Yue Yang;Chenhui Lin;Luo Xu;Xiaodong Yang;Wenchuan Wu;Bin Wang
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引用次数: 0
Abstract
This letter proposes a structure-aware automatic differentiation method to accelerate the solution of alternating current optimal power flow (ACOPF) with nonlinear programming (NLP) solvers. By exploiting the isomorphic structure of nonlinear power flow constraints in ACOPF, specialized binary code is generated to efficiently compute the Jacobian and Hessian matrix. Numerical tests show that our implementation achieves over 18% speedup in the total solution process and 40% speedup in automatic differentiation for large-scale ACOPF problems compared to state-of-the-art algebraic modeling languages of NLP.
期刊介绍：
The scope of IEEE Transactions on Power Systems covers the education, analysis, operation, planning, and economics of electric generation, transmission, and distribution systems for general industrial, commercial, public, and domestic consumption, including the interaction with multi-energy carriers. The focus of this transactions is the power system from a systems viewpoint instead of components of the system. It has five (5) key areas within its scope with several technical topics within each area. These areas are: (1) Power Engineering Education, (2) Power System Analysis, Computing, and Economics, (3) Power System Dynamic Performance, (4) Power System Operations, and (5) Power System Planning and Implementation.
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