serves as a precursor for the placement problem with PMU and traditional measurements in full generality. The placement problem in this paper is formulated as a graph connectivity problem, which is either solved exactly as a mixed integer linear program or approximately via minimum spanning tree and weighted bipartite matching calculations. A numerical case study on IEEE power system benchmarks demonstrates the solution methodologies for the placement problem.

Abstract Generating minimum-time and secure software update schedules for controllable power loads in low-voltage distribution grids is a problem of increasing importance because of accelerating integration of renewable energy resources. In this paper, we call such a problem the software update rollout problem and present a mathematical framework for its modeling and solution. First, it is shown that this problem can be understood as a multi-resource bin packing problem. Then several approximate and exact solution schemes are discussed, the former using greedy approximate algorithms and the later using integer linear programming (ILP). These schemes are then evaluated on benchmark networks of realistic size (CIGRE-LV, TPC 83-bus distribution system). Experimental results show that both greedy and ILP approaches perform well for real-time purposes. In particular, the greedy approach can attain high-quality approximate solutions almost instantly while the ILP approach can not only provide solutions with certifiable optimality gaps but also include extra constraints (e.g., precedence) as needed.

In this paper the problem of finding the sparsest (i.e., minimum cardinality) critical $k$-tuple including one arbitrarily specified measurement is considered. The solution to this problem can be used to identify weak points in the measurement set, or aid the placement of new meters. The critical $k$-tuple problem is a combinatorial generalization of the critical measurement calculation problem. Using topological network observability results, this paper proposes an efficient and accurate approximate solution procedure for the considered problem based on solving a minimum-cut (Min-Cut) problem and enumerating all its optimal solutions. It is also shown that the sparsest critical $k$-tuple problem can be formulated as a mixed integer linear programming (MILP) problem. This MILP problem can be solved exactly using available solvers such as CPLEX and Gurobi. A detailed numerical study is presented to evaluate the efficiency and the accuracy of the proposed Min-Cut and MILP calculations.

In this paper a procedure to detect and isolate data attacks on power network power flow measurements is proposed. This method can be used in conjunction with available bad data detection (BDD) methods to isolate multiple bad data which are otherwise difficult to handle. The proposed procedure relies on secure measurements of bus voltage magnitudes to define a measurement residual using potentially compromised active and reactive power flow measurements on transmission lines. The proposed residual can be calculated in real-time. In addition, the component of the proposed residual on any particular line depends only locally on the component of the data attack on the same line. This makes the proposed residual well-suited for distributed data attack isolation in large-scale power networks. Furthermore, it can be shown that the proposed procedure becomes more effective when measurements from multiple time instances can be utilized. A detailed numerical case study on the IEEE 14-bus benchmark system demonstrates the effectiveness of the proposed procedure.

Batteries are the core component of electric vehicles (EVs) and energy storage systems (ESSs), being crucial technologies contributing to carbon neutrality, energy security, power system reliability, economic efficiency, etc. The effective operation of batteries requires precise knowledge of the state of health (SOH) of the battery. A lack of proper knowledge of SOH may lead to the improper use of severely aged batteries, which may result in degraded system performance or even a risk of failure. This makes it important to accurately estimate battery SOH using only operational data, and this is the main topic of this study. In this study, we propose a novel method for online SOH estimation for batteries featuring simple online computation and robustness against measurement anomalies while avoiding the need for full cycle discharging and charging operation data. Our proposed method is based on incremental capacity analysis (ICA) to extract battery aging feature parameters and regression using simple piecewise linear interpolation. Our proposed method is compared with back-propagation neural network (BPNN) regression, a popular method for SOH estimation, in case studies involving actual data from battery aging experiments under realistic discharging and temperature conditions. In terms of accuracy, our method is on par with BPNN results (about 5% maximum relative error), while the simplicity of our method leads to better computation efficiency and robustness against data anomalies.