Optimal Operation of Islanded Microgrid Operation Based on the JAYA Optimization Algorithm

Islanded microgrid (MG) is one of the most important challenges in the power system operation as the network can be safe and disconnected from the conjected area. Also, in the case that the market price is high, the islanded MG can have a lower operational cost by islanding from the main grid. However, optimal operation of the islanded MG is very challenging as the MG is a nonlinear problem. Hence, this paper proposed a new heuristic method known as the JAYA optimization algorithm to solve the problem. Finally, the proposed model is examined on a modified IEEE 30 bus test network to show the merit of the model

Solving the Grid-Connected Microgrid Operation by JAYA Algorithm

This paper aims to investigate the optimal operation of grid-connected microgrids (MG). In the grid-connected mode, the MG can connect to the main utility and also can exchange energy with the main grid. This potential can lead to higher reliability and less operation cost. In order to show the effectiveness of the proposed model, it is tested on a modified IEEE 33 bus test system

Solving the Grid-Connected Microgrid Operation by JAYA Algorithm

This paper aims to investigate the optimal operation of grid-connected microgrids (MG). In the grid-connected mode, the MG can connect to the main utility and also can exchange energy with the main grid. This potential can lead to higher reliability and less operation cost. In order to show the effectiveness of the proposed model, it is tested on a modified IEEE 33 bus test system

Modified Genetic Algorithm Framework for Optimal Scheduling of Single Microgrid Combination with Distribution System Operator

In this paper, new reformed genetic algorithm (GA) according to the multicellular organism mechanism is developed for power management of single microgrid incorporation with the distribution system operator (DSO). Integration of single microgrid into the conventional grids cann enhance the complexity of the problem due to ability of disconnecting from the main grid as a standalone small electricity network. Hence, in this paper, a new evolutionary algorithm is developed to address the complexity of the problem. The main objective of the proposed model is to minimize the total operation cost of the microgrid in both utility connected and off utility connected modes; that means the objective is based on the economic consideration. To demonstrates the high performance and ability of the proposed method, a modified IEEE 33 distribution bus test network is selected and examined. Finally, the results are compared with the famous evolutionary algorithms such as particle swarm optimization (PSO). In it worth noting that the results are only based on the economic consideration

Islanded Microgrid Operation Based on the Chaotic Crow Search Algorithm

This paper investigates the optimal operation of the islanded microgrid. In order to find the optimal solution and also provide a fast response, a new heuristic method, which is known as the chaotic crow search optimization algorithm is developed. To show the merit of the model, it is tested on the IEEE 30 bus test network

Economic Operation of Islanded Microgrids Based on the Region Search Evolutionary Algorithm

In this paper a new evolutionary algorithm based on the region search evolutionary algorithm (RSEA) is developed for optimal energy management and operation of microgrids. Islanded microgrid operation is more crucial than the conventional distribution grids because of less dependency of the extremal (upstream) generation units. Thus, an effective economic operation of islanded microgrid need a strong algorithm to meet all constraint associated with the problem. The proposed developed RSEA technique is tested on the IEEE 33 bus test system. Results show the effectiveness of the RSEA technique, compared to the well-known evolutionary techniques

Region Search Optimization Algorithm for Economic Energy Management of Grid-Connected Mode Microgrid

Economic energy management of grid-connected microgrid has been widely investigated. However, due to the binary variables of the generation unit’s status, the optimal result of the grid-connected microgrid is very hard. Thus, in this paper, the region search optimization algorithm (RSOA) is developed and adopted for the energy management of the grid-connected microgrid. The developed technique has higher convergence speed and accuracy, compared to the well-known heuristic techniques, such as genetic algorithm and particle swarm optimization. Results shows the effectiveness of the developed model

Solving the Grid-Connected Microgrid Operation by Teaching Learning Based Optimization Algorithm

In this paper, the grid-connected operation of microgrid is investigated where the microgrid can exchange power with the main grid. The proposed problem is modeled as the mixed-integer linear programming (MILP) and is solved by an evolutionary algorithm known as the teaching learning-based optimization (TLBO). Finally, the proposed model is tested on a modified IEEE 33 bus test system to show the performance of the method

Economic Operation of Self-Sustained Microgrid Optimal Operation by Multiobjective Evolutionary Algorithm Based on Decomposition

This paper focuses on the optimal operation of the islanded microgrid. A novel heuristic method known as the Multiobjective Evolutionary Algorithm Based on Decomposition is presented to search for the optimal solution with a fast response. The efficiency of the method is tested on the IEEE 33 bus test network

A New Optimal Operation Structure For Renewable- Based Microgrid Operation based On Teaching Learning Based Optimization Algorithm

This paper proposes a new optimization framework for the optimal power dispatch in both grid-connected and islanded microgrid modes. Solving the microgrid operation by the evolutionary algorithms can be faster than analytical models due to the complexity of the problem. To demonstrate the efficiency and high performance of the proposed technique, it is applied on the IEEE 33 bus test network. Also, the proposed technique is compared with the analytical model, and also well-known heuristic methods such as particle swarm optimization (PSO), genetic algorithm (GA)