Optimal operation of distributed generation and storage systems in microgrids under real-time pricing using...

The usage of thermal and electrical energy sources in the form of distributed generation sources in microgrids has increased in recent years. As a result, many techniques have been developed to evaluate the information provided by these sources. It is essential that the most effective use of these sources, as well as any electrical or thermal storage, be carried out in such a manner as to provide a satisfactory explanation for the investment, which controlling it results in a reduction in usage and also, in turn, decreases consumption throughout specific periods in addition to the proper load curve. The optimal use of units to create electric power and heat in the microgrid, the best scheduling of the stored system, correct load management, and proper purchase as well as the sale from the power grid are the goals of the energy management system, and these objectives are what the system is designed to accomplish. The response program that includes real-time pricing (RTP) is used so that the adoption of energy management in the microgrid may be managed. In this study, biogeography algorithms and gene algorithms were utilized to achieve the optimal utilization of distributed generation resources of electrical and thermal devices in the microgrid for saving real-time pricing. This was done with the intention of reducing the amount of energy that was supplied by the microgrid. In the microgrid that was researched, distributed electrical generating sources like solar panels, diesel generators, Battery energy storage, and also thermal sources like boiler heat are used. In addition, a (CHP) system was used in this microgrid in order to generate both heat and electrical energy at the same time. A load response strategy should be taken into account while managing thermal and electrical energy sources in the microgrid to ensure a minimum source of energy supply. In addition, the suggested algorithm of biogeography has done well in terms of resource energy management in the microgrid; furthermore, the proposed algorithm of biogeography has lowered the expenses of the microgrid to a higher degree than the genetic algorithm did.