A hybrid IMFO-fuzzy algorithm for performance enhancement of microgrid under uncertainty via DGs integration and reconfiguration

Abstract

Microgrids (MGs) are distributed generation and distribution systems that include distributed generation (DG) units, energy storage systems (ESSs), distributed reactive sources (DRSs), and resilient loads that can operate in either connected or isolated modes. When dealing with uncontrolled DGs such as Wind Energy Systems (WES) and Photovoltaic Energy Systems (PVES), MGs planners have a difficult time making decisions. The work proposed in this paper addresses three interconnected works: (i) the implementation of a rigorous hybrid optimization approach for reconfiguration and DGs placement; (ii) the performance investigation under uncertain behavior of RES-based DGs and demand; and (iii) performance enhancement realization through the replacement of hybrid DGs for RES-based DGs. An Improved Moth Flame Optimization (IMFO), which is a multi-objective optimization method, has been linked with fuzzy logic in order to handle multiple objectives in an efficient manner. These objectives include the minimization of voltage deviation, the reduction of generation cost, and the reduction of loss. The quality of the power, the amount of money saved by consumers, and the benefits to the Distribution System Operator (DSO) might all be improved with the help of a hybrid algorithm. This research is also extended to address the uncertainties of RES-based DGs by replacing hybrid DGs in the most optimal locations. IEEE 33 bus RDS is used to test radial distribution system (RDS) microgrids. For validation purposes, uses 24-hour load patterns to mimic WES and PVES’ 24-hour load dispatching behavior. The research findings clearly demonstrate the advantages of microgrids over traditional architectures. The hybrid DG requires an average generating cost of 185.33 $/kW in order to produce 100 kW of power throughout the day with significantly reduced emissions.