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A strengthened primal-dual decomposition algorithm for solving bilevel SCUC problem


IEEE Transactions on Energy Markets, Policy and Regulation


Management Science and Operations

Authors / Editors

Goudarzi H;Hemsamzadeh M R;Bunn D;Fotuhi-Firuzabad M


Publication Year



Efficient nodal pricing models and short-term unit commitment planning face continuous needs for improvement as operational requirements evolve. This paper develops a Bilevel Security-Constrained Unit Commitment (BL-SCUC) model to include both revenue-adequacy and Fast Frequency Reserve (FFR) constraints. The upper level of the BL-SCUC model represents the non-convex UC decisions as well as the revenue-adequacy constraints of the market participants (generators, loads, and battery-storage owner). The lower level is a convex economic dispatch model which produces the nodal electricity prices. To solve the proposed BL-SCUC model, it is first reformulated as a single-level Mixed-Integer Linear Program (MILP) using the standard strong-duality approach. The resulting MILP model is hard to solve using standard off-the-shelf solvers such as Cplex, partly because the Big-M parameters’ optimal tuning for linearization in the strong duality method is NP-hard. To solve this, we propose a strengthened Primal-Dual Decomposition (PDD) algorithm, which takes benefit from both Benders-like and Lagrange Dual-like algorithms. The new PDD algorithm eliminates the Big-M parameters without affecting optimal values. Accordingly, the computational burden and optimal solution sensitivity resulting from Big-M parameters are mitigated. Results from the modified IEEE 24-bus system demonstrate the effectiveness of the proposed BL-SCUC model with its PDD algorithm, whilst results from the IEEE 118-bus system show the superiority of the proposed strengthened PDD algorithm over the classic Benders algorithm.


Disjunctive programming; Primal-dual decomposition; SCUC; Revenue adequacy; Fast reserve

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