Activate the Dual Cones: A Tight Reformulation of Conic ACOPF Constraints
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866912976533979136 |
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| author | Rafiei, Saba Chevalier, Samuel |
| author_facet | Rafiei, Saba Chevalier, Samuel |
| contents | By exploiting the observed tightness of dual rotated second-order cone (RSOC) constraints, this paper transforms the dual of a conic ACOPF relaxation into an equivalent, non-conic problem where dual constraints are implicitly enforced through eliminated dual RSOC variables. To accomplish this, we apply the RSOC-based Jabr relaxation of ACOPF, pose its dual, and then show that all dual RSOC constraints must be tight (i.e., active) at optimality. We then construct a reduced dual maximization problem with only non-negativity constraints, avoiding the explicit RSOC inequality constraints. Numerical experiments confirm that the tight formulation recovers the same dual objective values as a mature conic solver (e.g., MOSEK via PowerModels) on various PGLib benchmark test systems (ranging from 3- to 1354-buses). The proposed formulation has useful performance benefits, compared with its conic counterpart, and it allows us to define a bounding function which provides a guaranteed lower bound on system cost. While this paper focuses on demonstrating the correctness and validity of the proposed structural simplification, it lays the groundwork for future GPU-accelerated first-order optimization methods which can exploit the unconstrained nature of the proposed formulation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_20411 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Activate the Dual Cones: A Tight Reformulation of Conic ACOPF Constraints Rafiei, Saba Chevalier, Samuel Systems and Control Optimization and Control By exploiting the observed tightness of dual rotated second-order cone (RSOC) constraints, this paper transforms the dual of a conic ACOPF relaxation into an equivalent, non-conic problem where dual constraints are implicitly enforced through eliminated dual RSOC variables. To accomplish this, we apply the RSOC-based Jabr relaxation of ACOPF, pose its dual, and then show that all dual RSOC constraints must be tight (i.e., active) at optimality. We then construct a reduced dual maximization problem with only non-negativity constraints, avoiding the explicit RSOC inequality constraints. Numerical experiments confirm that the tight formulation recovers the same dual objective values as a mature conic solver (e.g., MOSEK via PowerModels) on various PGLib benchmark test systems (ranging from 3- to 1354-buses). The proposed formulation has useful performance benefits, compared with its conic counterpart, and it allows us to define a bounding function which provides a guaranteed lower bound on system cost. While this paper focuses on demonstrating the correctness and validity of the proposed structural simplification, it lays the groundwork for future GPU-accelerated first-order optimization methods which can exploit the unconstrained nature of the proposed formulation. |
| title | Activate the Dual Cones: A Tight Reformulation of Conic ACOPF Constraints |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2603.20411 |