Optimal Placement of SVC Incorporating Installation Cost

Sharma, Ankit Kumar; Saxena, Akash; Tiwari, Rajive

doi:10.14257/ijhit.2016.9.8.25

Cited by 14 publications

(13 citation statements)

References 20 publications

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“…To evaluate the objective function defined in (1) the parameters reported in Table 5 are considered. Some of these parameters have been taken from [6,33]. It is worth mentioning that in the evaluation of the f 2 component, the variable Q D-STATCOM k is defined in MVAr [33].…”

Section: Electric Distribution Test Feedersmentioning

confidence: 99%

Efficient Operative Cost Reduction in Distribution Grids Considering the Optimal Placement and Sizing of D-STATCOMs Using a Discrete-Continuous VSA

2021

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The problem of reactive power compensation in electric distribution networks is addressed in this research paper from the point of view of the combinatorial optimization using a new discrete-continuous version of the vortex search algorithm (DCVSA). To explore and exploit the solution space, a discrete-continuous codification of the solution vector is proposed, where the discrete part determines the nodes where the distribution static compensator (D-STATCOM) will be installed, and the continuous part of the codification determines the optimal sizes of the D-STATCOMs. The main advantage of such codification is that the mixed-integer nonlinear programming model (MINLP) that represents the problem of optimal placement and sizing of the D-STATCOMs in distribution networks only requires a classical power flow method to evaluate the objective function, which implies that it can be implemented in any programming language. The objective function is the total costs of the grid power losses and the annualized investment costs in D-STATCOMs. In addition, to include the impact of the daily load variations, the active and reactive power demand curves are included in the optimization model. Numerical results in two radial test feeders with 33 and 69 buses demonstrate that the proposed DCVSA can solve the MINLP model with best results when compared with the MINLP solvers available in the GAMS software. All the simulations are implemented in MATLAB software using its programming environment.

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Section: Electric Distribution Test Feedersmentioning

confidence: 99%

Efficient Operative Cost Reduction in Distribution Grids Considering the Optimal Placement and Sizing of D-STATCOMs Using a Discrete-Continuous VSA

2021

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“…The assessment of the component of the objective function z 2 can be made with the parameters reported in Table 4. Some of these values were adapted from [25] and complemented with the information provided in [16].…”

Section: Test Feedersmentioning

confidence: 99%

“…To evaluate the objective function defined in (1), the parameters reported in Table 4 are considered. Some of these parameters were taken from [16,25]. To avoid mistakes during the evaluation of this objective function, it is important to mention that the variable y j must be defined in Mvar.…”

Section: Test Feedersmentioning

confidence: 99%

An Approximate Mixed-Integer Convex Model to Reduce Annual Operating Costs in Radial Distribution Networks Using STATCOMs

2021

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The problem of optimal siting and sizing of distribution static compensators (STATCOMs) is addressed in this research from the point of view of exact mathematical optimization. The exact mixed-integer nonlinear programming model (MINLP) is decoupled into two convex optimization sub-problems, named the location problem and the sizing problem. The location problem is addressed by relaxing the exact MINLP model, assuming that all the voltages are equal to 1∠0∘, which allows obtaining a mixed-integer quadratic programming model as a function of the active and reactive power flows. The solution of this model provides the best set of nodes to locate all the STATCOMs. When all the nodes are selected, it solves the optimal reactive power problem through a second-order cone programming relaxation of the exact optimal power flow problem; the solution of the SOCP model provides the optimal sizes of the STATCOMs. Finally, it refines the exact objective function value due to the intrinsic non-convexities associated with the costs of the STATCOMs that were relaxed through the application of Taylor’s series expansion in the location and sizing stages. The numerical results in the IEEE 33- and 69-bus systems demonstrate the effectiveness and robustness of the proposed optimization problem when compared with large-scale MINLP solvers in GAMS and the discrete-continuous version of the vortex search algorithm (DCVSA) recently reported in the current literature. With respect to the benchmark cases of the test feeders, the proposed approach reaches the best reductions with 14.17% and 15.79% in the annual operative costs, which improves the solutions of the DCVSA, which are 13.71% and 15.30%, respectively.

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“…Bansal, Jagdish Chand et al [33] developed Spider Monkey Optimization which was based on the social behavior of spider monkeys and Simon, Dan [34] developed Biogeography Based Optimization which was based on Biogeography is the study of the geographical distribution of biological organisms. Saxena Akash et al [35] used a radial basis function neural network based optimization technique for optimal placement and sizing of DG, also performed a comprehensive study of chaos embedded bridging mechanisms and crossover operators for grasshopper optimization in [36], ambient air quality classification using Grey Wolf Optimization (GWO) in [37] and used Gravitational Search Algorithm (GSA) for optimal allocation of SVC in [38]. These researches are evidences of applications solved through bio inspired optimization techniques.…”

Section: Whale Optimization Algorithmmentioning

confidence: 99%

A demand side management control strategy using Whale optimization algorithm

Sharma

2019

SN Appl. Sci.

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In recent years, demand side management programs are in the spotlight due to the evolution of the smart grid and consumer-centric policies. Demand side management program contains many objectives one of the prime objective is to manage energy demand by certain change in consumer demand. This can be achieved by various methods such as financial discount and change in behavior through imparting education to support the stressed conditions of the grid. This paper demonstrates demand side management strategies based upon strategic conservation, peak clipping and load shifting techniques for future smart grids. The grid contains large number of controllable devices. The day before strategic conservation, peak clipping and load shifting techniques discussed in this paper are mathematically derived for minimization problem. A heuristic-based Whale optimization algorithm (WOA) was developed for solving this problem of minimization. Simulations are conducted on a test smart grid that contains a variation in loads in two service areas, one with residential consumers, and another with commercial consumers. WOA proves its efficacy by comparing the results with spider monkey optimization and biogeography based optimization. The simulation results show that proposed demand side management strategies achieve substantial savings, while reducing the peak load demand of the smart grid.

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Optimal Placement of SVC Incorporating Installation Cost

Abstract: Abstract

Cited by 14 publications

References 20 publications

Efficient Operative Cost Reduction in Distribution Grids Considering the Optimal Placement and Sizing of D-STATCOMs Using a Discrete-Continuous VSA

Efficient Operative Cost Reduction in Distribution Grids Considering the Optimal Placement and Sizing of D-STATCOMs Using a Discrete-Continuous VSA

An Approximate Mixed-Integer Convex Model to Reduce Annual Operating Costs in Radial Distribution Networks Using STATCOMs

A demand side management control strategy using Whale optimization algorithm

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