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

Abstract

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.

Energy Flexibility Management Based on Predictive Dispatch Model of Domestic Energy Management System

“…Hence, the plug-state of the EV has been modeled through a Markov chain in [17]. In [18], a DEMS has been presented as part of an organization-based multi-agent system. Besides, the uncertainty of distributed energy resources has been considered through an Modified Stochastic Predicted Band (MSPB) method used to model the DEM problem.…”

“…The interaction with the electricity market and the participation in demand response programs has also been explored, and this research is leading to promising outcomes, but modeling the electricity market at the local level has not been of interest to the authors in the previous works. However, the local electricity market has been introduced in [18], but the impact of the flexibility has not been evaluated in [18]. The research dealing with the uncertainty associated with the several resources is, however, still at an initial stage.…”

“…However, a stochastic scenario-based method is used to consider the uncertainty of PV power generation in the second stage, the real-time stage. Finally, the performance of the proposed hybrid Interval-Stochastic (InterStoch) method is compared with the MSPB method that was introduced in [18,19].…”

“…In this paper, the predicted data from [18] have been used. For simplicity, only the uncertainty of PV power generation is considered.…”

“…We consider that each smart home can participate in two different types of Local Electricity Market (LEM), not the wholesale market [18]. These LEMs are called day-ahead and real-time markets.…”