Optimal Allocation of Capacitor Banks using Genetic Algorithm and Sensitivity Analysis

Rosa, William Moreti da; Rossoni, Priscila; Teixeira, Júlio Carlos; Belati, Edmarcio Antônio; Asano, Patrícia Teixeira Leite

doi:10.1109/tla.2016.7786353

Cited by 21 publications

(7 citation statements)

References 22 publications

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“…GA is a global heuristics parameter search technique based on genetic operators to find optimal or near‐optimal solutions for each specific problem [19, 20]. Unlike the traditional optimisation approaches that require one starting point, GA uses a set of points (chromosomes) as the initial condition and each chromosome is evaluated for its performance according to the objective function which characterises the problem to be solved and defined by the designers.…”

Section: Methodsmentioning

confidence: 99%

Control parameterisation for POD via software‐in‐the‐loop simulation

Nguyen¹,

Yang²,

Nielsen³

et al. 2019

J. eng.

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Section: Methodsmentioning

confidence: 99%

Control parameterisation for POD via software‐in‐the‐loop simulation

Nguyen¹,

Yang²,

Nielsen³

et al. 2019

J. eng.

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“…The proposed MOPF is a non-linear, non-convex, large-scale problem involving discrete and integer variables. These characteristics make it a challenging problem to solve by conventional iterative methods [69]. Equation (1) represents the objective function that corresponds to the minimization of energy losses in a 24-h period denoted as T…”

Section: Problem Formulationmentioning

confidence: 99%

A Mathematical Programming Approach for the Optimal Operation of Storage Systems, Photovoltaic and Wind Power Generation

2023

Self Cite

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The ever-growing participation of Renewable Energy Sources (RES) in modern distribution networks is replacing an important portion of Conventional Generation (CG), which brings along new challenges in the planning and operation of distribution grids. As RES such as Photovoltaic Energy (PV) and Wind Power Generation (WPG) increase in distribution networks, studies regarding their integration and coordination become more important. In this context, the purpose of this paper is to propose a Multi-period Optimal Power Flow (MOPF) model for the optimal coordination of Battery Energy Storage Systems (BESSs) with PV, WPG, and CG in modern distribution networks. The model formulation was developed in A Modeling Language for Mathematical Programming (AMPL) and solved through the Knitro solver within a time horizon of 24 h. A distinctive feature and one of the main contributions of the proposed approach is the fact that BESSs can provide both active and reactive power. The proposed optimization model reduces power losses and improves voltage profiles. To show the applicability and effectiveness of the proposed model, several tests were carried out on the 33-bus distribution test system and a real distribution system of 141 buses located in the metropolitan area of Caracas. Power loss reductions of up to 58.4% and 77% for the test systems of 33 and 141 buses were obtained, respectively, when BESSs provided both active and reactive power. The results allow us to conclude that the proposed model for optimal coordination of BESSs with RES is suitable for real-life applications, resulting in important reductions of power losses and flattening of voltage profiles.

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“…As mentioned earlier, the first stage of the CBGA is selection, as described by [35], and consists of arbitrarily choosing a subset of individuals of the IP that will be submitted to a tournament for providing two individuals with the best fitness function (in this case, the individuals with the solutions that minimize the cost of the power losses to the greatest extent) [38].…”

Section: Selectionmentioning

confidence: 99%

Reduction of Losses and Operating Costs in Distribution Networks Using a Genetic Algorithm and Mathematical Optimization

et al. 2021

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This study deals with the minimization of the operational and investment cost in the distribution and operation of the power flow considering the installation of fixed-step capacitor banks. This issue is represented by a nonlinear mixed-integer programming mathematical model which is solved by applying the Chu and Beasley genetic algorithm (CBGA). While this algorithm is a classical method for resolving this type of optimization problem, the solutions found using this approach are better than those reported in the literature using metaheuristic techniques and the General Algebraic Modeling System (GAMS). In addition, the time required for the CBGA to get results was reduced to a few seconds to make it a more robust, efficient, and capable tool for distribution system analysis. Finally, the computational sources used in this study were developed in the MATLAB programming environment by implementing test feeders composed of 10, 33, and 69 nodes with radial and meshed configurations.

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Optimal Allocation of Capacitor Banks using Genetic Algorithm and Sensitivity Analysis

Cited by 21 publications

References 22 publications

Control parameterisation for POD via software‐in‐the‐loop simulation

Control parameterisation for POD via software‐in‐the‐loop simulation

A Mathematical Programming Approach for the Optimal Operation of Storage Systems, Photovoltaic and Wind Power Generation

Reduction of Losses and Operating Costs in Distribution Networks Using a Genetic Algorithm and Mathematical Optimization

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