TY - JOUR
T1 - A Two-Stage Decomposition Approach for AC Optimal Power Flow
AU - Tu, Shenyinying
AU - Wachter, Andreas
AU - Wei, Ermin
N1 - Funding Information:
This work was supported by Leslie and Mac McQuown. The work of Andreas Wachter was supported in part by National Science Foundation under Grant DMS-1522747 and in part by Los Alamos National Laboratory, its Center for Nonlinear Studies, and its Ulam Scholarship program. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. Department of Energy (Contract No. 89233218CNA000001) under the auspices of the NNSA of the U.S. DOE at LANL under Contract No. DE- AC52- 06NA25396. Paper no. TPWRS-01913-2019.
Publisher Copyright:
© 1969-2012 IEEE.
PY - 2021/1
Y1 - 2021/1
N2 - The alternating current optimal power flow (AC-OPF) problem is critical to power system operations and planning, but it is generally hard to solve due to its nonconvex and large-scale nature. This paper proposes a scalable decomposition approach in which the power network is decomposed into a master network and a number of subnetworks, where each network has its own AC-OPF subproblem. This formulates a two-stage optimization problem and requires only a small amount of communication between the master and subnetworks. The key contribution is a smoothing technique that renders the response of a subnetwork differentiable with respect to the input from the master problem, utilizing properties of the barrier problem formulation that naturally arises when subproblems are solved by a primal-dual interior-point algorithm. Consequently, existing efficient nonlinear programming solvers can be used for both the master problem and the subproblems. The advantage of this framework is that speedup can be obtained by processing the subnetworks in parallel, and it has convergence guarantees under reasonable assumptions. The formulation is readily extended to instances with stochastic subnetwork loads. Numerical results show favorable performance and illustrate the scalability of the algorithm which is able to solve instances with more than 11 million buses.
AB - The alternating current optimal power flow (AC-OPF) problem is critical to power system operations and planning, but it is generally hard to solve due to its nonconvex and large-scale nature. This paper proposes a scalable decomposition approach in which the power network is decomposed into a master network and a number of subnetworks, where each network has its own AC-OPF subproblem. This formulates a two-stage optimization problem and requires only a small amount of communication between the master and subnetworks. The key contribution is a smoothing technique that renders the response of a subnetwork differentiable with respect to the input from the master problem, utilizing properties of the barrier problem formulation that naturally arises when subproblems are solved by a primal-dual interior-point algorithm. Consequently, existing efficient nonlinear programming solvers can be used for both the master problem and the subproblems. The advantage of this framework is that speedup can be obtained by processing the subnetworks in parallel, and it has convergence guarantees under reasonable assumptions. The formulation is readily extended to instances with stochastic subnetwork loads. Numerical results show favorable performance and illustrate the scalability of the algorithm which is able to solve instances with more than 11 million buses.
KW - Optimal power flow
KW - decomposition
KW - distribution networks
KW - primal-dual interior point method
KW - smoothing technique
KW - stochastic optimization
KW - transmission networks
KW - two-stage optimization
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U2 - 10.1109/TPWRS.2020.3002189
DO - 10.1109/TPWRS.2020.3002189
M3 - Article
AN - SCOPUS:85099355781
VL - 36
SP - 303
EP - 312
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
SN - 0885-8950
IS - 1
M1 - 9115808
ER -