Optimal operation management of a microgrid based on MOPSO and Differential Evolution algorithms

Local aggregation of Distributed Energy Resources (DERs), storage devices, controllable and uncontrollable loads is known as Microgrid. Microgrid operation management in order to reduce both cost and emission simultaneously is a very challenging task considering smart utilization of available energy resources in a highly constrained environment along with the conflicting nature of objectives. This paper aims to optimize the operation of an interconnected microgrid which comprises a variety of DERs and storage devices in order to minimize both cost and emission resulted from supplying local demands. Furthermore we will try to achieve an intelligent schedule to charge and discharge storage devices that provides the opportunity to benefit from market price fluctuations. The presented optimization framework is based on Multiobjective Particle Swarm Optimization (MOPSO) approach which adopts Differential Evolution (DE) algorithm to improve the search capability of the developed methodology. Finally results from an illustrative case study are provided and analyzed.