Analytical model of power system hardening planning for long-term risk reduction

Hardening components is an effective way to decrease the load loss risk caused by component outages in power systems. The system hardening against the long-term risk usually faces the computation burden problem brought up by the risk evaluation. This paper proposes a deterministic hardening planning model to minimize the long term system risk of load curtailment, which eases the computational cost concern. To accomplish this task, a set of system events with load loss and corresponding load curtailments are obtained by an improved Monte Carlo simulation. An analytical function of system risk index with respect to the unavailability of components is derived and can calculate the risk index rapidly. Then, the deterministic model is constructed by applying the analytical function into the stochastic problem of system hardening planning. The greedy algorithm is developed to solve the model. The proposed model and method improve the computation efficiency by avoiding the repeated time-consuming risk evaluations. Case studies are conducted on the RBTS, RTS79, and a modified RTS96 system. Performances of the proposed model and method are compared with that of the conventional reliability optimization method (CROM). Results demonstrate that the model obtains better solution than the existing study on hardening planning for long-term risk, and has higher computation efficiency than CROM.