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The model with random variable coefficients has been solved via the suggested method and it has been implemented on a numeric example.
In Ref. [5], the optimal operation of typical MG with RESs has been solved via adaptive modified particle swarm optimization (AMPSO).
The total operating cost including emission price and start-up cost has been taken in Ref. [15] as objective function for evaluating the optimum operation of MG; the problem has been solved via mesh adaptive direct search algorithm (MADSA).
Traditionally nonlinear inverse problems in discrete form have been solved via local optimization methods with regularization, but linear analysis techniques failed to account for the uncertainty in the solution that it is adopted.
Solution methods for single chance constraints in optimal power flow problems have been considered in the literature, ensuring single constraints are satisfied with a prescribed probability; however, joint chance constraints, ensuring multiple constraints are simultaneously satisfied, have predominantly been solved via scenario-based approaches or by utilizing Boole's inequality as an upper bound.
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can be solved via subgradient method.
The synthesis problem is solved via LMI.
Problem (2) can be solved via the standard AL method.
The proposed algorithm is solved via dynamic programming.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com