Optimal DG placement under Standard Market Design using GA

Mohan, B.R.; Ramesh, M.

doi:10.1109/iceteeem.2012.6494460

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…It can be adapted to solve the electrical power system's problem, as revealed in many research areas. Genetic algorithms (GAs) are a popular OT that can be applied to determine optimal distributed generator (DG) placement [19,20]. Therefore, this paper aims to define the optimal conditions of FCSs and PVs by comparing the GA, CSA, and SAA methodology with the best number of models for calculation and time for computation.…”

Section: Formulamentioning

confidence: 99%

“…The GA is an optimization tool following the heuristic method based on the evolution algorithm. The GA was presented by Goldberg (1989) and developed by applying it to the problems of search, optimization, and machine learning as improvements in basic techniques and knowledge-based techniques [20,24]. The GA aims to follow natural evolution to select a specific number of chromosomes and adapt genetic algorithm operation.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…problems of search, optimization, and machine learning as improvements in basic techniques and knowledge-based techniques [20,24]. The GA aims to follow natural evolution to select a specific number of chromosomes and adapt genetic algorithm operation.…”

Section: Simulated Annealing Algorithm (Saa)mentioning

confidence: 99%

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Optimal Placement of Distributed Photovoltaic Systems and Electric Vehicle Charging Stations Using Metaheuristic Optimization Techniques

et al. 2021

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In this study, the concept of symmetry is introduced by finding the optimal state of a power system. An electric vehicle type load is present, where the supply stores’ electrical energy causes an imbalance in the system. The optimal conditions are related by adjusting the voltage of the bus location. The key variables are the load voltage deviation (LVD), the variation of the load and the power, and the sizing of the distributed photovoltaic (DPV), which are added to the system for power stability. Here, a method to optimize the fast-charging stations (FCSs) and DPV is presented using an optimization technique comparison. The system tests the distribution line according to the bus grouping in the IEEE 33 bus system. This research presents a hypothesis to solve the problem of the voltage level in the system using metaheuristic algorithms: the cuckoo search algorithm (CSA), genetic algorithm (GA), and simulated annealing algorithm (SAA) are used to determine the optimal position for DPV deployment in the grid with the FCSs. The LVD, computation time, and total power loss for each iteration are compared. The voltage dependence power flow is applied using the backward/forward sweep method (BFS). The LVD is applied to define the objective function of the optimization techniques. The simulation results show that the SAA showed the lowest mean computation time, followed by the GA and the CSA. A possible location of the DPV is bus no. 6 for FCSs with high penetration levels, and the best FCS locations can be found with the GA, with the best percentage of best hit counter on buses no. 2, 3, 13, 14, 28, 15, and 27. Therefore, FCSs can be managed and handled in optimal conditions, and this work supports future FCS expansion.

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Section: Formulamentioning

confidence: 99%

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%