Internet of things platform for energy management in multi-microgrid system to enhance power quality: ARBFNOCS technique

This manuscript proposes an Internet of Things (IoT) platform for energy management (EM) in multi-microgrid (MMG) system to enhance the power quality with hybrid method. The proposed method is the consolidation of opposition based crow search optimizer (OCSO) and radial basis functional neural network (RBFNN), hence it called RBFNOCS technique. The main aim of this manuscript is to optimally managing the power and resources of distribution system (DS) by constantly track the data from IoT-based communication framework. In the proposed work, every devices of home is interfaced with data acquisition module (DAM) that is IoT object along unique IP address resultant in large mesh wireless network. Here, the IoT-based communication framework is used for facilitating the development of a demand response (DR) energy management system (EMS) for distribution system. The transmitted data is processed by RBFNOCS technique. By utilizing the RBFNOCS method, the active with reactive power processing for optimal capacity unbalance compensation smart VSIs share the obtainable neutral current (NC). Likewise, the DS IoT framework enhances these networks flexibility and gives feasible use of obtainable resources. Moreover, the RBFNOCS method is responsible for satisfying the total supply with energy demand. The proposed model is activated in MATLAB/Simulink site and the performance is compared with existing models, namely improved artificial bee colony, squirrel search algorithm and gravitational search algorithm based artificial neural network (SOGSNN), GOAPSNN, fruit fly optimization, and FORDF technique. When compared with the existing methods, the efficiency of the RBFNOCS method is 93.4501%.