A GBDT‐SOA approach for the system modelling of optimal energy management in grid‐connected micro‐grid system

Abstract

In this manuscript, a hybrid approach for energy management in grid connected MG system is proposed. The grid connected MG system has photovoltaic (PV), wind turbine (WT), micro turbine (MT), battery. The proposed hybrid approach is the consolidation of both the Gradient Boosting Decision Trees (GBDT) and Sandpiper Optimization Algorithm (SOA) and this way the proposed technique is named as GBDT-SOA. Here, at grid connected microgrid configuration the required load demand is ever monitored by the GBDT approach. The perfect combination of the MG is optimized by SOA considering the predicted load requirement. The fuel cost including grid power hourly power variation, operation and maintenance cost of the grid connected microgrid system is defined as the objective of the proposed technique. The constraints are power demand, renewable energy sources, state of charge of storage elements. Batteries have been used as an energy source, to stabilize and allow the renewable power system units to maintain running in a steady, stable output power. At that point, the proposed model is executed in MATLAB/ Simulink work site and the performance is analyzed with existing techniques, such as BFO, SOA and SSA. The efficiency of the sources like photovoltaic, wind turbine, micro turbine, and battery using proposed technique is 95.9375%, 92.113%, 94.387% and 93.7560%.

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“…; Mutation = 𝛽 𝑔𝑐 𝑑 𝑐 (15) where, 𝛼 𝑔𝑐 is the number of gene crossover, 𝑑 𝑐 is the distance between the grasshoppers, 𝛽 𝑔𝑐 is the point of mutation. Utilizing the updated movement, determine and evaluate fitness.…”

“…To obtain optimal switching, one must design an optimal controller. In this, we designed an intelligent controller by combining AGOA [12,13] and GBDT [14,15], which is called the AGOA-GBDT method. The hunting activities of grasshoppers served as inspiration for the AGOA algorithm.…”

A new energy management technique for microgrid system using muddy soil fish optimization algorithm

“…Step3: Check the operational and network constraints stated in Equations ( 1) to ( 4), ( 6) to ( 9), ( 12) to (14), and (21).…”

“…Subsequently, load prediction is determined effectively by the sandpiper optimization algorithm (SOA) and advanced Salp swarm optimization algorithm (ASOA). Moreover, GBDT‐SOA 14 and ANFASO 15 approaches through a hybrid method have focused on merely optimizing the production overheads by completely neglecting the user preferences and the corresponding system constraints. In a recent scenario, the CEM framework has adopted a robust two‐stage decision approach 16 for solving the unit commitment and power flow challenges in a standalone microgrid that includes renewable sources, storage elements, and interruptible demand.…”

Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer

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