This study proposes a novel robusttransmission-constrainedunitcommitment model with adjustable conservatism (denoted as RAC-TCUC). Ellipsoidal uncertainty set (EUS) is adopted in this model to well fit the spatial-...
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This study proposes a novel robusttransmission-constrainedunitcommitment model with adjustable conservatism (denoted as RAC-TCUC). Ellipsoidal uncertainty set (EUS) is adopted in this model to well fit the spatial-temporal correlated wind power. The affine policy is utilised in the generation dispatch process of the model for the sake of computational tractability. To reduce the conservatism, a novel criterion for budget value selection of the EUS is presented. Moreover, this study discusses a crucial, yet barely addressed issue in the literature: the feasibility of the solution against the realisation of uncertainties beyond the prescribed EUS. To prove the validity of the criterion, an analytical relationship between the budget value of the EUS and the actual probability of solution's feasibility against all possible scenarios of uncertainties is presented. Finally, the testing results demonstrate the effectiveness and economic benefits of the proposed method.
With the rapid integration of power sources with uncertainty, robustness must be carefully considered in the transmissionconstrainedunitcommitment (TCUC) problem. The overall computational complexity of the robust ...
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With the rapid integration of power sources with uncertainty, robustness must be carefully considered in the transmissionconstrainedunitcommitment (TCUC) problem. The overall computational complexity of the robust TCUC methods is closely related to the vertex number of the uncertainty set. The vertex number is further associated with: (i) the period number in the scheduling horizon, (ii) the number of nodes with uncertain power injections. In this study, a column merging method (CMM) is proposed to reduce the computational burden by merging the uncertain nodes, while still guaranteeing the robustness of the solution. By the CMM, the transmission constraints are modified, with the parameters obtained based on an analytical solution of a uniform approximation problem, so that the computational time for obtaining the modified constraints is negligible. The CMM is applied under a greedy-algorithm-based framework, where the number of merged nodes and the approximation error can be well balanced. The CMM is designed as a preprocessing tool to improve the solution efficiency for robust TCUC problems and is compatible with many solution methods (like two-stage and multi-stage robust optimisation methods). Numerical tests show the method is effective.
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