This paper presents a mixed-integer LP approach to the solution of the long-term transmission expansion planning problem. In general, this problem is large-scale, mixed-integer, nonlinear, and nonconvex. We derive a mixed-integer linear formulation that considers losses and guarantees convergence to optimality using existing optimization software. The proposed model is applied to Garver's 6-bus system, the IEEE Reliability Test System, and a realistic Brazilian system. Simulation results show the accuracy as well as the efficiency of the proposed solution technique. Index Terms-Linearized power flow, mixed-integer linear programming, power loss modeling, transmission expansion planning.

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We consider here the problem of detecting changes in the status of switching devices, circuit breakers in particular, in distribution networks. The lack of measurements in distribution networks compared to transmission networks is the main challenge of this problem. Using expected values of power consumption, and their variance, we are able to quickly calculate the confidence level of identifying the correct topology, or the current status of switching devices, using any given configuration of real time measurements. This allows to compare between different configurations in order to select the optimal one. The main approach we propose relies on approximating the measurements as normal distributed random variables, and applying the maximum likelihood principle. We also discuss an alternative based on support vectors. Results are demonstrated using the IEEE 123 buses distribution test case.

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