Organization-based Multi-Agent structure of the Smart Home Electricity System

This paper proposes a Building Energy Management System (BEMS) as part of an organization-based Multi-Agent system that models the Smart Home Electricity System (MASHES). The proposed BEMS consists of an Energy Management System (EMS) and a Prediction Engine (PE). The considered Smart Home Electrici...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2017 IEEE Congress on Evolutionary Computation (CEC) s. 1327 - 1334
Hlavní autoři: Shokri Gazafroudi, Amin, Pinto, Tiago, Prieto-Castrillo, Francisco, Prieto, Javier, Corchado, Juan Manuel, Jozi, Aria, Vale, Zita, Kumar Venayagamoorthy, Ganesh
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2017
Témata:
Building energy management system
decision-making under uncertainty
Electricity supply industry
Energy management
interval optimization
Linear programming
local electricity market
Multi-stage noise shaping
multiagent system
Smart homes
Uncertainty
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper proposes a Building Energy Management System (BEMS) as part of an organization-based Multi-Agent system that models the Smart Home Electricity System (MASHES). The proposed BEMS consists of an Energy Management System (EMS) and a Prediction Engine (PE). The considered Smart Home Electricity System (SHES) consists of different agents, each with different tasks in the system. In this context, smart homes are able to connect to the power grid to sell/buy electrical energy to/from the Local Electricity Market (LEM), and manage electrical energy inside of the smart home. Moreover, a Modified Stochastic Predicted Bands (MSPB) interval optimization method is used to model the uncertainty in the Building Energy Management (BEM) problem. A demand response program (DRP) based on time of use (TOU) rate is also used. The performance of the proposed BEMS is evaluated using a JADE implementation of the proposed organization-based MASHES.
DOI:10.1109/CEC.2017.7969458