Relaxed convex model for optimal location and sizing of DGs in DC grids using sequential quadratic programming and random hyperplane approaches

Montoya, Oscar Danilo; Gil-González, Walter; Grisales-Noreña, Luis Fernando

doi:10.1016/j.ijepes.2019.105442

Cited by 27 publications

(28 citation statements)

References 49 publications

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“…The 21-node test feeder is a DC distribution network that is composed of 21 nodes and 20 branches that generates a radial configuration where the voltage-controlled source is connected at node 1. This source supports a voltage profile of 1.00 pu [19]. The complete information regarding constant power consumption and branches are presented in Figure 1 and Table 1, respectively [19].…”

Section: -Node Test Feedermentioning

confidence: 78%

“…This source supports a voltage profile of 1.00 pu [19]. The complete information regarding constant power consumption and branches are presented in Figure 1 and Table 1, respectively [19]. Note that all of the values in Table 1 are calculated when considering 1 kV and 100 kW as the voltage and power bases, respectively.…”

Section: -Node Test Feedermentioning

confidence: 90%

“…This analysis is known as tertiary control [13], which addresses power flow analysis [2,14], optimal power flow [15], economic dispatch strategies [16], and voltage stability analysis [17,18]. Furthermore, these analyses are integrated with other problems, such as the placement and sizing of multiple distributed generators (DGs) [19], as well as placement and operation of battery energy storage systems in DC networks [20].…”

Section: Introductionmentioning

confidence: 99%

“…In [23], a probabilistic optimization methodology for estimating uncertainties in loads, plug-in electric vehicles, and renewable generation in DC distribution networks was analyzed. In [19], a hybrid strategy for optimal location and dimensioning of DGs in DC grids was provided, which mixed a hyperplanes search method (HSM) with semidefinite programming (SDP) model. The HSM relaxed the binary variables, while the SDP computed the optimal power flow.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks

2020

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This research addresses the problem of the optimal location and sizing distributed generators (DGs) in direct current (DC) distribution networks from the combinatorial optimization. It is proposed a master–slave optimization approach in order to solve the problems of placement and location of DGs, respectively. The master stage applies to the classical Chu & Beasley genetic algorithm (GA), while the slave stage resolves a second-order cone programming reformulation of the optimal power flow problem for DC grids. This master–slave approach generates a hybrid optimization approach, named GA-SOCP. The main advantage of optimal dimensioning of DGs via SOCP is that this method makes part of the exact mathematical optimization that guarantees the possibility of finding the global optimal solution due to the solution space’s convex structure, which is a clear improvement regarding classical metaheuristic optimization methodologies. Numerical comparisons with hybrid and exact optimization approaches reported in the literature demonstrate the proposed hybrid GA-SOCP approach’s effectiveness and robustness to achieve the global optimal solution. Two test feeders compose of 21 and 69 nodes that can locate three distributed generators are considered. All of the computational validations have been carried out in the MATLAB software and the CVX tool for convex optimization.

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Section: -Node Test Feedermentioning

confidence: 78%

Section: -Node Test Feedermentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks

2020

Self Cite

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“…SQPM is known as a local search scheme and it has been implemented to solve constrained/unconstrained models. In recent years, SQPM has been implemented in the sizing and location of DGs [ 41 ], optimal gait based on bipedal robots through nonlinear system of predictive control [ 42 ], optimal organization of directional overcurrent communication incorporating spread generation [ 43 ], second order prediction differential system [ 44 ], central air-conditioning optimization [ 45 ], and flight vehicle management [ 46 ].…”

Section: Methodology: Ann-ga-sqpmmentioning

confidence: 99%

Artificial Neural Networks to Solve the Singular Model with Neumann–Robin, Dirichlet and Neumann Boundary Conditions

Nisar

Sabir

Raja

et al. 2021

Sensors

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The aim of this work is to solve the case study singular model involving the Neumann–Robin, Dirichlet, and Neumann boundary conditions using a novel computing framework that is based on the artificial neural network (ANN), global search genetic algorithm (GA), and local search sequential quadratic programming method (SQPM), i.e., ANN-GA-SQPM. The inspiration to present this numerical framework comes through the objective of introducing a reliable structure that associates the operative ANNs features using the optimization procedures of soft computing to deal with such stimulating systems. Four different problems that are based on the singular equations involving Neumann–Robin, Dirichlet, and Neumann boundary conditions have been occupied to scrutinize the robustness, stability, and proficiency of the designed ANN-GA-SQPM. The proposed results through ANN-GA-SQPM have been compared with the exact results to check the efficiency of the scheme through the statistical performances for taking fifty independent trials. Moreover, the study of the neuron analysis based on three and 15 neurons is also performed to check the authenticity of the proposed ANN-GA-SQPM.

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A MIQP model for optimal location and sizing of dispatchable DGs in DC networks

Montoya

Gil-González

2020

Energy Syst

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Relaxed convex model for optimal location and sizing of DGs in DC grids using sequential quadratic programming and random hyperplane approaches

Cited by 27 publications

References 49 publications

Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks

Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks

Artificial Neural Networks to Solve the Singular Model with Neumann–Robin, Dirichlet and Neumann Boundary Conditions

A MIQP model for optimal location and sizing of dispatchable DGs in DC networks

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