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Towards the Robust Situation Awareness in Distribution Management System
Title
Towards the Robust Situation Awareness in Distribution Management System
Creator
Yao, Yiyun
Date
2019
Description
In distribution systems, intermittent distributed energy resources (DERs) and vol-atile loads will result in a wide variation of system...
Show moreIn distribution systems, intermittent distributed energy resources (DERs) and vol-atile loads will result in a wide variation of system operating conditions. This motivates the establishment of modern distribution management system (DMS) for real-time net-work monitoring, resource optimization, and demand management. Three subproblems are mainly discussed when establishing the robust situation awareness in DMS. A measurement placement problem is proposed to decide the optimal locations and types of measurements to be placed in the distribution systems that minimize the worst-case estimation errors for DSSE over different system operating conditions. Four indices of the estimation error covariance matrix are chosen as the criteria of accuracy. The proposed measurement placement problem is formulated as a mixed-integer sem-idefinite programming (MISDP) problem. To avoid the combinatorial complexity, a con-vex relaxation, followed by a local optimization method, is employed to solve the MISDP problem. The proposed problem and the effectiveness of the proposed solution method are numerically demonstrated on the 33-bus distribution system.Distribution system state estimation (DSSE) is one of the vital components in the next-generation distribution management system (DMS), which allows the operators to monitor the entire system’s operating conditions. Due to the lack of real-time measurements, DSSE has to process measurements whose quality varies significantly across different sources, which causes convergence issue to the Gauss-Newton solver. In this chapter, a semidefinite programming (SDP) framework is developed to reformulate the DSSE problem into a rank- constrained SDP problem. One challenge of this technique is the nonconvex rank-one constraint, which is generally relaxed. However, the relaxed SDP-DSSE problem cannot guarantee a rank-one solution and hence lose optimality. Therefore, we propose two solution approaches, namely the rank reduction approach and the convex iteration approach, to obtain rank-one solutions for the SDP-DSSE problem. The proposed model and the effectiveness of the proposed solution approaches are numerically demonstrated on the IEEE 13-, 34-bus, and 123-bus distribution systems.A SE algorithm based on random measurements selection, which is inspired by the concept of moving target defense (MTD), is developed to prevent and mitigate stealthy cyber-attacks. With the proposed SE, a library of selected measurements scenarios is first generated offline given the available measurements and network topology. During online operation, multiple weighted least square (WLS) based SEs are processed in parallel with randomly picked scenarios from the library. The final solution is selected based on the largest normalized residuals with regard to individual scenarios. The effectiveness of the proposed SE is examined by attack-defense experiments on IEEE 14-bus, 39-bus, 57-bus, and 118-bus systems.
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