The introduction of energy storage devices represents an important element for supporting the operation of distribution networks. Considering the presence of renewable energy sources, the use of these devices provides...
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ISBN:
(纸本)9781467388481
The introduction of energy storage devices represents an important element for supporting the operation of distribution networks. Considering the presence of renewable energy sources, the use of these devices provides ancillary services and energy balance flexibility to the network. This work presents a mixedintegersecondorderconeprogramming model for solving the problem of allocating energy storage devices in radial distribution networks. The model provides the optimal allocation of the devices in the grid as well as their optimal operating cycle in order to minimize system operational costs considering also the preservation and the life of these devices. The use of existing classical optimization tools and the conic model ensures convergence to the optimum operating point. To demonstrate the accuracy of the mathematical model developed and the efficiency of the solution technique, an 11-bus test system with the presence of renewable energy sources is used in the simulations.
We formulate mixed-integer conic approximations to AC transmission system planning. The first applies lift-and-project relaxations to a nonconvex model built around a semidefinite power flow relaxation. We then employ...
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We formulate mixed-integer conic approximations to AC transmission system planning. The first applies lift-and-project relaxations to a nonconvex model built around a semidefinite power flow relaxation. We then employ a quadratically constrained approximation to the DistFlow equations in constructing a second-ordercone model that is convex without relaxation. We solve mixedinteger linear and second-ordercone programs using commercial software and assess their performance on two benchmark problems. As with DC power flow models and linear AC relaxations, the new models usually produce solutions which are infeasible under the original constraints. However, they are nearer to feasibility, and therefore represent stronger alternatives.
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