Application of the Artificial Bee Colony Algorithm to Scheduling Strategies for Energy-Storage Systems of a Microgrid With Self-Healing Functions

• Published in: IEEE transactions on industry applications Vol. 57; no. 3; pp. 2156 - 2167
• Main Author: Kuo, Ming-Tse
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial bee colony; Control equipment; distributed energy resource (DER); Distributed generation; Electric power grids; Energy; Energy sources; Energy storage; Fault detection; Inverters; Load modeling; master–slave control; Mathematical model; Matlab; microgrid (MG); Microgrids; Optimization algorithms; Peak load; Power flow; Renewable energy sources; Scheduling; Search algorithms; self-healing function; Storage equipment; Storage systems; Swarm intelligence; Voltage control
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2021.3058233

The aim of this article is to explore various operating situations of a microgrid based on master-slave control and the self-healing of the microgrid after fault detection. It also finds a scheduling strategy for the energy-storage equipment through an artificial bee colony algorithm to meet the needs of the microgrid. Many operating modes will face multiple instances of connection and disconnection operations. The disturbance generated by distributed energy resources can be effectively reduced through the regulation of the voltage, frequency, and phase angle. Moreover, when the microgrid is disconnected, its power flow will reduce the disturbance. In addition, a fault point is detected when there is a fault. The recovery and isolation actions are automatically carried out, which can reduce the area affected by the fault and enable the nonfault area to quickly restore the power supply. Therefore, the self-healing function of the power grid is achieved. In this article, the bee colony algorithm is applied to the scheduling strategy for the operation of the energy-storage equipment. Multiple demands need to be satisfied by the energy-storage equipment. Multiple assessment indicators are set up as the basis for the algorithm to assess the merits and demerits of the scheduling strategy. The experimental results show that the control of the energy-storage equipment through the bee colony algorithm can satisfy the demands of the various characteristics of the microgrid. It can also reduce the peak load during critical periods and maintain the reliability of the system.