Fuzzy stalking based wolf algorithm for economic and emission dispatch of microgrid

Abstract

A new concept in power system is MicroGrid (MG). The microgrid concept assumes a cluster of diversified loads comprising of electrical, cooling and thermal demands and various microsources. Microgrid operates as a single controllable system that provides both electric power and heat to its local area. Few primitive analysis on MG behavior as a whole system are conducted with certain component models. Thus this thesis aims to model the different components of microgrid and propose new methods to solve Generation Scheduling (GS) problem in the microgrid. The energy newlinemanagement system encounters additional challenges while scheduling microgrid with renewable energy sources. Solar and wind generation impose uncertainties due to its sporadic availability. The uncertainties introduced by the intermittent renewable energy outputs make it more difficult to realize optimal energy management of macrogrid and MGs. In addition, microgrid integrated with Smart Thermal Storage (STS) mechanisms like Thermo Chemical Heat Storage (TCHS) and Ice-Storage Air Conditioner (ISAC) introduces inevitable constraints to the GS problem. Thereby the GS of STS integrated microgrid is transformed into Coordinated Generation Scheduling (CGS) by satisfying the electrical, thermal and cooling demand. In this thesis, the CGS problem for microgrid with STS is formulated and solved using the Grey Wolf Optimization (GWO) algorithm. newline newline