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Chance Constrained Optimal Power Flow Using the Inner-Outer Approximation Approach
In recent years, there has been a huge trend to penetrate renewable energy
sources into energy networks. However, these sources introduce uncertain power
generation depending on environmental conditions. Therefore, finding 'optimal'
and 'feasible' operation strategies is still a big challenge for network
operators and thus, an appropriate optimization approach is of utmost
importance. In this paper, we formulate the optimal power flow (OPF) with
uncertainties as a chance constrained optimization problem. Since uncertainties
in the network are usually 'non-Gaussian' distributed random variables, the
chance constraints cannot be directly converted to deterministic constraints.
Therefore, in this paper we use the recently-developed approach of inner-outer
approximation to approximately solve the chance constrained OPF. The
effectiveness of the approach is shown using DC OPF incorporating uncertain
non-Gaussian distributed wind power
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