An Improved Spatial Branch-and-Bound Algorithm for Non-Convex Optimal Electricity-Gas Flow

Addressing non-convexity plays a fundamental role in solving the optimal electricity-gas flow models. In this paper, an improved spatial branch-and-bound algorithm is proposed to solve the non-convex problem, which is formulated as a mixed-integer bilinear programming, for its exact solution. The co...

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Bibliographic Details
Published in:IEEE transactions on power systems Vol. 37; no. 2; pp. 1326 - 1339
Main Authors: Liu, Pengxiang, Wu, Zhi, Gu, Wei, Lu, Yuping
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Approximation algorithms
branching strategy
Compressors
Computational geometry
Continuity (mathematics)
Convexity
Electricity
Exact solutions
Gas flow
Manganese
Mathematical model
Mixed integer
mixed-integer bilinear programming
non-convex optimization
Optimal electricity-gas flow
Optimization
Pipelines
Programming
spatial branch-and-bound
ISSN:0885-8950, 1558-0679
Online Access:Get full text
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Summary:Addressing non-convexity plays a fundamental role in solving the optimal electricity-gas flow models. In this paper, an improved spatial branch-and-bound algorithm is proposed to solve the non-convex problem, which is formulated as a mixed-integer bilinear programming, for its exact solution. The core of the algorithm is to divide the non-convex model into convex and small sub-models by branching on specific continuous variables, so that the non-convex problem can be equivalent to a rooted tree for exploration. The exactness of the algorithm is guaranteed by the same criterion as the classical branch-and-bound algorithm. To alleviate the computational burden, a novel two-stage spatial branching strategy is developed to improve the effectiveness and efficiency of the branching operations. The performance of the proposed algorithm is verified on two integrated electricity-gas systems with different sizes. Numerical results demonstrate that our method achieves a balance among feasibility, optimality, and efficiency. The comparison with another 6 convexification-based methods, 3 state-of-the-art non-convex optimization solvers, and 2 spatial branch-and-bound algorithms with classical branching rules further shows the superiority of our algorithm.
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2021.3101883