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A novel peak load shaving algorithm has been proposed in this study for peak shaving application in hybrid PV-BESS connected Isolated Microgrid (IMG) system. This algorithm will help an IMG system to operate its generation systems optimally and economically along with PV generation unit. An IMG model has been developed in MATLAB/Simulink environment with actual load data, conventional Gas Turbine Generator (GTG), Photovoltaic (PV) generation system and Battery Energy Storage System (BESS). The proposed algorithm has been tested with the designed IMG model. To evaluate the effectiveness of the algorithm, simulation case studies have been conducted with actual load data and actual PV generation data. The simulation results demonstrate that the algorithm can minimize the limitations of the existing methods and it can use the PV generation system effectively. Peak shaving service can be found from this algorithm as a simple and forcible way. A comparative analysis has been conducted of the proposed algorithm with the conventional methods. The comparison results can easily reflect that the proposed algorithm can ensure the optimal use of PV generation system and can serve peak shaving service effectively. It can also ensure an economic and environment friendly system where the existing algorithms are limited to serve these. The proposed algorithm can mitigate the available power issues for hybrid PV-BESS connected system and it can also minimize the operating cost by dispatching the generation units optimally.

The objective of this study is to propose a decision-tree-based peak shaving algorithm for islanded microgrid. The proposed algorithm helps an islanded microgrid to operate its generation units efficiently. Effectiveness of the proposed algorithm was tested with a BESS-based MATLAB/Simulink model of an actual microgrid under realistic load conditions which were recorded. To evaluate the performance, simulation case studies were conducted under various load conditions and results were compared with conventional techniques. Results showed that the proposed algorithm offers a simple and effective way of peak load shaving without heavy computational burdens often needed in other methods. The comparison analysis verified that the proposed algorithm can effectively mitigate the peak load demand regardless of the schedule of the generators, where conventional methods were limited. The financial benefit investigation shows that microgrid utility can enjoy substantial savings, while reducing of the peak demand of the microgrid. Thus, the islanded microgrid that include fuel-based generation can implement the proposed technique to reduce the consumption of fuel and increase the efficiency of fuel-based generation through peak load mitigation.