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

Abstract

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%.

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

“…Therefore, the emerging management solution should be efficient and low-cost for an economically viable smart grid. The main research directions of current MMG energy management and operation control in different cooperative approaches can be summarized as the following three aspects:1) energy transaction strategy [76][77][78][79][80]; 2) information interaction strategy [81][82][83][84]; 3) artificial intelligence technology in MMG [88][89][90][91][92][93][94]. Table 3 summarized and compared the different solution methods and optimization objectives of energy management and operation control of MMG under different cooperative approaches.…”

“…Therefore, the energy management problem of MMG is essentially a problem of multi-scenes, multi-objectives, nonlinear and mixed uncertainty. To solve this problem, enumeration method [144][145][146][147][148][149], mixed integer programming method [80], [163], heuristic algorithm [89][90][91][92] have been studied respectively. Enumeration, as the name implies, enumerates all possible combinations of optimized variables.…”

“…With the rapid development of artificial intelligence technology, deep learning, enhanced learning, and other theoretical methods showed great advantages in data analysis, prediction, classification, and other aspects, especially in the processing of massive data [88][89]. At present, many related technologies have been applied in MMG operation, energy management and other aspects [90][91][92]. Reference [90] proposed the combination of deep neural network and reinforcement learning.…”

“…Then the model-free reinforcement learning technique is used to optimize the retail price of local MG to maximize the profit by selling energy. Reference [91] proposes an Internet of Things (IoT) platform for energy management in multimicrogrid system to enhance the power quality. Reference [92] design a framework of integrated frequency control to reduce the frequency deviation of a multi-area multi-microgrid system.…”

Improving the quality of project management at energytech through marketing in support of sustainable and environmental development of energy economics

“…The established approach to project management belongs to the field of internal management (Anastasi et al, 2021;Khan et al, 2022;Qi et al, 2021;Rezaei et al, 2022;Sahoo et al, 2021). This approach ensures that the interests of the business are sufficiently fully respected when managing projects at EnergyTech (Hadini et al, 2021;Ji et al, 2021;Karthick et al, 2021;Vinjamuri and Burthi, 2022). At the same time, the interests of the external environment (society and the state) remain outside project management with the current approach, which is its disadvantage (Garcia-Bernabeu et al, 2019;Storch de Gracia et al, 2019;Hosseini et al, 2020;Izquierdo et al, 2020).…”