Convex Optimization Based Distributed Optimal Gas-Power Flow Calculation

Wang, Cheng; Wei, Wu; Wang, Jianhui; Bai, Linquan; Liang, Yile; Bi, Tianshu

doi:10.1109/tste.2017.2771954

Cited by 138 publications

(58 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…References [5]- [7], [15]- [20] propose different piecewise linear approximation methods to solve the non-convex and nonlinear IEGS optimization problem. These methods mainly include the following types, metaheuristic algorithm [5], [6], second-order cone (SOC) relaxation [15], [16], and piecewise linear approximation [7], [17]- [20] etc. References [18], [21] present the study of the coordinated scheduling strategy, in which, the models of the electricity network and gas network are developed in detail, and the operation constraints of the networks are fully considered.…”

Section: Nwmentioning

confidence: 99%

Two-Layer Optimization Scheduling Model of Integrated Electricity and Natural Gas Energy System Considering the Feasibility of Gas-Fired Units’ Reserve

Zhou

Bao

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The reserve scheduling is important for integrated energy systems to cope with sudden disturbances such as fluctuations of renewable energy. The existing reserve scheduling approaches for integrated energy systems need iterations of calculation to guarantee the feasibility of the gas-fired units' reserve, leading to problem of high computational burden. In this paper, a two-layer optimization model is proposed to set the boundary of the gas-fired units' power and reserve, so that the scheduling result can meet the gas network constraints. The proposed two-layer optimization framework can avoid repeated loop iterations, resulting in low computational complexity. Testing results verify the effectiveness of the proposed method.

show abstract

Section: Nwmentioning

confidence: 99%

Two-Layer Optimization Scheduling Model of Integrated Electricity and Natural Gas Energy System Considering the Feasibility of Gas-Fired Units’ Reserve

Zhou

Bao

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…x are the gas pressure and gas flow at the time of ij of pipe x, M 1 , M 2 are the pipe transfer constants. In reference [27], the Euler finite difference method is used to transform the partial differential equation 3, which is difficult to be solved directly into an algebraic equation. The pipe ij is divided into N segments, and the length of each segment x ij = l ij /N , the equation is expressed as follows:…”

Section: Natural Gas Network Modelmentioning

confidence: 99%

“…After the above transformation, formula (22) is convex constrained and (23) is still nonconvex. The convex relaxation of formula (23) is performed by using the sequential second-order cone programming method presented in the reference [27]. If the quadratic term is linearly expanded by the first order Taylor series, then expression (23) can be approximated as follows:…”

Section: A Pccp Algorithmmentioning

confidence: 99%

Distributed Optimal Dispatching of Interconnected Electricity-Gas-Heating System

Zhu

Chen

et al. 2020

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

“…However, in reality, electricity and NG subsystems are generally owned by different utilities [16,17]. If the centralised optimisation method is used, a system operator is required to collect all the information of electricity and NG subsystems for centralised decision-making, which cannot guarantee the information privacy and decisionmaking independence of each subsystem [17][18][19]. Therefore, it is a favourable choice to make dispatch decisions in a decentralised manner.…”

Section: Introductionmentioning

confidence: 99%

ADP‐based decentralised algorithm for the optimal energy flow of the electricity–natural gas system

Zhu

Guo

et al. 2020

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

Convex Optimization Based Distributed Optimal Gas-Power Flow Calculation

Cited by 138 publications

References 35 publications

Two-Layer Optimization Scheduling Model of Integrated Electricity and Natural Gas Energy System Considering the Feasibility of Gas-Fired Units’ Reserve

Two-Layer Optimization Scheduling Model of Integrated Electricity and Natural Gas Energy System Considering the Feasibility of Gas-Fired Units’ Reserve

Distributed Optimal Dispatching of Interconnected Electricity-Gas-Heating System

ADP‐based decentralised algorithm for the optimal energy flow of the electricity–natural gas system

Contact Info

Product

Resources

About