Energy Management System in Industrial Microgrids

Abstract

Large-sized industrial buildings with high amount of energy requirements are considered industrial microgrids (IμGs). Thus this type of customer needs to attempt to concentrate on optimum intra-building power handling as well as bi-directional energy transfer between the grid and IμG. For this purpose, a bi-level control is required that supervises building-level benefits as well as utility-level incentives at the same time by achieving an optimal compromise between resilience and performance. The proposed control is verified under deterministic and stochastic conditions. Recurrent outages on the electric and natural gas networks as well as intermittent solar irradiation are examples of unpredictable situations. To convert the risk-neutral controller into a risk-averse one and protect the system from load loss during unpredictable carrier interruptions, conditional value at risk has been applied to the objective function. According to simulations, the suggested risk-averse control improves the ability of station battery and plug-in hybrid electric automobiles to retain energy by +22.03% and +20.14%, respectively. To determine an ideal solution more speedily, this research also created a powerful solution methodology by fusing the revised flower pollination algorithm (FPA) and mixed-integer linear programming. By evaluating the results of the suggested unique hybrid algorithm with those of previously established algorithms such as the Salp Swarm Algorithm, Grasshopper Optimization Algorithm, Polar Bear Algorithm, Coyote Optimization, and Two Cored FPA, the proposed algorithm has been validated. Results show a 7.29% decrease in energy cost, a 22.93% decline in GHG emissions, and a 42.253% saving in execution time.