Residential Energy Consumption Controlling Techniques to Enable Autonomous Demand Side Management in Future Smart Grid Communications

Abstract

In this work, we present a survey of residential load controlling techniques to implement demand side management in future smart grid. Power generation sector facing important challenges both in quality and quantity to meet the increasing requirements of consumers. Energy efficiency, reliability, economics and integration of new energy resources are important issues to enhance the stability of power system infrastructure. Optimal energy consumption scheduling minimizes the energy consumption cost and reduce the peak-to-average ratio (PAR) as well as peak load demand in peak hours. In this work, we discuss different energy consumption scheduling schemes that schedule the household appliances in real-time to achieve minimum energy consumption cost and reduce peak load curve in peak hours to shape the peak load demand.

Home energy management systems: A review of modelling and complexity

“…Some works also partially survey the HEMS literature [87,88]. However, this paper focuses on the reviewing the modelling and complexity framework in HEMS.…”

Cooperative home energy management using batteries for a photovoltaic system considering the diversity of households

“…Such efforts can be classified by several factors [3], as listed below. (1) Controlled target appliance Many studies on the scheduling of storage systems, including residential batteries and pure/plug-in hybrid electric vehicles, have been proposed [4][5][6][7][8], mainly because of the low risk of compromising customer utilities.…”

A probabilistic demand side management approach by consumption admission control

“…There are many solutions proposed to DR and DSM like direct control of smart appliances, pricing and load scheduling. Good references can be found about different DSM approaches [3,4,5]. With direct control system operators can remove the extreme values in electricity consumption (peak shaving) and encourage additional energy use during periods of lowest system demand (valley filling).The load control as a demand response strategy is presented in [6], where simulating (summer period, air-conditioning units) is conducted with two control algorithms.…”