Bender's decomposition algorithm for model predictive control of a modular multi-level converter

Ma, Minyue; Fan, Lingling

doi:10.1109/naps.2017.8107218

Cited by 1 publication

(2 citation statements)

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“…In this paper, we propose a new strategy to improve the initial point, which is different from the conventional method of selecting zero point as the initial point. For the first iteration, we ignore formula (24) and construct the second-order cone programming (SOCP) model in order to find out the excellent initial point. After each iteration of ADMM, the output value of gas turbine and P2G is directly selected as the initial iteration point of the next iteration, which can ensure the speed and quality of the solution.…”

Section: A Pccp Algorithmmentioning

confidence: 99%

“…As a weakly-centralized computing technology, distributed computing can effectively solve the problem of information privacy. Distributed computing methods mainly include the principle method of auxiliary problems [19], [20], alternating direction method of multiplies algorithm [20]- [23], Benders decomposition algorithm [24], [25], etc. The ADMM algorithm is an important method for solving separable convex optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Optimal Dispatching of Interconnected Electricity-Gas-Heating System

Zhu

Chen

et al. 2020

IEEE Access

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In the researching background of integrated energy system, a single electricity-gas-heating system (EGHS) can be regarded as an active producer. In order to solve the joint optimization of integrated energy systems under the condition of incomplete information, this paper proposes a distributed optimal scheduling framework of EGHS. First, establish a coupling model of the interconnected EGHS, and perform strict second-order cone convexity of the complex natural gas flow model. Next, use the bus splitting method to realize the decoupling between different regions of the interconnected system, and employ the alternating direction multiplier method (ADMM) to solve the model. Then, construct two-region energy system (78-node grid + 40-node gas grid + 40-node heat grid) and three-region energy system (117-node grid + 60 node gas grid + 60-node heat grid) as simulation examples to verify the effectiveness of the distributed optimization framework. In the end, the algorithm solution process, the effectiveness of scheduling results, and the comparison of optimization results under different interconnection methods are analyzed in detail. INDEX TERMS Electricity-gas-heating system, alternating direction method of multiplies, distributed optimal scheduling, second-order cone programming.

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Section: A Pccp Algorithmmentioning

confidence: 99%