Title :
Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households
Author :
Kahrobaee, Salman ; Asgarpoor, Sohrab ; Wei Qiao
Author_Institution :
Dept. of Electr. Eng., Univ. of Nebraska-Lincoln, Lincoln, NE, USA
Abstract :
In the near future, a smart grid will accommodate customers who are prepared to invest in generation-battery systems and employ energy management systems in order to cut down on their electricity bills. The main objective of this paper is to determine the optimum capacity of a customer´s distributed-generation system (such as a wind turbine) and battery within the framework of a smart grid. The proposed approach involves developing an electricity management system based on stochastic variables, such as wind speed, electricity rates, and load. Then, a hybrid stochastic method based on Monte Carlo simulation and particle swarm optimization is proposed to determine the optimum size of the wind generation-battery system. Several sensitivity analyses demonstrate the proper performance of the proposed method in different conditions.
Keywords :
Monte Carlo methods; building management systems; demand side management; distributed power generation; home automation; particle swarm optimisation; secondary cells; smart power grids; wind turbines; Monte Carlo simulation; distributed-generation system; electricity bill; electricity management system; energy management system; hybrid stochastic method; particle swarm optimization; smart grid; smart household; stochastic variable; storage capacity; wind generation-battery system; Batteries; Distributed power generation; Electricity; Load modeling; Smart homes; Wind speed; Wind turbines; Capacity planning; Monte Carlo simulation; distributed generation; energy storage; load management; particle swarm optimization (PSO); smart homes;
Journal_Title :
Smart Grid, IEEE Transactions on
DOI :
10.1109/TSG.2013.2278783