| Applications . | Reference . | Year . | Objective . | Technique(s) . |
|---|---|---|---|---|
| Economic load dispatch | Alquthami et al. [59] | 2020 | Get the best results for the lowest amount of money and in the shortest amount of time compared with previous techniques | Artificial bee colony optimization |
| Sahay et al. [60] | 2018 | To reduce fuel costs, transmission costs, labour costs and maintenance costs | Genetic algorithm | |
| Mishra et al. [61] | 2015 | Better compromised solutions, i.e. cost and emissions, between the two objectives | Genetic algorithm | |
| Dixit et al. [62] | 2011 | The problem with a single equivalent objective function to address economic, emission and combination economic and emission dispatch problems | Artificial bee colony optimization | |
| Daniel et al. [63] | 2018 | The period in which the load shipment is computed varies dynamically for each selected time interval | Artificial neural network | |
| Ruiz-Abellón et al. [64] | 2019 | The objective is to minimize ELD losses using a GA-based optimum power flow system | Particle swarm optimization | |
| Ali et al. [65] | 2020 | To start with, ELD is solved by using a genetic algorithm with prevailing constraints, but no line flow constraint | Genetic algorithm | |
| Generator maintenance scheduling | Fu et al. [66] | 2020 | Performing preventive maintenance on components that are at risk helps distribution networks avoid failures by preventing them before they occur | FACTS devices |
| Esmaili et al. [67] | 2014 | A multi-target framework for congestion management is presented in which three competing target functions are simultaneously optimized, total operating expenses, voltage and margins for transient stability | FACTS devices | |
| Suresh et al. [68] | 2013 | For a power system, minimizing annual supply reserve ratio deviation and reducing loss of load probability | Particle swarm optimization | |
| Lakshminarayanan et al. [69] | 2018 | The aims are to maximize and distribute reserved electricity equally over 52 weeks while fulfilling the numerous limitations | Genetic algorithm | |
| Scalabrini Sampaio et al. [70] | 2019 | Data collection for the training and testing of an artificial neural network to anticipate and identify defects in future machinery | Artificial neural network | |
| Power flow | Fikri et al. [71] | 2019 | Implementation of ANN in the absence of some problem data and, more importantly, in the absence of convergence of numerical methods with high precision | Artificial neural network |
| Rahul et al. [72] | 2012 | The objective is to minimize transmission losses using a GA-based optimum power flow system for the IEEE 30-bus test power system | Genetic algorithm | |
| Nakawiro et al. [73] | 2009 | Offline neural artificial networks replace the power flow in the OPF, which is a non-linear mixture of integral optimization and a network reduction goal | Genetic algorithm and artificial neural network | |
| Sumpavakup et al. [74] | 2010 | This method has been used to identify the optimum solution for each producing unit and reduce the overall production cost | Artificial bee colony optimization | |
| Abdellah et al. [75] | 2015 | To be optimum, the standard power flow program must be increased (OPF) | Adaptive neuro-fuzzy interference system | |
| Unit commitment | Nemati et al. [76] | 2018 | Modern power systems such as microgrids must face a variety of strict hurdles due to the present energy revolution | Genetic algorithm and mixed-integer linear programming |
| Alshareef et al. [77] | 2011 | In this study, the cost, emission and both cost and emission of the system are all minimized | Particle swarm optimization | |
| Arora et al. [78] | 2016 | The use of neural network learning results on medium-term load forecasting is presented as a method for unit commitment | Artificial neural network | |
| Liu et al. [79] | 2008 | In this research, a hybrid ANN technique is given to tackle combinational optimization issues in power systems, including unit commitment | Lagrangian relaxation and artificial neural network | |
| Kumar et al. [80] | 2010 | To begin with, unit commitment is solved by using a genetic algorithm with prevailing constraints but no line flow constraint. In the second phase, using GA-based OPF, the number of violations in the lines is minimized for a committed schedule | Genetic algorithm |
| Applications . | Reference . | Year . | Objective . | Technique(s) . |
|---|---|---|---|---|
| Economic load dispatch | Alquthami et al. [59] | 2020 | Get the best results for the lowest amount of money and in the shortest amount of time compared with previous techniques | Artificial bee colony optimization |
| Sahay et al. [60] | 2018 | To reduce fuel costs, transmission costs, labour costs and maintenance costs | Genetic algorithm | |
| Mishra et al. [61] | 2015 | Better compromised solutions, i.e. cost and emissions, between the two objectives | Genetic algorithm | |
| Dixit et al. [62] | 2011 | The problem with a single equivalent objective function to address economic, emission and combination economic and emission dispatch problems | Artificial bee colony optimization | |
| Daniel et al. [63] | 2018 | The period in which the load shipment is computed varies dynamically for each selected time interval | Artificial neural network | |
| Ruiz-Abellón et al. [64] | 2019 | The objective is to minimize ELD losses using a GA-based optimum power flow system | Particle swarm optimization | |
| Ali et al. [65] | 2020 | To start with, ELD is solved by using a genetic algorithm with prevailing constraints, but no line flow constraint | Genetic algorithm | |
| Generator maintenance scheduling | Fu et al. [66] | 2020 | Performing preventive maintenance on components that are at risk helps distribution networks avoid failures by preventing them before they occur | FACTS devices |
| Esmaili et al. [67] | 2014 | A multi-target framework for congestion management is presented in which three competing target functions are simultaneously optimized, total operating expenses, voltage and margins for transient stability | FACTS devices | |
| Suresh et al. [68] | 2013 | For a power system, minimizing annual supply reserve ratio deviation and reducing loss of load probability | Particle swarm optimization | |
| Lakshminarayanan et al. [69] | 2018 | The aims are to maximize and distribute reserved electricity equally over 52 weeks while fulfilling the numerous limitations | Genetic algorithm | |
| Scalabrini Sampaio et al. [70] | 2019 | Data collection for the training and testing of an artificial neural network to anticipate and identify defects in future machinery | Artificial neural network | |
| Power flow | Fikri et al. [71] | 2019 | Implementation of ANN in the absence of some problem data and, more importantly, in the absence of convergence of numerical methods with high precision | Artificial neural network |
| Rahul et al. [72] | 2012 | The objective is to minimize transmission losses using a GA-based optimum power flow system for the IEEE 30-bus test power system | Genetic algorithm | |
| Nakawiro et al. [73] | 2009 | Offline neural artificial networks replace the power flow in the OPF, which is a non-linear mixture of integral optimization and a network reduction goal | Genetic algorithm and artificial neural network | |
| Sumpavakup et al. [74] | 2010 | This method has been used to identify the optimum solution for each producing unit and reduce the overall production cost | Artificial bee colony optimization | |
| Abdellah et al. [75] | 2015 | To be optimum, the standard power flow program must be increased (OPF) | Adaptive neuro-fuzzy interference system | |
| Unit commitment | Nemati et al. [76] | 2018 | Modern power systems such as microgrids must face a variety of strict hurdles due to the present energy revolution | Genetic algorithm and mixed-integer linear programming |
| Alshareef et al. [77] | 2011 | In this study, the cost, emission and both cost and emission of the system are all minimized | Particle swarm optimization | |
| Arora et al. [78] | 2016 | The use of neural network learning results on medium-term load forecasting is presented as a method for unit commitment | Artificial neural network | |
| Liu et al. [79] | 2008 | In this research, a hybrid ANN technique is given to tackle combinational optimization issues in power systems, including unit commitment | Lagrangian relaxation and artificial neural network | |
| Kumar et al. [80] | 2010 | To begin with, unit commitment is solved by using a genetic algorithm with prevailing constraints but no line flow constraint. In the second phase, using GA-based OPF, the number of violations in the lines is minimized for a committed schedule | Genetic algorithm |
| Applications . | Reference . | Year . | Objective . | Technique(s) . |
|---|---|---|---|---|
| Economic load dispatch | Alquthami et al. [59] | 2020 | Get the best results for the lowest amount of money and in the shortest amount of time compared with previous techniques | Artificial bee colony optimization |
| Sahay et al. [60] | 2018 | To reduce fuel costs, transmission costs, labour costs and maintenance costs | Genetic algorithm | |
| Mishra et al. [61] | 2015 | Better compromised solutions, i.e. cost and emissions, between the two objectives | Genetic algorithm | |
| Dixit et al. [62] | 2011 | The problem with a single equivalent objective function to address economic, emission and combination economic and emission dispatch problems | Artificial bee colony optimization | |
| Daniel et al. [63] | 2018 | The period in which the load shipment is computed varies dynamically for each selected time interval | Artificial neural network | |
| Ruiz-Abellón et al. [64] | 2019 | The objective is to minimize ELD losses using a GA-based optimum power flow system | Particle swarm optimization | |
| Ali et al. [65] | 2020 | To start with, ELD is solved by using a genetic algorithm with prevailing constraints, but no line flow constraint | Genetic algorithm | |
| Generator maintenance scheduling | Fu et al. [66] | 2020 | Performing preventive maintenance on components that are at risk helps distribution networks avoid failures by preventing them before they occur | FACTS devices |
| Esmaili et al. [67] | 2014 | A multi-target framework for congestion management is presented in which three competing target functions are simultaneously optimized, total operating expenses, voltage and margins for transient stability | FACTS devices | |
| Suresh et al. [68] | 2013 | For a power system, minimizing annual supply reserve ratio deviation and reducing loss of load probability | Particle swarm optimization | |
| Lakshminarayanan et al. [69] | 2018 | The aims are to maximize and distribute reserved electricity equally over 52 weeks while fulfilling the numerous limitations | Genetic algorithm | |
| Scalabrini Sampaio et al. [70] | 2019 | Data collection for the training and testing of an artificial neural network to anticipate and identify defects in future machinery | Artificial neural network | |
| Power flow | Fikri et al. [71] | 2019 | Implementation of ANN in the absence of some problem data and, more importantly, in the absence of convergence of numerical methods with high precision | Artificial neural network |
| Rahul et al. [72] | 2012 | The objective is to minimize transmission losses using a GA-based optimum power flow system for the IEEE 30-bus test power system | Genetic algorithm | |
| Nakawiro et al. [73] | 2009 | Offline neural artificial networks replace the power flow in the OPF, which is a non-linear mixture of integral optimization and a network reduction goal | Genetic algorithm and artificial neural network | |
| Sumpavakup et al. [74] | 2010 | This method has been used to identify the optimum solution for each producing unit and reduce the overall production cost | Artificial bee colony optimization | |
| Abdellah et al. [75] | 2015 | To be optimum, the standard power flow program must be increased (OPF) | Adaptive neuro-fuzzy interference system | |
| Unit commitment | Nemati et al. [76] | 2018 | Modern power systems such as microgrids must face a variety of strict hurdles due to the present energy revolution | Genetic algorithm and mixed-integer linear programming |
| Alshareef et al. [77] | 2011 | In this study, the cost, emission and both cost and emission of the system are all minimized | Particle swarm optimization | |
| Arora et al. [78] | 2016 | The use of neural network learning results on medium-term load forecasting is presented as a method for unit commitment | Artificial neural network | |
| Liu et al. [79] | 2008 | In this research, a hybrid ANN technique is given to tackle combinational optimization issues in power systems, including unit commitment | Lagrangian relaxation and artificial neural network | |
| Kumar et al. [80] | 2010 | To begin with, unit commitment is solved by using a genetic algorithm with prevailing constraints but no line flow constraint. In the second phase, using GA-based OPF, the number of violations in the lines is minimized for a committed schedule | Genetic algorithm |
| Applications . | Reference . | Year . | Objective . | Technique(s) . |
|---|---|---|---|---|
| Economic load dispatch | Alquthami et al. [59] | 2020 | Get the best results for the lowest amount of money and in the shortest amount of time compared with previous techniques | Artificial bee colony optimization |
| Sahay et al. [60] | 2018 | To reduce fuel costs, transmission costs, labour costs and maintenance costs | Genetic algorithm | |
| Mishra et al. [61] | 2015 | Better compromised solutions, i.e. cost and emissions, between the two objectives | Genetic algorithm | |
| Dixit et al. [62] | 2011 | The problem with a single equivalent objective function to address economic, emission and combination economic and emission dispatch problems | Artificial bee colony optimization | |
| Daniel et al. [63] | 2018 | The period in which the load shipment is computed varies dynamically for each selected time interval | Artificial neural network | |
| Ruiz-Abellón et al. [64] | 2019 | The objective is to minimize ELD losses using a GA-based optimum power flow system | Particle swarm optimization | |
| Ali et al. [65] | 2020 | To start with, ELD is solved by using a genetic algorithm with prevailing constraints, but no line flow constraint | Genetic algorithm | |
| Generator maintenance scheduling | Fu et al. [66] | 2020 | Performing preventive maintenance on components that are at risk helps distribution networks avoid failures by preventing them before they occur | FACTS devices |
| Esmaili et al. [67] | 2014 | A multi-target framework for congestion management is presented in which three competing target functions are simultaneously optimized, total operating expenses, voltage and margins for transient stability | FACTS devices | |
| Suresh et al. [68] | 2013 | For a power system, minimizing annual supply reserve ratio deviation and reducing loss of load probability | Particle swarm optimization | |
| Lakshminarayanan et al. [69] | 2018 | The aims are to maximize and distribute reserved electricity equally over 52 weeks while fulfilling the numerous limitations | Genetic algorithm | |
| Scalabrini Sampaio et al. [70] | 2019 | Data collection for the training and testing of an artificial neural network to anticipate and identify defects in future machinery | Artificial neural network | |
| Power flow | Fikri et al. [71] | 2019 | Implementation of ANN in the absence of some problem data and, more importantly, in the absence of convergence of numerical methods with high precision | Artificial neural network |
| Rahul et al. [72] | 2012 | The objective is to minimize transmission losses using a GA-based optimum power flow system for the IEEE 30-bus test power system | Genetic algorithm | |
| Nakawiro et al. [73] | 2009 | Offline neural artificial networks replace the power flow in the OPF, which is a non-linear mixture of integral optimization and a network reduction goal | Genetic algorithm and artificial neural network | |
| Sumpavakup et al. [74] | 2010 | This method has been used to identify the optimum solution for each producing unit and reduce the overall production cost | Artificial bee colony optimization | |
| Abdellah et al. [75] | 2015 | To be optimum, the standard power flow program must be increased (OPF) | Adaptive neuro-fuzzy interference system | |
| Unit commitment | Nemati et al. [76] | 2018 | Modern power systems such as microgrids must face a variety of strict hurdles due to the present energy revolution | Genetic algorithm and mixed-integer linear programming |
| Alshareef et al. [77] | 2011 | In this study, the cost, emission and both cost and emission of the system are all minimized | Particle swarm optimization | |
| Arora et al. [78] | 2016 | The use of neural network learning results on medium-term load forecasting is presented as a method for unit commitment | Artificial neural network | |
| Liu et al. [79] | 2008 | In this research, a hybrid ANN technique is given to tackle combinational optimization issues in power systems, including unit commitment | Lagrangian relaxation and artificial neural network | |
| Kumar et al. [80] | 2010 | To begin with, unit commitment is solved by using a genetic algorithm with prevailing constraints but no line flow constraint. In the second phase, using GA-based OPF, the number of violations in the lines is minimized for a committed schedule | Genetic algorithm |
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