Secure electric power grid operation
Author(s)Foo, Ming Qing
Massachusetts Institute of Technology. Computation for Design and Optimization Program.
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This thesis examines two problems concerning the secure and reliable operation of the electric power grid. The first part studies the distributed operation of the electric power grid using the power flow problem, which is vital to the operation of the grid. The power flow problem is a feasibility problem for finding an assignment of complex bus voltages that satisfies the power flow equations and is within operational and safety limits. For reliability and privacy reasons, it is desirable to solve the power flow problem in a distributed manner. Two novel distributed algorithms are presented for solving convex feasibility problems for networks based on the Method of Alternating Projections (MAP) and the Projected Consensus algorithm. These algorithms distribute computation among the nodes of the network and do not require any form of central coordination. The original problem is equivalently split into small local sub-problems, which are coordinated locally via a thin communication protocol. Although the power flow problem is non-convex, the new algorithms are demonstrated to be powerful heuristics using IEEE test beds. Quadratically Constrained Quadratic Programs (QCQP), which occur in the projection sub-problems, are studied and methods for solving them efficiently are developed. The second part addresses the robustness and resiliency of state estimation algorithms for cyber-physical systems. The operation of the electric power grid is modeled as a dynamical system that is supported by numerous feedback control mechanisms, which depend heavily on state estimation algorithms. The electric power grid is constantly under attack and, if left unchecked, these attacks may corrupt state estimates and lead to severe consequences. This thesis proposes a novel dynamic state estimator that is resilient against data injection attacks and robust to modeling errors and additive noise signals. By leveraging principles of robust optimization, the estimator can be formulated as a convex optimization problem and its effectiveness is demonstrated in simulations of an IEEE 14-bus system.
Thesis: S.M., Massachusetts Institute of Technology, School of Engineering, Center for Computational Engineering, Computation for Design and Optimization Program, 2015.Cataloged from PDF version of thesis.Includes bibliographical references (pages 87-91).
DepartmentMassachusetts Institute of Technology. Computation for Design and Optimization Program.
Massachusetts Institute of Technology
Computation for Design and Optimization Program.