Tuning successive linear programming to solve AC optimal power flow problem for large networks
Successive linear programming (SLP) is a practical approach for solving largescale nonlinear optimization problems. Alternating current optimal power flow (ACOPF) is no exception, particularly the large size of realworld networks. However, in order to achieve tractability, it is essential to tune...
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Successive linear programming (SLP) is a practical approach for solving largescale nonlinear optimization problems. Alternating current optimal power flow (ACOPF) is no exception, particularly the large size of realworld networks. However, in order to achieve tractability, it is essential to tune the SLP algorithm presented in the literature. This paper presents a modified SLP algorithm to solve the ACOPF problem, specified by the U.S. Department of Energy’s (DOE) Grid Optimization (GO) Competition Challenge 1, within strict time limits. The algorithm first finds a nearoptimal solution for the relaxed problem (i.e., Stage 1). Then, it finds a feasible solution in the proximity of the nearoptimal solution (i.e., Stage 2 and Stage 3). The numerical experiments on test cases ranging from 500bus to 30,000bus systems show that the algorithm is tractable. The results show that our proposed algorithm is tractable and can solve more than 80% of test cases faster than the wellknown Interior Point Method while significantly reduce the number of iterations required to solve ACOPF. The number of iterations is considered an important factor in the examination of tractability which can drastically reduce the computational time required within each iteration.
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Format: 
Electronic Article

Language: 
English

Physical Description: 
OnlineRessource

DOI / URN: 
10.1016/j.ijepes.2021.107807
