A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes

Abstract

The residential sector is a major contributor to the global energy demand. The energy demand for the residential sector is expected to increase substantially in the next few decades. As the residential sector is responsible for almost 40% of overall electricity consumption, the demand response solution is considered the most effective and reliable solution to meet the growing energy demands. Home energy management systems (HEMSs) help manage the electricity demand to optimize energy consumption without compromising consumer comfort. HEMSs operate according to multiple criteria, including electricity cost, peak load reduction, consumer comfort, social welfare, environmental factors, etc. The residential appliance scheduling problem (RASP) is defined as the problem of scheduling household appliances in an efficient manner at appropriate periods with respect to dynamic pricing schemes and incentives provided by utilities. The objectives of RASP are to minimize electricity cost and peak load, maximize local energy generation and improve consumer comfort. To increase the effectiveness of demand response programs for smart homes, various demand-side management strategies are used to enable consumers to optimally manage their loads. This study lists out DSM techniques used in the literature for appliance scheduling. Most of these techniques aim at energy management in residential sectors to encourage users to schedule their power consumption in an effective manner. However, the performance of these techniques is rarely analyzed. Additionally, various factors, such as consumer comfort and dynamic pricing constraints, need to be incorporated. This work surveys most recent literature on residential household energy management, especially holistic solutions, and proposes new viewpoints on residential appliance scheduling in smart homes. The paper concludes with key observations and future research directions.

A Novel Solution for Day-Ahead Scheduling Problems Using the IoT-Based Bald Eagle Search Optimization Algorithm

“…Eagles approach target prey from the best-obtained position in this stage. The following is the hunting model [39,40]: (19) where c 1 and c 2 are random numbers [1,2]. 1 and 1 are directional coordinates, and can be defined as:…”

“…The aforementioned objectives are optimization objectives. Thus, to solve this problem, the methodologies employed for the scheduling techniques involve optimization techniques [1]. In this paper, we use the bald eagle search optimization algorithm (BES) technique for appliance scheduling in the residential sector.…”

“…In other terms, it is used for connecting producers of electricity to consumers of electricity. However, the existing electric grid faces a few challenges [1]. These challenges vary from country to country based on the energy demand.…”

“…The smart grid (SG) represents one such solution that will make existing grids more responsive and intelligent. It is a relatively new concept with advanced information and communication technologies (ICT) and integrates two-way communication between a utility and its consumers [1]. SGs facilitate customer interactions with utilities in a bi-directional way to enhance the security, performance, reliability, and sustainability of the generation, transmission, and distribution of electricity.…”

An Elastic Energy Management Algorithm in a Hierarchical Control System with Distributed Control Devices

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