The effect of multi -uncertainties on battery energy storage system sizing in smart homes

Abstract

Energy storage can provide many services and solve a multitude of problems in today's power grid. Recent studies on battery energy storage system (BESS) evaluation, sizing and scheduling using energy management system (EMS), have focused on advanced modeling and optimization methods to maximize value streams accumulated across multiple services. In this paper, an algorithm is proposed for EMS of smart home community including BESS, photovoltaic system (PV) and electrical vehicle (EV) with multiple uncertainties based on real behavior of consumers. The suggested algorithm reduces consumers cost by selecting optimal BESS sizes regarding the uncertainty of the market price, power generation of the PV, the amount of charge of the EV and the electricity of the grid. It is so crucial that all uncertainties and consumers' life style are considered to achieve accurate results by taken in to account as double apply both in BESS optimal sizing and EMS of smart home community. Genetic algorithm is used as an optimization method for solving of compatibility -based EMS problem in the community of smart homes considering controllable and uncontrollable loads, and it has been developed for three different cases: on grid, off grid, and off grid in addition to EV connected. The introduced algorithm, by calculating the optimal size of the BESS with state of charge (SOC), not only reduces the consumer cost but also increases their profit by selling excess energy to the grid.