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Projects > ELECTRICAL > 2017 > IEEE > POWER SYSTEMS
Power system operation is facing increasing cyber and physical attack risks, so it becomes pressing to develop effective methods to improve the robustness of electric power infrastructure in the presence of significant attacks. As it is not guaranteed that attacks can always be detected and thwarted before it causes disturbances and damages to the power systems, increased robustness can contribute to reducing the consequence of attacks. In this study, a holistic robustness framework is proposed by extending the conventional security-constrained optimal power flow (SCOPF) analysis to incorporate the risk caused by attacks. The corresponding solution methodology is proposed by combining particle swarm optimization and primal-dual interior point (PDIP) methods. Case studies conducted based on several test systems demonstrate that the proposed SCOPF model is able to reduce the consequence of attacks. This study can provide some insight into improving the power system operation robustness in the face of significant attacks.
Particle Swarm Optimization.
In this paper, the conventional SCOPF considering N-1 contingencies was extended to incorporate probable attack scenarios. The proposed SCOPF model considered the generation operation cost in the normal state, conventional N-1 contingencies as well as the risk of malicious attacks. The possible attack scenarios and their probabilities were incorporated into the objective function of the proposed SCOPF model. An improved solution method was investigated based on PSO for conducting the global search as well as on PDIP for finding the local SCOPF solution. And parallel computing for speeding up the calculation was used in this study. The mathematical model and the computational strategy were verified based on three representative test systems. The simulation results demonstrated that the proposed SCOPF model is able to provide increased robustness to the power grid in the face of predictable cyberattacks.
SCOPF FRAMEWORK