Observability analysis (OA) is vital to obtaining the available input measurements of state estimation (SE) in an integrated electricity and heating system (IEHS). Considering the thermal quasi-dynamics in pipelines, the measurement equations in heating systems are dependent on the estimated results, leading to an interdependency between OA and SE. The conventional OA methods require that the measurement equations be known exactly before SE is performed, and they are not applicable to IEHSs. To bridge this gap, a scenario-based OA scheme for IEHSs is devised that yields reliable analysis results for a predefined set of time-delay scenarios to cope with this interdependency. As its core procedure, the observable state identification and observability restoration are formulated in terms of integer linear programming. Numerical tests are carried out to demonstrate the validity and superiority of the proposed formulation.

Line-commutated converter (LCC)-based high-voltage DC (HVDC) systems have been integrated with bulk AC power grids for the interregional transmission of renewable power generation. The nonlinear HVDC model brings additional nonconvexity to the optimal power flow (OPF) of hybrid AC-DC power grids. The convex relaxation of such an OPF model could be a remedy, which has been discussed by little work. In this paper, a convexification method for the OPF of LCC-based AC-DC power grids is proposed using second-order cone programing relaxation. A sufficient condition for the exactness of the proposed convex relaxation is proposed and proven. The properties, exactness and efficiency of the proposed model are verified in three numerical simulations. The simulation results demonstrate that a globally optimal solution of the original OPF can be obtained from the proposed relaxed model with high computational efficiency.