A Comparative Analysis of Peak Load Shaving Strategies for Isolated Microgrid Using Actual Data

Peak load reduction is one of the most essential obligations and cost-effective tasks for electrical energy consumers. An isolated microgrid (IMG) system is an independent limited capacity power system where the peak shaving application can perform a vital role in the economic operation. This paper presents a comparative analysis of a categorical variable decision tree algorithm (CVDTA) with the most common peak shaving technique, namely, the general capacity addition technique, to evaluate the peak shaving performance for an IMG system. The CVDTA algorithm deals with the hybrid photovoltaic (PV)-battery energy storage system (BESS) to provide the peak shaving service where the capacity addition technique uses a peaking generator to minimize the peak demand. An actual IMG system model is developed in MATLAB/Simulink software to analyze the peak shaving performance. The model consists of four major components such as, PV, BESS, variable load, and gas turbine generator (GTG) dispatch models for the proposed algorithm, where the BESS and PV models are not applicable for the capacity addition technique. Actual variable load data and PV generation data are considered to conduct the simulation case studies which are collected from a real IMG system. The simulation result exhibits the effectiveness of the CVDTA algorithm which can minimize the peak demand better than the capacity addition technique. By ensuring the peak shaving operation and handling the economic generation dispatch, the CVDTA algorithm can ensure more energy savings, fewer system losses, less operation and maintenance (O&M) cost, etc., where the general capacity addition technique is limited.

Automated summary

This paper presents a comparative analysis of a categorical variable decision tree algorithm (CVDTA) with the general capacity addition technique for peak shaving performance evaluation in an isolated microgrid (IMG) system. The results show that the CVDTA algorithm outperforms the capacity addition technique in reducing peak demand and achieving energy savings and cost reduction.