Abstract

Different distributed generation (DG) technologies, active loads, and storage devices create an independent microgrid (MG). Scheduling of an MG is an important issue in renewable energy sources (RESs) based systems. In this paper, MGs include RESs, plug-in hybrid electric vehicles (PHEVs), and electrical energy storage systems. The proposed scheduling framework utilizes the Monte Carlo simulation (MCS) to characterize the uncertain parameters of PHEVs and RESs. Three different charging strategies are investigated for modeling the impact of different behaviors of PHEVs in MGs. These schemes are smart, controlled, and uncontrolled charging. Due to the nonlinear feature of the suggested optimization problem, it needs an efficient optimization tool to tackle the problem appropriately. So, this paper uses the backtracking search optimization (BSO) algorithm for the short-term scheduling of an MG. The proper performance of the offered scheme is investigated in two scenarios with different time horizons. The BSO algorithm and other optimization algorithms are used for comparing the results to verify the presented method in solving the energy management problem of the MGs.

Issue Section:
Energy Systems Analysis
Keywords:
renewable energy, microgrid, electric vehicle, uncertainty, scheduling, energy conversion/systems, energy storage systems, energy systems analysis, heat energy generation/storage/transfer, hydrogen energy, power (co-) generation
Topics:
Algorithms, Batteries, Electric vehicles, Energy generation, Energy management, Energy storage, Microgrids, Optimization, Optimization algorithms, Renewable energy sources, Storage, Stress, Uncertainty, Simulation results

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