A Multiagent Modeling and Investigation of Smart Homes With Power Generation, Storage, and Trading Features

Abstract

Smart homes, as active participants in a smart grid, may no longer be modeled by passive load curves; because their interactive communication and bidirectional power flow within the smart grid affects demand, generation, and electricity rates. To consider such dynamic environmental properties, we use a multiagent-system-based approach in which individual homes are autonomous agents making rational decisions to buy, sell, or store electricity based on their present and expected future amount of load, generation, and storage, accounting for the benefits each decision can offer. In the proposed scheme, home agents prioritize their decisions based on the expected utilities they provide. Smart homes' intention to minimize their electricity bills is in line with the grid's aim to flatten the total demand curve. With a set of case studies and sensitivity analyses, we show how the overall performance of the home agents converges-as an emergent behavior-to an equilibrium benefiting both the entities in different operational conditions and determines the situations in which conventional homes would benefit from purchasing their own local generation-storage systems.

Cyber-Attacks on Smart Meters in Household Nanogrid: Modeling, Simulation and Analysis

“…An overview over related work in household simulation models is given in Table 2. A smart home model was developed by Kahrobaee et al [16]. It consists of a home, which is connected to a battery as well as a form of energy generation and the electric grid.…”

“…It consists of a home, which is connected to a battery as well as a form of energy generation and the electric grid. Furthermore, Kahrobaee et al [16] defined four actions, which are available to the household in order to meet its objective of minimizing its cost of electricity. These actions are buying electricity from the grid, saving energy by either charging or discharging the battery, meeting the household's energy demand, and selling energy to the grid.…”

“…The household's peak energy demand was modelled following the classification scheme of Zhang et al [17]. Furthermore, the household nanogrid model includes a photovoltaic energy generation and an energy storage option, as introduced by Kahrobaee et al [16]. A central decision unit has different operation actions to meet household energy demand [16].…”

“…Furthermore, the household nanogrid model includes a photovoltaic energy generation and an energy storage option, as introduced by Kahrobaee et al [16]. A central decision unit has different operation actions to meet household energy demand [16]. Also, the central decision unit communicates a predefined set of data to the SM and in turn receives data such as the grid energy price for the next time period from the SM through a pre-established network, as described by Asare-Bediako et al [15].…”

Description of Two-Peak Characteristics in Power Engineering

“…control of level of electricity generation [14]; choice of electric equipment in distributive networks [15]; installation of a network configuration [16]; definition of peak consumption [17]; calculation of the electricity cost [18]; design and adjustment of Smart Grid [19];…”